Feasibility of Using the Utah Array for Long-Term Fully Implantable Neuroprosthesis Systems
byAutumn Bullard

A dissertation submitted in partial fulfillmentof the requirements for the degree ofDoctor of Philosophy(Biomedical Engineering)in the University of Michigan
2019

Doctoral Committee:
Associate Professor Cynthia A. Chestek, Chair
Assistant Professor Joan Greve
Associate Professor Parag G. Patil
Assistant Research Scientist John Seymour

Autumn Bullard
ajbull@umich.edu
ORCID iD: 0000-0002-0453-2261© Autumn Bullard 2019

Dedication

To my grandparents: Conrad Byrd, Viola Byrd, Barbra Bullard, and Edward Bullard.

ii

Acknowledgements
First and foremost, I have to thank God. It is because of Him that any of this has been possible.
To my advisor, Cindy Chestek, thank you for seeing something in me back in 2013 andwelcoming me to join your lab. You have been an amazing advisor, mentor, and model of a fearless
woman in a male dominated field. Your enthusiasm for the science and your genuine care for yourstudents has made it an honor to work under your guidance. Thank you for being patient with meduring times when I wasn’t the best student and instilling a great deal of knowledge in me that I
can take with me throughout life. I’d like to thank the rest of my committee Parag Patil, Joan
Greve, and John Seymour for your help and support through this process, as well as career advice.
Joan, although we met towards the end of my time here you have played an integral role in me
becoming a more confident student and for that I thank you.
I would like to thank all of the past and present members of the Chestek lab. Thank you forall of your help, advice, and friendship. We’ve shared so many memories both in lab and outsideof lab. I have learned so much from everyone over the years. I couldn’t have asked for a bettergroup of people to work with every day.
I have to give a huge thank you to my family. Mom and Dad, thank you for all the work
you put in raising me into the person I am today. You have been an incredible support system forme in everything that I do. To my boyfriend Isiah, thank you for continuing to encourage me when
I felt like giving up and staying on me about procrastinating. Your love and support throughout
this journey means everything to me. Also thank you to the Overton family for taking me in andbeing my home away from home.
iii

Lastly, thank you to my friends, especially those I’ve had the pleasure to meet while hereat Michigan. Thank you for your calls, support, encouragement, and inspiration. Grad school has
been one of the most difficult things I’ve ever done and you all have made it a time that I will
never forget. I would like to especially thank my sisters Aeriel Murphy-Leonard and Ciara Sivels.
You two have been my rock and my backbone through this journey. Thank you for being a shoulder
to lean on and helping to keep me uplifted. I don’t know how I could have made it through without
you both. I can’t thank you enough for the many great memories we’ve made that has made thisjourney an amazing experience.
This experience has been a rollercoaster, but one thing is for certain. I would have not madeit without the village of people I have supporting me. I am forever grateful.

iv

Table of Contents
Dedication ....................................................................................................................................... ii
Acknowledgements ........................................................................................................................ iii
List of Figures .............................................................................................................................. viii
List of Tables ................................................................................................................................. ix
Abstract ........................................................................................................................................... x
Chapter 1. Introduction ................................................................................................................... 1
1.1 Recent Advances in Motor Restoration through Human Brain Machine Interfaces ............ 1
1.1.1 Human Intracortical Neural Interface Technology ........................................................ 1
1.1.2 Neural Prosthesis ........................................................................................................... 2
1.1.3 Functional Electrical Stimulation .................................................................................. 3
1.2 Clinical Limitations of Implantable Neural Recording Systems .......................................... 4
1.2.1 Design ............................................................................................................................ 5
1.2.2 Safety ............................................................................................................................. 6
1.3 Summary of thesis................................................................................................................. 8
Chapter 2. Design and Testing of a 96-Channel Neural Interface Module for the Networked
Neuroprosthesis System .................................................................................................................. 2
2.1 Abstract ................................................................................................................................. 2
2.2 Introduction ........................................................................................................................... 3
2.3 Methods............................................................................................................................... 7
2.3.1 Networked Neuroprosthesis System .............................................................................. 7
2.3.2 Cortical Controlled FES System Overview ................................................................... 9
2.3.3 Recording Module Hardware Design .......................................................................... 10
2.3.4 Printed Circuit Board Layout ....................................................................................... 13
2.3.5 Experimental Design and Device Validation ............................................................... 14
2.3.6 Surgical Implantation & Electrophysiology ................................................................ 15
2.3.7 Experimental Setup ...................................................................................................... 17
2.4 Results ................................................................................................................................. 18
2.4.1 Device Validation ........................................................................................................ 18v

2.4.2 Spiking Band Power Validation................................................................................... 21
2.4.3 Power Consumption ..................................................................................................... 24
2.5 Discussion ........................................................................................................................... 26
2.6 Conclusion .......................................................................................................................... 28
Chapter 3. Estimating Risk for Future Intracranial Neuroprosthetic Devices: A systematic review
of hardware complications in clinical deep brain stimulation and experimental humanintracortical arrays ........................................................................................................................ 30
3.1 Abstract ............................................................................................................................... 30
3.2 Introduction ......................................................................................................................... 31
3.3 Methods............................................................................................................................... 35
3.3.1 Search Strategy ............................................................................................................ 35
3.3.2 Study Selection ............................................................................................................ 37
3.3.3 Data Extraction ............................................................................................................ 37
3.4 Results ................................................................................................................................. 38
3.4.1 DBS Search Results and Study Characteristics ........................................................... 38
3.4.2 Hemorrhage.................................................................................................................. 43
3.4.3 Infection ....................................................................................................................... 43
3.4.4 Skin Erosion ................................................................................................................. 44
3.4.5 Other Hardware Failures .............................................................................................. 45
3.4.6 Utah Array Search Results and Study Characteristics ................................................. 45
3.5 Discussion ........................................................................................................................... 50
3.5.1 Hemorrhage.................................................................................................................. 51
3.5.2 Infection ....................................................................................................................... 52
3.5.3 Skin Erosion ................................................................................................................. 52
3.5.4 Extension Cable Malfunctions ..................................................................................... 53
3.5.5 Utah Array Safety and Longevity ................................................................................ 53
3.5.6 Study Limitations ......................................................................................................... 55
3.6 Conclusion .......................................................................................................................... 56
Chapter 4. Reporting of Incidental Safety Data of the Utah Array in a Rhesus Macaque ........... 57
4.1 Abstract ............................................................................................................................... 57
4.2 Introduction ......................................................................................................................... 58
4.3 Methods............................................................................................................................... 59
4.3.1 Surgical Implantation ................................................................................................... 59
4.3.2 Perfusion and Tissue Processing .................................................................................. 60
4.3.3 Explantation of Arrays. ................................................................................................ 61vi

4.3.4 Tissue Staining ............................................................................................................. 61
4.3.5 Imaging ........................................................................................................................ 62
4.3.6 Cell Counting ............................................................................................................... 62
4.4 Results ................................................................................................................................. 63
4.4.1 Gross Pathology ........................................................................................................... 63
4.4.2 Neuron Density ............................................................................................................ 64
4.5 Discussion ........................................................................................................................... 67
4.6 Conclusion .......................................................................................................................... 69
Chapter 5. Discussion ................................................................................................................... 71
5.1 Conclusion .......................................................................................................................... 71
5.2 Future Directions ................................................................................................................ 73
5.2.1 Modular Systems ......................................................................................................... 73
5.2.2 Neural Signal Origin .................................................................................................... 74
5.2.3 Innovative Electrode Technology ................................................................................ 76
Bibliography ................................................................................................................................. 78vii

List of Figures
Figure 2.1. Concept diagram for brain-controlled FES ............................................................... 10
Figure 2.2. Block diagram of the neural recording module ......................................................... 11
Figure 2.3. Prototype board used for testing .................................................................................... 15
Figure 2.4. Example mockup of the final design with flexible circuitry ..................................... 15
Figure 2.5. Surgical photo of Utah arrays implanted in the motor and sensory cortex of a rhesus
macaque.......................................................................................................................................... 16

Figure 2.6. Experimental Setup.................................................................................................... 18
Figure 2.7. Single channel data recorded through the device, sampled at 32 kSps ........................ 19
Figure 2.8. Single units recorded in vivo through the device ............................................................. 21
Figure 2.9. Spiking band power validation .................................................................................. 22
Figure 2.10. Comparison of mean spiking band power from the device normalized to the maximum
power and firing rate calculated offline normalized to the maximum firing rate over 100 ms windows forthe best single channel ..................................................................................................................... 23

Figure 3.1. Overview of the standard brain machine interfaces set up ........................................ 35
Figure 3.2. Flow diagram of the study selection for DBS hardware complications .................... 38
Figure 3.3. Flow diagram of the study selection for Utah arrays................................................. 46
Figure 3.4. Length of chronic human implants reported in literature across clinical study sites .... 50
Figure 4.1. Gross Histological Results......................................................................................... 64
Figure 4.2. Histological Stains ..................................................................................................... 64
Figure 4.3. Neuron density comparison between neural tissue under the array and surrounding the array(control) from slice 13 ..................................................................................................................... 65

Figure 4.4. Neuron density for each sampled location averaged across the three slices analyzed ....... 66

viii

List of Tables
Table 2.1. Neural Recording Module Specifications ................................................................... 13
Table 2.2. Power Saving Techniques ........................................................................................... 25
Table 3.1. Keywords and Search Structure for Hardware Complications in DBS ...................... 36
Table 3.2. Keywords and Search Structure for Utah Arrays in Humans ..................................... 36
Table 3.3. DBS Hardware Related Adverse Events ..................................................................... 42
Table 3.4. Human Utah Array Implantation Sites and Senior Author Involvement .................... 48
Table 3.5. Reported Duration of Chronic Human Implanted Utah Arrays .................................. 49
Table 3.6. Example Power Analysis ........................................................................................... 55ix

Abstract
Damage to the spinal cord can disrupt the pathway of signals sent between the brain andthe body and may result in partial or complete loss of both motor and sensory functions. The lossof these functions can have devastating implications on the quality of one’s life, interfering withactivities of daily living related to walking, bladder and bowel control, trunk stability, and arm andhand function. Current approaches used to help improve and restore mobility require residual
movement to control, which can be unintuitive and inoperative by individuals with higher level
cervical injuries. In order to develop technology used by individuals of all levels of injury, it isnecessary to generate control signals directly from the brain. This thesis is intended to address theclinical limitations of implantable neural recording systems, and thus lay the foundation for thedevelopment of a design and safety profile for a fully implantable intracortical system for motor
restoration.
We first present the design and testing of a 96-channel neural recording device used tomate with an existing functional electrical stimulation (FES) system in order to facilitate braincontrolled FES. By extracting signal power within a narrow frequency bandwidth and reducing
overhead processor operations, a 25% power reduction is achieved. This establishes the feasibility
for an implantable system and enables the integration of the neural recording device withimplantable FES system. The specifications of this platform can be used as a guide to develop
further application specific modules and dramatically accelerate the overall process to a clinically
viable system.
xWith a functional device, the next step is to move towards a clinical trial. Here we
investigate the potential safety risks of future modular, implantable neuroprosthetic systems. A
systematic review of 240 articles was used to identify and quantitatively summarize the hardwarerelated complications of the most established intracranial clinical system, deep brain stimulation,and the most widespread experimental human intracranial system, the NeuroPort, including the
Utah microelectrode array. The safety and longevity data collected here will be used to better
inform future device and clinical trial design and satisfy regulatory requirements.
The stability and longevity of the Utah array are critical factors for determining whether
the clinical benefit outweighs the risk for potential users. We investigate the biological adverse
response to the insertion of the Utah array in a rhesus macaque. We examined the density ofneurons around the shanks of the array in comparison to control brain. Non-human primate animalmodels allow us to further examine the effects of the implantation of the Utah array on neuraltissue, which cannot be done with humans. Information gained through this will continue toincrease the pool of safety data for the Utah array and emerging intracranial devices.
Overall, we developed a neural recording device to be used for brain-controlled FES andexamined the potential safety concerns reported in the human literature and experimentally using
non-human primates. These results represent significant progress towards a clinically-viable
system for motor restoration in people suffering from spinal cord injury.

xiChapter 1
Introduction

The prevalence of paralysis is represented by a large segment of the US population,affecting nearly 1 in 50 Americans (Armour, Courtney-Long, Fox, Fredine, & Cahill, 2016). Spinal
cord injury (SCI), the second leading cause of paralysis is estimated to affect approximately
288,000 persons with 17,700 new cases each year (National Spinal Cord Injury Statistical Center,2016). Damage to the spinal cord disrupts the signal pathway used in communication between thecentral and the peripheral nervous systems and can result in the loss of motor and sensory functions
below the level of injury. The location of the damage to the spinal cord will determine the severityof the paralysis experienced. This loss of motor function creates functional limitations that havethe ability to drastically impact the quality of one’s life. Currently there are no adequate clinicalsolutions available to people living with paralysis. Most individuals require around the clock
assistance to fulfill daily functional tasks, which can be cost intensive and deprive one of theirsense of independence. Based on surveys of the quadriplegic population, it was emphasized thatthe restoration of arm and hand function would be most important to improve their quality of life
(Anderson, 2004). This would help restore some level of self-sufficiency and allow them to interact
with their surroundings.
Current clinical approaches to restore hand function, outside of relying on a full-timecaretaker, have been limited to both muscle-controlled prosthesis and functional electricalstimulation (FES) systems. This technology has been useful in assisting those with partial
1

paralysis. However, these methods of control can be counterintuitive to the normal process of
movement. In addition, these methods require a level of residual movement of the shoulder orlimbs to use these assistive devices, therefore excluding a subset of the SCI population. In orderto address this fundamental limitation, it becomes necessary to extract motor intent directly fromthe brain. By interfacing cortical signals, we can provide a more natural control solution andexpand function to individuals with all levels of injury.
The following sections will discuss current experimental approaches to restoring handfunction after spinal cord injury, the limitations towards clinical adoption, and present
opportunities for improvements to accelerate the process towards a clinically-viable system.

1.1 Recent Advances in Motor Restoration through Human Brain Machine Interfaces
1.1.1 Human Intracortical Neural Interface Technology
Extracting signals directly from the brain for motor control has been done using a varietyof neural interface technologies. These neural interfaces range in their level of invasiveness andsignal specificity. Noninvasive technologies such as electroencephalography (EEG) andelectrocorticography (ECoG) have demonstrated the potential to control computer cursors androbotic arms (Farwell & Donchin, 1988; Hotson et al., 2016; Leuthardt, Schalk, Wolpaw,
Ojemann, & Moran, 2004; Pistohl, Ball, Schulze-Bonhage, Aertsen, & Mehring, 2008; Schalk etal., 2007; Wolpaw, McFarland, Neat, & Forneris, 1991). However, while these interfaces could
be readily adopted due to their less invasive implementation, they do not provide control signals
with the high specificity needed to execute more advanced tasks. Early experiments have shownthat a more invasive method, extracting signals using electrodes implanted in motor cortex, mightprovide a better source for control signals. (D. R. Humphrey, Schmidt, & Thompson, 1970; D. S.

1

Humphrey & Hochberg, 1995). Intracortical electrodes that penetrate the brain are in close
proximity, which enables them to record activity from individual neurons or a small population ofneurons, thus extracting the most information.
The first effort to translate invasive single neuron recordings to humans used a neurotrophic
electrode, consisting of a hollow glass conical tip with two gold recording wires. This technology
was used in patients with ALS and brainstem stroke to control computer cursors (Kennedy &Bakay, 1998; Kennedy, Bakay, Moore, Adams, & Goldwaithe, 2000). In order to control multiple
degrees of freedom and increase the difficulty of tasks, more recording electrodes are needed. The
development of the Utah intracortical electrode array permitted the implantation and simultaneousrecording of a larger number of electrodes (Nordhausen, Maynard, & Normann, 1996;
Nordhausen, Rousche, & Normann, 1994). The Utah array is a 4 mm x 4 mm silicon-based
microelectrode array with 100 recording electrode shanks that extend 1.5 mm. Once
commercialized, this recording structure became a breakthrough in translating brain machineinterfaces from animal studies to clinical research in humans. This is currently the only invasiveneural interface that has FDA approval for human testing (Cyberkinetics, 2005). Utah arrays havebeen used in people to control computer cursors and robotic arms (Hochberg et al., 2006; S.-P.
Kim, Simeral, Hochberg, Donoghue, & Black, 2008), as well as study epilepsy (Weiss et al., 2013),
memory (Rutishauser, Aflalo, Rosario, Pouratian, & Andersen, 2018), consciousness (Hanrahan
et al., 2013), and sensory responses (Armenta Salas et al., 2018a).
1.1.2 Neural Prosthesis
Extraction of motor intent directly from the brain, using brain machine interfaces (BMIs)
has shown to be very promising in generating control signals for prosthetic devices (Collinger etal., 2013a; Hochberg et al., 2012b; Z. T. Irwin…Bullard et al., 2017; Pandarinath et al., 2018).

2

This concept was first demonstrated during the BrainGate clinical trial, with the first implantationof the Utah array in a human. Intracortical signals were used to control a two-dimensional cursor
and rudimentary movements of a robotic arm by individuals with tetraplegia (Hochberg et al.,2006; S.-P. Kim et al., 2008). With ongoing research and improved interpretation of neuralensembles using machine learning algorithms, three-dimensional control of prosthetics has beenused by people with tetraplegia to perform reaching and grasping with increased degrees of
freedom for functional tasks (Collinger et al., 2013a; Hochberg et al., 2012b; Wodlinger et al.,2015a). These studies have focused on decoding movement to control external prosthetic arms andrestore motor function to tetraplegics. However, with SCI the anatomy to execute movement
remains intact and grants the option to reanimate the paralyzed limb. This alternative restores thenatural extremity rather than using an external assistive device and is more desirable to people
living with SCI (Blabe et al., 2015).
1.1.3 Functional Electrical Stimulation
Functional electrical stimulation allows the signal pathway within the spinal cord that
initiates movement to be bypassed. Electrical stimulation is applied directly to the nerve
innervating muscle to produce movement. FES has made great strides in restoring a variety of
motor functions, assisting patients with grasping objects (Peckham et al., 1998, Kilgore et al.,2008, Popovic et al., 2002, Alon et al., 2003), walking (Thrasher et al., 2006, Daly et al., 2008),
and controlling bladder functions (Gaunt et al., 2006).
Most commercially-available FES systems for hand function have been controlled by theuser via physical switches, shoulder motion, and wrist position which allow patients to cycle
through pre-programmed stimulation patterns (Snoek et al., 2000, Prochazka et al., 19997, Handa
et al., 1992, Smith et al., 1987). These previous methods provided somewhat coarse and binary3

control of the limb. Current FES systems use residual myoelectric activity or joint angles as amethod of control to achieve more finely tuned movements (Memberg et al., 2014, Smith et al.,1998). The Freehand system was one of the first implanted FES systems, and was part of the largest
clinical trial of an upper extremity neuroprosthesis (Taylor, Esnouf, & Hobby, 2002). It has since
been removed from the market. However, Case Western Reserve University recently developed
the Networked Neuroprosthesis (NNP), a next-generation system of implantable modules for FES.
This fully implantable system can record residual EMG and perform many combinations of neuralstimulation for controlling grasp and other motor functions (Smith, Crish, Buckett, Kilgore, &Peckham, 2005). Although these current methods perform well for individuals with partial
paralysis, as the level of SCI increases and residual motor function decreases, there are fewer
options for control sources of such systems.
More recently, groups have successfully recorded signals directly from motor cortex using
Utah arrays to predict movements or EMG patterns and control FES systems (Ethier et al., 2012,
Ajiboye et al., 2012, Bouton et al., 2016). Bouton et al. demonstrated that multiunit activity in aparalyzed human could be used to control muscle activation through surface stimulation andprovide continuous control of isolated finger movements and six different wrist and hand postures(Bouton et al., 2016). Ajiboye et al. enabled a person with a C3 level injury to perform self-feeding
activities using intracortically-controlled stimulation through percutaneous electrodes (Ajiboye etal., 2017).

1.2 Clinical Limitations of Implantable Neural Recording Systems
Although promising, all previous demonstrations of cortically-controlled systems to restore
arm and hand function have required the connection of indwelling electrodes to external hardware
outside of the body. This increases the potential risk of infection and impedes the portability of the4system, confining the user to a purely research setting. To avoid any transcutaneous leads andmove towards a practical device for clinical use, active electronics for processing the neural signals
must be fully implantable.
1.2.1 Design
Many groups have designed and built custom implantable, wireless neural recording
devices (Aziz et al., 2009; Borton, Yin, Aceros, & Nurmikko, 2013; Gao et al., 2012; Miranda,
Gilja, Chestek, Shenoy, & Meng, 2012; Moo Sung Chae, Zhi Yang, Yuce, Linh Hoang, & Liu,
2009; S.-Y. Park, Cho, Na, & Yoon, 2018; Rizk et al., 2009; Wattanapanitch & Sarpeshkar, 2011). However, none have been FDA approved or tested in humans. Almost all of these circuits weredeveloped around specifications from basic neuroscience, in which the action potential waveformmust be captured. In order to accomplish this, typical neural recording systems must sample thesignal at >20kHz and digitize at a 16-bit resolution.
One of the major challenges in translating these systems into clinical use is the highbandwidth needed to access individual neural waveforms. The high sampling rate required toprocess and transmit neural data dramatically increases the power consumption of the device andcan result in short battery life and increased device temperatures (Borton et al., 2013; Harrison etal., 2009; Miranda, Gilja, Chestek, Shenoy, & Meng, 2010). Borton et al. developed a 100 channel,
hermetically sealed, implantable neural recording system. This device transmits broadband data at
24Mbps, requires 90.6mW, and can last 7 hours on a medical grade 200mAh battery (Borton etal., 2013). Similarly, Miranda et al. developed a 32-channel system of primarily off the shelfcomponents that delivered broadband data at 24 Mbps, using 142mW (Miranda et al., 2010). Thissystem can last up to 33 hours, but requires two 1200 mAh batteries. Rizk et al. built a 96-channel
implantable system powered through inductively-coupled coils which requires 2000mW (Rizk et5al., 2009). When compared to a pacemaker, which can run on µW and last approximately six years,
the previous systems draw too much power to run on an implantable battery and are beyond thespecifications of existing implantable devices.
1.2.2 Safety
An implantable neural recording device must also meet regulatory requirements before
being introduced to the market. The FDA classifies medical devices on a scale of one to threebased on the risk they impose. This class will determine the path taken to market and therequirements of safety and efficacy to be demonstrated. These requirements can have major
implications on the time, money, and amount of participants needed. In order to test an implantableneural recording device in humans the FDA must grant an investigational device exemption (IDE).
The IDE proposes that the risk to human subjects does not outweigh the benefits and the knowledge
gained, and further allows the collection of safety and efficacy data on the device through a clinical
trial. Inherently, the main priority of the FDA is safety. They look for adverse and serious adverse
events within clinical trials to make judgements on the safety of the device. Therefore, to ensure
human clinical trials are conducted efficiently and effectively it is critical to identify potential
sources of complications and estimate safety risks in advance. Unfortunately, because there havenot been any other implantable neural recording devices approved by the FDA, there is a lack ofsafety data available. However, safety analyses of existing technology can be used as a benchmark
to infer important information about the safety of the device.
As a major component of an implantable neural recording device, the Utah array can be
examined. While human safety data is limited, animal models can be used to learn more. There
are still many safety and efficacy challenges that pose a threat to the longevity, stability, and qualityof the arrays for clinical use in BMI control; mechanical damage of the electrode, degradation of6

electrode materials, and the response of the brain against the implanted device. Baresse et al., 2013
investigated long-term modes of failure of 78 Utah arrays over 27 non-human primates. They
demonstrated that most arrays failed within a year of implantation, most commonly of mechanicalfailures due to connector issues. Biological issues were the second most common, accounting for
24% of failures. There were reported recordings for about 6 years, however a slow progressive
decline in spike amplitude and the number of viable channels was noted (Baresse, et al., 2013).
However, even with spike amplitude decrease throughout the first year, signals can still stabilize
without loss of information content because the multiunit amplitude remains well above the noise(Chestek et al., 2011). In a follow up study, they used SEM techniques to visualize structuralchanges of explanted arrays from non-human primates to identify potential reasons for this signal
attenuation. The SEM revealed material deficits of parylene cracking and platinum tip corrosion
which progressed the longer they were implanted. A considerable amount of tissue encapsulation
had grown into the platinum and parylene defects and was also suggested to have lifted the arrayout of the brain (Baresse et al., 2016). Consequently, the implantation of the array can also induce
neural tissue and vascular damage having adverse effects on the health of the brain. During
insertion of electrodes, neurons are killed, blood vessels are disrupted and the blood brain barrier(BBB) is compromised, which can result in micro hemorrhages and other cellular responses to theimplanted device (Fernandez et al., 2014). Research on BBB disruption and techniques to reduce
damage has been done as a result of this. Kozai et al., 2010 discovered that by inserting in areas
over 5 µm away from any major sub-surface vessels could reduce neurovascular damage by about83%.

7

1.3 Summary of thesis
In this thesis, we examine the feasibility of using the Utah microelectrode array for longterm implantable, modular neural prosthesis systems in humans. We intend to address the clinicallimitations of implantable neural recording systems, and thus lay the foundation for thedevelopment of a design and safety profile for a fully implantable intracortical system for motor
restoration.
In Chapter 2, we present the design and testing of a novel implantable neural recording
device. This neural recording device was designed to access 96 channels from the Utah
microelectrode array and process the data in low power. Power reduction is enabled by the
extraction of signal power in a narrow frequency bandwidth along with the use of several featuresof the microcontroller that reduce overhead processor operations. The device architecture wasdesigned to be used as a module in conjunction with an existing, fully implantable, functional
electrical stimulation system to facilitate cortical-controlled FES. Specifications expressed in thischapter represent the first attempt to combine designs from different groups. This platform can beused as a guide to develop further application specific modules and dramatically accelerate theoverall process to a clinically viable system.
In Chapter 3, we examine the safety profiles of the most widespread, establishedintracranial clinical system, deep brain stimulation, and the most widespread experimental humanintracranial system, the NeuroPort, including the Utah microelectrode array. We identify andquantitatively summarize the hardware-related complications of deep brain stimulation that can beused to estimate potential safety risks of future modular, implantable neuroprosthetic systems. In
addition, we collect longevity data for human Utah array implants. Due to the lack of data reported
in literature discussing human research using the Utah array, we determined in order to continue

8to establish a database of safety data for future and emerging neuromodulation technologies weneed to be gathering detailed information about the implantations during experiments such as
implant and explant dates, detailed per patient adverse events, the time frame in which adverse
events occurred, as well as the responsive action. This data will better inform future device andclinical trial design and satisfy regulatory requirements.
In Chapter 4, we study incidental safety data from a rhesus macaque implanted with the
Utah array. Histology was performed on neural tissue of the area beneath and surrounding the
explanted array. The tissue was stained for neurons, microglia, and nuclear cells. We examinedthe density of neurons around the shanks of the array in comparison to control brain from threedifferent slices of varying depths along the electrode array. Non-human primate animal models
allow us to further examine the effects of the implantation of the Utah array on neural tissue, whichcannot be done with humans. Information gained through this will continue to increase the pool ofsafety data for the Utah array and emerging intracranial devices.
Chapter 5 will summarize the results of each study and discuss future directions to advancethe development and distribution of implantable neuroprostheses.

9Chapter 2
Design and Testing of a 96-Channel Neural Interface Module for the Networked
Neuroprosthesis System
A version of this chapter has been published to Bioelectronic Medicine.
Bullard, A. J., Nason, S. R., Irwin, Z. T., Nu, C. S., Smith, B., Campean, A., Peckham, P. H.,
Kilgore, K. L., Willsey, M. S., Patil, P. G., Chestek, C. A. (2019). Design and testing of a 96channel neural interface module for the Networked Neuroprosthesis system. Bioelectronic
Medicine, 5(1), 3.
2.1 Abstract
The loss of motor functions resulting from spinal cord injury can have devastating
implications on the quality of one’s life. Functional electrical stimulation has been used to help
restore mobility, however, current functional electrical stimulation (FES) systems require residual
movements to control stimulation patterns, which may be unintuitive and not useful for individuals
with higher level cervical injuries. Brain machine interfaces (BMI) offer a promising approach forcontrolling such systems; however, they currently still require transcutaneous leads connecting
indwelling electrodes to external recording devices. While several wireless BMI systems havebeen designed, high signal bandwidth requirements limit clinical translation. Case Western
Reserve University has developed an implantable, modular FES system, the Networked
Neuroprosthesis (NNP), to perform combinations of myoelectric recording and neural stimulation
for controlling motor functions. However, currently the existing module capabilities are notsufficient for intracortical recordings. Here we designed and tested a 1x4 cm, 96-channel neuralrecording module prototype to fit within the specifications to mate with the NNP. The neuralrecording module extracts power between 0.3-1 kHz, instead of transmitting the raw, high2

bandwidth neural data to decrease power requirements. The module consumed 33.6 mW whilesampling 96 channels at approximately 2 kSps. We also investigated the relationship between
average spiking band power and neural spike rate, which produced a maximum correlation of
R=0.8656 (Monkey N) and R=0.8027 (Monkey W). Our experimental results show that we canrecord and transmit 96 channels at 2ksps within the power restrictions of the NNP system andsuccessfully communicate over the NNP network. We believe this device can be used as anextension to the NNP to produce a clinically viable, fully implantable, intracortically-controlled
FES system and advance the field of bioelectronic medicine.

2.2 Introduction
According to the National Spinal Cord Injury Statistical Center, there are approximately
282,000 people living with chronic spinal cord injury (SCI), with 17,000 new cases occurring eachyear (National Spinal Cord Injury Statistical Center, 2016). Damage to the spinal cord can disrupt
the pathway of signals sent between the brain and the body and may result in partial or complete
loss of both motor and sensory functions below the level of injury. The loss of these functions canhave a major impact and severely interfere with activities of daily living related to arm and handfunction, walking, bladder and bowel control, and trunk stability. Interestingly, restoration of armand hand function was ranked as the highest priority amongst individuals with tetraplegia andcould significantly improve quality of life (Anderson, 2004). Functional electrical stimulation(FES), a technique that uses pulses of electrical current to generate contractions of muscles, hasbeen beneficial in assisting and improving the impaired motor function in individuals with SCI.
FES has made great strides in improving not only hand function (Peckham, Mortimer, andMarsolais 1980; Alon and McBride 2003; Popovic, Popovic, and Keller 2002; Kilgore et al. 2008),

3

but also walking (Daly et al., 2011; Thrasher, Flett, & Popovic, 2006), and controlling bladderfunctions (Gaunt & Prochazka, 2006).
FES systems have been controlled by the user via physical switches, shoulder motion, andwrist position, which allow patients to cycle through pre-programmed stimulation patterns(Prochazka et al. 1997; Snoek et al. 2000; Handa et al. 1992; Johnson et al. 1999; Smith et al.
1987). These previous methods provided somewhat coarse and binary control of the limb. Current
FES systems use residual myoelectric activity or joint angles as a method of control to achieve
more finely tuned movements (Memberg et al., 2014; Smith et al., 1987). Although these current
methods may work for individuals with partial paralysis, they can be unintuitive for patients andonly provide a few degrees of freedom. Further, in high cervical SCI, there is little or no residual
motor function to provide an appropriate input stimulus to control such systems. Thus, a control
solution which can provide more function to patients with all levels of injury is needed.
Brain machine interfaces (BMIs) have demonstrated great potential for generating control
signals for prosthetic devices (Chapin, Moxon, Markowitz, & Nicolelis, 1999; Collinger et al.,2013a; Gilja et al., 2015b; Hochberg et al., 2012a; Velliste, Perel, Spalding, Whitford, & Schwartz,2008). The capability of BMIs to decode intended movement directly from the brain can also be
used to control FES systems, potentially restoring natural function to patients with all levels of
spinal cord injury. Recently, groups have successfully used intracortically recorded signals from
Utah microelectrode arrays (Blackrock Microsystems, Salt Lake City, UT) to predict movements
or electromyogram (EMG) patterns to control FES systems (Ajiboye, Simeral, Donoghue,
Hochberg, & Kirsch, 2012a; Bouton et al., 2016; Ethier, Oby, Bauman, & Miller, 2012). Bouton
et al. demonstrated that multiunit activity in a paralyzed human could be used to control muscle
activation directly and provide continuous control of isolated finger movements and six different

4wrist and hand postures (Bouton et al., 2016). Most recently, Ajiboye et al. enabled a person witha C3 level injury to perform self-feeding activities using Utah array-controlled FES (Ajiboye etal., 2017). Although cortical control of FES is promising, current BMIs still require percutaneous
leads connecting indwelling electrodes to external recording devices that can lead to risk infection
and limits portability. Therefore, wireless technology is required to move towards a fully
implantable and clinically viable device.
Many groups have designed and built custom implantable, wireless neural recording
devices (Aziz et al., n.d.; Gao et al., 2012; Moo Sung Chae et al., 2009; S.-Y. Park et al., 2018;
Wattanapanitch & Sarpeshkar, 2011) . However, none have been FDA approved or tested inhumans. One of the major challenges in translating these systems into clinical use is the highbandwidth needed to access individual neural waveforms. The need to acquire, process, andtransmit this broadband neural data dramatically increases the power requirements of the device,
which results in large batteries with low battery life. For example, Borton et al. developed a 100
channel, hermetically sealed, implantable neural recording system. This device transmits
broadband data at 24Mbps, requires 90.6mW, and can last 7 hours on a medical grade 200mAhbattery (Borton et al., 2013). Similarly, Miranda et al. developed a 32-channel system of primarily
off the shelf components that delivered broadband data at 24 Mbps, using 142mW (Miranda et al.,2010). This system can last up to 33 hours but requires two 1200 mAh batteries. In all cases, thehigh power requirement prevents the use of compact batteries with adequate battery life practicalfor an implantable device.
One method for saving power is to reduce the system bandwidth by focusing on relevant
BMI features of the intracortical signals. Intracortical BMIs typically analyze the action potential
or “spikes” frequencies. Spikes are detected in the broadband signal by setting thresholds and5counting the number of crossings in regular time intervals. These spike counts can be used to
predict both continuous and discrete movements. Spike sorting individual neurons may bebeneficial if an electrode is recording neurons with independent tuning patterns, otherwise, if
electrodes primarily have only one neuron or similarly tuned neurons then a substantial
performance gain is not expected. Numerous studies have used thresholded spikes in BMI
experiments and have shown that there is minimal or no performance loss when compared to sorted
action potentials extracted from the broadband data (Chestek et al., 2011; Fraser, Chase, Whitford,& Schwartz, 2009). In investigation of spike sorted data in comparison to thresholded data for theuse in BMIs we have previously shown that spike sorting does not substantially improve decoding
performance. Using a Naïve Bayes classifier with both thresholded and spike sorted data, wedemonstrated percent accuracy only changed by an average of 5% and the correlation coefficient
only differed by 0.015 (Christie et al., 2015). Thus, instead of transmitting the entire broadband
signal, only the spike counts are needed to generate commands (Harrison et al., 2009). Thisimmensely compresses the data, decreasing the required data rate for transmission. Beyond using
spike counts, Stark and Abeles found that most of the decoding information received from spikes
could also be found in the signal frequency band of 300-6,000 Hz, which includes the spike
waveform frequencies (Stark & Abeles, 2007). We have previously shown this bandwidth can befurther reduced to 300-1,000 Hz and extract similar intracortical information (Irwin…Bullard etal., 2016). We refer to this 300-1,000 Hz band as the “spiking band”. Specifically, we developed
a 16-channel wireless neural interface to assess the power savings and BMI decoder performanceof this approach. We compared the decode performance of continuous finger position using a
Wiener filter between high bandwidth data using spike counts and low bandwidth data using the
spiking band. That study showed that instead of transmitting the entire broadband signal, extracting

6

only signal power within a narrow frequency band allowed for a reduced sampling rate and resulted
in a power savings of roughly 90% with only a 5% performance loss of the accuracy of the decoder(Irwin…Bullard et al., 2016). This approach could be applied in the context of existing fully
implantable systems to decrease power consumption and allow for practical neural control of FES
devices.
One efficient way to move the field towards a fully implantable intracortical BMI-FES
system for clinical use may be to merge a neural recording device with an already existing fully
implantable FES system. Using similar signal processing techniques from our previous wirelessdevice (Irwin…Bullard et al., 2016), we developed a novel 96-channel intracortical recording
device to be used as an extension to the modular, fully implantable FES system developed at Case
Western Reserve University. In this paper we describe a feasibility study for power, performance,
and form factor to mate with their system and the prospect of an implantable cortical-controlled
FES system.

2.3 Methods
2.3.1 Networked Neuroprosthesis System
The Networked Neuroprosthesis (NNP) is a system of implantable modules used to record
residual EMG and perform many combinations of neural stimulation for controlling grasp andother motor functions (Smith et al., 2005). While other implantable FES systems and modular
networks have been developed (Ghoreishizadeh, Haci, Liu, Donaldson, & Constandinou, 2017;
Guiraud, Azevedo Coste, Benoussaad, & Fattal, 2014; Jovičić, Saranovac, & Popović, 2012), to
our knowledge, the NNP is currently the only fully implantable FES system in initial human testingfor hand function. It consists of a single central power module, and multiple actuator and sensor

7

modules that are all interconnected via a network cable. The central power module is used forbattery housing and wireless transfer. It manages power distribution and has the capability tomonitor and program functions to other modules throughout the network. The actuator module is
responsible for providing stimulation to muscles or nerves while the sensor module records
biopotential data. The network cable provides the power and high-speed data link for the modules
with a two wire Controller Area Network-like (CAN) bus protocol (US 7,260,436 B2, 2007; Smithet al., 2005).
The current NNP sensor modules are designed with two bipolar channels to record EMG.
These sensor modules are not equipped to support 96-channel intracortical neural recordings.
However, the NNP system architecture was designed as a platform technology to accept newmodules with added functionality. Theoretically, a separate 96-channel neural recording module
can be added to the NNP system to facilitate cortical controlled FES. This module would need toadhere to the guidelines of the standard NNP sensor modules: fit into the approved 1 cm x 3 cm
hermetically sealed packaging of the sensor modules, include CAN capabilities, and meet therequirements of the 50kbps network bandwidth and the target power consumption of about 30
mW. Because the NNP has been cleared for human testing and offers an open architecture, wechose this as our base system to test the feasibility of a fully implantable, cortically controlled
FES system. The final system will record neural data from a 96-channel Utah array and generatecommand signals for grasping using low power circuitry and the spiking band feature extraction
technique. Herein, we present the design and experimental results of this neural recording
module prototype, including validation using novel datasets with intracortical recordings fromrhesus macaques performing finger movements.

82.3.2 Cortical Controlled FES System Overview
The envisioned intracortical FES system is shown in figure 2.1. The existing NNP
architecture is used for muscle stimulation, power, and communication, while the novel
intracortical recording module presented here generates control signals based on user intention.
The novel recording module records data from a 96-channel Utah array in motor cortex, extracts
the signal power in the 300-1,000 Hz frequency band, and will ultimately predict the user’s motor
intention using decoding techniques similarly to (Irwin…Bullard et al., 2016). That intention isthen converted into appropriate stimulation patterns by the power module and stimulates theparalyzed limb via the actuator module. The central power module of the NNP provides power toall sensor and actuator modules and provides a communication pathway both between modules
and for external programming.
The Utah array will be directly wire bonded to the neural recording module in ahermetically sealed capsule which will be secured to the skull, similar to what is seen with the
Responsive Neurostimulator system (Neuropace). Securing this module to the skull should limit
cable length and the possibility of excessive noise or interferance of the signal. The neuralrecording module will then be connected to other modules of the NNP throughout the body via the
network cable.

9Figure 2.1. Concept diagram for brain-controlled FES. The grey box denotes the existing NNP systemand the red box denotes the planned novel module.

2.3.3 Recording Module Hardware Design
The initial design specifications for this module, summarized in table 2.1, were determinedbased on the existing design of the Networked Neuroprosthesis system from Case Western andfrom the design of our previous wireless system (Irwin…Bullard et al., 2016). The recording
module is described by the block diagram in figure 2.2. It has a 96-channel front-end which filtersand digitizes the incoming neural data. The absolute value of the data on each channel is binnedby the central microcontroller of the device by averaging over a given time interval to calculate

10the mean signal power. Decoding and communication over the CAN bus have not yet been
implemented. However, in the future, the power on all channels will be decoded by the central
microcontroller to predict the user’s intended grasp type. Finally, the decoded grasp type will besent over the network to the NNP modules for appropriate muscle stimulation. Here, we validate
the module’s ability to record the neural data at power levels that will work in conjunction withthe entire system.

Figure 2.2. Block diagram of the neural recording module. (*) indicates future work not presented here.

The module was implemented using entirely commercial, off-the-shelf components. The
front-end consists of three Intan RHD2132 32-channel bioamplifiers that combine analog anddigital filters and a multiplexed 16-bit ADC. The upper and lower bandwidths of the amplifiers
and number of active channels can be easily configured by the central microcontroller (MCU) via
a standard four-wire SPI interface. The MCU has three USART ports that are configured for SPI
to transmit data from all three amplifiers simultaneously. The lower cutoff frequency of theamplifier bank is adjustable from 0.1–500 Hz, while the upper cutoff range is 100–20,000 Hz. The
ADC sampling rate is controlled by the MCU, which was set to approximately 2 kSps per channel

11when using all 96 channels to measure signal power, or approximately 30 kSps for a single
channel’s single unit recording. The power consumption of the amplifiers is proportional to theupper cut off frequency and scales at 7.6 µA/kHz per channel. The chosen upper cut off frequency
is the major factor in power consumption of the amplifiers. The upper cut off frequency also
determines the minimum sampling rate that can be applied, which is a major factor on the MCU
and power consumption of the overall system.
A 32-bit Atmel ATUC3C2256C MCU serves as the central controller and data processor,
configuring the front-end amplifiers, controlling the rate of data flow, as well as eventuallycommunicating with existing NNP circuitry via a CAN-like bus. Feature extraction from the datais performed on the MCU by averaging the absolute value of the data over a specified bin size thatis programmed, which we define as spiking band power. The MCU is clocked via an external
crystal oscillator at 8 MHz which is internally divided for device operation and SPI
communication. The MCU is programmed from an external computer via a ten-pin AVR JTAG
interface, and system configuration settings can be easily modified in the application code.
The MCU’s Direct Memory Access (DMA) controller allows sampling of the bioamplifiers
without processor oversight and was used to save power. The system is configured to initialize
the 8 MHz clock, USART modules, and Intan amplifiers using the fully active MCU at startup.
After initialization, the DMA controller is then set up to sample data using the USART peripheral(in SPI mode) and transfer it to internal memory. Once it is enabled, the MCU enters a low-power
sleep mode. Since the USART module has a lower bit resolution than the Intan amplifiers, a small
amount of glue logic was required to drive the MCU’s SPI interface to each amplifier while the
MCU remained asleep. A D-flip flop, AND-gate, and necessary propagation delay circuitry wereadded to each chip select line on each SPI bus to drive the MCU’s SPI interface for compatibility

12with each amplifier while the MCU remained asleep.
The remaining circuitry is responsible for network communication and power
conditioning. This section uses identical components and a similar layout as the existing modulesof the NNP. The network is designed to be DC isolated and is used to communicate with other
NNP modules using the CAN bus protocol. The power conditioning circuitry will ultimately be
responsible for harvesting power from the network.
Table 2.1. Neural Recording Module Specifications
ParameterArea# channels
ADC resolution
Amplifier input noise
CPU clockLow-pass filter
High-pass filter
Sampling rate

Value
1.0 x 4.0 cm
96*
16 bits
2.4 µVRMS
8 MHz
0.1 – 20 kHz*
0.1 – 500 Hz*
2.17 kSps (96 channels)
32.05 kSps (1 channel)
3.3 V

Supply voltage*Configurable in software
2.3.4 Printed Circuit Board Layout
NNP remote modules are designed to have several 1cm wide rigid printed circuit board(PCB) panels, connected via flexible PCBs. This enables the modules to be folded to fit in a 1cm
wide enclosure, while maintaining component layout space. These modules can additionallyaccommodate variable length enclosures, and designers have the choice of trading off module
length for circuit board density and complexity.
The novel module described thus far was prototyped on a six-layer printed circuit board.
The prototype layout is shown in figure 2.3. All active circuitry fit within three 1cm x 4 cm panels
in order to test the feasibility of fitting within an NNP package. Ultimately, a fourth panel will be
13

used comprised of all bond pads to connect internal circuitry to the external electrode array. The
limiting factor was the 32-channel Intan chips. All three Intan bioamplifiers were put on one panel
with signal lines extending out from the side of the board. This solution required an extension of
the board length from 3 cm for a typical NNP module to 4 cm for the novel module. A total of 12
signal lines pass between the bioamplifier and microcontroller panels, four SPI lines for each
amplifier, all on one inner layer. In addition, only four lines pass between the microcontroller panel
and the communication and power panel. This will enable future versions to be fabricated with arigid-flex circuit board that allows folding to fit within an NNP remote module enclosure(illustrated in figure 2.4). Most of the components outside of these panels are only used to facilitate
benchtop testing and are not required for device operation. This includes test points, programmingheaders, and Samtec connectors that can connect directly to a Utah array head stage (Blackrock
Microsystems). The remaining glue logic components were later added unconstrained to theoutlined panels to avoid any major design changes; however, they could fit with the necessary
adjustments.
2.3.5 Experimental Design and Device Validation
We explored the module’s ability to record in single channel (30 kSps) and multichannel(2 kSps) modalities. The sampling rate is approximate and can vary slightly based on code
configurations. In each modality, the power consumption and its correlation to channel count was
analyzed. In addition, we used the module to investigate the relationship between spiking band
power and the firing rate of action potentials. This neural recording module was validated using
pre-recorded data that was played back through the module and in vivo intracortical recordings
directly from a rhesus macaque. All animal procedures were approved by the University ofMichigan Institutional Review Board and the Institutional Animal Care & Use Committee.

14

Figure 2.3. Prototype board used for testing. Three panels (left to right): bioamplifiers, microcontroller,
network and power circuitry.

Figure 2.4. Example mockup of the final design with flexible circuitry. Demonstration of folded board to
fit within the NNP module can.

2.3.6 Surgical Implantation & Electrophysiology
Two rhesus macaque monkeys were induced under general anesthesia and placed in astereotactic frame. The craniotomy site was located using the stereotactic frame to estimate thelocation of the central sulcus. The hand region of primary motor cortex (M1) was approximated15

by projecting a line from the genu of the arcuate sulcus posteriorly toward the gyrus immediately
anterior to the central sulcus. The location of hand in the somatosensory cortex (S1) wasapproximated as the gyrus immediately posterior to the central sulcus across from the motor handregion. Two 4 mm x 4mm, 96-channel, intracortical Utah arrays (Blackrock Microsystems) were
implanted in motor and sensory hand region as shown in figure 2.5.
Broadband data were recorded at 30 kSps from the arrays using a Cerebus Neural Signal
Processor (Blackrock Microsystems). Neural spikes were detected by high-pass filtering the raw
data at 250 Hz and thresholding the resulting signal at -4.5 times the RMS voltage on each channel,
similar to other experiments (Gilja et al., 2015b).

Figure 2.5. (Top) Surgical photo of Utah arrays implanted in the motor and sensory cortex of a rhesus
macaque. A – anterior, L – lateral, CS – central sulcus. (Bottom) Spike panel recorded from the M1 arrayusing a Cerebus Neural Signal Processor (Blackrock Microsystems) illustrating the number of single units
and their quality. Data from this monkey was used in later analysis to validate the device offline and in
vivo.

16

2.3.7 Experimental Setup
Data Playback
Validation of the module in both single channel and multichannel modes were performed
using pre-recorded data. Broadband data that had been previously recorded through the Cerebus(Blackrock Microsystems) and saved to an external computer were replayed through the moduleusing a National Instruments DAQ card (PCI-6711) and then sent to a computer to view for further
analyses, shown in figure 2.6 (A). The DAQ was a 12-bit, 4 channel, 1 MS/s analog output device.
This was used in conjunction with a shielded connector box (NI SCB-68A) to access the channels
and connected to the module via Samtec connectors. The output of the DAQ was adjusted via a
voltage divider in order to achieve the original signal amplitude and outputted at the originalrecorded sampling rate at 30 kHz.
Sending known pre-recorded data through the device allowed for verification that theoutput data was correct. Additionally, whereas the general spike waveform is known and can bevisually detected in the signal, the spiking band waveform is not and offline comparison with theexact same data is critical. In practice, the system will not need to send neural data off the device.
A motor command will be decoded from the data and sent over the NNP system to be transformed
into a stimulation pattern. However, for testing purposes, the recorded data was sent to a computerfor analyses and validation of the module. In 2 kSps mode, at the end of each bin period, themodule computes the average for each channel. At the completion of either 2 kSps or raw 30 kSps
data, the device passes all the recorded data to an external computer through USART, using a
Future Technology Devices International (FTDI) chip as a USB interface for analyses andvalidation. Neural action potentials are visible when in single channel mode and the spiking band
power is visible in multichannel mode and are later compared to PC-processed data.

17

In Vivo Recordings
Data was also recorded by the module in vivo directly from a monkey. During theserecordings, the monkey sat in a primate chair (Crist Instruments) with his head restrained not
performing any task. A Cereport breakout connector was used to connect the array pedestal to themodule through the Samtec connectors. Neural data was recorded directly through the device andsent to an external computer using an FTDI chip as a USB interface, shown in figure 2.6 (B).

Figure 2.6. Experimental Setup. (A) While the monkey performs the finger flexion task, broadband
neural data is recorded through the Cerebus and saved on a computer. The offline data is later replayed
through the module using a National Instruments DAQ card. (B) While the monkey sits still, neural data
is recorded through the device and sent to a computer.

2.4 Results
2.4.1 Device Validation
Figure 2.3 shows the final device, 1 cm x 4 cm for each of 3 panels. Currently this prototype
does not include the flexible connections between panels, but only 16 lines run between the panels,
such that the same layout could be fabricated in a flex design. An example of this flex design isillustrated in figure 2.4. This design was tested extensively on the benchtop and with animals as
described below.

18Pre-recorded DataFirst, we verified that signals were passing reliably through the Intan amplifiers to themicrocontroller. During these tests, the module was powered by a DC regulated power supply at
3.3 V, bypassing the built-in power conditioning circuitry. The amplifiers were configured to filtera passband of 0.1- 7,500 Hz and were sampled at 32 kSps. These settings enabled testing of the
wideband performance of the device. The sampled data was transmitted to an external computerfor storage and processing. In single channel mode, signals were introduced and verified at
individual inputs on each bioamplifier. Individual channel investigation was necessary, as the

Figure 2.7. Single channel data recorded through the device, sampled at 32 kSps. (A) 1 kHz sine wave.
(B) Simulated neural data from neural signal generator (Blackrock Microsystems). (C) Pre-recorded
neural data from rhesus macaque.

19

system was designed to process data at much lower rates and the microcontroller cannot handle
the throughput necessary for all 96 channels at 32 kSps. However, the spiking band of all 96
channels can be processed in multichannel mode at 2ksps. Figure 2.7 shows the results of testingin single channel mode, where details of the incoming signal can be easily viewed. First, a 1 kHzsine wave was introduced to the amplifier input and the sampled output at 32 kSps, shown in figure
2.7 (A). The device was next validated with simulated neural signals from a neural signal generator(Blackrock Microsystems). Figure 2.7 (B) shows an example recording where spikes were clearlyvisible. Figure 2.7 (C) shows an example using real, prerecorded neural data at 32 kSps from arhesus macaque performing finger flexion tasks. All channels looked and performed similarly.

In Vivo Data
For consideration as a future clinical system, the device also needs to be able to handle thenoise and impedances associated with live recordings. We tested this by recording directly from a
Utah array in a nonhuman primate. Figure 2.8 shows an example recording from six channels
showing clear single unit activity, manually picked out. Data recorded through the device had an
RMS noise of 3.2 µVRMS which is comparable to that of the Cerebus at 2.1 µVRMS. Thesechannels were recorded separately, as this device is not designed to process 30 kSps data on all 96
channels, as the MCU cannot sample that fast and it would consume too much power for the overallsystem. However, these results show it is possible to record one channel of broadband data forbasic science purposes, or for calibration purposes in a clinical system.

20

Figure 2.8. Single units recorded in vivo through the device. Each channel was recorded individually.

2.4.2 Spiking Band Power Validation
Module Outputs Spiking Band Power
Next, we tested the intended usage mode where all 96 channels are recorded at once, usinga spiking band filter similar to that used by Stark and Abeles (Stark & Abeles, 2007). The device
was configured to filter inputs between 300-1,000 Hz, which we previously showed to enable
decoding with 95% of the performance associated with decoding threshold crossing events(Irwin…Bullard et al., 2016). The microcontroller sampled all 96 channels at 2.17 kSps andtransmitted the averaged absolute value of the signal after 128 samples (~58 ms bins) to a computerfor analysis. This mode was tested with raw pre-recorded neural data from Utah arrays, playedback through the device, such that it could be compared in multiple ways. We performed the sameprocesses used on the device offline in Matlab. The same broadband dataset used to playback
through the device was filtered between 300-1,000 Hz with a 2nd order Butterworth filter,
downsampled to 2 kSps, and the absolute value was averaged over 58 ms to estimate the mean
signal power on each channel. The output from the pre-recorded data played back through the21device was compared to the offline processed result. The offline Matlab results and the device
output are shown in figure 2.9, where the two signals matched closely, with a Pearson’s correlation
coefficient of 0.9607.

Figure 2.9. Spiking band power validation. Power calculated from 2 kSps data offline compared to data
output from the device on a single channel.

Comparing Spiking Band to Spiking Rate
In Irwin et al, we showed that low bandwidth intracortical data could be used to predict amonkey’s continuous finger position. In comparison to decodes using spike counts acquired fromhigh bandwidth data, performance drops by only 4.9% (Irwin…Bullard et al., 2016). One
explanation of this is that signal power within 300-1,000Hz represents the firing rate of neurons
on a particular channel, leading to a similar decode performance. To further test whether the
spiking band power results from actual spikes, we compared the spiking band power output fromthe device to the firing rate obtained from thresholding the broadband data, as used in many online
BMI experiments. Datasets were chosen for the two monkeys (N, W) on days with similar tasks
and performance. Within these datasets, the channels with visualized single unit waveforms were22

used in the analyses. Using Matlab, the mean firing rate was calculated in 100 ms windows. The
same broadband datasets were replayed through our device, which returned the 2 kSps spiking
band power, which was then averaged offline in Matlab with the same 100 ms window size. We
compared the traces of mean firing rate and mean power over multiple channels and used thecorrelation coefficient to assess their agreement. This yielded a mean correlation of R = 0.8656 in
Monkey N and R = 0.8027 in Monkey W. The high correlation between firing rate and signal
power suggests that the spiking band is related to actual spiking events and is not only capturinglocal field potentials (LFP). The channel with the best performance in each monkey is shown infigure 2.10 (A). Histograms of per-channel correlations are shown in figure 2.10 (B). The varying
distribution of correlation across the channels and different animals can be attributed to thevariance of the amplitude and activity frequency of the neurons during the defined time window.

Figure 2.10. (A) Comparison of mean spiking band power from the device normalized to the maximum
power and firing rate calculated offline normalized to the maximum firing rate over 100 ms windows forthe best single channel. (B) Histograms of the correlation coefficients for all channels in the dataset.

23

2.4.3 Power Consumption
In each mode, power was calculated by measuring the total system current through ajumper. Most of the power was consumed by the three Intan RHD2132s and the Atmel
AT32UC3C2256C microcontroller. With all power saving techniques in use, system power wasmeasured at 33.6 mW while sampling 96 channels at 2 kSps, configured to filter between 3001000 Hz. As a comparison, the power required to transmit a single channel, filtered between 0.17,500 Hz at 30 kSps, using our system was 31.2 mW. To transmit a single channel at 2 kSps, only
22.1mW were needed. The primary single-channel power savings came from the reduced filter
bandwidth and sampling rate. While it would not be practical to stream 96 channels at 30 kSps, it
is possible to view single unit waveforms one at a time using this approach.
To achieve this lower power consumption and ease the burden on the overall system power
management of the NNP, we used several features of the MCU to decrease overall power
consumption, summarized in table 2.2. First, we enabled the DMA which allowed the device tocontrol sampling from the amplifiers without having to wake up the processor from a low-power
sleep mode. The DMA was more efficient at sampling and was necessary to acquire data from all
96 channels at 2 kSps. The core alone could manage a maximum of 1 kHz while at the full clock
rate, while the DMA could still exceed the sampling rate even after a major reduction in clock
speed. System power was measured at 45.3 mW while transmitting all 96 channels at 2 kSps using
the DMA. Next, we disabled all unused peripherals on the MCU. Using the DMA and turning off
unused peripherals decreased power consumption by 11% to 40.0 mW. Finally, the processor core
was configured to spend most of its time in IDLE sleep mode. Since the DMA automates sampling,
the MCU core only needs to wake up every 64 ms to bin the data. With all these power saving

24techniques enabled, system power was measured at 33.6 mW, a 16% reduction from when the
CPU is fully awake, and a 25% reduction overall.
In addition to the techniques discussed above, reducing the number of channels transmitted
could also reduce the amount of power consumed by the device. Selectively using only the welltuned channels has been shown to positively contribute to the decode performance (Wahnoun, He,& Helms Tillery, 2006). This concept of channel masking allows us to record from only a subsetof channels and retain the decode performance. We simulated this by recording from only a portionof the channels evenly distributed across all three amplifiers. Transmitting all 96 channels in thelowest power mode required 33.6 mW. Disabling roughly a third of the channels to transmit only
63 channels consumed 31.4 mW. Disabling approximately two thirds of the channels allowed a
50% reduction to the system clock speed, where transmitting 33 channels of data required 26.7
mW.
Table 2.2. Power Saving Techniques# Channels
1
1
96
96
96

Power Mode
30ksps2ksps
2ksps DMA sampling + CPU awake
2ksps DMA sampling + Disabled Peripherals + CPU awake
2ksps DMA sampling + Disabled Peripherals + CPU asleep

System Power31.2 mW
22.1 mW
45.3 mW
40.0 mW
33.6 mW

Additionally, for completeness, we tested functionality of the device’s CAN network
interface and power conditioning circuitry by connecting it exclusively to an NNP power moduleand validated that the devices could communicate. System power of the neural recording module
was measured at 43.6 mA, while being powered by the NNP power module at 3.98 V, andrecording and binning all 96 channels, using the DMA as described above.

25

2.5 Discussion
We have designed a 96-channel neural recording module prototype that can be used with
the NNP system. The neural recording module was designed using entirely off-the-shelfcomponents and all active circuitry fit within 1 cm x 4 cm panels. While the current NNP sensor
modules have a 1 cm x 3 cm hermetically sealed enclosure, increasing the length by 1 cm is notoutside of the capabilities of the manufacturer and does not compromise the design. Our device
extracts signal power in a narrow frequency band, which allows us to sample at a low rate of 2
kSps. We demonstrated the neural recording module consumed 33.6 mW when transmitting all 96
channels at 2 kSps using all power saving techniques. We have not yet implemented decoding andfull communication over the CAN network and expect these functions to draw some additional
power. However, we have tested the functionality of the CAN network while the device was being
powered exclusively by the NNP power module and found our device’s power consumption to be
similar to that of existing NNP permitting normal function of other modules (power, actuator, andsensor). Most demonstrations of wireless neural recording devices have been done using >50 Mbps
of digitized voltage traces, which is far beyond the specifications of implantable devices. The
ability to sample at a lower rate tremendously decreases the size of the data and will be key incommunication with the NNP network, which has a bandwidth of 100 kbps. Our current designuses three 8 mm x 8 mm 32-channel Intan bioamplifiers, which is a limiting factor in PCB lengthand power consumption of the module. However, the BGA packaging of the next generation 9 mm
x 7 mm 64-channel Intan bioamplifier offers the possibility of improving our current design bydecreasing the number of chips from 3 to 2. This will dramatically reduce the size of our board
without any major design changes, bringing us closer to the 1 cm x 3 cm package of the existing
NNP modules. Each individual bioamplifier consumes some baseline power, so the reduction in26

the number of Intan bioamplifiers required will eliminate a portion of this power consumed andenable significant power savings. In addition, if the design option were available to reduce the 3.3
V supply voltage required for the Intan bioamplifiers, that would lead to further reduction of theoverall power consumption of the device.
Additionally, channel masking provides opportunities to save even more power. Wahnoun
et al. found that less than 70% of recorded neurons are well tuned to movement direction and thatsome of these neurons actually decrease decoding performance. Selecting the best 20 neurons tocontrol their neuroprosthetic system performed better than using all neurons (Wahnoun et al.,2006). Reducing the number of channels transmitted will reduce the computational load and resultin a decrease in overall power consumption and prolonged battery life.
While it enables dramatic power savings, there are still open questions about exactly wherethe "spiking-band" signal is coming from. This is important in order to understand how far theapproach can go. Is it a local signal directly reflective of spikes? Or is it broader LFP likeelectrocorticography (ECoG), which can also contain some movement information (Chestek et al.,2011; Flint, Scheid, Wright, Solla, & Slutzky, 2016)? We have previously demonstrated that we
can drop the upper frequency of the spiking band filter from 6 kHz (Stark & Abeles, 2007) to 1
kHz with only a 4.9% decrease in performance (Irwin…Bullard et al., 2016). Here, we have shownthat the power in the spiking band is highly correlated with the firing rate of threshold crossing
spiking rate of the input high bandwidth data. This suggests that spiking band power may be mostreflective of the spikes themselves. This is not surprising since action potentials have a 1-1.5 ms
sinusoidal-like waveform, and we filtered between 300-1,000 Hz. Spike amplitude also falls off
quickly, theoretically as the reciprocal of the distance from the electrode (Holt & Koch, 1999;
Moffitt & McIntyre, 2005), which makes it possible that the spiking band signal is fairly local.

27

Based on the frequency components it is likely that the spiking band contains both localized spikes
and broader LFP. This may be a benefit to the traditional use of thresholded spikes because thespiking band may be used to still interpret useful data from channels that over time no longer havegood quality spikes.
Custom ASIC designs have been presented to be smaller in size and consume less power,
however, the noise, impedance, and large transient voltages that are present in live data within realworld settings make it difficult to also achieve signal integrity and stability under these conditions.
With academic bioamplifiers progressing to a more commercial platform, we can record live stablesignals under very realistic parameters using off the shelf components, as seen with this device.
Overall, using the signal processing techniques described here, custom ASIC-free designs may
have crossed the threshold of viability for multi-channel neural recording.
At the moment, percutaneous wires and power are arguably the most limiting factors intranslating BMI systems to clinical use. The best devices run for only a few hours on a battery or
rely on wearable components (Borton et al., 2013). So long as this is true, spiking band devices
may offer the fastest path to clinical viability. In the future, should the number of electrodes
become more of a limitation than the power, it may make more sense to extract the maximum
amount of information from each electrode by extracting single unit timing. Currently, however,
several hundred electrodes can be implanted percutaneously in humans (T Aflalo et al., 2015;
Collinger et al., 2013a; Gilja et al., 2015b; Hochberg et al., 2012a), without being able to processeven 100 of those channels with implantable electronics.
2.6 Conclusion
This device was designed to acquire data at configurable bandwidths, sampling rates, andchannel counts using the Intan bioamplifiers which could allow it to be used for applications

28

outside of Utah array recording. The freedom to choose the filter parameters supports a variety ofneural signal modalities, such as ECoG and EMG. While there are many interesting implantabledevices in development for EMG (Baker, Scheme, Englehart, Hutchinson, & Greger, 2010; Troyk,
DeMichele, Kerns, & Weir, 2007) as well as wireless ECoG for epilepsy (Mestais et al., 2015;
Vansteensel et al., 2010), these devices would serve relatively small markets by themselves.
However, the concept behind the NNP is that existing modules can be applied to multiple
applications and markets, as the stimulation and power modules can be used for a brain-controlled
FES device theoretically proposed in this work. The specifications in this paper, representing thefirst attempt to combine designs from different groups, can be used as a guide to develop further
application specific modules. While representing a departure from traditional medical devices, thereuse of components and circuitry from the existing NNP platform may dramatically accelerate
the overall process to a clinically viable system.

29

Chapter 3
Estimating Risk for Future Intracranial Neuroprosthetic Devices: A systematic review ofhardware complications in clinical deep brain stimulation and experimental humanintracortical arrays

A version of this chapter has been submitted and is currently under peer review to the Journal of
Neuromodulation.
Bullard, A. J., Hutchison, B. C., Lee, J., Chestek, C. A., Parag, P. G. “Estimating Risk for Future
Intracranial Modular Neuroprosthetic Devices: A systematic review of hardware complications inclinical deep brain stimulation and experimental human intracortical arrays.” Neuromodulation.
3.1 Abstract
A new age of neuromodulation is emerging: one of restorative neuroengineering andneuroprosthetics. As novel device systems move towards regulatory evaluation and clinical trials,
a critical need arises for evidence-based identification of potential sources of hardware-related
complications and advance estimation of safety risks to facilitate clinical trial design and fully
inform patient consent. The objective of this systematic review is to provide a detailed safety
analysis for future intracranial, fully implanted, modular neuroprosthetic systems. To achieve thisaim, we conducted an evidence-based analysis of hardware complications for the most establishedclinical intracranial modular system, deep brain stimulation (DBS), as well as the most widelyused intracranial human experimental system, the silicon-based (Utah) array. Of 2,328
publications identified, 240 articles met the inclusion criteria and were reviewed for DBS hardware
complications. The most reported adverse events were infection (4.57%), internal pulse generator
malfunction (3.25%), hemorrhage (2.86%), lead migration (2.58%), lead fracture (2.56%), skin30

erosion (2.22%), and extension cable malfunction (1.63%). Of 433 publications identified, 76
articles met the inclusion criteria and were reviewed for Utah array complications. Of 48 humansubjects implanted with the Utah array, 18 have chronic implants. Few specific complications aredescribed in the literature, hence implant duration served as a lower bound for complication-free
operation. The longest reported duration of a person with a Utah array implant is 1,975 days (~5.4
years). Through systematic review of the clinical and human-trial literature, our study provides themost comprehensive safety review to date of DBS hardware and human neuroprosthetic researchusing the Utah array. The evidence-based analysis serves as an important reference forinvestigators seeking to meet regulatory requirements and to design clinical trials for future
intracranial, fully implanted, modular neuroprosthetic systems.

3.2 Introduction
A new age of neuromodulation is emerging. Established open-loop neuromodulation
systems treat a broad range of neurological network disorders, including Parkinson disease, tremor,
dystonia, obsessive-compulsive disorder, epilepsy and pain. A newly approved closed-loop device
provides responsive neural control of epilepsy. A growing body of literature suggests promise forneuromodulation to treat intractable depression and enhance recovery from spinal-cord injury.
Experimental neuroprosthetic systems incorporate intracortical silicon-based arrays andnetworked sensing and stimulation modules to allow real-time neuroprosthetic control. As
technology advances and the number of modular systems grow, a need arises to anticipate thepotential safety features and shortcomings of future neuroprosthetic systems. Such analysis, based
upon all available evidence, is prerequisite to satisfying regulatory requirements, formulating
clinical-trial design and oversight, and fully informing patient consent.

31

The primary aim of this systematic review is to provide a detailed safety analysis to informfuture intracranial, fully implanted and modular neuroprosthetic systems. To accomplish this aim,
we examine the safety profiles of the most widespread intracranial clinical system, deep brain
stimulation, as well as the most widespread intracranial human experimental system, the siliconbased (Utah) array, to identify safety considerations inherent to emerging modular neuroprosthetic
systems and to derive the most reliable advance safety estimates possible for likely future
neuroprosthetic systems. Our comprehensive and systematic review of the safety literature for deepbrain stimulation and human trials of the Utah array provides greater detail and scope than many
earlier reviews by encompassing all indications for DBS and focusing upon the structuralcomponents of the DBS system. This review thereby achieves a secondary aim as the mostcomprehensive evaluation of DBS hardware safety to date. Detailed safety evaluation ofexperimental systems, such as the Utah array, has been difficult due to the dearth of complications
reported in the literature, which focuses upon scientific and technological advances. However,indirect indicators exist. For example, we can estimate the duration of complication-free Utah array
operation from reported periods of implant longevity in the literature. As a result, in addition toevaluating the potential safety of future modular intracranial device systems, this review alsoachieves an additional secondary goal of providing the most comprehensive safety and longevity
review to date of human neuroprosthetic research using the Utah array.
Currently FDA-approved chronically implanted intracranial neuromodulation systems
include deep brain stimulation (DBS) and responsive neurostimulation (RNS). DBS has been used
for decades to treat movement disorders (L. Ackermans et al., 2011; Rodríguez Cruz et al., 2016;
Weaver et al., 2009) and, more recently, to treat neuropsychiatric disorders and epilepsy (Abreu etal., 2017b; S. H. Kim et al., 2017b; Ooms et al., 2014). DBS systems are modular, consisting of a32

multi-contact lead, an internal pulse generator (IPG), and an extension cable. The multi-contact
lead is a depth electrode, typically 28 or 40 cm long, inserted into an intracranial target structure
through a burr hole in the skull. The lead is secured at the skull entry point via a burr hole cover.
The IPG is typically placed subcutaneously in the chest region. The extension cable connects thetwo via subcutaneous tunneling along the neck. As a related example, the responsive
neurostimulation system for epilepsy (RNS, NeuroPace Inc.) is similarly modular, consisting ofcortical strip or depth leads connected to a cranially-implantable neurostimulator unit (Heck et al.,2014).
To monitor and record brain electrical activity for neuroprosthetic applications, thecommercially available Utah array (NeuroPort, Blackrock Microsystems, Inc.) is FDA approvedfor human implantation up to 30 days, or longer with an investigational device exemption. The
NeuroPort Array consists of a 4.0 mm x 4.0 mm silicon-based microelectrode (Utah) array with
96 electrodes, extending 1.0 to 1.5 mm, and a wire bundle connecting the array to a pedestal (figure1). The pedestal penetrates the skin to provide electrical connectivity. A cable carries signals fromthe pedestal to front-end amplifiers and, ultimately, to a computer-based signal acquisition system
for recording and decoding (Hochberg et al., 2006; Truccolo, Friehs, Donoghue, & Hochberg,2008). Currently, the major limitation of the NeuroPort system is that tethering the pedestal toexternal hardware impedes mobility, constraining array use to laboratory settings. In addition, thetranscutaneous pedestal violates the barrier integrity of the skin, potentially raising the risk ofinfection. Any clinically adopted neuroprosthetic system will require the Utah array to beconnected to a fully implanted modular actuator system with the ability to record and respond to
stimuli, similar to closed-loop DBS or RNS.

33

An example of such a system is the Networked Neural Prosthesis (NNP). The NNP is afully implantable, modular functional electrical stimulation (FES) system which, in conjunction
with implanted Utah arrays, could form a fully-implanted future neuroprosthetic system. The
current NNP consists of a sensor module responsible for recording myoelectric activity, an actuator
module responsible for providing intramuscular stimulation, and a power module which functions
as the central power for all modules. The modules are connected by a serial cable, allowing
communication across modules. Combinations of these modules can be distributed around the
body to assist in a variety of functions lost by individuals due to spinal cord injury (US 7,260,436
B2, 2007; Smith et al., 2005). The current-version NNP only records residual myoelectric activity,
but because the system was designed to accept additional modules, it may be possible to add aneural recording module to record directly from the brain and facilitate cortical-controlled FES.
Hence, a combined Utah Array-NNP system becomes a useful exemplar system for safety analysis.
An overview of the exemplar, prototypical fully implantable, modular, neuromodulation systemthat can be used for FES is shown in figure 3.1 (B).
Our primary aim is to provide a detailed safety analysis for such a future intracranial, fully
implanted and modular neuroprosthetic system. To understand potential hardware complications
for such emerging systems, we have performed a systematic review focused on the hardware
mechanisms of DBS failure as well as longevity or safety of the Utah array in humans. DBS is awell-established, fully implantable system that is similarly modular to our exemplar prototypicalneuroprosthetic system. Hence, major safety concerns and potential failure modes of the Utah
Array-NNP system are hypothesized to be similar to those documented in DBS and Utah Array.

34

Figure 3.1. A) Overview of the standard brain machine interfaces set up. An electrode is implanted in thebrain and percutaneous connections are made between the patient and a series of computers. Thisparticular example is of brain-controlled FES (Ajiboye, 2017). B) An example of a potential future brain
machine interface set up using a modular network. An electrode is implanted in the brain and connected
to an implantable module for processing instead of a series of computers. This portrays the potential for a
fully implantable brain-controlled FES system using the NNP.

3.3 Methods
3.3.1 Search Strategy
A systematic review was conducted to identify the relevant literature on hardware
complications of DBS, by searching the electronic databases: Pubmed, ClincalTrials.gov, Scopus,
and Cochrane’s Library. The search was broken down into two concepts; device and risks. A
comprehensive list of keywords was generated to capture all synonyms of DBS and risks, including
both general terms and potential risks specific to DBS. All device related keywords were grouped
together by an OR operator and the same was done for the risk keywords. The device and risk
groups were then merged with the AND operator to identify all articles with mention of DBS and35

some form of risk or complication in the title or abstract (table 3.1).

Table 3.1. Keywords and Search Structure for Hardware Complications in DBS
DBS Synonyms

Risk Synonyms

Deep brain stimulation, Thalamic stimulation

Hematoma, bleed*, “short circuit”, fracture, breakage,
migration, infection, erosion, revision, risk, safety,
adverse event*, “adverse effects”, complication*,
hardware failure[DBS OR DBS synonym…] AND [ risk OR risk synonym…](*) symbol at the end of a word to include other terms that begin with the root word (i.e. –ing, -s).

A similar approach was taken to identify all articles including the use of the Utah array inhumans in the title or abstract. The search was again separated into two concepts: device andpatient, where a comprehensive list of all synonyms describing Utah arrays and humans was
generated. Each separate concept group was combined through the OR operator and then together
with the AND operator. In addition, a list of known principal investigators who have conducted
experiments with humans implanted with Utah arrays were identified. This was incorporated at
the end with the AND operator to help refine the search results (table 3.2).

Table 3.2. Keywords and Search Structure for Utah Arrays in Humans
Utah Array Synonyms

Human Synonyms

96 channel microelectrode array*,
96 channels electrode array*,microelectrode array*, intracortical
microelectrode array*, intracorticalbrain computer interface*,
Neuroport array*

Human, subject*, patient*,
tetraplegic*, quadriplegic*, person,
peopleSenior Authors
Donoghue, Hochberg, Kirsch,
Henderson, Shenoy, Greger,
Normann, House, Cash, Jang,
Zaghloul, Salas, Andersen,
Schwartz, Rezai, Collinger,
Schevon, Truccolo[Utah array OR Utah array synonym…] AND [human OR human synonyms…] AND [senior author OR seniorauthor…](*) symbol at the end of a word to include other terms that begin with the root word (i.e. –ing, -s).

36

3.3.2 Study Selection
The search results were critiqued through a two-reviewer process. Each reviewer
independently read the title and abstract of articles to screen for relevance. They were classified aseither possibly relevant or clearly irrelevant. Articles deemed as clearly irrelevant by both
reviewers were immediately excluded, and articles classified as possibly relevant by both
reviewers were immediately included. The articles where the reviewers disagreed were reviewed
again, discussed, and then resolved.
Predefined inclusion and exclusion criteria were created to further refine our search results
to include only the papers within the scope of the review, shown in figure 3.2. Selected articles
were required to have clinical data from a primary study, report on bleeding, infection, or hardware
complications related to DBS, and include a quantification of risk. Articles not reporting data on
hardware complications, and articles reporting data solely on revision procedures were excluded,
as well as case studies, review articles, and editorial letters. Only full-length articles in Englishthat met all criteria were eligible for inclusion. Two reviewers (AB and JL) independently read thefull-length articles and assessed against the inclusion criteria. Articles that met all criteria
according to both reviewers were included in this review. Articles that both reviewers agreed did
not meet one or more of the criteria were excluded. The remaining articles where the reviewers
disagreed were reviewed again, discussed, and then resolved.
3.3.3 Data Extraction
To avoid extraction errors, two reviewers independently extracted data from the eligible
articles and any discrepancies were discussed and resolved. Infection, hemorrhage, skin erosion,and hardware failures related to malfunctions of the extension cable and IPG, and fracture or
migration of the DBS electrode were the primary complications focused on in this review. For

37

each eligible article the following data were extracted about the adverse event: incidence rate,
location, the time of occurrence post initial surgery, if additional surgery was required, and if it
resulted in a total explant of the system.

Figure 3.2. Flow diagram of the study selection for DBS hardware complications.

3.4 Results
3.4.1 DBS Search Results and Study Characteristics
Our initial database search yielded 2,328 articles that contained our keywords and MeSH
terms in either the title or abstract. After screening titles and abstracts, 479 potentially relevant
articles were identified, and the full text was assessed against the inclusion criteria. Finally, 240
articles were chosen to be included in this meta-analyses (Abode-Iyamah et al., 2018; Abreu et al.,2017a; Linda Ackermans et al., 2011; Air, Ostrem, Sanger, & Starr, 2011; Akram, Limousin,
38

Hyam, Hariz, & Zrinzo, 2015; Alex Mohit, Samii, Slimp, Grady, & Goodkin, 2004; Allert et al.,2018; Alomar, Mullin, Smithason, & Gonzalez-Martinez, 2018; Alterman et al., 2007; Amirnovin,
Williams, Cosgrove, & Eskandar, 2006; Ashkan, Alamri, & Ughratdar, 2015; Aviles-Olmos et al.,2014; Baizabal-Carvallo, Kagnoff, Jimenez-Shahed, Fekete, & Jankovic, 2014; Baizabal Carvallo
et al., 2012; Ben-Haim, Asaad, Gale, & Eskandar, 2009; Bergey et al., 2015; Bergfeld et al., 2016;
Beric et al., 2001; R. Bhatia et al., 2011; S. Bhatia, Oh, Whiting, Quigley, & Whiting, 2008; S.
Bhatia, Zhang, Oh, Angle, & Whiting, 2010; Binder, Rau, & Starr, 2005, 2003; Bjerknes,
Skogseid, Sæhle, Dietrichs, & Toft, 2014; P Blomstedt & Hariz, 2005; Patric Blomstedt & Hariz,
2006; Bourne, Conrad, Konrad, Neimat, & Davis, 2012; Boviatsis, Stavrinou, Themistocleous,Kouyialis, & Sakas, 2010; Burdick, Fernandez, et al., 2010; Burdick, Okun, et al., 2010; Cersosimo
et al., 2008; Chan et al., 2009; Chang et al., 2011; D. Charles et al., 2014; P. D. Charles et al.,2012; Chen et al., 2018; Chen, Mirzadeh, Lambert, et al., 2017; Chen, Mirzadeh, Chapple,
Lambert, & Ponce, 2017; Chiou, 2016; Chou et al., 2007; Chowdhury, Wilkinson, & Cappellani,
2017; Chui, Alimiri, Parrent, & Craen, 2018; Cif et al., 2012; Constantoyannis, Berk, Honey,
Mendez, & Brownstone, 2005; Coubes et al., 2002; Cury et al., 2017; Dafsari et al., 2018; deQuintana-Schmidt et al., 2014; Deep-Brain Stimulation for Parkinson’s Disease Study Group etal., 2001; Delavallée, Abu-Serieh, de Tourchaninoff, & Raftopoulos, 2008; Delavallée, Delaunois,
Ruwet, Jeanjean, & Raftopoulos, 2016; DeLong et al., 2014; Deuschl et al., 2006; Diamond,
Shahed, Azher, Dat-Vuong, & Jankovic, 2006; Dlouhy, Reddy, Dahdaleh, & Greenlee, 2012;
Doshi, 2011; Dowd, Pourfar, & Mogilner, 2018; Downes et al., 2016; Egidi et al., 2007; Esselink
et al., 2004; S. M. Falowski & Bakay, 2016, 2016; S. M. Falowski, Ooi, & Bakay, 2015; S.
Falowski, Ooi, Smith, Verhargen Metman, & Bakay, 2012; Fenoy & Simpson, 2012, 2014;
Fernández-Pajarín et al., 2017; Fernández, Alvarez Vega, Antuña Ramos, Fernández González, &39

Lozano Aragoneses, 2010; Fily et al., 2011; Follett et al., 2010; Frizon et al., 2017; Fytagoridis &Blomstedt, 2010; Geller et al., 2017; Gervais-Bernard et al., 2009; Gocmen et al., 2014;
Gologorsky et al., 2011; Goodman, 2006; Gorgulho et al., 2009; Gorgulho, De Salles, Frighetto,& Behnke, 2005; Greenberg et al., 2006; Gubler et al., 2017; Guridi, Rodriguez-Oroz, Alegre, &Obeso, 2012; Hardaway, Raslan, & Burchiel, 2018; Harries et al., 2012; Henssen et al., 2018;
Higuchi et al., 2016; Hilliard et al., 2016; Holslag et al., 2018; Holtzheimer et al., 2017; K. Hu,
Moses, Hutter, & Williams, 2017; X. Hu et al., 2010; Iansek, Rosenfeld, & Huxham, 2002; Isaias,
Alterman, & Tagliati, 2009; Janson, Maxwell, Gupte, & Abosch, 2010; Jobst et al., 2017; Joint,
Nandi, Parkin, Gregory, & Aziz, 2002; Kahn et al., 2012; Kaminska et al., 2012, 2017; Kawakami
et al., 2005; Keen, Przekop, Olaya, Zouros, & Hsu, 2014; Kefalopoulou et al., 2015; Khatib et al.,2008; M. Kim et al., 2017; M. S. Kim, Jeong, Ryu, Choi, & Chung, 2017; S. H. Kim et al., 2017a;
Klein et al., 2017; Kochanski, Nazari, & Sani, 2018; Koller et al., 1997; Kondziolka, Whiting,
Germanwala, & Oh, 2002; Koy et al., 2017; Kramer, Halpern, Danish, Jaggi, & Baltuch, 2012;
Krause et al., 2016; Kumar et al., 2000; Kupsch et al., 2006; Landi et al., 2003; J. Y. K. Lee &Kondziolka, 2005; S.-W. Lee et al., 2014; Lefebvre et al., 2017; Lempka et al., 2017; Leone,Franzini, Proietti Cecchini, & Bussone, 2013; Levi, Carrabba, Rampini, & Locatelli, 2015; Levy,
Lamb, & Adams, 1987; Lezcano et al., 2016; Li et al., 2013, 2017, Lim et al., n.d., 2007; Limousin,
Speelman, Gielen, & Janssens, 1999; Linhares, Carvalho, & Vaz, 2013; Lipsman et al., 2017;
Loher et al., 2002; Lozano et al., 2016; K E Lyons, Koller, Wilkinson, & Pahwa, 2001; Kelly E
Lyons, Wilkinson, Overman, & Pahwa, 2004; Maldonado et al., 2009; Mandat et al., n.d.; Martin
et al., 2017; Martinez-Ramirez et al., 2018; J H Mehrkens et al., 2009; Jan Hinnerk Mehrkens,
Borggraefe, Feddersen, Heinen, & Bötzel, 2010; Mendes Martins et al., 2012; Meoni et al., 2017;
Merola et al., 2017; Messina, Rizzi, Dones, & Franzini, 2014; J. P. Miller, Acar, & Burchiel, 2009;40

P. M. Miller & Gross, 2009; S. Miller et al., 2016; Morishita et al., 2017; Moro et al., 2010;
Motlagh et al., 2013; Movement Disorder Group et al., 2014; Nahas et al., 2010; Nazzaro et al.,2010; Nunta-Aree, Sitthinamsuwan, Boonyapisit, & Pisarnpong, 2010; O’Sullivan & Pell, 2009;
Odekerken et al., 2013; Oh, Abosch, Kim, Lang, & Lozano, 2002; Okun et al., 2012; Oliveria etal., 2017; J L Ostrem et al., 2011; Jill L Ostrem et al., 2013, 2016, 2017; Oyama et al., 2011; Pahwa
et al., 1999, 2001; Paluzzi, Belli, Bain, Liu, & Aziz, 2006; Panov et al., 2013; C. K. Park, Jung,
Kim, & Chang, 2017; J. H. Park, Chung, Lee, & Jeon, 2011; Y. S. Park et al., 2011; D. M. Patelet al., 2015; Peña et al., 2008; Pepper et al., 2013; Petraglia et al., 2016; Petrossian et al., 2013;
Piacentino, Pilleri, & Bartolomei, 2011; Ponce et al., 2016; Putzke et al., 2003; Ramayya et al.,2017; Rasouli & Kopell, 2016; Reuter, Deuschl, Falk, Mehdorn, & Witt, 2015; Rosa et al., 2017;
Ryu et al., 2017; Sachdev et al., 2014; Salanova et al., 2015; Sansur et al., 2007; Schuurman,
Bosch, Merkus, & Speelman, 2008; F. Seijo et al., 2014; F. J. Seijo, Alvarez-Vega, Gutierrez,
Fdez-Glez, & Lozano, 2007; Servello, Sassi, Gaeta, Ricci, & Porta, 2011; Sillay, Larson, & Starr,
2008; Sixel-Döring, Trenkwalder, Kappus, & Hellwig, 2010; M. Sobstyl, Kmieć, Ząbek,Szczałuba, & Mossakowski, 2014; M. Sobstyl, Ząbek, Dzierzęcki, Koziara, & Mossakowski, n.d.;
M. R. Sobstyl, Ząbek, Brzuszkiewicz-Kuźmicka, & Pasterski, 2017; Solmaz et al., 2014; Son etal., 2012; Staudt, Pourtaheri, Lakin, Soltanian, & Miller, 2017; Stroop, Holms, Nakamura, &Lehrke, 2018; Sydow, Thobois, Alesch, & Speelman, 2003; Tanei et al., 2009; Temel et al., 2004;
Terao et al., 2003; Testini et al., 2016; Themistocleous et al., 2017; Tir et al., 2007; Tolleson et al.,2014; Tonge et al., 2015; Umemura et al., 2003, 2011; Velasco et al., 2007; Vergani et al., 2010;
Verla et al., 2015; J. Vesper, Klostermann, Wille, Funk, & Brock, 2004; J Vesper, Chabardes, etal., 2002; J Vesper, Klostermann, Stockhammer, Funk, & Brock, 2002; Jan Vesper, Haak,
Ostertag, & Nikkhah, 2007; Vidailhet et al., 2005, 2007; J Voges et al., 2006; Jürgen Voges et al.,41

2007; Volkmann et al., 2012, 2014; X. Wang et al., 2017; Welter et al., 2017; Wharen et al., 2017;
White-Dzuro, Lake, Eli, & Neimat, 2016; White-Dzuro, Lake, & Neimat, 2017; Williams et al.,2016; Wojtecki et al., 2015; Xiaowu et al., 2010; Yianni et al., 2004; Zhan et al., 2018; J. Zhang
et al., 2017; K. Zhang et al., 2010; Zrinzo, Foltynie, Limousin, & Hariz, 2012; Zsigmond, HemmOde, & Wårdell, 2017) (figure 3.2). The remaining 239 articles were excluded for the following
reasons: not DBS or related (n= 47), no quantification of risk (n=15), secondary revision
procedures (n= 16), case studies, review articles, or editorial letters (n=145), not published in
English (n= 5), full-length article unavailable (n=21). A total of 27,299 patients across articles
were included in this analysis. Notably, only 7 hardware related deaths occurred within thesepatients. All complication incidence rates are reported in table 3.3, with a breakdown of thereported locations of the complications. Because all articles reporting a complication, did notreport the location of the complication the incidence rate depicts what could be accounted for.
Table 3.3. DBS Hardware Related Adverse Events
Complication

Incidence (%)

No. of patients reported

Infection

4.57

951 (20805)

IPG

20.4

2957 (14495)

scalp/burr hole

9.701406 (14495)

extension cable

8.24

1194 (14495)

lead

1.03

149 (14495)

IPG malfunction

3.25

141 (4331)

Hemorrhage

2.86

425 (14831)

intracerebral (ICH)52.96340 (11963)

IPG

22.1

2655 (11963)

Extension cable2.35

281 (11963)

Lead migration

2.58

121 (4677)

Lead fracture or failure

2.56

178 (6940)

Skin erosion

2.22

198 (8924)

Extension cable malfunction

1.63

84 (5145)

Total Overall Complication

7.68

2098 (27299)

42

3.4.2 Hemorrhage
Bleeding is always a major concern when implanting electrodes into the brain. Hemorrhage
during and after surgery can lead to neurological damage and even death in severe cases. Of all
the articles included in the study, 100 consisting of 14,831 patients reported on hemorrhage. The
overall incidence rate was 2.86%. Intracerebral hemorrhage (ICH) was the most common of thereported occurrences (52.9%), during the duration of the implanted system. While hemorrhage ismost likely to occur in the brain for these procedures, it also occurred in other areas throughout
the body as well: at the site of the IPG (4.2%) and along the extension cable (2.35%). When a
bleed occurs, it is usually reported to happen intraoperatively or within a few days of the surgery.
Bleeding should always be taken seriously, however the risk posed by the reported hemorrhage
ranged in severity and the action taken to resolve it. Some hemorrhages resolve on their own
without any external intervention, while others are more serious and may require additional
surgeries or other procedures. For the purpose of this review, any adverse events that required an
additional surgery were deemed as serious adverse events. In serious cases, the device is normallyexplanted. Usually bleeds that occur outside of the brain can be resolved and then hardware canbe re-implanted. In all the studies, there were only 6 reported deaths due to ICH (0.04%).

3.4.3 Infection
Second to bleeding, infection is often considered the next most prominent adverse event tobe cautious of in any surgical procedure, especially when there are foreign objects introduced
inside the body, particularly the brain. With future modular systems expected to incorporate
multiple implantable devices around the body, this is of major concern. There were 140 articles
consisting of 20,805 patients that reported data on infection. It was the most frequently occurring

43

adverse event, with an incidence rate 4.57%, and was the main reason for ultimate device
explanation. There was only one reported death related to an untreated infection. The infections
observed varied widely in their location, time of incidence relative to the initial surgery, andseverity. Of the 4.57% of infections reported most were located at the site of the IPG (20.4%),
followed by the connector and extension cable (8.6 %), the scalp or burr hole (9.7 %), and in thebrain along the electrode lead (1.47 %). Of the 49 studies who reported time, infections areobserved within the first 30 days of surgery (10.2%), however it can also occur months thereafter.
Most cases of infections in the brain were reported early, within days, whereas infections that
occurred around hardware outside of the brain took longer to appear. The majority of the reported
infections were classified as severe, meaning they resulted in the patient having additional surgery.
However, although additional surgery was required, in 35.9% of cases the infection was revised,
and the hardware was ultimately re-implanted allowing DBS therapy to continue. Depending on
the location of the infection only a subset of the system would be explanted and re-implanted. It
was rare that the entire system had to be explanted and then re-implanted. However, if the infection
was extremely severe and widespread the entire system would be permanently explanted (20.7%).
This was typically seen in instances of infection that had tracked along the DBS electrode.

3.4.4 Skin Erosion
Skin erosion is defined here as any place where there was a breakage of the skin due toimplanted hardware nearby. Erosion of the skin is most commonly seen over the IPG and on thescalp at the site of the burr hole or the connector where the extension cable and electrode meet.
This was reported in a total of 2.22% of cases and was very commonly associated with infection.
In 62.6% of cases where skin erosion was reported there was also a case of infection reported near44

the site of erosion. An additional surgery or procedure was required in 25 % of cases with skinerosion. Typically, wound debridement or surgical closure was used to repair skin erosion,
however some instances required revisions and re-implantation of hardware. The more severecases stemmed from erosion around the burr hole or the connector site and, where the electrodelead or extension cable was replaced. There were only 18 cases of skin erosion (9%) that led to thepermanent explant of the entire system.
3.4.5 Other Hardware Failures
In addition to the previously mentioned hardware failures, there were also malfunctions of
the extension cable and IPG, and fracture or migration of the DBS electrode. This includes mostof the complications due to DBS hardware that are potentially relevant to future chronic tethereddevices. Overall, IPG malfunction occurred 3.25% of the time, extension cable malfunction
occurred 1.63% of the time, lead fracture occurred 2.56% of the time, and lead migration occurred
2.58% of the time. These are typically not dangerous in and of themselves, though in a single case,a patient was electrically shocked due to the malfunction of the IPG(K E Lyons et al., 2001). In
most cases however, these are complications that usually require additional routine surgeries,
which do have their own associated risks. In 22.6 % of reported cases (including the electricalshock incident), they were able to revise and fix. In only 2 cases was a complete explantationnecessary.

3.4.6 Utah Array Search Results and Study Characteristics
Our initial search identified 433 articles, which resulted in 76 articles after screening where
humans had been implanted with the Utah array (figure 3.3). The Utah array, a 96-channel
microelectrode array (Blackrock Microsystems), has been implanted intracortically in a total of 48

45

Figure 3.3. Flow diagram of the study selection for Utah arrays.

subjects as of September 2018. This consists of patients implanted for epilepsy and otherintraoperative opportunities where tissue would have been ablated, and patients with paralysis.
The demographic of Utah array implants is dominated by acute cases, usually to study epilepsy,
anesthesia, or cognition, memory or language. A smaller subset of the cases are chronic implants,
used to study brain machine interfaces for motor control of prosthesis and stimulation for sensory
mapping. Of the 48 people implanted with the Utah array, 30 were implanted for less than 30 days
and 18 people were implanted chronically for more than 30 days (Tyson Aflalo et al., 2015;
Ajiboye et al., 2017; Ajiboye, Simeral, Donoghue, Hochberg, & Kirsch, 2012b; Annetta et al.,2018; Armenta Salas et al., 2018b; Bacher et al., 2015; Bouton et al., 2016; Brandman et al., 2018;
Chadwick et al., 2011; Colachis et al., 2018; Collinger et al., 2013a; Downey et al., 2016, 2017;46

Downey, Schwed, Chase, Schwartz, & Collinger, 2018; Even-Chen et al., 2018; Flesher et al.,2016; Friedenberg et al., 2017; Gilja et al., 2015a; Hochberg et al., 2006, 2012a; Homer et al.,2014; Jarosiewicz et al., 2013, 2015; Jitkritsadakul et al., 2017; S.-P. Kim et al., 2011, 2008; Klaes
et al., 2015; Malik, Hochberg, Donoghue, & Brown, 2015; Masse et al., 2014; Milekovic et al.,2018; Pandarinath et al., 2015, 2017a, 2018, Perge et al., 2013, 2014; Rutishauser et al., 2018;
Shaikhouni, Donoghue, & Hochberg, 2013; Simeral, Kim, Black, Donoghue, & Hochberg, 2011;
Truccolo et al., 2008; Truccolo, Hochberg, & Donoghue, 2010; Willett, Murphy, Memberg, et al.,2017; Willett, Murphy, Young, et al., 2017; Willett, Pandarinath, et al., 2017; Wodlinger et al.,2015b; Yang et al., 2017; Young et al., 2018; C. Y. Zhang et al., 2017). We have identified all thesenior authors and the sites, to our knowledge, involved in studies with human implants in table
3.4.

47

Table 3.4. Human Utah Array Implantation Sites and Senior Author Involvement
Chronic/Acute

Site

Senior Authors

No. of Implants

Collinger JL
University of Pittsburgh
Schwartz AB

2

Gaunt RA
California Institute of Technology, Rancho Los
Andersen RA
Amigos National Rehabilitation Hospital (RLA)
Brown University, Massachusetts General
Hospital
Chronic

3

Donoghue JP
Hochberg LR
Henderson JM
12Stanford University
Shenoy KV
Kirsch RF
Case Western Reserve University
Ajiboye AB
Rezai AR
Ohio State University

1
Sharma G

Total Chronic Implants

18
House PA
2

University of Utah Health Sciences Center

Columbia University Medical Center
Acute

Greger B
Normann RA

6Schevon CA

6

Cash SS

3

Truccolo W

7

Massachusetts General Hospital
National Institute of Health6
Zaghloul KA

Total Acute Implants

30

Total Human Utah Array Implants

48

The longest reported duration of a person with a Utah array implant is at least 1,975 days(~5.4 years), shown in figure 3.4 and table 3.5. S3, a participant in the BrainGate2 pilot clinical
trial, was first implanted November 30, 2005, and while it has not been reported that her array has
48

actually been explanted, it was documented that she completed her enrollment in the BrainGate2
clinical trial (Masse et al., 2014). Of the chronic cases, there was only one report of an explanted
Utah array in the literature. Participant S1 at the University of Pittsburgh was implanted for 987
days and then explanted due to skin retraction around the pedestals. However, it was reported that
there was no sign of infection. The only other mention of the safety of the Utah array implant waswith participant EGS at California Institute of Technology. They reported that there was no device
related adverse events to occur throughout their study (T Aflalo et al., 2015; Klaes et al., 2015).
Table 3.5 shows the breakdown of chronic patients across different studies and the lengths ofTable 3.5. Reported Duration of Chronic Human Implanted Utah Arrays
Location

University ofPittsburgh

California Instituteof Technology

BrainGate2

Ohio State
University

Participant

Implantation
Date

Reported
Duration

Adverse Events

S1

Feb 10,2012

987

S2

-

673

S1 explanted due to skinretraction around the pedestals,
no sign of infection

EGS

-

630

NS

-

-

FG

-

56

S1

-

90

S3

Nov 30, 2005

1975

A1

Feb 2006

239

T1

-

270

T2

Jun 2011

474

T5

Aug 2016

70

T6

Dec 7, 2012

837

T7

Jul 30, 2013

548

T8

Dec 1, 2014

928

T9

-

-

T10

-

33

MN

Jun 2004

300

S1

-

1144

*Reported duration is not equivalent to Utah array failure

49

T7 death unrelated to researchreported implantation. The end of the reported duration of the implant does not mean that the Utaharray has failed, however just the last reported published date for that participant. There have beena reported 9,254 of total published implant days.

Figure 3.4. Length of chronic human implants reported in literature across clinical study sites.

3.5 Discussion
The safety surrounding neuromodulation technology is a critical question for bothestablished and emerging systems. Hardware-related complications can result in potentially injury
to the patient, repeated surgical procedures, and reduced clinical efficacy. In this comprehensive,
systematic review we found that DBS had an incidence rate of 7.68% for total hardware related50

complications. The most common adverse events were infection, followed by IPG malfunction,
hemorrhage, lead migration, lead fracture or failure, skin erosion, and malfunction of the extension
cable. Using this information, we are able to infer potential safety challenges that future
intracranial, fully implanted and modular neuroprosthetic systems may face and begin thediscussion on how to plan for and mitigate these risks when developing a clinical trial. We haveidentified the following adverse events to be potentially most salient to emerging systems:
hemorrhage, infections, skin erosions, and malfunctions of the extension cable.
3.5.1 Hemorrhage
Based on this review, bleeding in the brain during or immediately after the surgery is themost critical adverse event that can risk the safety of the patient. In the DBS cases reporting ICH,
the clot most often tracked along the lead and extended into the brain. Bleeding was not typicallyseen on the surface of the brain. The biggest difference between DBS and future neural implants
is the type of electrode used. Since the Utah array is currently the only device used chronically inhumans, and one of the arrays most likely to be incorporated into future modular devices, we will
focus on this array. While a DBS lead extends several centimeters into the brain, the Utah array ismuch shallower, inserting only 1.5 mm into the brain. Therefore, if most of the bleeds that occur
in DBS are not on the surface, it is likely that the DBS lead is perturbing blood vessels deeper inthe brain that the Utah array would miss (Kozai et al., 2010). It is possible that the incidence rateof ICH experienced in DBS could be an overestimate of what we would see in future intracranial,
modular neuroprosthetic systems. In any case, bleeds can be very serious, but they are expected,
and protocols have been established to manage them.

51

3.5.2 Infection
Infections pose the highest risk for terminal explantation of the DBS entire system (20.7%).
Reported infections were predominantly found at the site of the IPG, followed by the burr hole,and then the extension cable. While in DBS there is only one IPG, in the case of the NNP and otherfuture modular systems, there will be many more “IPGs” and extension cables routed throughout
the body. This has the potential to increase the rate at which infections arise and possibly affecttheir ability to spread throughout the body. Infections in DBS are typically managed with
antibiotics, or portions of the system may be explanted while antibiotics is administered and thensuccessfully re-implanted. Re-implantation is the main treatment for infection in such systems.
While they are categorized as serious adverse events, they are very common. Future modular
systems may have the potential for increased incidence of infection, however this does notnecessarily have to be a failure of the system. As seen in DBS, revision procedures occur frequently
without ultimately ending the therapy.
3.5.3 Skin Erosion
Skin erosion, while occurring less frequently, commonly occurs with infection. When there
is erosion or breakage of the skin the area becomes susceptible and leads to infection of the area.
Since skin erosion was most likely to occur over the IPG, a modular system with multiple IPGs,
as proposed with the NNP and potential future devices, may experience an increased incidence
rate than reported in DBS. Knowing this we can begin to investigate surgical procedures for thebest placement of these IPGs throughout the body and how to implant them deeper as to reduce
the risk of erosion. Similar to infection, cases of skin erosion are also categorized as serious,
requiring a surgical revision. This may be treated with wound debridement but is most likely tolead to explantation of a portion or the whole system.

52

3.5.4 Extension Cable Malfunctions
Although there was a low incidence rate of extension cable malfunctions in comparison tothe other adverse events in DBS, this is vital information. The addition of a wire to connect the
Utah array to an implantable module is where most of the uncertainty lies with the safety of future
intracranial, modular devices. Malfunctions of the extension cable usually involve Twiddler’s
syndrome or bowstringing which can lead to fracture of the cable or displacement of the electrode.
One of the biggest risks with future Utah array tethered devices is that tension on the extension
cable has the potential to dislodge the implanted array. However, of all the reported extension
cable malfunctions, none led to the displacement of a DBS electrode, likely due to anchoring at
the burr hole. All complications were due to breakage of the cable, which were then replaced. Mostof the complications with the extension cable occur in the neck, however with future Utah array
tethered devices the module directly connected to the Utah array will be secured to the skull. This
smaller device may decrease the risk for potential electrode array dislodgement.

3.5.5 Utah Array Safety and Longevity
Utah arrays have been implanted in substantially fewer individuals, for shorter duration,and the literature does not contain much about the safety of these implants as compared to DBS,
which has a sufficiently large population to reveal rare safety events. Since there has been no
publication to date that explicitly discusses the risks or adverse events that occur in chronic humanimplants, we systematically reviewed all the published literature to address this topic. We
identified 48 individuals implanted with a Utah array and determined the duration of implantation
at the time of the study. This represents a starting point for a safety dataset of all FDA monitored
studies. Acute studies are more common and have been the dominant contributor to the population53

of subjects with Utah arrays. While long-term effects are not observed in these subjects,
observations from short term studies help to estimate intra- hemorrhage, although not explicitly
mentioned or discussed in any papers we reviewed. Infection typically prompts explantation of thearray, so array longevity provides a lower bound on the period of time without serious infection orother adverse event. The mean number of days of Utah array implantation across all participants
was 578. This underestimates implant time. With the exception of one paper, no study reported
array explantation. In the absence of complication, participants typically remain implanted
following the conclusion of the study.
Because DBS systems are clinically available and have been implanted in many patients,
incidence rates of DBS adverse events serve as a risk profile benchmark for future Utah array
modular systems. Understanding the potential risks and failure modes of a device and how many
people must be observed to witness such risks is important information when designing a clinical
trial. For example, we conducted a power analysis to estimate the number of patients implantedwith the Utah array needed to see similar incidence rates as DBS. We found that it would take avery large amount of people within a clinical trial before we would begin to see complications
with similar incidence rates as DBS systems. By contrast, if Utah array tethered devices introduced
5x the risk of DBS we would be able to see it much earlier and with fewer people (table 3.6). Given
the low incidence rate of infection in DBS, we would not have expected to see any complications
in cases with as few as 18 chronically implanted Utah arrays. It is also notable that these Utaharrays were all percutaneous and would likely have a higher infection rate than a fully implanted
system.

54

The large number of people needed to do a true safety study is far off. However, few
individuals are required to demonstrate the efficacy of neuroprosthetic systems. Efficacy may
therefore have to be established before safety studies can begin. Early feasibility human trials
would be beneficial in not only moving the needle in technology surrounding the future of
implantable intracortical devices, but also helping to increase the population of people with thesedevices for a comprehensive understanding of safety over time.
Table 3.6. Example Power Analysis
Complication

Sample size needed to reject Null

Power (1-β)

α

1.5x

2x

5x

Infection

602

167

15

.80

.05

Skin erosion

692

193

18

.80

.05

Hemorrhage

956

267

25

.80

.05

Extension cable malfunctions

1555

435

41

.80

.05

3.5.6 Study Limitations
There is currently no standard reporting for adverse events related to DBS hardware or
Utah array safety, thus this review is incomplete. There were some DBS articles that contained
data on hardware-related complications that were excluded because either the information was too
general (i.e. grouping infections and skin erosions and other skin complications together) or thedata was per electrode lead and not per patient. Papers also generally lacked the time in which
adverse events took place. In addition, papers discussing human research with the Utah array did
not disclose any adverse events and some lacked important details such as the implantation date.
Ultimately, safety questions will be best addressed in a sufficiently powered, prospective clinical
trial. In the meantime, pilot studies will continue to contribute valuable data points over time by55

including implant and explant dates and the time frame of experiments. As this literature grows,
these data will better inform future device and clinical trial design.
3.6 Conclusion
Through systematic review of the clinical and human-trial literature, our study provides themost comprehensive safety review to date of DBS hardware and human neuroprosthetic researchusing the Utah array. The evidence-based analysis serves as an important reference forinvestigators seeking to meet regulatory requirements and to design clinical trials for future
intracranial, fully implanted, modular neuroprosthetic systems.

56

Chapter 4
Reporting of Incidental Safety Data of the Utah Array in a Rhesus Macaque

4.1 Abstract
Brain machine interfaces have the potential to restore function and improve the quality oflife for many people. However, there is much that still needs to be explored in terms of the tissue
responses that contribute to the failure of intracortical electrodes in order to transition to routine
clinical adoption. The stability and longevity of these arrays are critical factors for determining
whether the clinical benefit outweighs the risk for potential users. Here we investigate the extent
of damage in neural tissue from post-mortem histology of a rhesus macaque implanted with two
Utah arrays for 818 days. Tissue was stained for neurons, microglia, and nuclei. We quantified theneuron density from eight sampled locations under the array in comparison to tissue surrounding
the array on three different slices using ImageJ. The neuron density for the sampled tissue
surrounding the Utah array averaged across all three slices was 37,559 neurons/mm3. The mean
neuron density of the 8 samples from under the array averaged across the three slices was 8,192
neurons/mm3, which is a 78.3% reduction. Due to large amounts of remodeling and uncertainty inthe exact depth of the slices used for sampling, the layer of brain cannot be determined. These
initial histologic findings give preliminary insight into the potential damage of a chronic Utaharray. However, additional histology from more primates are needed for further investigation.

57

4.2 Introduction
Brain machine interfaces (BMIs) have shown great potential to restore motor and sensory
functions to those suffering from neurological disorders. These devices require an intracorticalelectrode array to record neural activity with a high level of specificity across a large populationof neurons in order to control computer cursors and robotic arms (Nordhausen et al., 1996). The
Utah array (Blackrock Microsystems), a silicon based intracortical microelectrode array, is thecurrent state-of-the-art electrode array that has been used to study memory, anesthesia, cognition,
as well as for BMI applications in numerous animal and human clinical studies (Collinger et al.,2013b; Gilja et al., 2012; Hochberg et al., 2012b; Z. T. Irwin…Bullard et al., 2017; S.-P. Kim etal., 2008; Velliste et al., 2008; Schroeder…Bullard et al., 2017). Reportedly, the longest lastingcase of a functional Utah array in a human has been approximately 5.4 years during the BrainGate2
clinical trial (Masse et al., 2014). While this provides encouragement around the concept of multiyear recordings with the Utah array, studies with non-human primates show indications that thearray fails at many different time intervals.
The stability and longevity of these arrays are critical factors for determining whether theclinical benefit outweighs the risk for potential users. Despite improvements in electrodetechnology, an intracortical microelectrode that can function reliably for at least a decade has yet
to be introduced to the field of BMI. As a consequence, many studies have been done to investigate
the failure mechanisms of silicon electrodes that threaten long-term recordings (Barrese et al.,2013; Prasad et al., 2012). Previous studies have demonstrated that the signal quality recorded
from the Utah array generally attenuates over time (Chestek et al., 2011), which may ultimately
lead to the functional loss of the array.

58

Among the key factors implicated in the failure of the arrays, tissue response andintegration prior to implantation is suggested to be one of the main contributors (Barrese et al.,2013; Welle, Street, Ruda, Civillico, & Takmakov, 2017). This tissue response to the electrodes
has been well studied, investigating mechanisms consisting of fibrotic tissue encapsulation, glial
scarring, inflammation, and neuronal migration and death (Barrese, Aceros, & Donoghue, 2016;
Biran, Martin, & Tresco, 2005; Black et al., 2018; Nolta, Christensen, Crane, Skousen, & Tresco,
2015; Polikov, Tresco, & Reichert, 2005; Stiller et al., 2018; Szarowski et al., 2003; Turner et al.,1999). Histological data used to study these mechanisms chronically, have been primarily fromrats and cats. Due to ethical reasons and cost it is very difficult to obtain post-mortem histological
data to study these tissue responses in non-human primates or humans.
While monkeys are rarely perfused, in this rare case, we performed a euthanasia andperfusion on a rhesus macaque chronically implanted with Utah microelectrode arrays at the endof their experimental lifetime. Here we investigate the long-term neuronal damage from tissue
under the implant site and compare neuron densities with control brain areas within the same
animal. This explores the question of safety surrounding chronic implants that can’t be studied inhumans. Additionally, the findings of this work will help further expand the small pool of data forhistology of chronically implanted arrays in non-human primates.

4.3 Methods
4.3.1 Surgical Implantation
All animal procedures were approved by the University of Michigan Institutional Review
Board and the Institutional Animal Care & Use Committee. Four Utah arrays (Blackrock
Microsystems) were implanted in a male rhesus macaque. Two were implanted in the primary59motor (M1) and sensory (S1) cortex of the left hemisphere on August 20, 2015. The remaining
two were implanted similarly on the right hemisphere on May 4, 2016. Only the tissue from thearrays in the left hemisphere were stained and imaged, and included in the results below. These
two arrays were implanted for 818 days. The electrode tips of the array in M1 consisted of IrOx,
while the electrode tips of the array in S1 consisted of IrOx and aluminum oxide coating.
For each original implantation, the monkey was induced under general anesthesia andplaced in a stereotactic frame. The craniotomy site was located using the stereotactic frame toestimate the location of the central sulcus. Following the craniotomy, the dura was resected andarrays were inserted using a pneumatic inserter (Blackrock Microsystems). The dura was closed
and duragen was laid over the craniotomy site. The bone flap was then replaced and secured withtitanium screws. Dental acrylic was applied to secure the connectors and build up a head cap.

4.3.2 Perfusion and Tissue Processing
The animal was perfused transcardially, December 15, 2017, with 1X Phosphate Buffer
Saline (PBS) until the exudate was clear and then fixed with 4% paraformaldehyde (~1000 ml,
Electron Microscope Sciences). Due to the protocol, perfusion could not be performed underanesthesia, thus ex vivo perfusion began approximately 4 minutes after the death was confirmedby the veterinarian. The brain was carefully removed from the skull approximately 4 hours after
perfusion and was further immersion fixed in 4% paraformaldehyde for 72 hours, followed by anadditional 48 hours after the removal of the skull and arrays to further fix the tissue under the arrayand bone growth. Gross dissection of the tissue surrounding the implantation sites was performed
and the tissue was fixed in 4% paraformaldehyde (PFA) for 5 days and then stored in 1X PBS for
8 days at 4oC. To cryoprotect the tissue, samples were put in 30% sucrose (Sigma) in 1X PBS at60

4oC for 26 days to reach equilibrium. The tissue was frozen at -80oC in optimal cutting temperaturecompound (Sakura Finetek) and sliced perpendicular to the implantation sites in 100 μm sections
at -16oC. Slices were stored in 0.02% azide (Dot Scientific) in 1X PBS at 4oC until
immunohistochemical labeling.

4.3.3 Explantation of Arrays.
Explanatation of the Utah arrays began after perfusion of the rhesus macaque. The methyl
was carefully removed with a handheld drill. A 2-3 cm bone flap, overlaying the arrays, wasoutlined by drilling the bone down to the dura. Dura growth on top of the arrays was cut away andthe arrays were excised using forceps. Following explantation, the arrays were placed in Benz-All
overnight and switched to 1X PBS the next day, to be preserved for future analysis.

4.3.4 Tissue Staining
Tissue slices were blocked and permeabilized overnight in StartingBlock-PBS (Thermo
Fisher) containing 0.1% Triton-X 100 (Sigma) at 4oC. Then, the tissue was washed three times(30 min per wash) in PBS containing 0.5% Triton X-100 (1X PBS-T) at room temperature. The
tissue was then incubated in primary antibodies with 0.5% PBS-T for three days at 4oC. Primary
antibodies used were mouse anti-neuronal nuclei (NeuN) (1:250, Millipore) for neurons, rabbit
anti-Iba-1 (1:250, Wako) for all microglia/macrophages, and rabbit anti-glial fibrillary acidic
protein (GFAP) (1:250, Dako) for astrocytes. The tissue was washed three times (30 min per wash)
in 0.5% PBS-T at room temperature. Tissue was then incubated in secondary antibodies with 0.5%
PBS-T for one day at 4oC. Secondary antibodies used were anti-mouse Alexa Fluor 647 (1:250,
Jackson) and anti-rabbit Alexa Fluor 546 (1:250, Life Technologies). Hoechst (1:250, Thermo
Fisher) a stain for all cellular nuclei, was added as well. Following incubation, the tissue slices
61were washed in 0.5% PBS-T two times at two hour intervals and then kept in PBS overnight. All
slices were stored at 4oC in PBS with 0.02% azide until ready to image.
4.3.5 Imaging
All imaging was performed by the Cai Lab. Slices were mounted in Vectashield mounting
medium (Vector Labs) and imaged using a 20X objective on a confocal microscope systematic 1µm intervals in the z-dimension on a Zeiss LSM780 using 405nm and 633nm lasers for excitation
together with -405 and 488/543/633 dichroic mirrors. Images were then stitched using the ImageJ
Grid/Collection Stitching Plugin.
4.3.6 Cell Counting
Slices were collected beginning from the top of the tissue sample along the depth of thearray, each 100 µm thick. Due to the uncertainty of the exact location of the initial slice in relation
to the surface of the brain and large amounts of remodeling, we have no landmarks to determine
the layer of brain captured in the following slices. Slices 9, 12, and 13 were used in this analysis
at depths of 800-900 µm, 1100-1200 µm, and 1200-1300 µm from the top of the tissue sample.
Images stained for NeuN were analyzed using Image J. Neurons were directly counted manually
within a known volume using techniques from the fractionator approach (West, 1999). Using theImage J Cell Counter Plugin and the multi-point function, neurons were labeled when first visible
within the z-stack of a slice to ensure they were only counted once. In addition, a fluorescentintensity threshold was set to remove some of the counting bias in determining neurons frombackground.

624.4 Results
4.4.1 Gross Pathology
Upon explantation of the arrays we found that the array implanted in M1 was completely
encapsulated by dura or scar tissue as seen in figure 4.1 (B). This growth formed around the arrayand ultimately pushed it out of the brain. There was an impression left on the brain of the generaloutline of the array, but no visible holes on the surface of the brain left from the penetrating shanks,
shown in figure 4.1 (C). The imaged slices from neural tissue under this M1 array was consistent
with this as well. While the tissue did contain neurons, with no holes from the electrode shankspresent in the images, we were unable to determine orientation or the location of the array forfurther analyses. Therefore, this tissue was not use in the results below.
The S1 array remained embedded in the brain with typical growth on top as seen in figure
4.1 (D). This array did leave an impression and holes where the shanks had penetrated the brain,
shown in figure 4.1 (C). This impression was used in analysis of the stained images to determine
the orientation of the array and decipher between tissue under the implant and the surroundingarea, which was used as control brain in this study.

63

Figure 4.1. Gross Histological Results. A) Surgical photo of implanted Utah arrays. B) M1 array fully
encapsulated and displaced from the brain at 818 days post implant. C) Impressions left on the brain afterarray extraction. D) S1 implant covered with dura.

4.4.2 Neuron Density
We used multiple stains to look at the extent of damage under the implanted array. The
Hoechst stained for nuclei of cell bodies, Iba-1stained for microglia, and NeuN stained for neurons(figure 4.2).

Figure 4.2. Histological Stains. Hoechst stains for nuclei. Iba-1 stains for microglia. NeuN stains forneurons.

64

Damage to the neural tissue from the implantation of the Utah array was characterized byinvestigating the neuron densities from areas under the array and areas surrounding the array ofthe same monkey. Neuron density was quantified using the number of neurons per cubic
millimeter. Three 100 µm thick slices estimated to be at depths of 800-900 µm, 1100-1200 µm,
and 1200-1300 µm along the electrode array were analyzed to determine any damage. The layerof brain captured in these three slices are unknown. Due to the compromised images on the top
and bottom of each slice, potentially from tissue degradation, the first and last few z-stacks of eachslice were discarded. The remaining 70 µm thick slices were used for further analysis. The imaging
window of each slice varied slightly, resulting in differences in the amount of tissue surrounding
the array available as a control sample. Thus, a standard area of approximately 600 µm x 400 µm
was used to sample control brain for each slice. The exact same area boundary was used to sample
the neural tissue from eight locations under the array for each slice, shown in Figure 3. The array
was broken down into four quadrants and two locations from each quadrant were chosen randomly
with uniform distribution using Matlab.

Figure 4.3. Neuron density comparison between neural tissue under the array and surrounding the array(control) from slice 13. The location from where the samples are taken is outlined in the full image.

65

In the comparison of neuron densities in the tissue under the array and the surroundingarea, it is immediately visible that there is a drastic decrease in the number of neurons present in
the neural tissue that was under the Utah array, as seen in figure 4.3. The neuron density for the
sampled tissue surrounding the Utah array averaged across all three slices was 37,559
neurons/mm3. The mean neuron density of the 8 samples from under the array averaged across thethree slices was 8,192 neurons/mm3. The variance of the averaged neuron densities across the
samples under the array in comparison to sampled tissue surrounding the array are depicted infigure 4.4. The sampled area under the array of the three depths (800-900 µm, 1100-1200 µm,
1200-1300 µm) experienced a decline of 78.3% in the number of neurons present in the same
volume.

Figure 4.4. Neuron density for each sampled location averaged across the three slices analyzed.

66

4.5 Discussion
The longevity of the Utah array used for BMI experiments has always been in question.
Will it last long enough for the benefits to outweigh the risk of surgical implantation? Mechanisms
previously reported to contribute to the failure of the array consist of tissue response, material
degradation, and mechanical failures (Barrese et al., 2013). The purpose of this study was to further
explore the reactive tissue response threatening long-term recordings of the Utah array.
Specifically, we took the rare opportunity to analyze post-mortem histology of neural tissue froma rhesus macaque implanted with the Utah array for approximately 818 days to investigate damage
during chronic implantation.
Upon extraction, the array implanted in M1 was noticeably encapsulated and had been
displaced from the brain. This failure mechanism adheres to the literature, with reports ofencapsulation of an array as early as 1956 (Collias & Manuelidis, 1956). Kim et al 2004 explainedthat meningeal fibroblasts migrate down the shank of the electrode from the top of the cortex
enclosing the array, which was seen here as well (Y.-T. Kim, Hitchcock, Bridge, & Tresco, 2004).
Due to the displacement, the histology lacked an electrode footprint in order for us to determineorientation and continue our analysis.
There have been numerous studies on the formation of the glial scar as a response to theelectrode. The glial scar is typically completely formed within 6-12 weeks of implantation and ismost severe 50-100 µm from the array (Turner et al., 1999). This increases the distance between
the electrode and neurons and usually results in a graded loss of neurons closest to the electrode(Biran et al., 2005; Biran, Martin, & Tresco, 2007; Potter-Baker et al., 2014). Our histologic
findings from the tissue under the array implanted in S1support the claims of progressive neuralremodeling as a long-term failure mechanism. We believe something is occurring after the 1267weeks once the glial scar is already completely formed, having a longstanding effect on the neuron
density at a time point further out. The lack of neurons in close proximity to the electrode has beenshown to reduce recorded signal quality and can further lead to the loss of an array (Buzsáki, 2004).
We quantified the gross neuronal damage under the array. Our results suggest that in thelayers we analyzed the neural tissue under the chronic Utah array implant had a decreased neuron
density and size in comparison to the surrounding area. The size of the neurons from tissue under
the array were also visibly smaller in comparison to the tissue surrounding the array. While tissue
slices more dorsal had overall smaller neurons, typical to a higher layer (García-Cabezas & Barbas,
2014), the size between tissue under the array was very different than surrounding areas. We
measured a 78.3 % smaller number of neurons from three different depths under the arraycompared to adjacent tissue. While this is a dramatic reduction, there are many limitations of thisstudy that make it difficult to understand exactly what is happening. Neuron death is a possibility;
however, the neurons could also be experiencing migration down past the array. This is consistent
with results seen by the Welle Lab and Donoghue Lab (Barrese et al., 2013; Welle, 2018). The
exact distance of the slices used in this analyses are unknown and we do not know the layer ofbrain, however we can estimate based on the slice thickness and number that the samples werefrom a tissue depth of 800-1300 µm. This range is not large enough to include the electrode tips
or tissue further ventral, therefore we could not investigate the possible migration of neurons
downward. In addition, the sample of surrounding tissue used as our control contained about half
the neuron density than what is reported in the S1 of a rhesus macaque with no implant (Collins,
Airey, Young, Leitch, & Kaas, 2010). The inconsistency in neuron density for our control could
be a reflection of the location, immediately adjacent to the array, the size of the sample, or simply

68differences between monkeys. However, the limited histological reports in the literature make it
difficult to determine.
Based on our initial observations that neural tissue from a chronically implanted Utah arrayexperience a drastic decrease in the number of neurons, questions of potential motor deficits as aresult and the appropriate patient population arise. Currently, people implanted with these arrays
already have severe loss of motor functions due to spinal cord injury. However, as technology
advances and the applications of BMI expand, discussions of the injury threshold suitable for sucha device may become more critical. For example, can brain-controlled functional electricalstimulation only be used in people with high cervical level injury (C1-C3) or can it be expanded
to others (C4-C6) with slightly more residual motor function? Contrary to the human clinicalstudies, in this study, the Utah array was implanted in an able-bodied rhesus macaque.
Interestingly, despite the dramatic decrease of neurons present, the animal showed no signs of any
motor deficits and continued to move freely over a year after the array was no longer in use due topoor signals. This behavioral response has also been shown in studies investigating neuronal deathfrom moderate traumatic brain injury and resection in rats and similarly in humans, wheremoderate motor deficits recovered over time or were unnoticeable (Feeney, Gonzalez, & Law,
1982; Magill, Han, Li, & Berger, 2018; Ouyang, Yan, Zhang, & Fan, 2017). While there areconsiderable complexities of the brain, the recovery or lack of motor deficits experienced may be
the outcome of the transition of motor control to another intact area.
4.6 Conclusion
Brain machine interfaces have the potential to restore function and improve the quality oflife for many people. However, there is much that still needs to be explored in terms of the tissue
responses that contribute to the failure of intracortical electrodes in order to transition to routine

69clinical adoption. The initial histologic findings of the one rhesus macaque reported here give
preliminary insight into the extent of possible neural damage under a Utah array. Due to the limited
body of literature, no true judgements can be made. Additional histology from more primates areneeded, however, this work can contribute to minimal existing dataset.

70

Chapter 5
Discussion
5.1 Conclusion
Brain machine interfaces have provided an intuitive alternative to restore arm and handfunction to those suffering from paralysis due to spinal cord injury. However, there are still many
challenges involved in translating this technology into a system that can be used practically in aclinical setting. In this thesis, we attempt to address these challenges and present a plausible
solution to the development of fully implantable technology. By investigating the design and safety
specifications to meet regulatory requirements we hope to expedite the path to a clinically-viable
system.
First, In Chapter 2 we designed a neural recording device to access a 96-channel Utaharray, the state-of–the-art electrode for human BMI experiments, and mate with the NNP, anexisting implantable FES system. Using the spiking band as a neural feature provided an
opportunity to decimate the data while preserving the important neural information to predict
motor intent. Extracting signals in a smaller bandwidth, instead of the full broadband waveform,
allowed for a reduction in the large processing overhead of the front-end leading to power savings,
as seen previously in a 16-channel system (Irwin…Bullard et al., 2016). This construct formed
the basis of the device architecture, which fit within the packaging guidelines and manufacturer
capabilities of existing NNP modules. We also tested the device could record and transmit 96
channels of data in a power consumption range similar to existing NNP modules withoutcompromising signal integrity71Naturally, with the goal of creating a functional fully implantable device for clinical use,
the next step is to design a clinical trial. This will require an exhaustive analysis of the safety risks
associated with a fully implantable cortical-controlled FES system. Both the NNP system and Utaharray have been implanted in humans and have gone through clinical trials, however they havenever been conjoined together in a single system. It is unclear what safety risks may be associatedwith combining these two systems. There are currently no adequate or standard quantifications ofrisks and the percent chance of having a major safety problem with the conjoined system is
unknown. Thus, Chapter 3 analyzed the safety profiles of existing implantable, intracranial devices
in humans. Using a systematic approach, we identified potential sources of hardware-related
complications in the literature and quantified the incidence rate and severity of adverse events.
This study provided the most comprehensive safety review to date of DBS hardware and human
BMI research using the Utah array. Understanding the potential risks and failure modes of a device
and how many people must be observed to witness such risks is important information when
designing a clinical trial. Therefore, the evidence-based analysis serves as an important reference
for investigators seeking to meet regulatory requirements for an intracranial, fully implanted,
modular neuroprosthetic system.
While much information on the safety of a device can be gained through observing humanclinical trials, there are limitations, in which animal models may offer more opportunities for
additional analyses. In Chapter 4 we take a deeper look at the biological response of the brain after
the implantation of the Utah array. We quantified neuron density around the shanks of the array to
examine damage of the neural tissue. Histological results from the three tissue slices analyzed
suggest there is a reduction in the number of neurons in neural tissue under the array compared to72

surrounding tissue. These initial results can be used to increase the database of safety data for the
Utah array and can be used to inform the development of future devices.
5.2 Future Directions
5.2.1 Modular Systems
Over the years there has been an increased interest in research surrounding body area sensor
networks (Ha, 2015) . This concept of incorporating a network of multiple sensors throughout the
body has great potential to revolutionize the future of the healthcare system. The advancement of
implantable electronics and wireless communication provide opportunities to develop novel andinnovative sensor platforms for various applications. These modular systems have been adapted
for use in continuous sensing and monitoring for smart health care, assisted elderly living, andemergency response (Fortino & Gravina, 2015; Gyselinckx, Vullers, Hoof, Ryckaert, &Yazicioglu, 2008; Hadjem, Salem, & Nait-Abdesselam, 2014; Milenković, Otto, & Jovanov, 2006;
Ullah et al., 2010).
The modular, implantable brain-controlled FES system described in Chapter 2 further
adheres to this trend of body area sensor networks. With the ultimate goal of establishing one
system with the capability to address multiple function loss instead of several implantable systems
for each individual impairment, the development of our neural recording module can be used as a
guide to develop future application specific modules. This is important because spinal cord injury
severely interferes with a number of daily functions, not only arm and hand mobility, but
potentially trunk stability, bowel and bladder control, and walking.
Our neural recording device was designed to acquire data at configurable bandwidths,
sampling rates, and channel counts using the Intan bioamplifiers, offering the possibility to be used
for applications outside of Utah array recordings. This technology expands the functionality of the73device to accept different signal modalities and can be adapted to support other closed loop
applications such as closed loop DBS, seizure prevention, and bladder state monitoring(DiLorenzo, Mangubat, Rossi, & Byrne, 2014; Khurram et al., 2017; Stanslaski et al., 2012; Sun& Morrell, 2014). Reconfigurable systems provide an interface to multiple implantable platforms
and are an attractive new capability of the next generation of devices. Draper has developed an
implantable, wireless, radio-powered neural stimulation device that can be used for multi-modal
recording and stimulation, controlled by an external transmitter (Bjune et al., 2015; Wheeler et al.,2015). The combination of high density electrodes, multi-modal recordings, and adaptive closedloop stimulation will enable therapies to address a number of neurological disorders in both thecentral and peripheral nervous system and make it a great contender to function as a network ofdevices in the future.
5.2.2 Neural Signal Origin
While promising, there are still many avenues of research in need of exploration to advanceour understanding and further improve our proposed implantable brain-controlled FES system andinfluence emerging neuromodulation technologies. The use of the spiking band power to decrease
the size of the data transmitted is essentially the driving force in the architecture of our neuralrecording device. This feature allows for a dramatic reduction in power which makes our device
feasible for use as an implantable module. If embraced, this concept could be used to further
advance current experimental systems still using the typical broadband signal extraction (Borton
et al., 2013; Rizk et al., 2009). In addition, because of the preferred spiking band frequency range,
devices originally designed to process LFP and ECoG could use this technology to accesssufficient neural information for brain machine interface applications as well (B. C. Johnson et al.,2017; Mestais et al., 2015; Robinet et al., 2011).

74

We have shown that signal power within 300-1000 Hz can be used to predict motor intent
just as well as threshold crossings (Irwin…Bullard et al., 2016; Stark & Abeles, 2007). However,these studies used fairly simple tasks, which may not have exposed any discrepancies just yet.
Currently we do not know if spiking band power will perform well enough as a neural feature to
decode more complex tasks needed to fulfill functional daily activities. The neural origin andcontent within the 300-1000 Hz frequencies is not well understood. This lack of understanding in
the fundamentals of the recorded signal provide difficulties when predicting the limitations of its
decoding performance. Speculations around whether the spiking band power is a relatively local
signal similar to threshold crossings or a broader LFP signal persist. However, the comparison of
spiking band power and averaged thresholded crossings investigated in Chapter 2 support thenotion that spiking band is mostly reflective of spiking activity.
Studies have shown that while correlation between LFPs and single unit recordings may
be weak, LFPs may be more useful in encoding information like speed (Perel et al., 2015). Because
the spiking band frequency range encompasses those of LFPs and spikes, it may have the potential
to provide more information on both speed and velocity. This may be a possible advantage overusing only threshold crossings for BMI decoding. Additionally, acquiring the spiking band power
does not require spikes to be visible within the single, which allows it to still interpret useful datafrom channels that over time no longer have good quality spikes, ultimately extending recording
lifetime. Similar approaches have been made, adding high frequency local field potential activity
to decoding models to improve performance as the array condition diminishes (Ajiboye et al.,2017; Pandarinath et al., 2017b; D. Wang et al., 2014).

75

5.2.3 Innovative Electrode Technology
The longevity of the Utah array used for BMI experiments has always been in question.
Will it last long enough for the benefits to outweigh the risk of surgical implantation? Baresse etal. investigated factors that posed a threat to the longevity of the array including mechanicaldamage of the electrode, degradation of electrode materials, and the response of the brain after
implantation. Biological issues were reported as the second most common, accounting for 24% ofarray failures (Barrese et al., 2013). Studies have shown that after the insertion of the silicon-basedarray into the brain a glial scar forms in response, around the shank of the electrode. This scar can
extend up to 250 µm and make it difficult to record signals within a 100-200 µm radius surrounding
the electrode (Biran et al., 2005). Chapter 4, quantifies the damage of the neurons surrounding theelectrodes in response to insertion and gliosis, which ultimately led to poor signal quality and asubstantial reduction in efficacy of the BMI. This reactive response seen with multi-electrodearrays such as the Utah array, led groups to explore different electrode designs to decrease damageof neural tissue.
Carbon fiber electrodes may be well suited to address these challenges seen in siliconbased electrode arrays. The small diameter of the fiber allows for insertion without the negative
biological response of scarring and neuronal death around the electrode (P. R. Patel et al., 2016).
Therefore, there is increased neuronal density in the zone of recording which could lead to a longer
recording lifetime for use in BMI. In addition, because the large radius of neuronal death aroundthe electrode has been eliminated, carbon fibers may be used to increase channel count in a highdensity array (Massey et al., 2019). Having access to more channels may in return lead to a larger
resolution of the neural information and more accurate and advanced decoding.

76

Additional novel experimental electrodes have been designed with goal of eliminating thenegative biological response to implantation by decreasing the size and improving the flexibilityof the electrode. Neural dust, consists of ultra-miniature, free-floating, independent sensor nodes
for recording and communication and have been used in the brain and nerve (Seo et al., 2016; Seo,
Carmena, Rabaey, Maharbiz, & Alon, 2015). While this new neural recording platform addresses
many challenges seen with the Utah array, scaling this system to include hundreds of individual
electrodes may not be ideal for surgical implantation. One potential way to record from a large
number of neurons is with a high density mesh electrode (Fu, Hong, Viveros, Zhou, & Lieber,2017). The development of mesh electronics bridge the gap between scalability and flexibility andhave shown to integrate successfully with the brain without chronic gliosis (Liu et al., 2015; Xieet al., 2015).
There has been significant progress made in the field of brain machine interfaces and thegoal to restore arm and hand function to those suffering from paralysis. While there is still a long
path towards a clinically viable system, continuing the research on these challenges will help
advance the field. Additionally, building upon the results of this work, as we continue to collectdata on the safety and efficacy of neuromodulation systems, we lay the foundation for a strong
database of information that can be used to help groups meet the regulatory requirements needed
to translate emerging, innovative technologies to human clinical trials.

77

Bibliography
Abode-Iyamah, K. O., Chiang, H.-Y., Woodroffe, R. W., Park, B., Jareczek, F. J., Nagahama, Y.,… Greenlee, J. D. W. (2018). Deep brain stimulation hardware-related infections: 10-year
experience at a single institution. Journal of Neurosurgery, 1–10.
https://doi.org/10.3171/2017.9.JNS1780
Abreu, V., Vaz, R., Rebelo, V., Rosas, M. J., Chamadoira, C., Gillies, M. J., … Pereira, E. A. C.
(2017a). Thalamic Deep Brain Stimulation for Neuropathic Pain: Efficacy at Three Years’
Follow-Up. Neuromodulation : Journal of the International Neuromodulation Society,20(5), 504–513. https://doi.org/10.1111/ner.12620
Abreu, V., Vaz, R., Rebelo, V., Rosas, M. J., Chamadoira, C., Gillies, M. J., … Pereira, E. A. C.
(2017b). Thalamic Deep Brain Stimulation for Neuropathic Pain: Efficacy at Three Years’
Follow-Up. Neuromodulation: Technology at the Neural Interface, 20(5), 504–513.
https://doi.org/10.1111/ner.12620
Ackermans, L., Duits, A., van der Linden, C., Tijssen, M., Schruers, K., Temel, Y., … VisserVandewalle, V. (2011). Double-blind clinical trial of thalamic stimulation in patients with
Tourette syndrome. Brain : A Journal of Neurology, 134(Pt 3), 832–844.
https://doi.org/10.1093/brain/awq380
Ackermans, L., Duits, A., van der Linden, C., Tijssen, M., Schruers, K., Temel, Y., … VisserVandewalle, V. (2011). Double-blind clinical trial of thalamic stimulation in patients with
Tourette syndrome. Brain, 134(3), 832–844. https://doi.org/10.1093/brain/awq380
Aflalo, T., Kellis, S., Klaes, C., Lee, B., Shi, Y., Pejsa, K., … Andersen, R. A. (2015). Decoding
motor imagery from the posterior parietal cortex of a tetraplecig human. Science,
348(6237), 906–910. https://doi.org/10.7910/DVN/GJDUTV
Aflalo, T., Kellis, S., Klaes, C., Lee, B., Shi, Y., Pejsa, K., … Andersen, R. A. (2015).
Neurophysiology. Decoding motor imagery from the posterior parietal cortex of atetraplegic human. Science (New York, N.Y.), 348(6237), 906–910.
https://doi.org/10.1126/science.aaa5417
Air, E. L., Ostrem, J. L., Sanger, T. D., & Starr, P. A. (2011). Deep brain stimulation in children:
experience and technical pearls. Journal of Neurosurgery. Pediatrics, 8(6), 566–574.
https://doi.org/10.3171/2011.8.PEDS11153
Ajiboye, A. B., Simeral, J. D., Donoghue, J. P., Hochberg, L. R., & Kirsch, R. F. (2012a).
Prediction of imagined single-joint movements in a person with high-level tetraplegia. IEEE
Transactions on Bio-Medical Engineering, 59(10), 2755–2765.
https://doi.org/10.1109/TBME.2012.2209882
Ajiboye, A. B., Simeral, J. D., Donoghue, J. P., Hochberg, L. R., & Kirsch, R. F. (2012b).
Prediction of imagined single-joint movements in a person with high-level tetraplegia. IEEE
Transactions on Bio-Medical Engineering, 59(10), 2755–2765.
https://doi.org/10.1109/TBME.2012.2209882
Ajiboye, A. B., Willett, F. R., Young, D. R., Memberg, W. D., Murphy, B. A., Miller, J. P., …
Kirsch, R. F. (2017). Restoration of reaching and grasping movements through brain78

controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration. The
Lancet, 389(10081), 1821–1830. https://doi.org/10.1016/S0140-6736(17)30601-3
Akram, H., Limousin, P., Hyam, J., Hariz, M. I., & Zrinzo, L. (2015). Aim for the Suprasternal
Notch: Technical Note to Avoid Bowstringing after Deep Brain Stimulation. Stereotactic
and Functional Neurosurgery, 93(4), 227–230. https://doi.org/10.1159/000381680
Alex Mohit, A., Samii, A., Slimp, J. C., Grady, M. S., & Goodkin, R. (2004). Mechanical failureof the electrode wire in deep brain stimulation. Parkinsonism & Related Disorders, 10(3),153–156. https://doi.org/10.1016/j.parkreldis.2003.11.001
Allert, N., Jusciute, E., Weirich, O., Daryaeitabar, M., Nolden, B. M., & Karbe, H. (2018). LongTerm Stability of Short Circuits in Deep Brain Stimulation. Neuromodulation : Journal of
the International Neuromodulation Society, 21(6), 562–567.
https://doi.org/10.1111/ner.12709
Alomar, S., Mullin, J. P., Smithason, S., & Gonzalez-Martinez, J. (2018). Indications, technique,and safety profile of insular stereoelectroencephalography electrode implantation in
medically intractable epilepsy. Journal of Neurosurgery, 128(4), 1147–1157.
https://doi.org/10.3171/2017.1.JNS161070
Alon, G., & McBride, K. (2003). Persons with C5 or C6 tetraplegia achieve selected functional
gains using a neuroprosthesis. Archives of Physical Medicine and Rehabilitation, 84(1),119–124. https://doi.org/10.1053/apmr.2003.50073
Alterman, R. L., Miravite, J., Weisz, D., Shils, J. L., Bressman, S. B., & Tagliati, M. (2007).
Sixty hertz pallidal deep brain stimulation for primary torsion dystonia. Neurology, 69(7),681–688. https://doi.org/10.1212/01.wnl.0000267430.95106.ff
Amirnovin, R., Williams, Z. M., Cosgrove, G. R., & Eskandar, E. N. (2006). Experience with
Microelectrode Guided Subthalamic Nucleus Deep Brain Stimulation. Operative
Neurosurgery, 58(1 Suppl), ONS-96-ONS-102.
https://doi.org/10.1227/01.NEU.0000192690.45680.C2
Anderson, K. D. (2004). Targeting recovery: priorities of the spinal cord-injured population.
Journal of Neurotrauma, 21(10), 1371–1383. https://doi.org/10.1089/neu.2004.21.1371
Annetta, N., Friend, J., Schimmoeller, A., Buck, V. S., Friedenberg, D., Bouton, C. E., …
Sharma, G. (2018). A High Definition Non-invasive Neuromuscular Electrical Stimulation
System for Cortical Control of Combinatorial Rotary Hand Movements in a Human with
Tetraplegia. IEEE Transactions on Bio-Medical Engineering, 1–1.
https://doi.org/10.1109/TBME.2018.2864104
Armenta Salas, M., Bashford, L., Kellis, S., Jafari, M., Jo, H., Kramer, D., … Andersen, R. A.
(2018a). Proprioceptive and cutaneous sensations in humans elicited by intracortical
microstimulation. ELife, 7, e32904. https://doi.org/10.7554/eLife.32904
Armenta Salas, M., Bashford, L., Kellis, S., Jafari, M., Jo, H., Kramer, D., … Andersen, R. A.
(2018b). Proprioceptive and cutaneous sensations in humans elicited by intracortical
microstimulation. ELife, 7. https://doi.org/10.7554/eLife.32904
Armour, B. S., Courtney-Long, E. A., Fox, M. H., Fredine, H., & Cahill, A. (2016). Prevalenceand Causes of Paralysis-United States, 2013. American Journal of Public Health, 106(10),1855–1857. https://doi.org/10.2105/AJPH.2016.303270
Ashkan, K., Alamri, A., & Ughratdar, I. (2015). Anti-Coagulation and Deep Brain Stimulation:
Never the Twain Shall Meet. Stereotactic and Functional Neurosurgery, 93(6), 373–377.
https://doi.org/10.1159/000441232
Aviles-Olmos, I., Kefalopoulou, Z., Tripoliti, E., Candelario, J., Akram, H., Martinez-Torres, I.,
79

… Limousin, P. (2014). Long-term outcome of subthalamic nucleus deep brain stimulation
for Parkinson’s disease using an MRI-guided and MRI-verified approach. Journal of
Neurology, Neurosurgery & Psychiatry, 85(12), 1419–1425. https://doi.org/10.1136/jnnp2013-306907
Aziz, J. N. Y., Abdelhalim, K., Shulyzki, R., Genov, R., Bardakjian, B. L., Derchansky, M., …
Carlen, P. L. (2009). 256-Channel Neural Recording and Delta Compression Microsystem
With 3D Electrodes. IEEE Journal of Solid-State Circuits, 44(3), 995–1005.
https://doi.org/10.1109/JSSC.2008.2010997
Aziz, J. N. Y., Genov, R., Bardakjian, B. L., Derchansky, M., Carlen, P. L., Aziz, J. N. Y., …
Carlen, P. L. (n.d.). 256-Channel Neural Recording and Delta Compression Microsystemwith 3D Electrodes. IEEE Journal of Solid-State Circuits. Retrieved fromhttp://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.90.8929&rep=rep1&type=pdf
Bacher, D., Jarosiewicz, B., Masse, N. Y., Stavisky, S. D., Simeral, J. D., Newell, K., …
Hochberg, L. R. (2015). Neural Point-and-Click Communication by a Person With
Incomplete Locked-In Syndrome. Neurorehabilitation and Neural Repair, 29(5), 462–471.
https://doi.org/10.1177/1545968314554624
Baizabal-Carvallo, J. F., Kagnoff, M. N., Jimenez-Shahed, J., Fekete, R., & Jankovic, J. (2014).
The safety and efficacy of thalamic deep brain stimulation in essential tremor: 10 years andbeyond. Journal of Neurology, Neurosurgery, and Psychiatry, 85(5), 567–572.
https://doi.org/10.1136/jnnp-2013-304943
Baizabal Carvallo, J. F., Mostile, G., Almaguer, M., Davidson, A., Simpson, R., & Jankovic, J.
(2012). Deep brain stimulation hardware complications in patients with movement
disorders: risk factors and clinical correlations. Stereotactic and Functional Neurosurgery,
90(5), 300–306. https://doi.org/10.1159/000338222
Baker, J. J., Scheme, E., Englehart, K., Hutchinson, D. T., & Greger, B. (2010). Continuous
detection and decoding of dexterous finger flexions with implantable myoelectric sensors.
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18(4), 424–432.
https://doi.org/10.1109/TNSRE.2010.2047590
Barrese, J. C., Aceros, J., & Donoghue, J. P. (2016). Scanning electron microscopy ofchronically implanted intracortical microelectrode arrays in non-human primates. J Neural
Eng, 13(2), 26003. https://doi.org/10.1088/1741-2560/13/2/026003
Barrese, J. C., Rao, N., Paroo, K., Triebwasser, C., Vargas-Irwin, C., Franquemont, L., &Donoghue, J. P. (2013). Failure mode analysis of silicon-based intracortical microelectrodearrays in non-human primates. Journal of Neural Engineering, 10(6).
https://doi.org/10.1088/1741-2560/10/6/066014
Ben-Haim, S., Asaad, W. F., Gale, J. T., & Eskandar, E. N. (2009). Risk factors for hemorrhage
during microelectrode-guided deep brain stimulation and the introduction of an improved
microelectrode design. Neurosurgery, 64(4), 754-62; discussion 762-3.
https://doi.org/10.1227/01.NEU.0000339173.77240.34
Bergey, G. K., Morrell, M. J., Mizrahi, E. M., Goldman, A., King-Stephens, D., Nair, D., …
Seale, C. G. (2015). Long-term treatment with responsive brain stimulation in adults withrefractory partial seizures. Neurology, 84(8), 810–817.
https://doi.org/10.1212/WNL.0000000000001280
Bergfeld, I. O., Mantione, M., Hoogendoorn, M. L. C., Ruhé, H. G., Notten, P., van Laarhoven,
J., … Denys, D. (2016). Deep Brain Stimulation of the Ventral Anterior Limb of theInternal Capsule for Treatment-Resistant Depression. JAMA Psychiatry, 73(5), 456.
80

https://doi.org/10.1001/jamapsychiatry.2016.0152
Beric, A., Kelly, P. J., Rezai, A., Sterio, D., Mogilner, A., Zonenshayn, M., & Kopell, B. (2001).
Complications of deep brain stimulation surgery. Stereotactic and Functional
Neurosurgery, 77(1–4), 73–78. https://doi.org/10.1159/000064600
Bhatia, R., Dalton, A., Richards, M., Hopkins, C., Aziz, T., & Nandi, D. (2011). The incidence
of deep brain stimulator hardware infection: the effect of change in antibiotic prophylaxis
regimen and review of the literature. British Journal of Neurosurgery, 25(5), 625–631.
https://doi.org/10.3109/02688697.2011.566384
Bhatia, S., Oh, M., Whiting, T., Quigley, M., & Whiting, D. (2008). Surgical complications ofdeep brain stimulation. A longitudinal single surgeon, single institution study. Stereotactic
and Functional Neurosurgery, 86(6), 367–372. https://doi.org/10.1159/000175799
Bhatia, S., Zhang, K., Oh, M., Angle, C., & Whiting, D. (2010). Infections and hardware salvage
after deep brain stimulation surgery: a single-center study and review of the literature.
Stereotactic and Functional Neurosurgery, 88(3), 147–155.
https://doi.org/10.1159/000303528
Binder, D. K., Rau, G. M., & Starr, P. A. (2005). Risk factors for hemorrhage during
microelectrode-guided deep brain stimulator implantation for movement disorders.
Neurosurgery, 56(4), 722-32; discussion 722-32. Retrieved fromhttp://www.ncbi.nlm.nih.gov/pubmed/15792511
Binder, D. K., Rau, G., & Starr, P. A. (2003). Hemorrhagic Complications of MicroelectrodeGuided Deep Brain Stimulation. Stereotactic and Functional Neurosurgery, 80(1–4), 28–
31. https://doi.org/10.1159/000075156
Biran, R., Martin, D. C., & Tresco, P. A. (2005). Neuronal cell loss accompanies the brain tissue
response to chronically implanted silicon microelectrode arrays.
https://doi.org/10.1016/j.expneurol.2005.04.020
Biran, R., Martin, D. C., & Tresco, P. A. (2007). The brain tissue response to implanted silicon
microelectrode arrays is increased when the device is tethered to the skull. Journal of
Biomedical Materials Research Part A, 82A(1), 169–178.
https://doi.org/10.1002/jbm.a.31138
Bjerknes, S., Skogseid, I. M., Sæhle, T., Dietrichs, E., & Toft, M. (2014). Surgical site infections
after deep brain stimulation surgery: frequency, characteristics and management in a 10year period. PloS One, 9(8), e105288. https://doi.org/10.1371/journal.pone.0105288
Bjune, C. K., Marinis, T. F., Sriram, T. S., Brady, J. M., Moran, J., Parks, P. D., … Eskandar, E.
N. (2015). Packaging Architecture for an Implanted System that Monitors Brain Activity
and Applies Therapeutic Stimulation. In 48th Annual International Symposium on
Microelectronics. Orlando. Retrieved from https://imapsource.org/doi/pdf/10.4071/isom2015-THA13
Blabe, C. H., Gilja, V., Chestek, C. A., Shenoy, K. V, Anderson, K. D., & Henderson, J. M.
(2015, August 1). Assessment of brain-machine interfaces from the perspective of people
with paralysis. Journal of Neural Engineering. https://doi.org/10.1088/17412560/12/4/043002
Black, B. J., Kanneganti, A., Joshi-Imre, A., Rihani, R., Chakraborty, B., Abbott, J., … Cogan,
S. F. (2018). Chronic recording and electrochemical performance of Utah microelectrodearrays implanted in rat motor cortex. Journal of Neurophysiology, 120(4), 2083–2090.
https://doi.org/10.1152/jn.00181.2018
Blomstedt, P., & Hariz, M. I. (2005). Hardware-related complications of deep brain stimulation:81

a ten year experience. Acta Neurochirurgica, 147(10), 1061–4; discussion 1064.
https://doi.org/10.1007/s00701-005-0576-5
Blomstedt, P., & Hariz, M. I. (2006). Are complications less common in deep brain stimulation
than in ablative procedures for movement disorders? Stereotactic and Functional
Neurosurgery, 84(2–3), 72–81. https://doi.org/10.1159/000094035
Borton, D. A., Yin, M., Aceros, J., & Nurmikko, A. (2013). An implantable wireless neuralinterface for recording cortical circuit dynamics in moving primates. Journal of NeuralEngineering, 10(2), 1–25. https://doi.org/10.1088/1741-2560/10/2/026010
Bourne, S. K., Conrad, A., Konrad, P. E., Neimat, J. S., & Davis, T. L. (2012). Ventricular widthand complicated recovery following deep brain stimulation surgery. Stereotactic andFunctional Neurosurgery, 90(3), 167–172. https://doi.org/10.1159/000338094
Bouton, C. E., Shaikhouni, A., Annetta, N. V., Bockbrader, M. A., Friedenberg, D. A., Nielson,
D. M., … Rezai, A. R. (2016). Restoring cortical control of functional movement in ahuman with quadriplegia. Nature, 533(7602), 247–250. https://doi.org/10.1038/nature17435
Boviatsis, E. J., Stavrinou, L. C., Themistocleous, M., Kouyialis, A. T., & Sakas, D. E. (2010).
Surgical and hardware complications of deep brain stimulation. A seven-year experienceand review of the literature. Acta Neurochirurgica, 152(12), 2053–2062.
https://doi.org/10.1007/s00701-010-0749-8
Brandman, D. M., Hosman, T., Saab, J., Burkhart, M. C., Shanahan, B. E., Ciancibello, J. G., …
Hochberg, L. R. (2018). Rapid calibration of an intracortical brain–computer interface forpeople with tetraplegia. Journal of Neural Engineering, 15(2), 026007.
https://doi.org/10.1088/1741-2552/aa9ee7
Burdick, A. P., Fernandez, H. H., Okun, M. S., Chi, Y.-Y., Jacobson, C., & Foote, K. D. (2010).
Relationship between higher rates of adverse events in deep brain stimulation using
standardized prospective recording and patient outcomes. Neurosurgical Focus, 29(2), E4.
https://doi.org/10.3171/2010.4.FOCUS10100
Burdick, A. P., Okun, M. S., Haq, I. U., Ward, H. E., Bova, F., Jacobson, C. E., … Foote, K. D.
(2010). Prevalence of Twiddler’s Syndrome as a Cause of Deep Brain Stimulation
Hardware Failure. Stereotactic and Functional Neurosurgery, 88(6), 353–359.
https://doi.org/10.1159/000319039
Buzsáki, G. (2004). Large-scale recording of neuronal ensembles. Nature Neuroscience, 7(5),446–451. https://doi.org/10.1038/nn1233
Cersosimo, M. G., Raina, G. B., Piedimonte, F., Antico, J., Graff, P., & Micheli, F. E. (2008).
Pallidal surgery for the treatment of primary generalized dystonia: Long-term follow-up.
Clinical Neurology and Neurosurgery, 110(2), 145–150.
https://doi.org/10.1016/j.clineuro.2007.10.003
Chadwick, E. K., Blana, D., Simeral, J. D., Lambrecht, J., Kim, S. P., Cornwell, A. S., … Kirsch,R. F. (2011). Continuous neuronal ensemble control of simulated arm reaching by a humanwith tetraplegia. Journal of Neural Engineering, 8(3), 034003.
https://doi.org/10.1088/1741-2560/8/3/034003
Chan, D. T. M., Zhu, X. L., Yeung, J. H. M., Mok, V. C. T., Wong, E., Lau, C., … Poon, W. S.
(2009). Complications of deep brain stimulation: a collective review. Asian Journal ofSurgery, 32(4), 258–263. https://doi.org/10.1016/S1015-9584(09)60404-8
Chang, W. S., Kim, H. Y., Kim, J. P., Park, Y. S., Chung, S. S., & Chang, J. W. (2011). Bilateralsubthalamic deep brain stimulation using single track microelectrode recording. Acta
Neurochirurgica, 153(5), 1087–1095. https://doi.org/10.1007/s00701-011-0953-1
82

Chapin, J. K., Moxon, K. A., Markowitz, R. S., & Nicolelis, M. A. (1999). Real-time control of arobot arm using simultaneously recorded neurons in the motor cortex. Nature Neuroscience,2(7), 664–670. https://doi.org/10.1038/10223
Charles, D., Konrad, P. E., Neimat, J. S., Molinari, A. L., Tramontana, M. G., Finder, S. G., …
Davis, T. L. (2014). Subthalamic nucleus deep brain stimulation in early stage Parkinson’s
disease. Parkinsonism & Related Disorders, 20(7), 731–737.
https://doi.org/10.1016/j.parkreldis.2014.03.019
Charles, P. D., Dolhun, R. M., Gill, C. E., Davis, T. L., Bliton, M. J., Tramontana, M. G., …
Konrad, P. E. (2012). Deep brain stimulation in early Parkinson’s disease: enrollment
experience from a pilot trial. Parkinsonism & Related Disorders, 18(3), 268–273.
https://doi.org/10.1016/j.parkreldis.2011.11.001
Chen, T., Mirzadeh, Z., Chapple, K., Lambert, M., & Ponce, F. A. (2017). Complication rates,
lengths of stay, and readmission rates in &quot;awake&quot; and &quot;asleep&quot; deepbrain simulation. Journal of Neurosurgery, 127(2), 360–369.
https://doi.org/10.3171/2016.6.JNS152946
Chen, T., Mirzadeh, Z., Chapple, K. M., Lambert, M., Evidente, V. G. H., Moguel-Cobos, G., …
Ponce, F. A. (2018). Intraoperative test stimulation versus stereotactic accuracy as a surgicalend point: a comparison of essential tremor outcomes after ventral intermediate nucleus
deep brain stimulation. Journal of Neurosurgery, 129(2), 290–298.
https://doi.org/10.3171/2017.3.JNS162487
Chen, T., Mirzadeh, Z., Lambert, M., Gonzalez, O., Moran, A., Shetter, A. G., & Ponce, F. A.
(2017). Cost of Deep Brain Stimulation Infection Resulting in Explantation. Stereotactic
and Functional Neurosurgery, 95(2), 117–124. https://doi.org/10.1159/000457964
Chestek, C. A., Gilja, V., Nuyujukian, P., Foster, J. D., Fan, J. M., Kaufman, M. T., … Shenoy,
K. V. (2011). Long-term stability of neural prosthetic control signals from silicon corticalarrays in rhesus macaque motor cortex. Journal of Neural Engineering, 8(4), 45005.
https://doi.org/10.1088/1741-2560/8/4/045005
Chiou, S.-M. (2016). Benefits of subthalamic stimulation for elderly parkinsonian patients aged
70 years or older. Clinical Neurology and Neurosurgery, 149, 81–86.
https://doi.org/10.1016/j.clineuro.2016.07.028
Chou, Y.-C., Lin, S.-Z., Hsieh, W. A., Lin, S. H., Lee, C. C., Hsin, Y. L., … Chen, S.-Y. (2007).
Surgical and hardware complications in subthalamic nucleus deep brain stimulation.
Journal of Clinical Neuroscience, 14(7), 643–649.
https://doi.org/10.1016/j.jocn.2006.02.016
Chowdhury, T., Wilkinson, M., & Cappellani, R. B. (2017). Hemodynamic Perturbations in
Deep Brain Stimulation Surgery: First Detailed Description. Frontiers in Neuroscience, 11,477. https://doi.org/10.3389/fnins.2017.00477
Christie, B. P., Tat, D. M., Irwin, Z. T., Gilja, V., Nuyujukian, P., Foster, J. D., … Chestek, C. A.
(2015). Comparison of spike sorting and thresholding of voltage waveforms for intracorticalbrain-machine interface performance. Journal of Neural Engineering J. Neural Eng, 12, 10.
https://doi.org/10.1088/1741-2560/12/1/016009
Chui, J., Alimiri, R., Parrent, A., & Craen, R. A. (2018). The Effects of Intraoperative Sedation
on Surgical Outcomes of Deep Brain Stimulation Surgery. The Canadian Journal of
Neurological Sciences. Le Journal Canadien Des Sciences Neurologiques, 45(2), 168–175.
https://doi.org/10.1017/cjn.2017.269
Cif, L., Gonzalez-Martinez, V., Vasques, X., Corlobé, A., Moura, A. M., Bonafé, A., & Coubes,83

P. (2012). Staged implantation of multiple electrodes in the internal globus pallidus in thetreatment of primary generalized dystonia. Journal of Neurosurgery, 116(5), 1144–1152.
https://doi.org/10.3171/2012.1.JNS102045
Colachis, S. C., Bockbrader, M. A., Zhang, M., Friedenberg, D. A., Annetta, N. V, Schwemmer,
M. A., … Sharma, G. (2018). Dexterous Control of Seven Functional Hand Movements
Using Cortically-Controlled Transcutaneous Muscle Stimulation in a Person With
Tetraplegia. Frontiers in Neuroscience, 12, 208. https://doi.org/10.3389/fnins.2018.00208
Collias, J. C., & Manuelidis, E. E. (1956). Histopathological Changes Produced by Implanted
Electrodes in Cat Brains. Journal of Neurosurgery. Retrieved fromhttps://thejns.org/view/journals/j-neurosurg/14/3/article-p302.xml
Collinger, J. L., Wodlinger, B., Downey, J. E., Wang, W., Tyler-Kabara, E. C., Weber, D. J., …
Schwartz, A. B. (2013a). High-performance neuroprosthetic control by an individual withtetraplegia. Lancet (London, England), 381(9866), 557–564. https://doi.org/10.1016/S01406736(12)61816-9
Collinger, J. L., Wodlinger, B., Downey, J. E., Wang, W., Tyler-Kabara, E. C., Weber, D. J., …
Schwartz, A. B. (2013b). High-performance neuroprosthetic control by an individual withtetraplegia. The Lancet, 381(9866), 557–564. https://doi.org/10.1016/S01406736(12)61816-9
Collins, C. E., Airey, D. C., Young, N. A., Leitch, D. B., & Kaas, J. H. (2010). Neuron densities
vary across and within cortical areas in primates. Proceedings of the National Academy of
Sciences of the United States of America, 107(36), 15927–15932.
https://doi.org/10.1073/pnas.1010356107
Constantoyannis, C., Berk, C., Honey, C. R., Mendez, I., & Brownstone, R. M. (2005). Reducing
hardware-related complications of deep brain stimulation. The Canadian Journal of
Neurological Sciences. Le Journal Canadien Des Sciences Neurologiques, 32(2), 194–200.
Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/16018154
Coubes, P., Vayssiere, N., El Fertit, H., Hemm, S., Cif, L., Kienlen, J., … Frerebeau, P. (2002).
Deep brain stimulation for dystonia. Surgical technique. Stereotactic and Functional
Neurosurgery, 78(3–4), 183–191. https://doi.org/10.1159/000068962
Cury, R. G., Fraix, V., Castrioto, A., Pérez Fernández, M. A., Krack, P., Chabardes, S., … Moro,
E. (2017). Thalamic deep brain stimulation for tremor in Parkinson disease, essential
tremor, and dystonia. Neurology, 89(13), 1416–1423.
https://doi.org/10.1212/WNL.0000000000004295
Cyberkinetics. (2005). NeuroPort Cortical Microelectrode Array System (Neuroport Electroe)
510K Summary. Retrieved fromhttps://www.accessdata.fda.gov/cdrh_docs/pdf4/K042384.pdf
Dafsari, H. S., Reker, P., Silverdale, M., Reddy, P., Pilleri, M., Martinez-Martin, P., …
EUROPAR and the IPMDS Non-Motor PD Study Group. (2018). Subthalamic Stimulation
Improves Quality of Life of Patients Aged 61 Years or Older With Short Duration ofParkinson’s Disease. Neuromodulation : Journal of the International Neuromodulation
Society, 21(6), 532–540. https://doi.org/10.1111/ner.12740
Daly, J. J., Zimbelman, J., Roenigk, K. L., McCabe, J. P., Rogers, J. M., Butler, K., … Ruff, R.
L. (2011). Recovery of coordinated gait: randomized controlled stroke trial of functional
electrical stimulation (FES) versus no FES, with weight-supported treadmill and overground training. Neurorehabilitation and Neural Repair, 25(7), 588–596.
https://doi.org/10.1177/1545968311400092
84

de Quintana-Schmidt, C., Pascual-Sedano, B., Alvarez-Holzapfel, M. J., Gironell, A., Leidinger,
A., Benito, N., … Molet-Teixidó, J. (2014). [Complications related with implanted devices
in patients with Parkinson’s disease treated with deep brain stimulation. A study of a seriesof 124 patients over a period of 16 years]. Revista de Neurologia, 59(2), 49–56. Retrieved
from http://www.ncbi.nlm.nih.gov/pubmed/25005315
Deep-Brain Stimulation for Parkinson’s Disease Study Group, Obeso, J. A., Olanow, C. W.,
Rodriguez-Oroz, M. C., Krack, P., Kumar, R., & Lang, A. E. (2001). Deep-brain
stimulation of the subthalamic nucleus or the pars interna of the globus pallidus in
Parkinson’s disease. The New England Journal of Medicine, 345(13), 956–963.
https://doi.org/10.1056/NEJMoa000827
Delavallée, M., Abu-Serieh, B., de Tourchaninoff, M., & Raftopoulos, C. (2008). Subdural
motor cortex stimulation for central and peripheral neuropathic pain: a long-term follow-up
study in a series of eight patients. Neurosurgery, 63(1), 101–108.
https://doi.org/10.1227/01.NEU.0000335076.24481.B6
Delavallée, M., Delaunois, J., Ruwet, J., Jeanjean, A., & Raftopoulos, C. (2016). STN DBS for
Parkinson’s disease: results from a series of ten consecutive patients implanted undergeneral anaesthesia with intraoperative use of 3D fluoroscopy to control lead placement.
Acta Neurochirurgica, 158(9), 1783–1788. https://doi.org/10.1007/s00701-016-2889-y
DeLong, M. R., Huang, K. T., Gallis, J., Lokhnygina, Y., Parente, B., Hickey, P., … Lad, S. P.
(2014). Effect of advancing age on outcomes of deep brain stimulation for Parkinson
disease. JAMA Neurology, 71(10), 1290–1295.
https://doi.org/10.1001/jamaneurol.2014.1272
Deuschl, G., Schade-Brittinger, C., Krack, P., Volkmann, J., Schäfer, H., Bötzel, K., … German
Parkinson Study Group, Neurostimulation Section. (2006). A randomized trial of deep-brain
stimulation for Parkinson’s disease. The New England Journal of Medicine, 355(9), 896–
908. https://doi.org/10.1056/NEJMoa060281
Diamond, A., Shahed, J., Azher, S., Dat-Vuong, K., & Jankovic, J. (2006). Globus pallidus deepbrain stimulation in dystonia. Movement Disorders : Official Journal of the Movement
Disorder Society, 21(5), 692–695. https://doi.org/10.1002/mds.20767
DiLorenzo, D. J., Mangubat, E. Z., Rossi, M. A., & Byrne, R. W. (2014). Chronic unlimited
recording electrocorticography–guided resective epilepsy surgery: technology-enabledenhanced fidelity in seizure focus localization with improved surgical efficacy. Journal of
Neurosurgery, 120(6), 1402–1414. https://doi.org/10.3171/2014.1.JNS131592
Dlouhy, B. J., Reddy, A., Dahdaleh, N. S., & Greenlee, J. D. W. (2012). Antibiotic impregnatedcatheter coverage of deep brain stimulation leads facilitates lead preservation after hardware
infection. Journal of Clinical Neuroscience : Official Journal of the Neurosurgical Societyof Australasia, 19(10), 1369–1375. https://doi.org/10.1016/j.jocn.2012.02.008
Doshi, P. K. (2011). Long-term surgical and hardware-related complications of deep brain
stimulation. Stereotactic and Functional Neurosurgery, 89(2), 89–95.
https://doi.org/10.1159/000323372
Dowd, R. S., Pourfar, M., & Mogilner, A. Y. (2018). Deep brain stimulation for Tourette
syndrome: a single-center series. Journal of Neurosurgery, 128(2), 596–604.
https://doi.org/10.3171/2016.10.JNS161573
Downes, A. E., Pezeshkian, P., Behnke, E., Bordelon, Y., Tagliati, M., Mamelak, A., &Pouratian, N. (2016). Acute Ischemic Stroke During Deep Brain Stimulation Surgery of
Globus Pallidus Internus: Report of 5 Cases. Operative Neurosurgery (Hagerstown, Md.),85

12(4), 383–390. https://doi.org/10.1227/NEU.0000000000001359
Downey, J. E., Brane, L., Gaunt, R. A., Tyler-Kabara, E. C., Boninger, M. L., & Collinger, J. L.
(2017). Motor cortical activity changes during neuroprosthetic-controlled object interaction.
Scientific Reports, 7(1), 16947. https://doi.org/10.1038/s41598-017-17222-3
Downey, J. E., Schwed, N., Chase, S. M., Schwartz, A. B., & Collinger, J. L. (2018).
Intracortical recording stability in human brain–computer interface users. Journal of NeuralEngineering, 15(4), 046016. https://doi.org/10.1088/1741-2552/aab7a0
Downey, J. E., Weiss, J. M., Muelling, K., Venkatraman, A., Valois, J.-S., Hebert, M., …
Collinger, J. L. (2016). Blending of brain-machine interface and vision-guided autonomous
robotics improves neuroprosthetic arm performance during grasping. Journal of
Neuroengineering and Rehabilitation, 13(1), 28. https://doi.org/10.1186/s12984-016-0134-9
Egidi, M., Franzini, A., Marras, C., Cavallo, M., Mondani, M., Lavano, A., … Functional
Neurosurgery Study Group of the Italian Society of Neurosurgery. (2007). A survey of
Italian cases of dystonia treated by deep brain stimulation. Journal of Neurosurgical
Sciences, 51(4), 153–158. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/18176524
Esselink, R. A. J., de Bie, R. M. A., de Haan, R. J., Lenders, M. W. P. M., Nijssen, P. C. G.,
Staal, M. J., … Speelman, J. D. (2004). Unilateral pallidotomy versus bilateral subthalamic
nucleus stimulation in PD: a randomized trial. Neurology, 62(2), 201–207. Retrieved fromhttp://www.ncbi.nlm.nih.gov/pubmed/14745054
Ethier, C., Oby, E. R., Bauman, M. J., & Miller, L. E. (2012). Restoration of grasp following
paralysis through brain-controlled stimulation of muscles. Nature, 485.
https://doi.org/10.1038/nature10987
Even-Chen, N., Stavisky, S. D., Pandarinath, C., Nuyujukian, P., Blabe, C. H., Hochberg, L. R.,… Shenoy, K. V. (2018). Feasibility of Automatic Error Detect-and-Undo System in
Human Intracortical Brain-Computer Interfaces. IEEE Transactions on Bio-Medical
Engineering, 65(8), 1771–1784. https://doi.org/10.1109/TBME.2017.2776204
Falowski, S. M., & Bakay, R. A. E. (2016). Revision Surgery of Deep Brain Stimulation Leads.
Neuromodulation : Journal of the International Neuromodulation Society, 19(5), 443–450.
https://doi.org/10.1111/ner.12404
Falowski, S. M., Ooi, Y. C., & Bakay, R. A. E. (2015). Long-Term Evaluation of Changes in
Operative Technique and Hardware-Related Complications With Deep Brain Stimulation.
Neuromodulation : Journal of the International Neuromodulation Society, 18(8), 670–677.
https://doi.org/10.1111/ner.12335
Falowski, S., Ooi, Y. C., Smith, A., Verhargen Metman, L., & Bakay, R. A. E. (2012). An
evaluation of hardware and surgical complications with deep brain stimulation based on
diagnosis and lead location. Stereotactic and Functional Neurosurgery, 90(3), 173–180.
https://doi.org/10.1159/000338254
Farwell, L., & Donchin, E. (1988). Talking off the top of your head: toward a mental prosthesis
utilizing event-related brain potentials (Vol. 70). Retrieved fromhttp://www.farwellbrainfingerprinting.com/pdf/Farwell-Donchin-1988-Talking-Off-theTop-of-Your-Head-BCI-brain-computer-interface.pdf
Feeney, D. M., Gonzalez, A., & Law, W. A. (1982). Amphetamine, haloperidol, and experienceinteract to affect rate of recovery after motor cortex injury. Science (New York, N.Y.),
217(4562), 855–857. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/7100929
Fenoy, A. J., & Simpson, R. K. (2012). Management of device-related wound complications indeep brain stimulation surgery. Journal of Neurosurgery, 116(6), 1324–1332.
86https://doi.org/10.3171/2012.1.JNS111798
Fenoy, A. J., & Simpson, R. K. (2014). Risks of common complications in deep brain
stimulation surgery: management and avoidance. Journal of Neurosurgery, 120(1), 132–
139. https://doi.org/10.3171/2013.10.JNS131225
Fernández-Pajarín, G., Sesar, A., Ares, B., Relova, J. L., Arán, E., Gelabert-González, M., &Castro, A. (2017). Delayed complications of deep brain stimulation: 16-year experience in
249 patients. Acta Neurochirurgica, 159(9), 1713–1719. https://doi.org/10.1007/s00701017-3252-7
Fernández, F. S., Alvarez Vega, M. A., Antuña Ramos, A., Fernández González, F., & Lozano
Aragoneses, B. (2010). Lead Fractures in Deep Brain Stimulation during Long-Term
Follow-Up. Parkinson’s Disease, 2010, 409356. https://doi.org/10.4061/2010/409356
Fily, F., Haegelen, C., Tattevin, P., Buffet-Bataillon, S., Revest, M., Cady, A., & Michelet, C.
(2011). Deep brain stimulation hardware-related infections: a report of 12 cases and reviewof the literature. Clinical Infectious Diseases : An Official Publication of the Infectious
Diseases Society of America, 52(8), 1020–1023. https://doi.org/10.1093/cid/cir065
Flesher, S. N., Collinger, J. L., Foldes, S. T., Weiss, J. M., Downey, J. E., Tyler-Kabara, E. C.,… Gaunt, R. A. (2016). Intracortical microstimulation of human somatosensory cortex.
Science Translational Medicine, 8(361), 361ra141.
https://doi.org/10.1126/scitranslmed.aaf8083
Flint, R. D., Scheid, M. R., Wright, Z. A., Solla, S. A., & Slutzky, M. W. (2016). Long-Term
Stability of Motor Cortical Activity: Implications for Brain Machine Interfaces and Optimal
Feedback Control. The Journal of Neuroscience : The Official Journal of the Society for
Neuroscience, 36(12), 3623–3632. https://doi.org/10.1523/JNEUROSCI.2339-15.2016
Follett, K. A., Weaver, F. M., Stern, M., Hur, K., Harris, C. L., Luo, P., … CSP 468 Study
Group. (2010). Pallidal versus subthalamic deep-brain stimulation for Parkinson’s disease.
The New England Journal of Medicine, 362(22), 2077–2091.
https://doi.org/10.1056/NEJMoa0907083
Fortino, G., & Gravina, R. (2015). Fall-MobileGuard: a Smart Real-Time Fall Detection System.
In Proceedings of the 10th EAI International Conference on Body Area Networks (pp. 44–
50). ICST. https://doi.org/10.4108/eai.28-9-2015.2261462
Fraser, G. W., Chase, S. M., Whitford, A., & Schwartz, A. B. (2009). Control of a brain–computer interface without spike sorting. Journal of Neural Engineering, 6(5), 055004.
https://doi.org/10.1088/1741-2560/6/5/055004
Friedenberg, D. A., Schwemmer, M. A., Landgraf, A. J., Annetta, N. V, Bockbrader, M. A.,
Bouton, C. E., … Sharma, G. (2017). Neuroprosthetic-enabled control of graded arm
muscle contraction in a paralyzed human. Scientific Reports, 7(1), 8386.
https://doi.org/10.1038/s41598-017-08120-9
Frizon, L. A., Hogue, O., Wathen, C., Yamamoto, E., Sabharwal, N. C., Jones, J., … Nagel, S. J.
(2017). Subsequent Pulse Generator Replacement Surgery Does Not Increase the Infection
Rate in Patients With Deep Brain Stimulator Systems: A Review of 1537 Unique Implants
at a Single Center. Neuromodulation : Journal of the International Neuromodulation
Society, 20(5), 444–449. https://doi.org/10.1111/ner.12605
Fu, T.-M., Hong, G., Viveros, R. D., Zhou, T., & Lieber, C. M. (2017). Highly scalable
multichannel mesh electronics for stable chronic brain electrophysiology. Proceedings of
the National Academy of Sciences of the United States of America, 114(47), E10046–
E10055. https://doi.org/10.1073/pnas.1717695114
87

Fytagoridis, A., & Blomstedt, P. (2010). Complications and side effects of deep brain stimulationin the posterior subthalamic area. Stereotactic and Functional Neurosurgery, 88(2), 88–93.
https://doi.org/10.1159/000271824
Gao, H., Walker, R. M., Nuyujukian, P., Makinwa, K. A. A., Shenoy, K. V., Murmann, B., &Meng, T. H. (2012). HermesE: A 96-Channel Full Data Rate Direct Neural Interface in 0.13$\mu$m CMOS. IEEE Journal of Solid-State Circuits, 47(4), 1043–1055.
https://doi.org/10.1109/JSSC.2012.2185338
García-Cabezas, M. Á., & Barbas, H. (2014). Area 4 has layer IV in adult primates. The
European Journal of Neuroscience, 39(11), 1824–1834. https://doi.org/10.1111/ejn.12585
Gaunt, R. A., & Prochazka, A. (2006). Control of urinary bladder function with devices:successes and failures. Progress in Brain Research, 152, 163–194.
https://doi.org/10.1016/S0079-6123(05)52011-9
Geller, E. B., Skarpaas, T. L., Gross, R. E., Goodman, R. R., Barkley, G. L., Bazil, C. W., …
Morrell, M. J. (2017). Brain-responsive neurostimulation in patients with medicallyintractable mesial temporal lobe epilepsy. Epilepsia, 58(6), 994–1004.
https://doi.org/10.1111/epi.13740
Gervais-Bernard, H., Xie-Brustolin, J., Mertens, P., Polo, G., Klinger, H., Adamec, D., …
Thobois, S. (2009). Bilateral subthalamic nucleus stimulation in advanced Parkinson’s
disease: five year follow-up. Journal of Neurology, 256(2), 225–233.
https://doi.org/10.1007/s00415-009-0076-2
Ghoreishizadeh, S. S., Haci, D., Liu, Y., Donaldson, N., & Constandinou, T. G. (2017). FourWire Interface ASIC for a Multi-Implant Link. IEEE Transactions on Circuits and Systems
I: Regular Papers, 64(12), 3056–3067. https://doi.org/10.1109/TCSI.2017.2731659
Gilja, V., Nuyujukian, P., Chestek, C. A., Cunningham, J. P., Yu, B. M., Fan, J. M., … Shenoy,
K. V. (2012). A high-performance neural prosthesis enabled by control algorithm design.
Nature Neuroscience, 15(12), 1752–1757. https://doi.org/10.1038/nn.3265
Gilja, V., Pandarinath, C., Blabe, C. H., Nuyujukian, P., Simeral, J. D., Sarma, A. A., …
Henderson, J. M. (2015a). Clinical translation of a high-performance neural prosthesis.
Nature Medicine, 21(10), 1142–1145. https://doi.org/10.1038/nm.3953
Gilja, V., Pandarinath, C., Blabe, C. H., Nuyujukian, P., Simeral, J. D., Sarma, A. a, …
Henderson, J. M. (2015b). Clinical translation of a high-performance neural prosthesis.
Nature Medicine, 21(10), 1142–1145. https://doi.org/10.1038/nm.3953
Gocmen, S., Celiker, O., Topcu, A., Panteli, A., Acar, G., & Acar, F. (2014). Reuse of internal
pulse generator in cases of infection after deep brain stimulation surgery. Stereotactic andFunctional Neurosurgery, 92(3), 140–144. https://doi.org/10.1159/000360585
Gologorsky, Y., Ben-Haim, S., Moshier, E. L., Godbold, J., Tagliati, M., Weisz, D., & Alterman,
R. L. (2011). Transgressing the Ventricular Wall During Subthalamic Deep Brain
Stimulation Surgery for Parkinson Disease Increases the Risk of Adverse NeurologicalSequelae. Neurosurgery, 69(2), 294–300. https://doi.org/10.1227/NEU.0b013e318214abda
Goodman, R. R. (2006). Operative techniques and morbidity with subthalamic nucleus deepbrain stimulation in 100 consecutive patients with advanced Parkinson’s disease. Journal of
Neurology, Neurosurgery & Psychiatry, 77(1), 12–17.
https://doi.org/10.1136/jnnp.2005.069161
Gorgulho, A., De Salles, A. A. F., Frighetto, L., & Behnke, E. (2005). Incidence of hemorrhage
associated with electrophysiological studies performed using macroelectrodes andmicroelectrodes in functional neurosurgery. Journal of Neurosurgery, 102(5), 888–896.
88https://doi.org/10.3171/jns.2005.102.5.0888
Gorgulho, A., Juillard, C., Uslan, D. Z., Tajik, K., Aurasteh, P., Behnke, E., … De Salles, A. A.
F. (2009). Infection following deep brain stimulator implantation performed in theconventional versus magnetic resonance imaging–equipped operating room. Journal of
Neurosurgery, 110(2), 239–246. https://doi.org/10.3171/2008.6.17603
Greenberg, B. D., Malone, D. A., Friehs, G. M., Rezai, A. R., Kubu, C. S., Malloy, P. F., …
Rasmussen, S. A. (2006). Three-Year Outcomes in Deep Brain Stimulation for Highly
Resistant Obsessive–Compulsive Disorder. Neuropsychopharmacology, 31(11), 2384–
2393. https://doi.org/10.1038/sj.npp.1301165
Gubler, F. S., Ackermans, L., Kubben, P. L., Damci, A., Kuijf, M. L., Oosterloo, M., … Temel,
Y. (2017). Infections in deep brain stimulation: Shaving versus not shaving. Surgical
Neurology International, 8(1), 249. https://doi.org/10.4103/sni.sni_172_17
Guiraud, D., Azevedo Coste, C., Benoussaad, M., & Fattal, C. (2014). Implanted functional
electrical stimulation: case report of a paraplegic patient with complete SCI after 9 years.
Journal of NeuroEngineering and Rehabilitation, 11(1), 15. https://doi.org/10.1186/17430003-11-15
Guridi, J., Rodriguez-Oroz, M. C., Alegre, M., & Obeso, J. A. (2012). Hardware complications
in deep brain stimulation: electrode impedance and loss of clinical benefit. Parkinsonism &Related Disorders, 18(6), 765–769. https://doi.org/10.1016/j.parkreldis.2012.03.014
Gyselinckx, B., Vullers, R., Hoof, C. Van, Ryckaert, J., & Yazicioglu, R. F. (2008). HUMAN++:EMERGING TECHNOLOGY FOR BODY AREA NETWORKS, 249(November), 175–
180. https://doi.org/10.1007/978-0-387-74909-9
Ha, I. (2015). Technologies and Research Trends in Wireless Body Area Networks for
Healthcare: A Systematic Literature Review. International Journal of Distributed Sensor
Networks, 11(6), 573538. https://doi.org/10.1155/2015/573538
Hadjem, M., Salem, O., & Nait-Abdesselam, F. (2014). An ECG monitoring system forprediction of cardiac anomalies using WBAN. In 2014 IEEE 16th International Conferenceon e-Health Networking, Applications and Services (Healthcom) (pp. 441–446). IEEE.
https://doi.org/10.1109/HealthCom.2014.7001883
Handa, Y., Handa, T., Ichie, M., Murakami, H., Hoshimiya, N., Ishikawa, S., & Ohkubo, K.
(1992). Functional electrical stimulation (FES) systems for restoration of motor function ofparalyzed muscles--versatile systems and a portable system. Frontiers of Medical andBiological Engineering : The International Journal of the Japan Society of Medical
Electronics and Biological Engineering, 4(4), 241–255.
Hanrahan, S. J., Greger, B., Parker, R. A., Ogura, T., Obara, S., Egan, T. D., & House, P. A.
(2013). The Effects of Propofol on Local Field Potential Spectra, Action Potential Firing
Rate, and Their Temporal Relationship in Humans and Felines. Frontiers in Human
Neuroscience, 7, 136. https://doi.org/10.3389/FNHUM.2013.00136
Hardaway, F. A., Raslan, A. M., & Burchiel, K. J. (2018). Deep Brain Stimulation-Related
Infections: Analysis of Rates, Timing, and Seasonality. Neurosurgery, 83(3), 540–547.
https://doi.org/10.1093/neuros/nyx505
Harries, A. M., Kausar, J., Roberts, S. A. G., Mocroft, A. P., Hodson, J. A., Pall, H. S., &Mitchell, R. D. (2012). Deep brain stimulation of the subthalamic nucleus for advanced
Parkinson disease using general anesthesia: long-term results. Journal of Neurosurgery,116(1), 107–113. https://doi.org/10.3171/2011.7.JNS11319
Harrison, R. R., Kier, R. J., Chestek, C. A., Gilja, V., Nuyujukian, P., Ryu, S., … Shenoy, K. V.
89

(2009). Wireless neural recording with single low-power integrated circuit. IEEE
Transactions on Neural Systems and Rehabilitation Engineering, 17(4), 322–329.
https://doi.org/10.1109/TNSRE.2009.2023298
Heck, C. N., King-Stephens, D., Massey, A. D., Nair, D. R., Jobst, B. C., Barkley, G. L., …
Morrell, M. J. (2014). Two-year seizure reduction in adults with medically intractablepartial onset epilepsy treated with responsive neurostimulation: final results of the RNS
System Pivotal trial. Epilepsia, 55(3), 432–441. https://doi.org/10.1111/epi.12534
Henssen, D. J. H. A., Kurt, E., van Cappellen van Walsum, A.-M., Arnts, I., Doorduin, J.,
Kozicz, T., … Bartels, R. H. M. A. (2018). Long-term effect of motor cortex stimulation inpatients suffering from chronic neuropathic pain: An observational study. PloS One, 13(1),e0191774. https://doi.org/10.1371/journal.pone.0191774
Higuchi, M.-A., Martinez-Ramirez, D., Morita, H., Topiol, D., Bowers, D., Ward, H., … Okun,
M. S. (2016). Interdisciplinary Parkinson’s Disease Deep Brain Stimulation Screening andthe Relationship to Unintended Hospitalizations and Quality of Life. PloS One, 11(5),e0153785. https://doi.org/10.1371/journal.pone.0153785
Hilliard, J. D., Bona, A., Vaziri, S., Walz, R., Okun, M. S., & Foote, K. D. (2016). 138
Delayed Scalp Erosion After Deep Brain Stimulation Surgery. Neurosurgery,
63(CN_suppl_1), 156. https://doi.org/10.1227/01.neu.0000489708.21831.66
Hochberg, L. R., Bacher, D., Jarosiewicz, B., Masse, N. Y., Simeral, J. D., Vogel, J., …
Donoghue, J. P. (2012a). Reach and grasp by people with tetraplegia using a neurally
controlled robotic arm. Nature, 485(7398), 372–375. https://doi.org/10.1038/nature11076
Hochberg, L. R., Bacher, D., Jarosiewicz, B., Masse, N. Y., Simeral, J. D., Vogel, J., …
Donoghue, J. P. (2012b). Reach and grasp by people with tetraplegia using a neurally
controlled robotic arm. Nature, 485(7398), 372–375. https://doi.org/10.1038/nature11076
Hochberg, L. R., Serruya, M. D., Friehs, G. M., Mukand, J. A., Saleh, M., Caplan, A. H., …
Donoghue, J. P. (2006). Neuronal ensemble control of prosthetic devices by a human withtetraplegia. Nature, 442(7099), 164–171. https://doi.org/10.1038/nature04970
Holslag, J. A. H., Neef, N., Beudel, M., Drost, G., Oterdoom, D. L. M., Kremer, N. I., … van
Dijk, J. M. C. (2018). Deep Brain Stimulation for Essential Tremor: A Comparison of
Targets. World Neurosurgery, 110, e580–e584. https://doi.org/10.1016/j.wneu.2017.11.064
Holt, G. R., & Koch, C. (1999). Electrical interactions via the extracellular potential near cellbodies. Journal of Computational Neuroscience, 6(2), 169–184.
Holtzheimer, P. E., Husain, M. M., Lisanby, S. H., Taylor, S. F., Whitworth, L. A., McClintock,
S., … Mayberg, H. S. (2017). Subcallosal cingulate deep brain stimulation for treatmentresistant depression: a multisite, randomised, sham-controlled trial. The Lancet. Psychiatry,
4(11), 839–849. https://doi.org/10.1016/S2215-0366(17)30371-1
Homer, M. L., Perge, J. A., Black, M. J., Harrison, M. T., Cash, S. S., & Hochberg, L. R. (2014).
Adaptive offset correction for intracortical brain-computer interfaces. IEEE Transactions on
Neural Systems and Rehabilitation Engineering : A Publication of the IEEE Engineering in
Medicine and Biology Society, 22(2), 239–248.
https://doi.org/10.1109/TNSRE.2013.2287768
Hotson, G., McMullen, D. P., Fifer, M. S., Johannes, M. S., Katyal, K. D., Para, M. P., … Crone,
N. E. (2016). Individual finger control of a modular prosthetic limb using high-density
electrocorticography in a human subject. Journal of Neural Engineering, 13(2), 02601726017. https://doi.org/10.1088/1741-2560/13/2/026017
Hu, K., Moses, Z. B., Hutter, M. M., & Williams, Z. (2017). Short-Term Adverse Outcomes90

After Deep Brain Stimulation Treatment in Patients with Parkinson Disease. World
Neurosurgery, 98, 365–374. https://doi.org/10.1016/j.wneu.2016.10.138
Hu, X., Jiang, X., Zhou, X., Liang, J., Wang, L., Cao, Y., … Yang, P. (2010). Avoidance andmanagement of surgical and hardware-related complications of deep brain stimulation.
Stereotactic and Functional Neurosurgery, 88(5), 296–303.
https://doi.org/10.1159/000316762
Humphrey, D. R., Schmidt, E. M., & Thompson, W. D. (1970). Predicting measures of motor
performance from multiple cortical spike trains. Science (New York, N.Y.), 170(3959), 758–
762. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/4991377
Humphrey, D. S., & Hochberg, L. R. (1995). Intracortical recording of brain activity for controlof limb prostheses. Rehabilitation Engineering Society of North America, 650–658.
Iansek, R., Rosenfeld, J. V, & Huxham, F. E. (2002). Deep brain stimulation of the subthalamic
nucleus in Parkinson’s disease. The Medical Journal of Australia, 177(3), 142–146.
Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/12149083
Irwin, Z. T., Schroeder, K. E., Vu, P. P., Bullard, A. J., Tat, D. M., Nu, C. S., … Chestek, C. A.
(2017). Neural control of finger movement via intracortical brain-machine interface.
Journal of Neural Engineering. https://doi.org/10.1088/1741-2552/aa80bd
Irwin, Z. T., Schroeder, K. E., Vu, P. P., Tat, D. M., Bullard, A. J., Woo, S. L., … Chestek, C. A.
(2016). Chronic recording of hand prosthesis control signals via a regenerative peripheral
nerve interface in a rhesus macaque. Journal of Neural Engineering.
Irwin, Z. T., Thompson, D. E., Schroeder, K. E., Tat, D. M., Hassani, A., Bullard, A. J., …
Chestek, C. A. (2016). Enabling Low-Power, Multi-Modal Neural Interfaces Through a
Common, Low-Bandwidth Feature Space. IEEE Transactions on Neural Systems andRehabilitation Engineering, 24(5), 521–531. https://doi.org/10.1109/TNSRE.2015.2501752
Isaias, I. U., Alterman, R. L., & Tagliati, M. (2009). Deep brain stimulation for primary
generalized dystonia: long-term outcomes. Archives of Neurology, 66(4), 465–470.
https://doi.org/10.1001/archneurol.2009.20
Janson, C., Maxwell, R., Gupte, A. A., & Abosch, A. (2010). Bowstringing as a complication ofdeep brain stimulation: case report. Neurosurgery, 66(6), E1205; discussion E1205.
https://doi.org/10.1227/01.NEU.0000369199.72783.F9
Jarosiewicz, B., Masse, N. Y., Bacher, D., Cash, S. S., Eskandar, E., Friehs, G., … Hochberg, L.
R. (2013). Advantages of closed-loop calibration in intracortical brain-computer interfaces
for people with tetraplegia. Journal of Neural Engineering, 10(4), 046012.
https://doi.org/10.1088/1741-2560/10/4/046012
Jarosiewicz, B., Sarma, A. A., Bacher, D., Masse, N. Y., Simeral, J. D., Sorice, B., … Hochberg,
L. R. (2015). Virtual typing by people with tetraplegia using a self-calibrating intracorticalbrain-computer interface. Science Translational Medicine, 7(313), 313ra179-313ra179.
https://doi.org/10.1126/scitranslmed.aac7328
Jitkritsadakul, O., Bhidayasiri, R., Kalia, S. K., Hodaie, M., Lozano, A. M., & Fasano, A. (2017).
Systematic review of hardware-related complications of Deep Brain Stimulation: Do new
indications pose an increased risk? Brain Stimulation, 10(5), 967–976.
https://doi.org/10.1016/j.brs.2017.07.003
Jobst, B. C., Kapur, R., Barkley, G. L., Bazil, C. W., Berg, M. J., Bergey, G. K., … Morrell, M.
J. (2017). Brain-responsive neurostimulation in patients with medically intractable seizures
arising from eloquent and other neocortical areas. Epilepsia, 58(6), 1005–1014.
https://doi.org/10.1111/epi.13739
91

Johnson, B. C., Gambini, S., Izyumin, I., Moin, A., Zhou, A., Alexandrov, G., … Muller, R.
(2017). An implantable 700μW 64-channel neuromodulation IC for simultaneous recording
and stimulation with rapid artifact recovery. In IEEE Symposium on VLSI Circuits.
Retrieved from https://escholarship.org/uc/item/3mf489zc
Johnson, M. W., Peckham, P. H., Bhadra, N., Kilgore, K. L., Gazdik, M. M., Keith, M. W., &Strojnik, P. (1999). Implantable transducer for two-degree of freedom joint angle sensing.
IEEE Transactions on Rehabilitation Engineering : A Publication of the IEEE Engineeringin Medicine and Biology Society, 7(3), 349–359.
Joint, C., Nandi, D., Parkin, S., Gregory, R., & Aziz, T. (2002). Hardware-related problems ofdeep brain stimulation. Movement Disorders : Official Journal of the Movement DisorderSociety, 17 Suppl 3, S175-80. Retrieved fromhttp://www.ncbi.nlm.nih.gov/pubmed/11948774
Jovičić, N. S., Saranovac, L. V, & Popović, D. B. (2012). Wireless distributed functional
electrical stimulation system. Journal of Neuroengineering and Rehabilitation, 9, 54.
https://doi.org/10.1186/1743-0003-9-54
Kahn, E., D’Haese, P.-F., Dawant, B., Allen, L., Kao, C., Charles, P. D., & Konrad, P. (2012).
Deep brain stimulation in early stage Parkinson’s disease: operative experience from aprospective randomised clinical trial. Journal of Neurology, Neurosurgery, and Psychiatry,
83(2), 164–170. https://doi.org/10.1136/jnnp-2011-300008
Kaminska, M., Lumsden, D. E., Ashkan, K., Malik, I., Selway, R., & Lin, J.-P. (2012).
Rechargeable deep brain stimulators in the management of paediatric dystonia: welltolerated with a low complication rate. Stereotactic and Functional Neurosurgery, 90(4),233–239. https://doi.org/10.1159/000337768
Kaminska, M., Perides, S., Lumsden, D. E., Nakou, V., Selway, R., Ashkan, K., & Lin, J.-P.
(2017). Complications of Deep Brain Stimulation (DBS) for dystonia in children – The
challenges and 10 year experience in a large paediatric cohort. European Journal of
Paediatric Neurology, 21(1), 168–175. https://doi.org/10.1016/j.ejpn.2016.07.024
Kawakami, N., Jessen, H., Bordini, B., Gallagher, C., Klootwyk, J., & Garell, C. P. (2005). Deepbrain stimulation of the subthalamic nucleus in Parkinson’s disease. WMJ : Official
Publication of the State Medical Society of Wisconsin, 104(6), 35–38. Retrieved fromhttp://www.ncbi.nlm.nih.gov/pubmed/16218314
Keen, J. R., Przekop, A., Olaya, J. E., Zouros, A., & Hsu, F. P. K. (2014). Deep brain stimulation
for the treatment of childhood dystonic cerebral palsy. Journal of Neurosurgery. Pediatrics,
14(6), 585–593. https://doi.org/10.3171/2014.8.PEDS141
Kefalopoulou, Z., Zrinzo, L., Jahanshahi, M., Candelario, J., Milabo, C., Beigi, M., … Foltynie,
T. (2015). Bilateral globus pallidus stimulation for severe Tourette’s syndrome: a doubleblind, randomised crossover trial. The Lancet. Neurology, 14(6), 595–605.
https://doi.org/10.1016/S1474-4422(15)00008-3
Kennedy, P. R., & Bakay, R. A. E. (1998). Restoration of neural output from a paralyzed patient
by a direct brain connection. NeuroReport, 9, 1707–1711. Retrieved fromhttps://insights.ovid.com/pubmed?pmid=9665587
Kennedy, P. R., Bakay, R. A., Moore, M. M., Adams, K., & Goldwaithe, J. (2000). Directcontrol of a computer from the human central nervous system. IEEE Transactions on
Rehabilitation Engineering : A Publication of the IEEE Engineering in Medicine andBiology Society, 8(2), 198–202. Retrieved fromhttp://www.ncbi.nlm.nih.gov/pubmed/10896186
92Khatib, R., Ebrahim, Z., Rezai, A., Cata, J. P., Boulis, N. M., John Doyle, D., … Farag, E.
(2008). Perioperative Events During Deep Brain Stimulation: The Experience at Cleveland
Clinic. Journal of Neurosurgical Anesthesiology, 20(1), 36–40.
https://doi.org/10.1097/ANA.0b013e318157a15a
Khurram, A., Ross, S. E., Sperry, Z. J., Ouyang, A., Stephan, C., Jiman, A. A., & Bruns, T. M.
(2017). Chronic monitoring of lower urinary tract activity via a sacral dorsal root ganglia
interface. Journal of Neural Engineering, 14(3), 036027. https://doi.org/10.1088/17412552/aa6801
Kilgore, K. L., Hoyen, H. A., Bryden, A. M., Hart, R. L., Keith, M. W., & Peckham, P. H.
(2008). An Implanted Upper-Extremity Neuroprosthesis Using Myoelectric Control. The
Journal of Hand Surgery, 33(4), 539–550. https://doi.org/10.1016/j.jhsa.2008.01.007
Kilgore, K. L., Peckham, P. H., Crish, T. J., & Smith, B. (2007). US 7,260,436 B2. United States
Patent. https://doi.org/10.1016/j.(73)Kim, M., Jung, N. Y., Park, C. K., Chang, W. S., Jung, H. H., & Chang, J. W. (2017).
Comparative Evaluation of Magnetic Resonance-Guided Focused Ultrasound Surgery for
Essential Tremor. Stereotactic and Functional Neurosurgery, 95(4), 279–286.
https://doi.org/10.1159/000478866
Kim, M. S., Jeong, J. S., Ryu, H.-S., Choi, S.-H., & Chung, S. J. (2017). Infection related to deepbrain stimulation in patients with Parkinson disease: Clinical characteristics and risk factors.
Journal of the Neurological Sciences, 383, 135–141.
https://doi.org/10.1016/j.jns.2017.10.031
Kim, S.-P., Simeral, J. D., Hochberg, L. R., Donoghue, J. P., & Black, M. J. (2008). Neuralcontrol of computer cursor velocity by decoding motor cortical spiking activity in humans
with tetraplegia. Journal of Neural Engineering, 5(4), 455–476.
https://doi.org/10.1088/1741-2560/5/4/010
Kim, S.-P., Simeral, J. D., Hochberg, L. R., Donoghue, J. P., Friehs, G. M., & Black, M. J.
(2011). Point-and-click cursor control with an intracortical neural interface system byhumans with tetraplegia. IEEE Transactions on Neural Systems and Rehabilitation
Engineering : A Publication of the IEEE Engineering in Medicine and Biology Society,19(2), 193–203. https://doi.org/10.1109/TNSRE.2011.2107750
Kim, S. H., Lim, S. C., Kim, J., Son, B.-C., Lee, K. J., & Shon, Y.-M. (2017a). Long-term
follow-up of anterior thalamic deep brain stimulation in epilepsy: A 11-year, single center
experience. Seizure, 52, 154–161. https://doi.org/10.1016/j.seizure.2017.10.009
Kim, S. H., Lim, S. C., Kim, J., Son, B.-C., Lee, K. J., & Shon, Y.-M. (2017b). Long-term
follow-up of anterior thalamic deep brain stimulation in epilepsy: A 11-year, single center
experience. Seizure, 52, 154–161. https://doi.org/10.1016/j.seizure.2017.10.009
Kim, Y.-T., Hitchcock, R. W., Bridge, M. J., & Tresco, P. A. (2004). Chronic response of adultrat brain tissue to implants anchored to the skull. Biomaterials, 25(12), 2229–2237.
Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/14741588
Klaes, C., Kellis, S., Aflalo, T., Lee, B., Pejsa, K., Shanfield, K., … Andersen, R. A. (2015).
Hand Shape Representations in the Human Posterior Parietal Cortex. The Journal of
Neuroscience : The Official Journal of the Society for Neuroscience, 35(46), 15466–15476.
https://doi.org/10.1523/JNEUROSCI.2747-15.2015
Klein, J., Büntjen, L., Jacobi, G., Galazky, I., Panther, P., Zaehle, T., … Kupsch, A. (2017).
Bilateral thalamic deep brain stimulation for essential tremor in elderly patients. Journal of
Neural Transmission (Vienna, Austria : 1996), 124(9), 1093–1096.
93https://doi.org/10.1007/s00702-017-1741-8
Kochanski, R. B., Nazari, P., & Sani, S. (2018). The Utility of Vancomycin Powder in Reducing
Surgical Site Infections in Deep Brain Stimulation Surgery. Operative Neurosurgery(Hagerstown, Md.). https://doi.org/10.1093/ons/opx293
Koller, W., Pahwa, R., Busenbark, K., Hubble, J., Wilkinson, S., Lang, A., … Olanow, C. W.
(1997). High-frequency unilateral thalamic stimulation in the treatment of essential andparkinsonian tremor. Annals of Neurology, 42(3), 292–299.
https://doi.org/10.1002/ana.410420304
Kondziolka, D., Whiting, D., Germanwala, A., & Oh, M. (2002). Hardware-Related
Complications after Placement of Thalamic Deep Brain Stimulator Systems. Stereotactic
and Functional Neurosurgery, 79(3–4), 228–233. https://doi.org/10.1159/000070836
Koy, A., Weinsheimer, M., Pauls, K. A. M., Kühn, A. A., Krause, P., Huebl, J., … GEPESTIM
Consortium. (2017). German registry of paediatric deep brain stimulation in patients withchildhood-onset dystonia (GEPESTIM). European Journal of Paediatric Neurology :EJPN : Official Journal of the European Paediatric Neurology Society, 21(1), 136–146.
https://doi.org/10.1016/j.ejpn.2016.05.023
Kozai, T. D. Y., Marzullo, T. C., Hooi, F., Langhals, N. B., Majewska, A. K., Brown, E. B., &Kipke, D. R. (2010). Reduction of neurovascular damage resulting from microelectrodeinsertion into the cerebral cortex using in vivo two-photon mapping. Journal of NeuralEngineering, 7(4), 046011. https://doi.org/10.1088/1741-2560/7/4/046011
Kramer, D. R., Halpern, C. H., Danish, S. F., Jaggi, J. L., & Baltuch, G. H. (2012). The Effect ofIntraventricular Trajectory on Brain Shift in Deep Brain Stimulation. Stereotactic andFunctional Neurosurgery, 90(1), 20–24. https://doi.org/10.1159/000332056
Krause, P., Lauritsch, K., Lipp, A., Horn, A., Weschke, B., Kupsch, A., … Kühn, A. A. (2016).
Long-term results of deep brain stimulation in a cohort of eight children with isolated
dystonia. Journal of Neurology, 263(11), 2319–2326. https://doi.org/10.1007/s00415-0168253-6
Kumar, R., Lang, A. E., Rodriguez-Oroz, M. C., Lozano, A. M., Limousin, P., Pollak, P., …
Obeso, J. A. (2000). Deep brain stimulation of the globus pallidus pars interna in advanced
Parkinson’s disease. Neurology, 55(12 Suppl 6), S34-9. Retrieved fromhttp://www.ncbi.nlm.nih.gov/pubmed/11188973
Kupsch, A., Benecke, R., Müller, J., Trottenberg, T., Schneider, G.-H., Poewe, W., … DeepBrain Stimulation for Dystonia Study Group. (2006). Pallidal Deep-Brain Stimulation inPrimary Generalized or Segmental Dystonia. New England Journal of Medicine, 355(19),1978–1990. https://doi.org/10.1056/NEJMoa063618
Landi, A., Parolin, M., Piolti, R., Antonini, A., Grimaldi, M., Crespi, M., … Gaini, S. M. (2003).
Deep brain stimulation for the treatment of Parkinson’s disease: the experience of the
Neurosurgical Department in Monza. Neurological Sciences, 24(0), s43–s44.
https://doi.org/10.1007/s100720300039
Lee, J. Y. K., & Kondziolka, D. (2005). Thalamic deep brain stimulation for management ofessential tremor. Journal of Neurosurgery, 103(3), 400–403.
https://doi.org/10.3171/jns.2005.103.3.0400
Lee, S.-W., Lee, M.-K., Seo, I., Kim, H.-S., Kim, J.-H., & Kim, Y.-S. (2014). A groove
technique for securing an electrode connector on the cranial bone: case analysis of efficacy.
Journal of Korean Neurosurgical Society, 56(2), 130–134.
https://doi.org/10.3340/jkns.2014.56.2.130
94

Lefebvre, J., Buffet-Bataillon, S., Henaux, P. L., Riffaud, L., Morandi, X., & Haegelen, C.
(2017). Staphylococcus aureus screening and decolonization reduces the risk of surgical site
infections in patients undergoing deep brain stimulation surgery. The Journal of Hospital
Infection, 95(2), 144–147. https://doi.org/10.1016/j.jhin.2016.11.019
Lempka, S. F., Malone, D. A., Hu, B., Baker, K. B., Wyant, A., Ozinga, J. G., … Machado, A.
G. (2017). Randomized clinical trial of deep brain stimulation for poststroke pain. Annals of
Neurology, 81(5), 653–663. https://doi.org/10.1002/ana.24927
Leone, M., Franzini, A., Proietti Cecchini, A., & Bussone, G. (2013). Success, failure, andputative mechanisms in hypothalamic stimulation for drug-resistant chronic cluster
headache. Pain, 154(1), 89–94. https://doi.org/10.1016/j.pain.2012.09.011
Leuthardt, E. C., Schalk, G., Wolpaw, J. R., Ojemann, J. G., & Moran, D. W. (2004). A brain–computer interface using electrocorticographic signals in humans. Journal of NeuralEngineering, 1(2), 63–71. https://doi.org/10.1088/1741-2560/1/2/001
Levi, V., Carrabba, G., Rampini, P., & Locatelli, M. (2015). &quot;Short term surgicalcomplications after subthalamic deep brain stimulation for Parkinson’s disease: does old
age matter?&quot;. BMC Geriatrics, 15(1), 116. https://doi.org/10.1186/s12877-015-0112-2
Levy, R. M., Lamb, S., & Adams, J. E. (1987). Treatment of chronic pain by deep brain
stimulation: long term follow-up and review of the literature. Neurosurgery, 21(6), 885–
893. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/3325851
Lezcano, E., Gómez-Esteban, J. C., Tijero, B., Bilbao, G., Lambarri, I., Rodriguez, O., …
Zarranz, J. J. (2016). Long-term impact on quality of life of subthalamic nucleus stimulationin Parkinson’s disease. Journal of Neurology, 263(5), 895–905.
https://doi.org/10.1007/s00415-016-8077-4
Li, D., Cao, C., Zhang, J., Zhan, S., Chen, S., & Sun, B. (2013). Subthalamic nucleus deep brain
stimulation for Parkinson’s disease: 8 years of follow-up. Translational Neurodegeneration,
2(1), 11. https://doi.org/10.1186/2047-9158-2-11
Li, D., Zhang, C., Gault, J., Wang, W., Liu, J., Shao, M., … Sun, B. (2017). Remotely
Programmed Deep Brain Stimulation of the Bilateral Subthalamic Nucleus for theTreatment of Primary Parkinson Disease: A Randomized Controlled Trial Investigating the
Safety and Efficacy of a Novel Deep Brain Stimulation System. Stereotactic and Functional
Neurosurgery, 95(3), 174–182. https://doi.org/10.1159/000475765
Lim, S.-N., Lee, S.-T., Tsai, Y.-T., Chen, I.-A., Tu, P.-H., Chen, J.-L., … Wu, T. (n.d.). Longterm anterior thalamus stimulation for intractable epilepsy. Chang Gung Medical Journal,
31(3), 287–296. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/18782952
Lim, S.-N., Lee, S.-T., Tsai, Y.-T., Chen, I.-A., Tu, P.-H., Chen, J.-L., … Wu, T. (2007).
Electrical stimulation of the anterior nucleus of the thalamus for intractable epilepsy: along-term follow-up study. Epilepsia, 48(2), 342–347. https://doi.org/10.1111/j.15281167.2006.00898.x
Limousin, P., Speelman, J. D., Gielen, F., & Janssens, M. (1999). Multicentre European study ofthalamic stimulation in parkinsonian and essential tremor. Journal of Neurology,
Neurosurgery, and Psychiatry, 66(3), 289–296. Retrieved fromhttp://www.ncbi.nlm.nih.gov/pubmed/10084526
Linhares, P., Carvalho, B., & Vaz, R. (2013). One-step tunneling of DBS extensions--a technicalnote. Acta Neurochirurgica, 155(5), 837–40; discussion 840.
https://doi.org/10.1007/s00701-013-1667-3
Lipsman, N., Lam, E., Volpini, M., Sutandar, K., Twose, R., Giacobbe, P., … Lozano, A. M.
95

(2017). Deep brain stimulation of the subcallosal cingulate for treatment-refractory anorexia
nervosa: 1 year follow-up of an open-label trial. The Lancet Psychiatry, 4(4), 285–294.
https://doi.org/10.1016/S2215-0366(17)30076-7
Liu, J., Fu, T.-M., Cheng, Z., Hong, G., Zhou, T., Jin, L., … Lieber, C. M. (2015). Syringeinjectable electronics. Nature Nanotechnology, 10(7), 629–636.
https://doi.org/10.1038/nnano.2015.115
Loher, T. J., Burgunder, J.-M., Pohle, T., Weber, S., Sommerhalder, R., & Krauss, J. K. (2002).
Long-term pallidal deep brain stimulation in patients with advanced Parkinson disease: 1year follow-up study. Journal of Neurosurgery, 96(5), 844–853.
https://doi.org/10.3171/jns.2002.96.5.0844
Lozano, A. M., Fosdick, L., Chakravarty, M. M., Leoutsakos, J.-M., Munro, C., Oh, E., …
Smith, G. S. (2016). A Phase II Study of Fornix Deep Brain Stimulation in Mild
Alzheimer’s Disease. Journal of Alzheimer’s Disease, 54(2), 777–787.
https://doi.org/10.3233/JAD-160017
Lyons, K. E., Koller, W. C., Wilkinson, S. B., & Pahwa, R. (2001). Long term safety andefficacy of unilateral deep brain stimulation of the thalamus for parkinsonian tremor.
Journal of Neurology, Neurosurgery, and Psychiatry, 71(5), 682–684. Retrieved fromhttp://www.ncbi.nlm.nih.gov/pubmed/11606685
Lyons, K. E., Wilkinson, S. B., Overman, J., & Pahwa, R. (2004). Surgical and hardware
complications of subthalamic stimulation: a series of 160 procedures. Neurology, 63(4),612–616. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/15326230
Magill, S. T., Han, S. J., Li, J., & Berger, M. S. (2018). Resection of primary motor cortex
tumors: feasibility and surgical outcomes. Journal of Neurosurgery, 129, 961–972.
Retrieved from https://thejns.org/view/journals/j-neurosurg/129/4/article-p961.xml
Maldonado, I. L., Roujeau, T., Cif, L., Gonzalez, V., El-Fertit, H., Vasques, X., … Coubes, P.
(2009). Magnetic resonance-based deep brain stimulation technique: a series of 478
consecutive implanted electrodes with no perioperative intracerebral hemorrhage.
Neurosurgery, 65(6 Suppl), 196-201; discussion 201-2.
https://doi.org/10.1227/01.NEU.0000342404.14347.FB
Malik, W. Q., Hochberg, L. R., Donoghue, J. P., & Brown, E. N. (2015). Modulation depth
estimation and variable selection in state-space models for neural interfaces. IEEE
Transactions on Bio-Medical Engineering, 62(2), 570–581.
https://doi.org/10.1109/TBME.2014.2360393
Mandat, T., Tykocki, T., Koziara, H., Koziorowski, D., Brodacki, B., Rola, R., … Nauman, P.
(n.d.). Subthalamic deep brain stimulation for the treatment of Parkinson disease.
Neurologia i Neurochirurgia Polska, 45(1), 32–36. Retrieved fromhttp://www.ncbi.nlm.nih.gov/pubmed/21384291
Martin, A. J., Larson, P. S., Ziman, N., Levesque, N., Volz, M., Ostrem, J. L., & Starr, P. A.
(2017). Deep brain stimulator implantation in a diagnostic MRI suite: infection history overa 10-year period. Journal of Neurosurgery, 126(1), 108–113.
https://doi.org/10.3171/2015.7.JNS15750
Martinez-Ramirez, D., Jimenez-Shahed, J., Leckman, J. F., Porta, M., Servello, D., Meng, F.-G.,… Okun, M. S. (2018). Efficacy and Safety of Deep Brain Stimulation in TouretteSyndrome: The International Tourette Syndrome Deep Brain Stimulation Public Databaseand Registry. JAMA Neurology, 75(3), 353–359.
https://doi.org/10.1001/jamaneurol.2017.4317
96

Masse, N. Y., Jarosiewicz, B., Simeral, J. D., Bacher, D., Stavisky, S. D., Cash, S. S., …
Donoghue, J. P. (2014). Non-causal spike filtering improves decoding of movement
intention for intracortical BCIs. Journal of Neuroscience Methods, 236, 58–67.
https://doi.org/10.1016/j.jneumeth.2014.08.004
Massey, T. L., Santacruz, S. R., Hou, J. F., Pister, K. S. J., Carmena, J. M., & Maharbiz, M. M.
(2019). A high-density carbon fiber neural recording array technology. Journal of NeuralEngineering, 16(1), 016024. https://doi.org/10.1088/1741-2552/aae8d9
Mehrkens, J. H., Borggraefe, I., Feddersen, B., Heinen, F., & Bötzel, K. (2010). Early globus
pallidus internus stimulation in pediatric patients with generalized primary dystonia: longterm efficacy and safety. Journal of Child Neurology, 25(11), 1355–1361.
https://doi.org/10.1177/0883073810365369
Mehrkens, J. H., Bötzel, K., Steude, U., Zeitler, K., Schnitzler, A., Sturm, V., & Voges, J.
(2009). Long-term efficacy and safety of chronic globus pallidus internus stimulation indifferent types of primary dystonia. Stereotactic and Functional Neurosurgery, 87(1), 8–17.
https://doi.org/10.1159/000177623
Memberg, W. D., Polasek, K. H., Hart, R. L., Bryden, A. M., Kilgore, K. L., Nemunaitis, G. A.,… Kirsch, R. F. (2014). Implanted Neuroprosthesis for Restoring Arm and Hand Functionin People With High Level Tetraplegia. Archives of Physical Medicine and Rehabilitation,
95(6), 1201–1211.e1. https://doi.org/10.1016/j.apmr.2014.01.028
Mendes Martins, V., Coste, J., Derost, P., Ulla, M., Gabrillargues, J., Durif, F., … Lemaire, J.-J.
(2012). [Surgical complications of deep brain stimulation: clinical experience of 184 cases].
Neuro-Chirurgie, 58(4), 219–224. https://doi.org/10.1016/j.neuchi.2012.02.004
Meoni, S., Fraix, V., Castrioto, A., Benabid, A. L., Seigneuret, E., Vercueil, L., … Moro, E.
(2017). Pallidal deep brain stimulation for dystonia: a long term study. Journal of
Neurology, Neurosurgery, and Psychiatry, 88(11), 960–967. https://doi.org/10.1136/jnnp2016-315504
Merola, A., Fasano, A., Hassan, A., Ostrem, J. L., Contarino, M. F., Lyons, M., … Espay, A. J.
(2017). Thalamic deep brain stimulation for orthostatic tremor: A multicenter international
registry. Movement Disorders : Official Journal of the Movement Disorder Society, 32(8),1240–1244. https://doi.org/10.1002/mds.27082
Messina, G., Rizzi, M., Dones, I., & Franzini, A. (2014). Cosmetic posterior implant of internal
pulse generators in deep brain stimulation procedures: technical report. Neuromodulation :Journal of the International Neuromodulation Society, 17(8), 729–730.
https://doi.org/10.1111/ner.12156
Mestais, C. S., Charvet, G., Sauter-Starace, F., Foerster, M., Ratel, D., & Benabid, A. L. (2015).
WIMAGINE: Wireless 64-channel ECoG recording implant for long term clinicalapplications. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23(1),
10–21. https://doi.org/10.1109/TNSRE.2014.2333541
Milekovic, T., Sarma, A. A., Bacher, D., Simeral, J. D., Saab, J., Pandarinath, C., … Hochberg,
L. R. (2018). Stable long-term BCI-enabled communication in ALS and locked-in
syndrome using LFP signals. Journal of Neurophysiology, 120(1), 343–360.
https://doi.org/10.1152/jn.00493.2017
Milenković, A., Otto, C., & Jovanov, E. (2006). Wireless sensor networks for personal healthmonitoring: Issues and an implementation. Computer Communications, 29(13–14), 2521–
2533. https://doi.org/10.1016/j.comcom.2006.02.011
Miller, J. P., Acar, F., & Burchiel, K. J. (2009). Significant reduction in stereotactic and97

functional neurosurgical hardware infection after local neomycin/polymyxin application.
Journal of Neurosurgery, 110(2), 247–250. Retrieved fromhttp://www.ncbi.nlm.nih.gov/pubmed/19263587
Miller, P. M., & Gross, R. E. (2009). Wire tethering or “bowstringing” as a long-term hardwarerelated complication of deep brain stimulation. Stereotactic and Functional Neurosurgery,87(6), 353–359. https://doi.org/10.1159/000236369
Miller, S., Akram, H., Lagrata, S., Hariz, M., Zrinzo, L., & Matharu, M. (2016). Ventral
tegmental area deep brain stimulation in refractory short-lasting unilateral neuralgiform
headache attacks. Brain : A Journal of Neurology, 139(Pt 10), 2631–2640.
https://doi.org/10.1093/brain/aww204
Miranda, H., Gilja, V., Chestek, C. A., Shenoy, K. V., & Meng, T. H. (2010). HermesD: A highrate long-range wireless transmission system for simultaneous multichannel neuralrecording applications. IEEE Transactions on Biomedical Circuits and Systems, 4(3), 181–
191. https://doi.org/10.1109/TBCAS.2010.2044573
Miranda, H., Gilja, V., Chestek, C. A., Shenoy, K. V., & Meng, T. H. (2012). HermesD: A HighRate Long-Range Wireless Transmission System for Simultaneous Multichannel Neural
Recording Applications. IEEE Journal of Solid-State Circuits, 47(4), 1043–1055.
https://doi.org/10.1109/JSSC.2012.2185338
Moffitt, M. A., & McIntyre, C. C. (2005). Model-based analysis of cortical recording withsilicon microelectrodes. Clinical Neurophysiology, 116(9), 2240–2250.
https://doi.org/10.1016/j.clinph.2005.05.018
Moo Sung Chae, Zhi Yang, Yuce, M. R., Linh Hoang, & Liu, W. (2009). A 128-Channel 6 mW
Wireless Neural Recording IC With Spike Feature Extraction and UWB Transmitter. IEEE
Transactions on Neural Systems and Rehabilitation Engineering, 17(4), 312–321.
https://doi.org/10.1109/TNSRE.2009.2021607
Morishita, T., Hilliard, J. D., Okun, M. S., Neal, D., Nestor, K. A., Peace, D., … Foote, K. D.
(2017). Postoperative lead migration in deep brain stimulation surgery: Incidence, risk
factors, and clinical impact. PLOS ONE, 12(9), e0183711.
https://doi.org/10.1371/journal.pone.0183711
Moro, E., Lozano, A. M., Pollak, P., Agid, Y., Rehncrona, S., Volkmann, J., … Lang, A. E.
(2010). Long-term results of a multicenter study on subthalamic and pallidal stimulation in
Parkinson’s disease. Movement Disorders : Official Journal of the Movement DisorderSociety, 25(5), 578–586. https://doi.org/10.1002/mds.22735
Motlagh, M. G., Smith, M. E., Landeros-Weisenberger, A., Kobets, A. J., King, R. A., Miravite,
J., … Leckman, J. F. (2013). Lessons Learned from Open-label Deep Brain Stimulation forTourette Syndrome: Eight Cases over 7 Years. Tremor and Other Hyperkinetic Movements(New York, N.Y.), 3. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/24255802
Movement Disorder Group, Chan, A. Y. Y., Yeung, J. H. M., Mok, V. C. T., Ip, V. H. L., Wong,A., … Poon, W. S. (2014). Subthalamic nucleus deep brain stimulation for Parkinson’s
disease: evidence for effectiveness and limitations from 12 years’ experience. Hong Kong
Medical Journal = Xianggang Yi Xue Za Zhi, 20(6), 474–480.
https://doi.org/10.12809/hkmj144242
Nahas, Z., Anderson, B. S., Borckardt, J., Arana, A. B., George, M. S., Reeves, S. T., & Takacs,
I. (2010). Bilateral epidural prefrontal cortical stimulation for treatment-resistantdepression. Biological Psychiatry, 67(2), 101–109.
https://doi.org/10.1016/j.biopsych.2009.08.021
98National Spinal Cord Injury Statistical Center. (2016). Spinal Cord Injury Facts and Figures at a
Glance. The Journal of Spinal Cord Medicine. Birmingham,AL: University of Alabama at
Birmingham. https://doi.org/10.1179/1079026813Z.000000000136
Nazzaro, J. M., Lyons, K. E., Honea, R. A., Mayo, M. S., Cook-Wiens, G., Harsha, A., …
Pahwa, R. (2010). Head positioning and risk of pneumocephalus, air embolism, andhemorrhage during subthalamic deep brain stimulation surgery. Acta Neurochirurgica,
152(12), 2047–2052. https://doi.org/10.1007/s00701-010-0776-5
Nolta, N. F., Christensen, M. B., Crane, P. D., Skousen, J. L., & Tresco, P. A. (2015). BBB
leakage, astrogliosis, and tissue loss correlate with silicon microelectrode array recording
performance. Biomaterials, 53, 753–762.
https://doi.org/10.1016/J.BIOMATERIALS.2015.02.081
Nordhausen, C. T., Maynard, E. M., & Normann, R. A. (1996). Single unit recording capabilities
of a 100 microelectrode array. Brain Research, 726(1–2), 129–140. Retrieved fromhttp://www.ncbi.nlm.nih.gov/pubmed/8836553
Nordhausen, C. T., Rousche, P. J., & Normann, R. A. (1994). Optimizing recording capabilitiesof the Utah Intracortical Electrode Array. Brain Research, 637(1–2), 27–36. Retrieved fromhttp://www.ncbi.nlm.nih.gov/pubmed/8180807
Nunta-Aree, S., Sitthinamsuwan, B., Boonyapisit, K., & Pisarnpong, A. (2010). SW2-year
outcomes of subthalamic deep brain stimulation for idiopathic Parkinson’s disease. Journalof the Medical Association of Thailand = Chotmaihet Thangphaet, 93(5), 529–540.
Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/20524438
O’Sullivan, D., & Pell, M. (2009). Long-term follow-up of DBS of thalamus for tremor and STN
for Parkinson’s disease. Brain Research Bulletin, 78(2–3), 119–121.
https://doi.org/10.1016/j.brainresbull.2008.09.001
Odekerken, V. J. J., van Laar, T., Staal, M. J., Mosch, A., Hoffmann, C. F. E., Nijssen, P. C. G.,… de Bie, R. M. A. (2013). Subthalamic nucleus versus globus pallidus bilateral deep brain
stimulation for advanced Parkinson’s disease (NSTAPS study): a randomised controlled
trial. The Lancet. Neurology, 12(1), 37–44. https://doi.org/10.1016/S1474-4422(12)70264-8
Oh, M. Y., Abosch, A., Kim, S. H., Lang, A. E., & Lozano, A. M. (2002). Long-term hardwarerelated complications of deep brain stimulation. Neurosurgery, 50(6), 1268-74; discussion
1274-6. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/12015845
Okun, M. S., Gallo, B. V, Mandybur, G., Jagid, J., Foote, K. D., Revilla, F. J., … SJM DBS
Study Group. (2012). Subthalamic deep brain stimulation with a constant-current device in
Parkinson’s disease: an open-label randomised controlled trial. The Lancet. Neurology,
11(2), 140–149. https://doi.org/10.1016/S1474-4422(11)70308-8
Oliveria, S. F., Rodriguez, R. L., Bowers, D., Kantor, D., Hilliard, J. D., Monari, E. H., … Foote,
K. D. (2017). Safety and efficacy of dual-lead thalamic deep brain stimulation for patientswith treatment-refractory multiple sclerosis tremor: a single-centre, randomised, singleblind, pilot trial. The Lancet. Neurology, 16(9), 691–700. https://doi.org/10.1016/S14744422(17)30166-7
Ooms, P., Mantione, M., Figee, M., Schuurman, P. R., van den Munckhof, P., & Denys, D.
(2014). Deep brain stimulation for obsessive-compulsive disorders: long-term analysis ofquality of life. Journal of Neurology, Neurosurgery & Psychiatry, 85(2), 153–158.
https://doi.org/10.1136/jnnp-2012-302550
Ostrem, J. L., Galifianakis, N. B., Markun, L. C., Grace, J. K., Martin, A. J., Starr, P. A., &Larson, P. S. (2013). Clinical outcomes of PD patients having bilateral STN DBS using99

high-field interventional MR-imaging for lead placement. Clinical Neurology and
Neurosurgery, 115(6), 708–712. https://doi.org/10.1016/j.clineuro.2012.08.019
Ostrem, J. L., Racine, C. A., Glass, G. A., Grace, J. K., Volz, M. M., Heath, S. L., & Starr, P. A.
(2011). Subthalamic nucleus deep brain stimulation in primary cervical dystonia.
Neurology, 76(10), 870–878. https://doi.org/10.1212/WNL.0b013e31820f2e4f
Ostrem, J. L., San Luciano, M., Dodenhoff, K. A., Ziman, N., Markun, L. C., Racine, C. A., …
Starr, P. A. (2017). Subthalamic nucleus deep brain stimulation in isolated dystonia: A 3year follow-up study. Neurology, 88(1), 25–35.
https://doi.org/10.1212/WNL.0000000000003451
Ostrem, J. L., Ziman, N., Galifianakis, N. B., Starr, P. A., Luciano, M. S., Katz, M., … Larson,
P. S. (2016). Clinical outcomes using ClearPoint interventional MRI for deep brain
stimulation lead placement in Parkinson’s disease. Journal of Neurosurgery, 124(4), 908–
916. https://doi.org/10.3171/2015.4.JNS15173
Ouyang, W., Yan, Q., Zhang, Y., & Fan, Z. (2017). Moderate injury in motor-sensory cortex
causes behavioral deficits accompanied by electrophysiological changes in mice adulthood.
PloS One, 12(2), e0171976. https://doi.org/10.1371/journal.pone.0171976
Oyama, G., Okun, M. S., Zesiewicz, T. A., Tamse, T., Romrell, J., Zeilman, P., & Foote, K. D.
(2011). Delayed clinical improvement after deep brain stimulation-related subdural
hematoma. Report of 4 cases. Journal of Neurosurgery, 115(2), 289–294.
https://doi.org/10.3171/2011.3.JNS101424
Pahwa, R., Lyons, K. E., Wilkinson, S. B., Tröster, A. I., Overman, J., Kieltyka, J., & Koller, W.
C. (2001). Comparison of thalamotomy to deep brain stimulation of the thalamus inessential tremor. Movement Disorders : Official Journal of the Movement Disorder Society,16(1), 140–143. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11215575
Pahwa, R., Lyons, K. L., Wilkinson, S. B., Carpenter, M. A., Tröster, A. I., Searl, J. P., …
Koller, W. C. (1999). Bilateral thalamic stimulation for the treatment of essential tremor.
Neurology, 53(7), 1447–1450. Retrieved fromhttp://www.ncbi.nlm.nih.gov/pubmed/10534249
Paluzzi, A., Belli, A., Bain, P., Liu, X., & Aziz, T. M. (2006). Operative and hardware
complications of deep brain stimulation for movement disorders. British Journal of
Neurosurgery, 20(5), 290–295. https://doi.org/10.1080/02688690601012175
Pandarinath, C., Gilja, V., Blabe, C. H., Nuyujukian, P., Sarma, A. A., Sorice, B. L., … Shenoy,
K. V. (2015). Neural population dynamics in human motor cortex during movements inpeople with ALS. ELife, 4, e07436. https://doi.org/10.7554/eLife.07436
Pandarinath, C., Nuyujukian, P., Blabe, C. H., Sorice, B. L., Saab, J., Willett, F. R., …
Henderson, J. M. (2017a). High performance communication by people with paralysis using
an intracortical brain-computer interface. ELife, 6. https://doi.org/10.7554/eLife.18554
Pandarinath, C., Nuyujukian, P., Blabe, C. H., Sorice, B. L., Saab, J., Willett, F. R., …
Henderson, J. M. (2017b). High performance communication by people with paralysis using
an intracortical brain-computer interface. ELife, 6, e18554.
https://doi.org/10.7554/eLife.18554
Pandarinath, C., O’Shea, D. J., Collins, J., Jozefowicz, R., Stavisky, S. D., Kao, J. C., …
Sussillo, D. (2018). Inferring single-trial neural population dynamics using sequential autoencoders. Nature Methods, 15(10), 805–815. https://doi.org/10.1038/s41592-018-0109-9
Panov, F., Gologorsky, Y., Connors, G., Tagliati, M., Miravite, J., & Alterman, R. L. (2013).
Deep brain stimulation in DYT1 dystonia: a 10-year experience. Neurosurgery, 73(1), 86–100

93; discussion 93. https://doi.org/10.1227/01.neu.0000429841.84083.c8
Park, C. K., Jung, N. Y., Kim, M., & Chang, J. W. (2017). Analysis of Delayed Intracerebral
Hemorrhage Associated with Deep Brain Stimulation Surgery. World Neurosurgery, 104,
537–544. https://doi.org/10.1016/j.wneu.2017.05.075
Park, J. H., Chung, S. J., Lee, C. S., & Jeon, S. R. (2011). Analysis of hemorrhagic risk factors
during deep brain stimulation surgery for movement disorders: comparison of thecircumferential paired and multiple electrode insertion methods. Acta Neurochirurgica,
153(8), 1573–1578. https://doi.org/10.1007/s00701-011-0997-2
Park, S.-Y., Cho, J., Na, K., & Yoon, E. (2018). Modular 128-Channel $\Delta$ - $\Delta\Sigma$ Analog Front-End Architecture Using Spectrum Equalization Scheme for 1024Channel 3-D Neural Recording Microsystems. Undefined. Retrieved fromhttps://www.semanticscholar.org/paper/Modular-128-Channel-%24%5CDelta%24%24%5CDelta-%5CSigma%24-Analog-ParkCho/c5fcc3b581361b6ef896f32a6e739f9fe429dde2
Park, Y. S., Kang, J.-H., Kim, H. Y., Kang, D. W., Chang, W. S., Kim, J. P., & Chang, J. W.
(2011). A combination procedure with double C-shaped skin incision and dual-floor burrhole method to prevent skin erosion on the scalp and reduce postoperative skincomplications in deep brain stimulation. Stereotactic and Functional Neurosurgery, 89(3),178–184. https://doi.org/10.1159/000324903
Patel, D. M., Walker, H. C., Brooks, R., Omar, N., Ditty, B., & Guthrie, B. L. (2015). Adverse
events associated with deep brain stimulation for movement disorders: analysis of 510
consecutive cases. Neurosurgery, 11 Suppl 2, 190–199.
https://doi.org/10.1227/NEU.0000000000000659
Patel, P. R., Zhang, H., Robbins, M. T., Nofar, J. B., Marshall, S. P., Kobylarek, M. J., …
Chestek, C. A. (2016). Chronic in vivo stability assessment of carbon fiber microelectrodearrays. Journal of Neural Engineering, 13(6), 066002. https://doi.org/10.1088/17412560/13/6/066002
Peckham, P. H., Mortimer, J. T., & Marsolais, E. B. (1980). Controlled prehension and release in
the C5 quadriplegic elicited by functional electrical stimulation of the paralyzed forearm
musculature. Annals of Biomedical Engineering, 8(4–6), 369–388.
Peña, E., Pastor, J., Hernando, V., Gallego, I., Pedrosa, M., Carrasco, R., & Sola, R. G. (2008).
Skin erosion over implants in deep brain stimulation patients. Stereotactic and Functional
Neurosurgery, 86(2), 120–126. https://doi.org/10.1159/000116216
Pepper, J., Zrinzo, L., Mirza, B., Foltynie, T., Limousin, P., & Hariz, M. (2013). The risk of
hardware infection in deep brain stimulation surgery is greater at impulse generator
replacement than at the primary procedure. Stereotactic and Functional Neurosurgery,91(1), 56–65. https://doi.org/10.1159/000343202
Perel, S., Sadtler, P. T., Oby, E. R., Ryu, S. I., Tyler-Kabara, E. C., Batista, A. P., & Chase, S.
M. (2015). Single-unit activity, threshold crossings, and local field potentials in motor
cortex differentially encode reach kinematics. Journal of Neurophysiology, 114(3), 1500–
1512. https://doi.org/10.1152/jn.00293.2014
Perge, J. A., Homer, M. L., Malik, W. Q., Cash, S., Eskandar, E., Friehs, G., … Hochberg, L. R.
(2013). Intra-day signal instabilities affect decoding performance in an intracortical neuralinterface system. Journal of Neural Engineering, 10(3), 036004.
https://doi.org/10.1088/1741-2560/10/3/036004
Perge, J. A., Zhang, S., Malik, W. Q., Homer, M. L., Cash, S., Friehs, G., … Hochberg, L. R.
101

(2014). Reliability of directional information in unsorted spikes and local field potentials
recorded in human motor cortex. Journal of Neural Engineering, 11(4), 046007.
https://doi.org/10.1088/1741-2560/11/4/046007
Petraglia, F. W., Farber, S. H., Han, J. L., Verla, T., Gallis, J., Lokhnygina, Y., … Lad, S. P.
(2016). Comparison of Bilateral vs. Staged Unilateral Deep Brain Stimulation (DBS) in
Parkinson’s Disease in Patients Under 70 Years of Age. Neuromodulation: Technology at
the Neural Interface, 19(1), 31–37. https://doi.org/10.1111/ner.12351
Petrossian, M. T., Paul, L. R., Multhaupt-Buell, T. J., Eckhardt, C., Hayes, M. T., Duhaime, A.C., … Sharma, N. (2013). Pallidal deep brain stimulation for dystonia: a case series. Journalof Neurosurgery. Pediatrics, 12(6), 582–587. https://doi.org/10.3171/2013.8.PEDS13134
Piacentino, M., Pilleri, M., & Bartolomei, L. (2011). Hardware-related infections after deep brain
stimulation surgery: review of incidence, severity and management in 212 single-center
procedures in the first year after implantation. Acta Neurochirurgica, 153(12), 2337–2341.
https://doi.org/10.1007/s00701-011-1130-2
Pistohl, T., Ball, T., Schulze-Bonhage, A., Aertsen, A., & Mehring, C. (2008). Prediction of arm
movement trajectories from ECoG-recordings in humans. Journal of Neuroscience
Methods, 167(1), 105–114. https://doi.org/10.1016/j.jneumeth.2007.10.001
Polikov, V. S., Tresco, P. A., & Reichert, W. M. (2005). Response of brain tissue to chronicallyimplanted neural electrodes. Journal of Neuroscience Methods, 148, 1–18.
https://doi.org/10.1016/j.jneumeth.2005.08.015
Ponce, F. A., Asaad, W. F., Foote, K. D., Anderson, W. S., Rees Cosgrove, G., Baltuch, G. H.,… ADvance Research Group. (2016). Bilateral deep brain stimulation of the fornix for
Alzheimer’s disease: surgical safety in the ADvance trial. Journal of Neurosurgery, 125(1),75–84. https://doi.org/10.3171/2015.6.JNS15716
Popovic, M. R., Popovic, D. B., & Keller, T. (2002). Neuroprostheses for grasping. NeurologicalResearch, 24(5), 443–452. https://doi.org/10.1179/016164102101200311
Potter-Baker, K. A., Ravikumar, M., Burke, A. A., Meador, W. D., Householder, K. T., Buck, A.
C., … Capadona, J. R. (2014). A comparison of neuroinflammation to implanted
microelectrodes in rat and mouse models. Biomaterials, 35(22), 5637–5646.
https://doi.org/10.1016/j.biomaterials.2014.03.076
Prasad, A., Xue, Q.-S., Sankar, V., Nishida, T., Shaw, G., Streit, W. J., & Sanchez, J. C. (2012).
Comprehensive characterization and failure modes of tungsten microwire arrays in chronicneural implants. Journal of Neural Engineering, 9(5), 056015. https://doi.org/10.1088/17412560/9/5/056015
Prochazka, A., Gauthier, M., Wieler, M., & Kenwell, Z. (1997). The bionic glove: an electricalstimulator garment that provides controlled grasp and hand opening in quadriplegia.
Archives of Physical Medicine and Rehabilitation, 78(6), 608–614.
Putzke, J. D., Wharen, R. E., Wszolek, Z. K., Turk, M. F., Strongosky, A. J., & Uitti, R. J.
(2003). Thalamic deep brain stimulation for tremor-predominant Parkinson’s disease.
Parkinsonism & Related Disorders, 10(2), 81–88. Retrieved fromhttp://www.ncbi.nlm.nih.gov/pubmed/14643997
Ramayya, A. G., Abdullah, K. G., Mallela, A. N., Pierce, J. T., Thawani, J., Petrov, D., &Baltuch, G. H. (2017). Thirty-Day Readmission Rates Following Deep Brain Stimulation
Surgery. Neurosurgery, 81(2), 259–267. https://doi.org/10.1093/neuros/nyx019
Rasouli, J. J., & Kopell, B. H. (2016). The Adjunctive Use of Vancomycin Powder Appears Safeand May Reduce the Incidence of Surgical-Site Infections After Deep Brain Stimulation
102Surgery. World Neurosurgery, 95, 9–13. https://doi.org/10.1016/j.wneu.2016.07.063
Reuter, S., Deuschl, G., Falk, D., Mehdorn, M., & Witt, K. (2015). Uncoupling of dopaminergicand subthalamic stimulation: Life-threatening DBS withdrawal syndrome. Movement
Disorders : Official Journal of the Movement Disorder Society, 30(10), 1407–1413.
https://doi.org/10.1002/mds.26324
Rizk, M., Bossetti, C. A., Jochum, T. A., Callender, S. H., Nicolelis, M. A. L., Turner, D. A., &Wolf, P. D. (2009). A fully implantable 96-channel neural data acquisition system. Journalof Neural Engineering, 6(2), 26002. https://doi.org/10.1088/1741-2560/6/2/026002
Robinet, S., Audebert, P., Regis, G., Zongo, B., Beche, J.-F., Condemine, C., … Charvet, G.
(2011). A Low-Power 0.7 _ 32-Channel Mixed-Signal Circuit for ECoG Recordings. In
IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND
SYSTEMS (pp. 451–461). Retrieved from https://www.semanticscholar.org/paper/A-LowPower-0.7-%24/mu-%7B/rm-V_%7Brms%7D%7D%24-32-Channel-for-RobinetAudebert/0f4a0ba649dd31158b5d9b793f74835d1705647e
Rodríguez Cruz, P. M., Vargas, A., Fernández-Carballal, C., Garbizu, J., De La Casa-Fages, B.,& Grandas, F. (2016). Long-term Thalamic Deep Brain Stimulation for Essential Tremor:
Clinical Outcome and Stimulation Parameters. Movement Disorders Clinical Practice, 3(6),567–572. https://doi.org/10.1002/mdc3.12337
Rosa, M., Scelzo, E., Locatelli, M., Carrabba, G., Levi, V., Arlotti, M., … Priori, A. (2017). Risk
of Infection After Local Field Potential Recording from Externalized Deep Brain
Stimulation Leads in Parkinson’s Disease. World Neurosurgery, 97, 64–69.
https://doi.org/10.1016/j.wneu.2016.09.069
Rutishauser, U., Aflalo, T., Rosario, E. R., Pouratian, N., & Andersen, R. A. (2018). SingleNeuron Representation of Memory Strength and Recognition Confidence in Left Human
Posterior Parietal Cortex. Neuron, 97(1), 209–220.e3.
https://doi.org/10.1016/j.neuron.2017.11.029
Ryu, H.-S., Kim, M.-S., You, S., Kim, M.-J., Kim, Y. J., Kim, J., … Chung, S. J. (2017).
Comparison of Pallidal and Subthalamic Deep Brain Stimulation in Parkinson’s Disease:
Therapeutic and Adverse Effects. Journal of Movement Disorders, 10(2), 80–86.
https://doi.org/10.14802/jmd.17001
Sachdev, P. S., Mohan, A., Cannon, E., Crawford, J. D., Silberstein, P., Cook, R., … Silburn, P.
A. (2014). Deep brain stimulation of the antero-medial globus pallidus interna for Tourette
syndrome. PloS One, 9(8), e104926. https://doi.org/10.1371/journal.pone.0104926
Salanova, V., Witt, T., Worth, R., Henry, T. R., Gross, R. E., Nazzaro, J. M., … SANTE Study
Group. (2015). Long-term efficacy and safety of thalamic stimulation for drug-resistantpartial epilepsy. Neurology, 84(10), 1017–1025.
https://doi.org/10.1212/WNL.0000000000001334
Sansur, C. A., Frysinger, R. C., Pouratian, N., Fu, K.-M., Bittl, M., Oskouian, R. J., … Elias, W.
J. (2007). Incidence of symptomatic hemorrhage after stereotactic electrode placement.
Journal of Neurosurgery, 107(5), 998–1003. https://doi.org/10.3171/JNS-07/11/0998
Schalk, G., Kubánek, J., Miller, K. J., Anderson, N. R., Leuthardt, E. C., Ojemann, J. G., …
Wolpaw, J. R. (2007). Decoding two-dimensional movement trajectories using
electrocorticographic signals in humans. Journal of Neural Engineering, 4(3), 264–275.
https://doi.org/10.1088/1741-2560/4/3/012
Schroeder, K. E., Irwin, Z. T., Bullard, A. J., Thompson, D. E., Bentley, J. N., Stacey, W. C., …
Chestek, C. A. (2017). Robust tactile sensory responses in finger area of primate motor
103cortex relevant to prosthetic control. Journal of Neural Engineering.
https://doi.org/10.1088/1741-2552/aa7329
Schuurman, P. R., Bosch, D. A., Merkus, M. P., & Speelman, J. D. (2008). Long-term follow-up
of thalamic stimulation versus thalamotomy for tremor suppression. Movement Disorders :Official Journal of the Movement Disorder Society, 23(8), 1146–1153.
https://doi.org/10.1002/mds.22059
Seijo, F., Alvarez de Eulate Beramendi, S., Santamarta Liébana, E., Lozano Aragoneses, B., Saiz
Ayala, A., Fernández de León, R., & Alvarez Vega, M. A. (2014). Surgical adverse eventsof deep brain stimulation in the subthalamic nucleus of patients with Parkinson’s disease.
The learning curve and the pitfalls. Acta Neurochirurgica, 156(8), 1505–12; discussion
1512. https://doi.org/10.1007/s00701-014-2082-0
Seijo, F. J., Alvarez-Vega, M. A., Gutierrez, J. C., Fdez-Glez, F., & Lozano, B. (2007).
Complications in subthalamic nucleus stimulation surgery for treatment of Parkinson’s
disease. Review of 272 procedures. Acta Neurochirurgica, 149(9), 867–876.
https://doi.org/10.1007/s00701-007-1267-1
Seo, D., Carmena, J. M., Rabaey, J. M., Maharbiz, M. M., & Alon, E. (2015). Model validationof untethered, ultrasonic neural dust motes for cortical recording. Journal of Neuroscience
Methods, 244, 114–122. https://doi.org/10.1016/J.JNEUMETH.2014.07.025
Seo, D., Neely, R. M., Shen, K., Singhal, U., Alon, E., Rabaey, J. M., … Maharbiz, M. M.
(2016). Wireless Recording in the Peripheral Nervous System with Ultrasonic Neural Dust.
Neuron, 91(3), 529–539. https://doi.org/10.1016/j.neuron.2016.06.034
Servello, D., Sassi, M., Gaeta, M., Ricci, C., & Porta, M. (2011). Tourette syndrome (TS) bears ahigher rate of inflammatory complications at the implanted hardware in deep brain
stimulation (DBS). Acta Neurochirurgica, 153(3), 629–632. https://doi.org/10.1007/s00701010-0851-y
Shaikhouni, A., Donoghue, J. P., & Hochberg, L. R. (2013). Somatosensory responses in ahuman motor cortex. Journal of Neurophysiology, 109(8), 2192–2204.
https://doi.org/10.1152/jn.00368.2012
Sillay, K. A., Larson, P. S., & Starr, P. A. (2008). Deep brain stimulator hardware-related
infections: incidence and management in a large series. Neurosurgery, 62(2), 360–367.
https://doi.org/10.1227/01.neu.0000316002.03765.33
Simeral, J. D., Kim, S.-P., Black, M. J., Donoghue, J. P., & Hochberg, L. R. (2011). Neuralcontrol of cursor trajectory and click by a human with tetraplegia 1000 days after implant of
an intracortical microelectrode array. Journal of Neural Engineering, 8(2), 025027.
https://doi.org/10.1088/1741-2560/8/2/025027
Sixel-Döring, F., Trenkwalder, C., Kappus, C., & Hellwig, D. (2010). Skin complications indeep brain stimulation for Parkinson’s disease: frequency, time course, and risk factors.
Acta Neurochirurgica, 152(2), 195–200. https://doi.org/10.1007/s00701-009-0490-3
Smith, B., Crish, T. J., Buckett, J. R., Kilgore, K. L., & Peckham, P. H. (2005). Development of
an implantable networked neuroprosthesis. 2nd International IEEE EMBS Conference on
Neural Engineering, 2005, 454–457. https://doi.org/10.1109/CNE.2005.1419657
Smith, B., Peckham, P. H., Keith, M. W., & Roscoe, D. D. (1987). An externally powered,multichannel, implantable stimulator for versatile control of paralyzed muscle. IEEE
Transactions on Bio-Medical Engineering, 34(7), 499–508.
Snoek, G. J., IJzerman, M. J., in ’t Groen, F. A., Stoffers, T. S., & Zilvold, G. (2000). Use of theNESS handmaster to restore handfunction in tetraplegia: clinical experiences in ten patients.
104

Spinal Cord, 38(4), 244–249.
Sobstyl, M., Kmieć, T., Ząbek, M., Szczałuba, K., & Mossakowski, Z. (2014). Long-term
outcomes of bilateral pallidal stimulation for primary generalised dystonia. Clinical
Neurology and Neurosurgery, 126, 82–87. https://doi.org/10.1016/j.clineuro.2014.08.027
Sobstyl, M. R., Ząbek, M., Brzuszkiewicz-Kuźmicka, G., & Pasterski, T. (2017). Dual Anchor
Internal Pulse Generator Technique May Lower Risk of Twiddler’s Syndrome: A Case
Series and Literature Review. Neuromodulation : Journal of the International
Neuromodulation Society, 20(6), 606–612. https://doi.org/10.1111/ner.12581
Sobstyl, M., Ząbek, M., Dzierzęcki, S., Koziara, H., & Mossakowski, Z. (n.d.). Chronic bilateralpallidal stimulation in patients with generalized primary dystonia - multi-contact cathodal
stimulation is superior to bipolar stimulation mode. Preliminary results. Neurologia i
Neurochirurgia Polska, 45(3), 252–259. Retrieved fromhttp://www.ncbi.nlm.nih.gov/pubmed/21866482
Solmaz, B., Tatarli, N., Ceylan, D., Bayri, Y., Ziyal, M. I., & Şeker, A. (2014). A sine-waveshaped skin incision for inserting deep-brain stimulators. Acta Neurochirurgica, 156(8),1523–1525. https://doi.org/10.1007/s00701-014-2123-8
Son, B., Han, S., Choi, Y., Kim, H., Kim, M., Yang, S., & Lee, S. (2012). Transaxillarysubpectoral implantation of implantable pulse generator for deep brain stimulation.
Neuromodulation : Journal of the International Neuromodulation Society, 15(3), 260–6;
discussion 266. https://doi.org/10.1111/j.1525-1403.2011.00420.x
Stanslaski, S., Afshar, P., Cong, P., Giftakis, J., Stypulkowski, P., Carlson, D., … Denison, T.
(2012). Design and Validation of a Fully Implantable, Chronic, Closed-Loop
Neuromodulation Device With Concurrent Sensing and Stimulation. IEEE Transactions on
Neural Systems and Rehabilitation Engineering, 20(4), 410–421.
https://doi.org/10.1109/TNSRE.2012.2183617
Stark, E., & Abeles, M. (2007). Predicting movement from multiunit activity. The Journal of
Neuroscience : The Official Journal of the Society for Neuroscience, 27(31), 8387–8394.
https://doi.org/10.1523/JNEUROSCI.1321-07.2007
Staudt, M. D., Pourtaheri, N., Lakin, G. E., Soltanian, H. T., & Miller, J. P. (2017). SurgicalManagement of Deep Brain Stimulator Scalp Erosion without Hardware Removal.
Stereotactic and Functional Neurosurgery, 95(6), 385–391.
https://doi.org/10.1159/000484323
Stiller, A. M., Usoro, J., Frewin, C. L., Danda, V. R., Ecker, M., Joshi-Imre, A., … Black, B. J.
(2018). Chronic intracortical recording and electrochemical stability of thiol-ene/acrylate
shape memory polymer electrode arrays. Micromachines, 9(10), 1–14.
https://doi.org/10.3390/mi9100500
Stroop, R., Holms, F., Nakamura, M., & Lehrke, R. (2018). A Submammarian Approach for
Cosmetically Improved Implantation of Deep Brain Stimulation Generators. World
Neurosurgery, 109, e699–e706. https://doi.org/10.1016/j.wneu.2017.10.057
Sun, F. T., & Morrell, M. J. (2014). The RNS System: responsive cortical stimulation for thetreatment of refractory partial epilepsy. Expert Review of Medical Devices, 11(6), 563–572.
https://doi.org/10.1586/17434440.2014.947274
Sydow, O., Thobois, S., Alesch, F., & Speelman, J. D. (2003). Multicentre European study ofthalamic stimulation in essential tremor: a six year follow up. Journal of Neurology,
Neurosurgery, and Psychiatry, 74(10), 1387–1391. Retrieved fromhttp://www.ncbi.nlm.nih.gov/pubmed/14570831
105

Szarowski, D. H., Andersen, M. D., Retterer, S., Spence, A. J., Isaacson, M., Craighead, H. G.,… Shain, W. (2003). Brain responses to micro-machined silicon devices. Brain Research,983(1–2), 23–35. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/12914963
Tanei, T., Kajita, Y., Kaneoke, Y., Takebayashi, S., Nakatsubo, D., & Wakabayashi, T. (2009).
Staged bilateral deep brain stimulation of the subthalamic nucleus for the treatment of
Parkinson’s disease. Acta Neurochirurgica, 151(6), 589–594.
https://doi.org/10.1007/s00701-009-0293-6Taylor, P., Esnouf, J., & Hobby, J. (2002). The functional impact of the Freehand System ontetraplegic hand function. Clinical Results. Spinal Cord, 40(11), 560–566.
https://doi.org/10.1038/sj.sc.3101373
Temel, Y., Ackermans, L., Celik, H., Spincemaille, G. H., van der Linden, C., Walenkamp, G.
H., … Visser-Vandewalle, V. (2004). Management of hardware infections following deepbrain stimulation. Acta Neurochirurgica, 146(4), 355–61; discussion 361.
https://doi.org/10.1007/s00701-004-0219-2
Terao, T., Takahashi, H., Yokochi, F., Taniguchi, M., Okiyama, R., & Hamada, I. (2003).
Hemorrhagic complication of stereotactic surgery in patients with movement disorders.
Journal of Neurosurgery, 98(6), 1241–1246. https://doi.org/10.3171/jns.2003.98.6.1241
Testini, P., Zhao, C. Z., Stead, M., Duffy, P. S., Klassen, B. T., & Lee, K. H. (2016).
Centromedian-Parafascicular Complex Deep Brain Stimulation for Tourette Syndrome:
A Retrospective Study. Mayo Clinic Proceedings, 91(2), 218–225.
https://doi.org/10.1016/j.mayocp.2015.11.016
Themistocleous, M. S., Sakas, D. E., Boviatsis, E., Tagaris, G., Kouyialis, A., Psachoulia, C., &Stathis, P. (2017). The Insertion of Electrodes in the Brain for Electrophysiological
Recording or Chronic Stimulation Is Not Associated With Any Biochemically Detectable
Neuronal Injury. Neuromodulation : Journal of the International Neuromodulation Society,20(5), 424–428. https://doi.org/10.1111/ner.12598
Thrasher, T. A., Flett, H. M., & Popovic, M. R. (2006). Gait training regimen for incomplete
spinal cord injury using functional electrical stimulation. Spinal Cord, 44(6), 357–361.
https://doi.org/10.1038/sj.sc.3101864
Tir, M., Devos, D., Blond, S., Touzet, G., Reyns, N., Duhamel, A., … Krystkowiak, P. (2007).
Exhaustive, one-year follow-up of subthalamic nucleus deep brain stimulation in a large,
single-center cohort of parkinsonian patients. Neurosurgery, 61(2), 297-304; discussion
304-5. https://doi.org/10.1227/01.NEU.0000285347.50028.B9
Tolleson, C., Stroh, J., Ehrenfeld, J., Neimat, J., Konrad, P., & Phibbs, F. (2014). The factors
involved in deep brain stimulation infection: a large case series. Stereotactic and Functional
Neurosurgery, 92(4), 227–233. https://doi.org/10.1159/000362934
Tonge, M., Ackermans, L., Kocabicak, E., van Kranen-Mastenbroek, V., Kuijf, M., Oosterloo,
M., … Temel, Y. (2015). A detailed analysis of intracerebral hemorrhages in DBS
surgeries. Clinical Neurology and Neurosurgery, 139, 183–187.
https://doi.org/10.1016/j.clineuro.2015.10.017
Troyk, P. R., DeMichele, G. A., Kerns, D. A., & Weir, R. F. (2007). IMES: An implantablemyoelectric sensor. In Annual International Conference of the IEEE Engineering in
Medicine and Biology - Proceedings (pp. 1730–1733).
https://doi.org/10.1109/IEMBS.2007.4352644
Truccolo, W., Friehs, G. M., Donoghue, J. P., & Hochberg, L. R. (2008). Primary motor cortex
tuning to intended movement kinematics in humans with tetraplegia. The Journal of106Neuroscience : The Official Journal of the Society for Neuroscience, 28(5), 1163–1178.
https://doi.org/10.1523/JNEUROSCI.4415-07.2008
Truccolo, W., Hochberg, L. R., & Donoghue, J. P. (2010). Collective dynamics in human andmonkey sensorimotor cortex: predicting single neuron spikes. Nature Neuroscience, 13(1),105–111. https://doi.org/10.1038/nn.2455
Turner, J. N., Shain, W., Szarowski, D. H., Andersen, M., Martins, S., Isaacson, M., &Craighead, H. (1999). Cerebral Astrocyte Response to Micromachined Silicon Implants.
Experimental Neurology, 156(1), 33–49. https://doi.org/10.1006/exnr.1998.6983
Ullah, S., Khan, P., Ullah, N., Saleem, S., Higgins, H., & Kwak, K. S. (2010). A Review ofWireless Body Area Networks for Medical Applications.
https://doi.org/10.4236/ijcns.2009.28093
Umemura, A., Jaggi, J. L., Hurtig, H. I., Siderowf, A. D., Colcher, A., Stern, M. B., & Baltuch,
G. H. (2003). Deep brain stimulation for movement disorders: morbidity and mortality in
109 patients. Journal of Neurosurgery, 98(4), 779–784.
https://doi.org/10.3171/jns.2003.98.4.0779
Umemura, A., Oka, Y., Yamamoto, K., Okita, K., Matsukawa, N., & Yamada, K. (2011).
Complications of subthalamic nucleus stimulation in Parkinson’s disease. Neurologia
Medico-Chirurgica, 51(11), 749–755. Retrieved fromhttp://www.ncbi.nlm.nih.gov/pubmed/22123476
Vansteensel, M. J., Hermes, D., Aarnoutse, E. J., Bleichner, M. G., Schalk, G., Van Rijen, P. C.,… Ramsey, N. F. (2010). Brain-computer interfacing based on cognitive control. Annals of
Neurology, 67(6), 809–816. https://doi.org/10.1002/ana.21985
Velasco, A. L., Velasco, F., Velasco, M., Trejo, D., Castro, G., & Carrillo-Ruiz, J. D. (2007).
Electrical stimulation of the hippocampal epileptic foci for seizure control: a double-blind,
long-term follow-up study. Epilepsia, 48(10), 1895–1903. https://doi.org/10.1111/j.15281167.2007.01181.x
Velliste, M., Perel, S., Spalding, M. C., Whitford, A. S., & Schwartz, A. B. (2008). Corticalcontrol of a prosthetic arm for self-feeding. Nature, 453(7198), 1098–1101.
https://doi.org/10.1038/nature06996
Vergani, F., Landi, A., Pirillo, D., Cilia, R., Antonini, A., & Sganzerla, E. P. (2010). Surgical,
medical, and hardware adverse events in a series of 141 patients undergoing subthalamic
deep brain stimulation for Parkinson disease. World Neurosurgery, 73(4), 338–344.
https://doi.org/10.1016/j.wneu.2010.01.017
Verla, T., Marky, A., Farber, H., Petraglia, F. W., Gallis, J., Lokhnygina, Y., … Lad, S. P.
(2015). Impact of advancing age on post-operative complications of deep brain stimulation
surgery for essential tremor. Journal of Clinical Neuroscience : Official Journal of the
Neurosurgical Society of Australasia, 22(5), 872–876.
https://doi.org/10.1016/j.jocn.2014.11.005
Vesper, J., Chabardes, S., Fraix, V., Sunde, N., Østergaard, K., & Kinetra Study Group. (2002).
Dual channel deep brain stimulation system (Kinetra) for Parkinson’s disease and essential
tremor: a prospective multicentre open label clinical study. Journal of Neurology,
Neurosurgery, and Psychiatry, 73(3), 275–280. Retrieved fromhttp://www.ncbi.nlm.nih.gov/pubmed/12185158
Vesper, J., Haak, S., Ostertag, C., & Nikkhah, G. (2007). Subthalamic nucleus deep brain
stimulation in elderly patients – analysis of outcome and complications. BMC Neurology,
7(1), 7. https://doi.org/10.1186/1471-2377-7-7
107

Vesper, J., Klostermann, F., Stockhammer, F., Funk, T., & Brock, M. (2002). Results of chronic
subthalamic nucleus stimulation for Parkinson’s disease: a 1-year follow-up study. Surgical
Neurology, 57(5), 306-11; discussion 311-3. Retrieved fromhttp://www.ncbi.nlm.nih.gov/pubmed/12128300
Vesper, J., Klostermann, F., Wille, C., Funk, T., & Brock, M. (2004). Long-Term Suppression ofExtrapyramidal Motor Symptoms with Deep Brain Stimulation (DBS). Zentralblatt FürNeurochirurgie, 65(3), 117–122. https://doi.org/10.1055/s-2004-822789
Vidailhet, M., Vercueil, L., Houeto, J.-L., Krystkowiak, P., Benabid, A.-L., Cornu, P., … French
Stimulation du Pallidum Interne dans la Dystonie (SPIDY) Study Group. (2005). Bilateraldeep-brain stimulation of the globus pallidus in primary generalized dystonia. The New
England Journal of Medicine, 352(5), 459–467. https://doi.org/10.1056/NEJMoa042187
Vidailhet, M., Vercueil, L., Houeto, J.-L., Krystkowiak, P., Lagrange, C., Yelnik, J., … French
SPIDY Study Group. (2007). Bilateral, pallidal, deep-brain stimulation in primary
generalised dystonia: a prospective 3 year follow-up study. The Lancet. Neurology, 6(3),223–229. https://doi.org/10.1016/S1474-4422(07)70035-2
Voges, J., Hilker, R., Bötzel, K., Kiening, K. L., Kloss, M., Kupsch, A., … Pinsker, M. O.
(2007). Thirty days complication rate following surgery performed for deep-brainstimulation. Movement Disorders, 22(10), 1486–1489. https://doi.org/10.1002/mds.21481
Voges, J., Waerzeggers, Y., Maarouf, M., Lehrke, R., Koulousakis, A., Lenartz, D., & Sturm, V.
(2006). Deep-brain stimulation: long-term analysis of complications caused by hardware
and surgery--experiences from a single centre. Journal of Neurology, Neurosurgery, andPsychiatry, 77(7), 868–872. https://doi.org/10.1136/jnnp.2005.081232
Volkmann, J., Mueller, J., Deuschl, G., Kühn, A. A., Krauss, J. K., Poewe, W., … DBS study
group for dystonia. (2014). Pallidal neurostimulation in patients with medication-refractory
cervical dystonia: a randomised, sham-controlled trial. The Lancet. Neurology, 13(9), 875–
884. https://doi.org/10.1016/S1474-4422(14)70143-7
Volkmann, J., Wolters, A., Kupsch, A., Müller, J., Kühn, A. A., Schneider, G.-H., … DBS study
group for dystonia. (2012). Pallidal deep brain stimulation in patients with primary
generalised or segmental dystonia: 5-year follow-up of a randomised trial. The Lancet.
Neurology, 11(12), 1029–1038. https://doi.org/10.1016/S1474-4422(12)70257-0
Vu, P. P., Irwin, Z. T., Bullard, A. J., Ambani, S. W., Sando, I. C., Urbanchek, M. G., …
Chestek, C. A. (2018). Closed-Loop Continuous Hand Control via Chronic Recording ofRegenerative Peripheral Nerve Interfaces. IEEE Transactions on Neural Systems andRehabilitation Engineering. https://doi.org/10.1109/TNSRE.2017.2772961
Wahnoun, R., He, J., & Helms Tillery, S. I. (2006). Selection and parameterization of corticalneurons for neuroprosthetic control. Journal of Neural Engineering, 3(2), 162–171.
https://doi.org/10.1088/1741-2560/3/2/010
Wang, D., Zhang, Q., Li, Y., Wang, Y., Zhu, J., Zhang, S., & Zheng, X. (2014). Long-term
decoding stability of local field potentials from silicon arrays in primate motor cortex
during a 2D center out task. Journal of Neural Engineering, 11(3), 036009.
https://doi.org/10.1088/1741-2560/11/3/036009
Wang, X., Wang, J., Zhao, H., Li, N., Ge, S., Chen, L., … Wang, X. (2017). Clinical analysis
and treatment of symptomatic intracranial hemorrhage after deep brain stimulation surgery.
British Journal of Neurosurgery, 31(2), 217–222.
https://doi.org/10.1080/02688697.2016.1244252
Wattanapanitch, W., & Sarpeshkar, R. (2011). A Low-Power 32-Channel Digitally108

Programmable Neural Recording Integrated Circuit. IEEE Transactions on Biomedical
Circuits and Systems, 5(6), 592–602. https://doi.org/10.1109/TBCAS.2011.2163404
Weaver, F. M., Follett, K., Stern, M., Hur, K., Harris, C., Marks, W. J., … CSP 468 Study
Group. (2009). Bilateral deep brain stimulation vs best medical therapy for patients withadvanced Parkinson disease: a randomized controlled trial. JAMA, 301(1), 63–73.
https://doi.org/10.1001/jama.2008.929
Weiss, S. A., Banks, G. P., McKhann, G. M., Goodman, R. R., Emerson, R. G., Trevelyan, A. J.,& Schevon, C. A. (2013). Ictal high frequency oscillations distinguish two types of seizure
territories in humans. Brain : A Journal of Neurology, 136(Pt 12), 3796–3808.
https://doi.org/10.1093/brain/awt276
Welle, C. (2018). The Living Interface - Dynamic Alterations of Neural and VascularMorphology During Chronic Microelectrode Implantation in the Brain. In Neuroelectronic
Interfaces Conference GRC. Galveston. Retrieved fromhttps://www.grc.org/neuroelectronic-interfaces-conference/2018/
Welle, C., Street, M. G., Ruda, K., Civillico, E., & Takmakov, P. A. (2017). Invasive Cortical
Microelectrode Array Longitudinal Performance: Temporal Dynamics of Electrical
Impedance Spectroscopy and Multiunit Activity. In Electrochemical Society (Vol.
MA2017-02, pp. 2291–2291). The Electrochemical Society. Retrieved fromhttp://ma.ecsdl.org/content/MA2017-02/55/2291.short
Welter, M.-L., Houeto, J.-L., Thobois, S., Bataille, B., Guenot, M., Worbe, Y., … Palfi, S.
(2017). Anterior pallidal deep brain stimulation for Tourette’s syndrome: a randomised,double-blind, controlled trial. The Lancet. Neurology, 16(8), 610–619.
https://doi.org/10.1016/S1474-4422(17)30160-6
West, M. J. (1999). Stereological methods for estimating the total number of neurons andsynapses: issues of precision and bias. Trends Neuroscience, 22(2), 51–61.
https://doi.org/10.1002/jemt.20167
Wharen, R. E., Okun, M. S., Guthrie, B. L., Uitti, R. J., Larson, P., Foote, K., … SJM DBS ET
Study Group. (2017). Thalamic DBS with a constant-current device in essential tremor:
A controlled clinical trial. Parkinsonism & Related Disorders, 40, 18–26.
https://doi.org/10.1016/j.parkreldis.2017.03.017
Wheeler, J. J., Baldwin, K., Kindle, A., Guyon, D., Nugent, B., Segura, C., … Eskandar, E. N.
(2015). An implantable 64-channel neural interface with reconfigurable recording andstimulation. In 2015 37th Annual International Conference of the IEEE Engineering in
Medicine and Biology Society (EMBC) (Vol. 2015, pp. 7837–7840). IEEE.
https://doi.org/10.1109/EMBC.2015.7320208
White-Dzuro, G. A., Lake, W., Eli, I. M., & Neimat, J. S. (2016). Novel Approach to Securing
Deep Brain Stimulation Leads: Technique and Analysis of Lead Migration, Breakage, andSurgical Infection. Stereotactic and Functional Neurosurgery, 94(1), 18–23.
https://doi.org/10.1159/000442893
White-Dzuro, G. A., Lake, W., & Neimat, J. S. (2017). Subpectoral Implantation of Internal
Pulse Generators for Deep Brain Stimulation: Technical Note for Improved Cosmetic
Outcomes. Operative Neurosurgery (Hagerstown, Md.), 13(4), 529–534.
https://doi.org/10.1093/ons/opx018
Willett, F. R., Murphy, B. A., Memberg, W. D., Blabe, C. H., Pandarinath, C., Walter, B. L., …
Ajiboye, A. B. (2017). Signal-independent noise in intracortical brain-computer interfaces
causes movement time properties inconsistent with Fitts’ law. Journal of Neural109

Engineering, 14(2), 026010. https://doi.org/10.1088/1741-2552/aa5990
Willett, F. R., Murphy, B. A., Young, D. R., Memberg, W. D., Blabe, C. H., Pandarinath, C., …
Ajiboye, A. B. (2017). A Comparison of Intention Estimation Methods for Decoder
Calibration in Intracortical Brain-Computer Interfaces. IEEE Transactions on Bio-Medical
Engineering, 65(9), 2066–2078. https://doi.org/10.1109/TBME.2017.2783358
Willett, F. R., Pandarinath, C., Jarosiewicz, B., Murphy, B. A., Memberg, W. D., Blabe, C. H.,… Ajiboye, A. B. (2017). Feedback control policies employed by people using intracorticalbrain-computer interfaces. Journal of Neural Engineering, 14(1), 016001.
https://doi.org/10.1088/1741-2560/14/1/016001
Williams, N. R., Short, E. B., Hopkins, T., Bentzley, B. S., Sahlem, G. L., Pannu, J., … Nahas,Z. (2016). Five-Year Follow-Up of Bilateral Epidural Prefrontal Cortical Stimulation for
Treatment-Resistant Depression. Brain Stimulation, 9(6), 897–904.
https://doi.org/10.1016/j.brs.2016.06.054
Wodlinger, B., Downey, J. E., Tyler-Kabara, E. C., Schwartz, A. B., Boninger, M. L., &Collinger, J. L. (2015a). Ten-dimensional anthropomorphic arm control in a humanbrain−machine interface: difficulties, solutions, and limitations. Journal of NeuralEngineering, 12(1), 016011. https://doi.org/10.1088/1741-2560/12/1/016011
Wodlinger, B., Downey, J. E., Tyler-Kabara, E. C., Schwartz, A. B., Boninger, M. L., &Collinger, J. L. (2015b). Ten-dimensional anthropomorphic arm control in a humanbrain−machine interface: difficulties, solutions, and limitations. Journal of NeuralEngineering, 12(1), 016011. https://doi.org/10.1088/1741-2560/12/1/016011
Wojtecki, L., Groiss, S. J., Ferrea, S., Elben, S., Hartmann, C. J., Dunnett, S. B., … SurgicalApproaches Working Group of the European Huntington’s Disease Network (EHDN).
(2015). A Prospective Pilot Trial for Pallidal Deep Brain Stimulation in Huntington’s
Disease. Frontiers in Neurology, 6, 177. https://doi.org/10.3389/fneur.2015.00177
Wolpaw, J. R., McFarland, D. J., Neat, G. W., & Forneris, C. A. (1991). An EEG-based braincomputer interface for cursor control. Electroencephalography and Clinical
Neurophysiology, 78(3), 252–259. Retrieved fromhttp://www.ncbi.nlm.nih.gov/pubmed/1707798
Xiaowu, H., Xiufeng, J., Xiaoping, Z., Bin, H., Laixing, W., Yiqun, C., … Jianmin, L. (2010).
Risks of intracranial hemorrhage in patients with Parkinson’s disease receiving deep brain
stimulation and ablation. Parkinsonism & Related Disorders, 16(2), 96–100.
https://doi.org/10.1016/j.parkreldis.2009.07.013
Xie, C., Liu, J., Fu, T.-M., Dai, X., Zhou, W., & Lieber, C. M. (2015). Three-Dimensional
Macroporous Nanoelectronic Networks as Minimally Invasive Brain Probes. Nat Mater,
14(12), 1286–1292. https://doi.org/10.1038/nmat4427
Yang, Y., Dickey, M. W., Fiez, J., Murphy, B., Mitchell, T., Collinger, J., … Wang, W. (2017).
Sensorimotor experience and verb-category mapping in human sensory, motor and parietal
neurons. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior, 92,
304–319. https://doi.org/10.1016/j.cortex.2017.04.021
Yianni, J., Nandi, D., Shad, A., Bain, P., Gregory, R., & Aziz, T. (2004). Increased risk of leadfracture and migration in dystonia compared with other movement disorders following deepbrain stimulation. Journal of Clinical Neuroscience, 11(3), 243–245.
https://doi.org/10.1016/j.jocn.2003.10.003
Young, D., Willett, F., Memberg, W. D., Murphy, B., Walter, B., Sweet, J., … Ajiboye, A. B.
(2018). Signal processing methods for reducing artifacts in microelectrode brain recordings
110caused by functional electrical stimulation. Journal of Neural Engineering, 15(2), 026014.
https://doi.org/10.1088/1741-2552/aa9ee8
Zhan, S., Sun, F., Pan, Y., Liu, W., Huang, P., Cao, C., … Sun, B. (2018). Bilateral deep brain
stimulation of the subthalamic nucleus in primary Meige syndrome. Journal of
Neurosurgery, 128(3), 897–902. https://doi.org/10.3171/2016.12.JNS16383
Zhang, C. Y., Aflalo, T., Revechkis, B., Rosario, E. R., Ouellette, D., Pouratian, N., & Andersen,
R. A. (2017). Partially Mixed Selectivity in Human Posterior Parietal Association Cortex.
Neuron, 95(3), 697–708.e4. https://doi.org/10.1016/j.neuron.2017.06.040
Zhang, J., Wang, T., Zhang, C.-C., Zeljic, K., Zhan, S., Sun, B.-M., & Li, D.-Y. (2017). The
safety issues and hardware-related complications of deep brain stimulation therapy: asingle-center retrospective analysis of 478 patients with Parkinson’s disease. ClinicalInterventions in Aging, 12, 923–928. https://doi.org/10.2147/CIA.S130882
Zhang, K., Bhatia, S., Oh, M. Y., Cohen, D., Angle, C., & Whiting, D. (2010). Long-term resultsof thalamic deep brain stimulation for essential tremor. Journal of Neurosurgery, 112(6),1271–1276. https://doi.org/10.3171/2009.10.JNS09371
Zrinzo, L., Foltynie, T., Limousin, P., & Hariz, M. I. (2012). Reducing hemorrhagic
complications in functional neurosurgery: a large case series and systematic literature
review. Journal of Neurosurgery, 116(1), 84–94. https://doi.org/10.3171/2011.8.JNS101407
Zsigmond, P., Hemm-Ode, S., & Wårdell, K. (2017). Optical Measurements during Deep Brain
Stimulation Lead Implantation: Safety Aspects. Stereotactic and Functional Neurosurgery,95(6), 392–399. https://doi.org/10.1159/000484944

111