Creating the feedback loop: closed-loop neurostimulation.

Implantable devices for electrical stimulation of the brain have been in routine clinical use since 1997, when the first commercial deep brain stimulation (DBS) system was approved for the treatment of tremor1. These DBS devices provide an invariant train of stimulatory pulses at a fixed frequency. This “open-loop” mode (meaning unidirectional signal generated from the device and delivered to the brain) of DBS therapy has proven to be effective for treatment of essential tremor2, Parkinson’s disease3,4, and dystonia5,6. As we expand our understanding of the neurophysiological mechanisms of both DBS and movement disorders, the shortcomings of open loop therapy DBS are evident and will be discussed in this review. The design of a “closed-loop” implantable pulse generator (IPG) to sense and respond to physiological signals (“closed-loop” meaning bidirectional signals moving in both sensing and responding directions, allowing for the use of sensor signals to provide feedback modulation of stimulation) within or outside the brain is considered the next frontier in brain stimulation research and will likely broaden the field to include new applications for neuromodulation. Implantable closed loop stimulation systems are well established in the treatment of cardiac arrhythmias. Cardiac pacemaker devices capable of sensing and responding to atrial activity are closed loop mode cardiac stimulation devices and have been in clinical use since 19637. Despite the precedent for an IPG with dual sensing and stimulating functionality set 50 years ago, efforts to bring similar concepts to DBS devices8,9 have been delayed 50-years in part due to the complexity of brain signals. Whereas cardiac pacemakers detect the P-wave signal of the atrial pacemaker, brain-generated signals are statistically complex. Perhaps more importantly, the clinical meaningfulness of recordable brain signals is not immediately obvious. Strategy development for interpreting neuronal signals in closed-loop neurostimulation application is underway. In broadest terms, an understanding of the relationship between a patient’s clinical state and a neuronal signal under the influence of external stimulation is fundamental to any future utilization of the signal as a surrogate marker for clinical states. Clinical states are disease specific but collectively can be categorized by pathological expressions of the disease (e.g. the magnitude of tremor) and behavioral intentions (i.e. attempting a task at hand such as walking, talking or writing). Therefore, closed-loop neurostimulation relates available neuronal recording to meaningful clinical states and uses the surrogate measurements to update neurostimulation as the device is operating. Recordable neurophysiological signals are available from multiple levels of the brain, including a single neuron, multiple individual neurons, a localized population of neurons, or a large-scale population of neurons. Single neuron recordings have been shown to be related to certain specific aspects of movement10 and cognition11. Technical challenges of chronic recording from single neurons exist, such as increased sampling rate requirements, difficulty maintaining recordings from the same neuron for extended periods of time, and degradation at the neuron-electrode interface. These challenges contribute to the overall difficulty in maintaining sustained recordings from a single neuron. Recording from large populations of neurons, or local field potential (LFP) recordings, are much more stable over time. Oscillatory components of LFP recordings from highly specialized cortex, such as motor cortex or visual cortex, have been successfully related to clinical states such as movement and visual percepts12–14. However, recordings from these specialized cortical regions of the brain are limited because these regions are not typically accessed during routine surgery for neurostimulation. This review article will present current developments in closed loop neurostimulation and strategies for manipulation of recordable signals in order to relate this information to a patient’s clinical state (Figure 1). Specifically, this review will cover the rationale for closed loop stimulation, meaningful categories of clinical patient states, brain signals available for recording, signal processing for prediction of patient states, and interventional DBS patterns aimed at restoring a desired state, or facilitating a desired state. Parkinson’s Disease will be a primary focus; however, principles can be applied to other movement disorders, as well as epilepsy, mood disorders, and other neuropsychiatric diseases. This review article is intended as an introduction of engineering issues for clinicians and of clinical issues for engineers. Figure 1 General control system diagram representing a conceptualized closed loop neurostimulation system for DBS. In this diagram, the reference signal (* LFP Feature) is the feature extracted from the basal ganglia local field potential. This reference signal* ... Rationale for closed loop stimulation: Parkinson’s Disease Open-loop DBS is effective for treating the motor signs of Parkinson’s disease, but side effects of this therapy and its inefficiencies may be diminished within a closed loop system. Side effects of open-loop DBS experienced by some patients include impaired cognition, speech, gait and balance15. Open loop DBS could potentially disrupt decision making, learning, and cognitive association through its effect on LFP oscillations of the brain. Open-loop DBS therapy was developed prior to an understanding of how LFP oscillations influence the precise timing of neuronal action potentials (integral to the mechanism of Hebbian learning16,17), and influence communication between distant neuronal ensembles (the basis of cognitive association18,19). Studies have demonstrated that open-loop DBS impairs learning and object-naming during stimulation of the pulvinar20, and impairs verbal fluency and reactive inhibition during stimulation of the subthalamic nucleus. These impairments may be due to disruption of cortico-cortical or cortico-subcortical oscillatory synchronization between LFPs of connected brain regions20. As a result of dopaminergic depletion in Parkinson’s disease, the basal ganglia are characterized by radical changes at the level of a single neuron and above. Neuronal firing patterns change significantly in animal models and in humans with Parkinson’s disease. In the pathological state, neuronal pairs within and across boundaries of basal ganglia nuclei fire together with increased synchrony21. This phenomenon is reflected in the abnormal oscillatory activity revealed by LFPs within and between those structures. The method by which high frequency stimulation influences cortico-basal ganglia circuits and leads to a therapeutic outcome is under debate22,23. DBS effects are likely a combination of a local inhibitory effect and an excitatory effect on distal connected nuclei. The local inhibitory effect of DBS may be evident by the fact that both stimulation and lesioning of the globus pallidus lead to similar therapeutic effects in the clinical treatment of Parkinson’s disease. For the treatment of essential tremor, clinical effects of stimulation and lesioning of the thalamus are roughly equivalent. The distal excitatory effect of subthalamic nucleus DBS is evident by its observed effect of increasing the firing rate of neurons in the globus pallidus, a distal connected nucleus24. Though the mechanism of DBS therapeutic benefit is poorly understood, an end-result of open-loop DBS may be a neuronal network that is able to retain partial functionality despite insufficient dopaminergic innervation. If this is true, the present therapeutic methods may not be restoring basal ganglia functionality to the fullest extent possible, due to limitations of the static approach of open-loop DBS in an inherently dynamic system. It is therefore compelling to seek a treatment that could enhance “surviving” functionality in the basal ganglia. Dynamic restoration of neuronal rhythms by the integration of responsive signal processing might be an advantage of a closed-loop system. Conversely, damage to the basal ganglia may be the result of irreversible loss of neuronal circuitry involved in highly specialized information processing25. In this case, dynamic and responsive signal processing may not present an advantage compared to current open-loop DBS, especially if a damaged neuronal network generates faulty signal outputs.