Cortical Physiology as a Therapeutic Target in Parkinson's Disease Related Dementia and Cognitive Dysfunction
Phase: NA
Conditions: Parkinson's Disease
Sponsor: University of Colorado, Denver

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w
COMIRB Protocol
PROTOCOL #:
13-2724
COLORADO MULTIPLE INSTITUTIONAL REVIEW BOARD
CAMPUS BOX F-490    TELEPHONE:  303-724-1055  Fax:  
303-724-0990
Project Title: Cortical physiology as a therapeutic target in Parkinson’s disease related 
dementia and cognitive dysfunction
Principal Investigator: 
Benzi Kluger
Version 3  30Jan2014
 
I. Hypotheses and Specific Aims:  
Aim 1. Determine whether graph theory measures of intrinsic network functional 
connectivity are associated with cognitive dysfunction in PD.
Hypothesis 1: Increased randomness and decreased efficiency of cortical networks 
contribute to PD cognitive dysfunction and will be associated with neuropsychological 
measures.
Aim 2. Develop a novel state-defining biomarker for cognitive dysfunction in PD 
based on measures of intrinsic regional activity and network connectivity using a 
machine learning approach.
Hypothesis 2: Cognitive dysfunction in PD can be characterized through a unique 
combination of regional and network features as measured by MEG and optimized 
through machine learning.
Aim 3. Determine the effects of rTMS on intrinsic network functional connectivity 
and cognitive dysfunction in PD.
Hypothesis 3: High frequency bifrontal rTMS will increase the efficiency of cortical 
networks including frontal nodes and will be associated with improvements in 
neuropsychological measures.
Exploratory Aim. Investigate how MRI measures of white matter, GABA function 
and resting state networks relate to MEG measures of functional connectivity.
Hypothesis 4: MEG measures of intrinsic functional connectivity will correlate with MRI 
measures and be reflective of underlying structural (white matter) and neurotransmitter 
(GABA) integrity.
II.
Background and Significance: 
Parkinson’s disease (PD) affects 1-2% of people over age 65, representing over 1 million 
Americans.1 Although traditionally characterized by its motor symptoms, over the course 
of PD cognitive symptoms have a greater impact on patient suffering and caregiver 
burden.2 Three-fourths of patients surviving 20 years or longer develop dementia making 
it the leading cause of nursing home placement.3 Despite the high prevalence of cognitive 
dysfunction in PD, its pathophysiology is poorly understood and current treatments have 
minimal effects on symptoms or progression.4 Moreover, standard medical and surgical 
PD treatments have no effect on cognition and may in fact worsen cognition.5 Better 
treatment strategies for PD-related cognitive dysfunction based on an improved 
knowledge of neurobiological mechanisms are clearly needed. 
COMIRB
APPROVED
23-Oct-2015

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Functional connections between cortical regions are important for normal cognition and 
memory6, 7 and that alterations in functional cortical connectivity may contribute to 
cognitive dysfunction in PD.8 In the past decade, modern network theory has emerged as 
a powerful tool to describe the organization of functional brain networks using the 
mathematics of graph theory.9 Two fundamental measures are path length, defined as the 
average distance between any two nodes in a network and a measure of global efficiency 
and integration; and the clustering coefficient defined as the average number of mutually 
connected nodes surrounding each node and a measure of local efficiency and 
modularity. Figure 1 shows an optimal balance between global and local efficiency can be 
achieved through clustered modules connected through integrating links, a design termed 
small-world architecture.10 It is well-established that healthy brain networks demonstrate 
small-world architecture.11 Importantly, loss of small world features are associated with 
cognitive dysfunction in multiple sclerosis and Alzheimer’s disease (AD).12-14 Known 
features of PD neurophysiology relevant to network connectivity include reduced cortical 
inhibition15, which may lead to disrupted modularity, and pathological subcortical 
oscillations16, which may drive increased randomness. Our pilot data and other studies17 
suggest that network architecture is abnormal in PD but its relationship to cognitive 
dysfunction has not been examined. 
Magnetoencephalography (MEG) measures cortical activity on a millisecond time scale 
and can provide highly informative data for graph theory analyses.13, 14 Advantages of 
MEG versus fMRI include high temporal resolution, ability to separate oscillatory spectra 
and direct measurement of neuronal activity (vs. blood flow).18 One of the strengths and 
challenges of MEG analysis is the high volume of potentially relevant data generated. 
Machine learning refers to automated algorithms designed to facilitate pattern recognition 
and classification and are particularly advantageous for large and complex data sets 
where traditional statistical approaches are rapidly overwhelmed.19 Machine learning has 
been successfully applied to many complex biological data sets including genomics, 
proteomics, and neuroimaging.20-22 Notably, machine-learning algorithms have been 
successfully applied to develop diagnostic AD biomarkers from MEG data.23 Our group 
has further shown that machine-learning MEG biomarkers can also track AD progression 
over time.24 Machine-learning approaches to MEG data offers significant promise to 
develop biomarkers relevant to cognitive dysfunction in PD.
Repetitive Transcanial Magnetic Stimulation (rTMS) modulates brain activity using 
repeated pulses from a magnetic coil over the scalp. rTMS may induce effects through 
changes in cortical excitability25, neuroplasticity26, gene expression27, neurotrophic 
factors28, and neurotransmitters including dopamine29. Recent studies further show that 
rTMS can modulate focal oscillatory activity30 and network connectivity31, 32. Our 
Preliminary Data suggests that rTMS similarly affects network connectivity in PD. The 
therapeutic potential of rTMS is established in certain neuropsychiatric illnesses, including 
FDA approval for depression, with responses following a course of rTMS lasting over 6 
Figure 1. Diagrams of 
random network (left: low 
path length and clustering), 
small-world network (middle: 
low path length, high 
clustering), and lattice 
network (right: high path 
length, high clustering).

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months.33 rTMS can also improve cognitive function in healthy older adults34, AD35 (with 
benefits lasting up to 4 months), and PD36, 37. Limitations of the prior PD studies include 
not specifically enrolling subjects with cognitive impairment, not controlling for depression, 
not assessing the durability of benefits and not tying rTMS effects to mechanisms. In this 
proposal we address these limitations in a clinical and mechanistic trial of rTMS in PD 
patients with mild cognitive impairment (MCI).
As patients will be required to obtain an anatomical MRI for TMS and MEG localization, 
we are adding an exploratory Aim to better understand the underpinnings of MEG 
findings. First, we would like to compare how MEG measures of resting state networks 
compare to fMRI resting state measures which have previously been reported as 
abnormal in PD (Gottlich et al., 2013). Second to see how structural connectivity, 
measured with diffusion tensor imaging (DTI) of white matter tracts, relates to MEG 
measures of functional connectivity. White matter has also recently shown to be altered in 
PD (Kim et al., 2013). Finally, oscillatory activity within the beta- and gamma-range, as 
measured non-invasively using EEG and MEG methods, has been associated with 
intrinsic glutamatergic and GABAergic activity in animal and computational models of the 
neocortex (Lally et al., 2014; Gaetz et al., 2011). We thus aim to characterize biochemical 
levels in the motor cortex of our population samples by using  proton magnetic resonance 
spectroscopy (1H-MRS).
III.
Preliminary Studies/Progress Report:  
The grant associated with this proposal has just been funded by the NIH/NINDS. It builds 
on preliminary data from my CCTSI KL2 grant showing that MEG measures of cortical 
function differ between healthy controls and PD patients, correlate with cognitive function 
in PD patients without cognitive impairment, and are partially normalized by rTMS.
IV. Research Methods
A. Outcome Measure(s):  
Neuropsychological and Neurological Testing: Our primary cognitive outcome will be the 
total score on the Mattis Dementia Rating Scale (MDRS)99 which measures multiple 
cognitive domains and is validated in PD.100 An important secondary question is whether 
connectivity in specific networks are associated with domain specific deficits. We will thus 
perform tests in the following domains: 1) Executive function (Trails B, Verbal Fluency, 
Stroop)101; 2) Memory (Hopkins Verbal Learning Test)102; 3) Language (Boston Naming 
Test)103; 4) Attention (Brief Test of Attention)104; and 5) Visuospatial (Judgement of Line 
Orientation)105. Finally, we will perform the Unified Parkinson’s Disease Rating motor 
section106 to use as a covariate in models for disease severity. 
MRI: 
MRI scans will be acquired using a G.E. 3.0 T Signa, whole body magnet with an Excite 
upgrade and 8-channel head coil using a 3-D, extended dynamic range, inversion 
recovery SPGR ASSET parallel imaging sequence with excellent gray/white contrast. The 
T1-weighted series will consist of .94 mm thick coronal images and can be acquired in 9 
minutes. MRI images will be aligned with MEG using SPM8 software.107
The following additional sequences will be optional for participants and add approximately one hour 
to the MRI scan but do not add risk and the patient does not have to do anything except lie still. 

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Participants will be informed that these scans are not essential to the study and that we can stop 
scanning at any point should they become uncomfortable.
Diffusion tensor imaging (DTI) is a MRI scanning technique that enables one to assess the 
diffusion of water in brain tissue. The risks to subjects are identical to those in standard T1-
weighted MRI scanning (i.e., no more than minimal risk). The purpose of DTI is to better quantify 
the white matter, or axon segments, of the brain. 
1H-MRS can be used to quantify a range of brain metabolites, including n-acetylaspartate and 
glutamine/glutamate, markers of neuronal density and/or neuronal integrity. The MRS scan will 
allow us to crudely quantify neuronal integrity within the motor cortex. We will focus a single voxel 
of interest on the left prefrontal cortex because whole brain human MRS is prohibitively time-
consuming and impractical. We will test the hypothesis that changes in connectivity are correlated 
to decreased levels of glutamate and GABA within the frontal cortex in persons with Parkinson’s 
disease.
For purposes of comparison to MEG resting state, echo-planar imaging-gradient recalled echo 
(EPI-GRE) data normally acquired during functional MRI scanning (fMRI) will be acquired during 
the MRI session. Subjects will simply rest in the prone position with their eyes open for 2 minutes 
and closed for 2 minutes. The 10 minute baseline MRI scan will allow us to compare the generators 
of resting state MEG gamma-band to resting state activation in fMRI.
The total MRI procedure, if patients consent to the additional sequences, will last approximately 70 
minutes and will begin with the anatomical portion. In case the study subjects feel any discomfort 
anytime after this first acquisition, they will be freed of terminating the additional scans without 
being disqualified from the study.
MEG Data Collection: Magnetic field data will be recorded using a whole head 
neuromagnetometer (4D Neuroimaging) with an array of 248 sensors at rest with eyes 
open for 2 minutes and closed for 2 minutes at a 678 Hz sampling rate. Data will be 
processed offline using a 0.1-100 Hz band pass filter. Data will be manually examined and 
five 6-second artifact free epochs for both eye-open and closed conditions will be selected 
for further analyses. Artifact correction if needed will be performed using independent 
components analysis.108 Sensors will be realigned to a common sensor array to allow 
group level analysis in a shared brain space.109
MEG Data Processing: Following data acquisition, there are 4 major steps for graph 
theory analysis.63 First, data will be transformed into the frequency domain using Morlet 
wavelets and parceled into six standard frequency bands: delta (1-4 Hz), theta (4-8 Hz), 
alpha (8-12 Hz), low beta (12-20 Hz), high beta (20-30 Hz) and gamma (30-60 Hz).110 
Further analyses will be performed separately for each band. Second, the strength of 
functional connectivity must be calculated between all sensors. Granger causality will be 
used for this step, providing a directional weighted measure of connectivity suitable for 
graph theory.111, 112 Third, functional connections between nodes will be used to create a 
network graph. This is matrix representing the directed weighted connection strengths 
between all sensors for each frequency band. Advantages of creating a weighted network 
graph include ensuring full connectedness and avoiding potentially arbitrary thresholds 
required for binary networks.61 Finally, graph analyses can be performed on the network 
graph using the Matlab-based Brain Connectivity Toolbox61 and custom Matlab code. Our 
primary MEG outcome measure will be network small-worldness, calculated as the ratio of 
the mean clustering coefficient to the mean path length normalized to a randomly 
connected network.113 Planned secondary MEG outcome measures will include global 
efficiency (a measure of global integration related to path length), nodal efficiency (a 
measure of modularity)60, and degree distribution (a measure of network resilience)114. 

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B. Description of Population to be Enrolled:  
We will enroll 25 PD subjects over the age of 40 without cognitive impairment diagnosed 
with probable PD using UK Brain Bank Criteria38 and 55 PD subjects over the age of 40 
with MCI or mild dementia using the Movement Disorder Task Force 2012 criteria and a 
Clinical Dementia Rating scale score (CDR) less than 2.39, 40 All study visits will be 
performed in subjects’ best dopaminergic On state. PD MCI and mild dementia subjects 
will be tested on anticholinesterase therapy if already taking these medications given the 
potential difficulty of finding drug naïve subjects; long half-life of these medications; and 
desire to find measures robust to medication effects as seen in prior MEG studies.41 
Exlusion criteria:
1) features suggestive of other neurological disorders
2) DBS
3) evidence for active depression or Hospital Anxiety Depression Scale42 score > 11
4) motor symptoms expected to interfere with scanning
5) contraindications to TMS: 

history of seizures or status epilepticus, 

unstable coronary artery disease

hydrocephalus

implantable electrodes, cerebral spinal fluid shunts, skull plates or other 
intracranial metal

medical devices including pacemakers or other implantable stimulators or 
pumps

subjects taking medications known to lower seizure threshold

pregnancy
In order to complete recruitment including consent screen failures, drop-outs and 
unusable datasets we request the capacity to recruit up to 150 participants.
C. Study Design and Research Methods  
Aim 1 Rationale and Design: Even while not engaged in a task the brain exhibits spatially 
and temporally organized patterns of activity.84 The past 15 years has seen remarkable 
progress in understanding these intrinsic functional networks (also termed resting-state 
networks) in both health and disease.85 Intrinsic networks are consistent within and across 
individuals, correlate with behavioral and clinical outcomes, and can be analyzed with 
graph theory measures to define global and local aspects of network organization.59 MEG 
can be used to identify multiple intrinsic networks similar to fMRI but with additional 
information regarding the power spectra of connections.86, 87 Importantly, MEG measures 
of intrinsic activity can sensitively discriminate AD from other neurological disorders and 
correlate with cognitive function in both AD and multiple sclerosis.40, 47, 64 Prior studies of 
PD suggest changes in cortical connectivity may be associated with dementia but did not 
define network organization using graph theory.88 Our objective for this experiment is to 
determine whether graph theory measures of intrinsic MEG activity correlate with 
cognitive dysfunction in PD. We hypothesize that loss of small-world architecture 
contributes to cognitive dysfunction in PD through loss of local efficiency and increased 
randomness. To test this hypothesis, 25 PD subjects with MCI and 25 with normal 
cognition will undergo neuropsychological testing and a resting MEG scan for analysis for 
this aim. MEG and neuropsychological testing may be done on the same day or separate 
days, not to exceed 1 week in between. We will use graph theory measures to model the 

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relationship between network connectivity and cognitive function. The data generated by 
this Aim will fundamentally advance our understanding of the role of cortical physiology in 
PD cognitive dysfunction, will provide a modern network framework for future studies in 
this field and may provide insights leading to future therapeutic targets.
Aim 2 Rationale and Design: While finding group differences or clinical associations in 
brain connectivity using MEG will improve our understanding of the pathophysiology of PD 
cognitive dysfunction it is unlikely to provide sufficient discriminant ability to serve as a 
useful diagnostic or predictive biomarker. Machine learning algorithms offer several 
advantages in terms of biomarker development including: 1) Characterization of data at 
the level of the individual; 2) Ability to simultaneously optimize multiple features from 
complex datasets; and 3) Allows data to drive feature selection rather than theoretical 
assumptions.72 Support Vector Machine (SVM) is a machine learning method that works 
by determining the maximum separation between classes in a high dimensional space 
composed of the features of interest.119 Our objective for this Aim is to develop a novel 
state-defining biomarker for PD-MCI based on MEG measures of regional activity and 
network connectivity using SVM. We hypothesize that cognitive dysfunction in PD can be 
characterized through a unique combination of MEG regional and network features and 
optimized through SVM. To test this hypothesis, we will reanalyze the MEG data collected 
in Aim 1 using SVM to classify PD patients with and without MCI. 
AIM 3 Rationale and Design: rTMS can improve cognition in PD without dementia52, PD 
with depression80, older adults122 and in AD123 but has not been tested in PD-MCI. Given 
the need for improved cognitive treatments and possible differences in rTMS effects on 
PD-MCI pathophysiology it is important to specifically test rTMS in this population. Prior 
studies as well as our own pilot data suggest that rTMS modulates cortical 
connectivity;16,17 thus rTMS provides not only a potentially effective intervention but an 
important causal test of our associative data and theoretical link between cortical 
connectivity and cognitive dysfunction in PD. Our clinical objective for this experiment is to 
determine whether rTMS improves cognitive outcomes in PD-MCI. Our mechanistic 
objective is to determine whether rTMS normalizes patterns of cortical connectivity. We 
hypothesize that high-frequency bifrontal rTMS will partially normalize patterns of cortical 
connectivity linked to frontal nodes and will improve cognitive measures. To test this 
hypothesis 55 PD-MCI subjects will be randomized to a 10-day course of real or sham 
bifrontal high frequency rTMS. We will measure cognitive and MEG outcomes pre-
treatment and on the day of the last treatment (neuropsych testing and MEG procedures 
may be separated by 1 day if patient is fatigued) and repeat cognitive measures 28 days 
post rTMS to assess the durability of cognitive changes.
TMS: TMS will be administered using a 70-mm diameter air-cooled figure-of-8 coil and 
SuperRapid2 Magstim Stimulator. We will first define the subject’s resting motor threshold 
(MT) by localizing the hand area of motor cortex and determing the minimal TMS 
amplitude needed to produce a motor-evoked potential of 50 microV on 4 of 8 trials 
measured by EMG over the first dorsal interosseous muscle (Biopac, Goleta, CA). Once 
the MT is determined, repetitive pulses will be delivered to the right and left pre-frontal 
cortex (Brodman area 46) using a frameless stereotactic navigation system and the 
subject’s MRI in Brainsight software. Depending on our preliminary results from Aim 1, we 
may change our target if it is evident that a particular frontal node shows high clustering 
within beta or theta frequency bands. This type of targeting based on functional data has 
been previously performed and may enhance TMS effects on intrinsic connectivity.16 
Stimuli will be delivered at 20 Hz at 90% rMT for 25 trains of 30 pulses per train, inter-train 

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interval of 30 seconds for a total of 750 pulses per hemisphere, which is well within the 
most recent international TMS safety guidelines.129 This dose and duration of rTMS is 
based on physiological studies of healthy adults and treatment studies of cognition in PD 
and Alzheimer’s disease.52, 123 Side of first stimulation (left vs right hemisphere) will be 
counterbalanced across subjects. Sham stimulation will be delivered using a sham coil 
fitted with electrodes to mimic both the auditory and somatic sensation of real TMS as I 
have performed in prior TMS trials.19 
Schedule of Procedures
2 weeks
Baseline 
Procedures2
rTMS 
Session 11,
rTMS 
Sessions 2-
91
rTMS 
Session 
101
28 days 
post-rTMS 
Session 
101
Informed 
consent
X
MRI 
(anatomical 
and optional 
functional 
scans)
X
MEG
X
X1
Neuropsych 
tests
X
X1
X1
rTMS (real or 
sham)1
X1
X1
X1
Randomization 
to real or 
sham TMS1
X
1PD-MCI subjects only
2Baseline procedures to be completed within 1 week of rTMS Session 1
D. Description, Risks and Justification of Procedures and Data Collection 
Tools:
PD-MCI and mild dementia patients may be considered a vulnerable population due to 
cognitive dysfunction but are necessary to answering our research questions. We are 
cognizant of this issue and will carefully assess potential participant’s understanding of the 
study, particularly of study activities and risks. If there are any concerns on the part of the 
investigator or potential participant, we will require that their guardian participate in the 
consenting process and sign an informed consent. We will also require that PD-MCI 
subjects give consent, or at minimum assent if fully informed consent is not possible. Our 
protocol and consent forms will be approved by our local IRB before approaching any 
potential subjects.

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After enrollment and screening assessment, PD and PD-MCI subjects will undergo 
neuropsychological testing, magnetoencephalography (MEG) recordings, and MRI. PD-
MCI subjects will then be randomized in a 1:1 ratio to real or sham TMS groups using 
MAPLE 9.0 software (Waterloo, Canada). Baseline neuropsychological testing, MEG, and 
MRI may be performed over multiple days, but all will be completed within 1 week and 
within 1 week of first TMS session for PD-MCI subjects. 
TMS will consist of 10 sessions (over two weeks) of 20 Hz repetitive TMS given at 90% of 
motor threshold in 25 trains of 30 pulses per train with a 28.5 second inter-train interval to 
each prefrontal cortex (Brodman area 46) localized using MRI and Brainsight software. 
There will be a total of 750 pulses per hemisphere per session. These TMS parameters 
are well within the most recent published safety guidelines.129 This dose and duration was 
chosen on the basis of prior physiological studies in healthy adults78 and in treatment 
studies of cognition in PD52 and Alzheimer’s Disease.123
There are minimal risks involved in the proposed research. There are no known risks from 
MEG recordings. In appropriately screened subjects, the primary risk of MRI is 
claustrophobia and anxiety which is remediable by removal from the scanner. Cognitive 
testing as proposed in this study may induce boredom or restlessness. Any new 
neuropsychiatric diagnoses detected during screening (e.g. depression) will be referred 
for appropriate treatment, including emergent treatment if indicated. TMS as proposed in 
this study has a minimal risk (less than 1 in 1000) of inducing seizures in appropriately 
screened subjects or other adverse events.129 Noatbly, there have no cases of epilepsy 
(recurrent seizures) or status epilepticus (prolonged seizure considered a medical 
emergency) with TMS. The risks of TMS in PD are similar to that of the general 
population.3 Transient motor worsening has been reported with supplementary motor area 
TMS in PD, but not with the frontal targets we have proposed. There have been reports of 
hearing loss with repeated TMS pulses and thus all subjects will be required to wear ear 
plugs, similar to MRI. There is a slight risk of headache and neck pain which is typically 
self-resolving and/or treatable with over the counter analgesics. 
All subjects will be required to wear hearing protection and will be supervised by study 
personnel during all aspects of this study. A neurologist will be available either in person 
or on-call within the building in the event of a seizure or other adverse study event. Study 
personnel involved in human subject interactions will be trained not only in human 
subjects protection but will be also BLS or ACLS certified and specifically trained in 
seizure safety and what to do in the event of other medical emergencies. There is a COR 
cart available outside of the TMS and MEG laboratories. All adverse events will be 
reported to the IRB within 5 days and seizures or other significant adverse events 
associated with TMS will additionally be reported to the FDA.
E. Potential Scientific Problems:  
Prior TMS trials are often questioned regarding the adequacy of patient blinding with 
certain sham procedures. Our sham involves both auditory and tactile stimulation, and in 
fact resulted in a similar improvement in depression as real TMS in our recent trial.19 
Subject retention, particularly with PD-MCI, may be difficult with 2 weeks of daily 
treatment. We will use similar procedures to our PD apathy rTMS trial where we 
maintained 100% retention. Finally, our sample size is relatively small to detect clinically 
significant changes. This is in part due to budgetary imitations and one goal of this trial is 
to serve to develop a more definitive, larger trial with longer-term follow-up. Changes in 
sensor location over repeated recordings may result in false attribution of brain activity 

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changes. To address this issue, sensors will be realigned to a common sensor array to 
allow repeated group level analysis in a shared brain space.109
F. Data Analysis Plan:  
AIM 1 Statistical Analysis and Sample Size: To address our primary research question, 
Spearman correlations will be used to identify frequency spectra small-worldness 
measures associated with the MDRS in eyes-closed data with a false detection rate set at 
0.05 to handle the issue of multiple comparisons. We will then use linear regression to 
determine whether small-worldness in these specified frequency bands remain associated 
with the MDRS when controlling for each other as well as other important covariates, 
namely age and motor severity. A sample size of 45 (allowing for 5 drop-outs and/or 
unusable data-sets from the original 50) will allow us to detect correlations of 0.18 or 
higher between our MEG features and MDRS with 80% power 0.05 significance. This is 
comparable to correlations of 0.19 and higher seen in other resting state MEG studies of 
cognition.115 Planned secondary analyses will parallel our primary analysis to determine 
whether global efficiency, nodal efficiency or degree distribution are significantly 
associated with MDRS in linear regression models. Exploratory analyses will assess 
reactivity of these measures between eyes open and closed conditions and assess 
whether domain specific neuropsychological test scores are associated with connectivity 
within networks defined by domain related nodes (e.g. occipital nodes and visuospatial 
tests).
AIM 2 Statistical Analysis and Sample Size: We will use SVM to determine what 
combination of MEG features yields the most robust classifier for discriminating PD with 
and without MCI. The principle behind SVM learning is that features of interest can be 
modeled in a high-dimensional feature space (see Figure 2 for a visualization of a very 
simple 2 feature space). SVM then uses a recursive algorithm to define a hyper-plane of 
select features creating the greatest distance between groups of interest.119 Given the 
large potential combination of MEG features and sensors this will be a computationally 
intensive but tractable SVM application. As the optimal number of features to build an 
SVM classifier is not known a priori, we will use a procedure called bagging-cross-
validation to determine the optimal number of features and maximize the information 
gained from SVM in this relatively small sample size.121 This approach uses a boot-strap 
leave one out procedure to create n – 1 training sets and n test subjects, where n is the 
total sample size, to optimize feature number and maximize the final receiver operating 
curve. Given the large effect sizes seen in our pilot data and other MEG studies of PDD58, 
we anticipate that our sample size of 45 (allowing for 5 unusable data-sets from the 
original 50) will allow us to create a classifier with an area under the ROC curve (AUC) of 
0.95 or higher comparable to AUCs seen in other SVM applications in dementia and MCI 
populations with as few as 15 subjects per group.68
AIM 3 Statistical Analysis and Sample Size: We will address our primary clinical research 
question by determining whether there is a significant group difference between the real 
and sham treated PD-MCI patients on the MDRS change following rTMS using repeated 
measures ANOVA. We will address our primary physiologic research question by 
determining whether there is a significant difference between the real and sham treated 
PD-MCI patients on change in MEG measures of small-worldness using a repeated 
measures ANOVA. Planned secondary analyses will include determining group 
differences in change on measures of quality of life, domain specific neuropsychological 
results and durability at the 28-day post-TMS time point. If cognition is improved we will 
use regression modeling to determine if these improvements were associated in changes 

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in small-worldness or other MEG features. With a total sample size of 50 (25 per group 
allowing for 5 drop-outs from original 55) we will be able to determine a between group 
difference of 10 points on the MDRS (assuming a SD of 13 as per prior trials and an effect 
size 0.77)124 with 80% power and 0.05 significance. This sample size is comparable to 
prior PDD studies. For example, a study of rivastigmine in PDD detected significant 
change on the Mattis with only 28 subjects.124
Exploratory Aim Statistical Analyses and Sample Size: We will address our exploratory 
research questions by looking at Spearman correlations or linear models of MEG and MRI 
measures. Power considerations are similar as for Aim 1 with detection of correlations of 
0.18 or higher between our MEG and MRI features with 80% power 0.05 significance.
G. Summarize Knowledge to be Gained:  
PD is the second most common neurodegenerative disease, affecting over one million 
Americans and dementia is the most common cause of nursing home placement in this 
population.53, 54 Our understanding of the neurobiological mechanisms of cognitive 
dysfunction in PD are limited and we do not have efficacious treatments. This research 
with address this significant public health issue in three major ways: 1) Identify 
abnormalities in cortical connectivity associated with cognitive dysfunction in PD; 2) 
Develop a MEG biomarker for cognitive dysfunction in PD; and 3) Evaluate the potential 
for TMS to induce durable changes in PD cognitive function and connectivity. Given the 
burden of dementia in this population, we feel that the minimal risks involved are justified 
both to individual PD and PD-MCI subjects, the larger population of PD and PD-MCI 
patients, and to society as a whole. Recent TMS studies using published safety guidelines 
suggest that the risks of TMS may be even lower than previously described. Even using 
conservative estimates and anticipating some discomfort from prolonged sitting and 
cognitive testing, we argue that this research is imperative to advance this field and 
ultimately to provide relief to these patients and their caregivers.
 H. References:
1.
Dorsey ER, Constantinescu R, Thompson JP, et al. Projected number of people 
with Parkinson disease in the most populous nations, 2005 through 2030. Neurology 
2007;68:384-386.
2.
Docherty MJ, Burn DJ. Parkinson's disease dementia. Curr Neurol Neurosci Rep 
2010;10:292-298.
3.
Hely MA, Reid WG, Adena MA, Halliday GM, Morris JG. The Sydney multicenter 
study of Parkinson's disease: the inevitability of dementia at 20 years. Mov Disord 
2008;23:837-844.
4.
Emre M, Aarsland D, Albanese A, et al. Rivastigmine for dementia associated with 
Parkinson's disease. N Engl J Med 2004;351:2509-2518.
5.
Kluger BM, Foote KD, Jacobson CE, Okun MS. Lessons learned from a large 
single center cohort of patients referred for DBS management. Parkinsonism Relat Disord 
2011;17:236-239.
6.
Friederici AD. The brain basis of language processing: from structure to function. 
Physiol Rev 2011;91:1357-1392.
7.
He H, Sui J, Yu Q, et al. Altered small-world brain networks in schizophrenia 
patients during working memory performance. PLoS One 2012;7:e38195.

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8.
Bosboom JL, Stoffers D, Wolters E, Stam CJ, Berendse HW. MEG resting state 
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