Elevated Gaussian-modeled beta power in the cortex characterizes aging, but not Parkinson's disease.

Aging is a key risk factor for the development of Parkinson's disease (PD). PD is characterized by excessive synchrony of beta oscillations (13-30 Hz) in the basal ganglia thalamo-cortical network. However, cortical beta power is not reliably elevated in individuals with PD. Here, we sought to disentangle how resting cortical beta power compares in younger controls, older controls, and individuals with PD using scalp electroencephalogram (EEG) and a novel approach for quantifying beta power. Specifically, we used a Gaussian model to determine if sensorimotor beta power distinguishes these groups. In addition, we looked at the distribution of beta power across the entire cortex. Our findings showed that Gaussian-modeled beta power does not differentiate individuals with PD (on medication) from healthy younger or older controls in sensorimotor cortex. However, beta power (and not theta or alpha) was higher in healthy older versus younger controls. This effect was most pronounced in regions near sensorimotor cortex including the frontal and parietal areas [P < 0.05, false discovery rate (FDR) corrected]. In addition, the bandwidth of the periodic beta was also higher in healthy older than young individuals in parietal regions. Finally, the aperiodic component, specifically the exponent of the signal, was higher (steeper) in younger controls than in individuals with PD in the right parietal-occipital region (P < 0.05, FDR corrected), possibly reflecting differences in neuronal spiking. Our findings suggest that cortical Gaussian beta power is possibly modulated by age and could be further explored in longitudinal studies to determine whether sensorimotor beta increases with increasing age.NEW & NOTEWORTHY Altered sensorimotor beta activity has been shown to be a feature in aging and PD. Using a novel approach, we clarify that resting sensorimotor beta power does not distinguish subjects with PD from healthy younger and older controls. However, beta power was higher in older compared with younger controls in central sensorimotor, frontal, and parietal regions. These results provide a clearer picture of sensorimotor beta power, demonstrating that it is elevated in aging but not PD.

Advances in human intracranial electroencephalography research, guidelines and good practices.

Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.

Novel approaches for quantifying beta synchrony in Parkinson's disease.

Despite the clinical and financial burden of Parkinson's disease (PD), there is no standardized, reliable biomarker to diagnose and track PD progression. Instead, PD is primarily assessed using subjective clinical rating scales and patient self-report. Such approaches can be imprecise, hindering diagnosis and disease monitoring. An objective biomarker would be beneficial for clinical care, refining diagnosis, and treatment. Due to widespread electrophysiological abnormalities both within and between brain structures in PD, development of electrophysiologic biomarkers may be feasible. Basal ganglia recordings acquired with neurosurgical approaches have revealed elevated power in the beta frequency range (13-30 Hz) in PD, suggesting that beta power could be a putative PD biomarker. However, there are limitations to the use of beta power as a biomarker. Recent advances in analytic approaches have led to novel methods to quantify oscillatory synchrony in the beta frequency range. Here we describe some of these novel approaches in the context of PD and explore how they may serve as electrophysiological biomarkers. These novel signatures include (1) interactions between beta phase and broadband (> 50 Hz, "gamma") amplitude (i.e., phase amplitude coupling, PAC), (2) asymmetries in waveform shape, (3) beta coherence, and (4) beta "bursts." Development of a robust, reliable, and readily accessible electrophysiologic biomarker would represent a major step towards more precise and personalized care in PD.

Effect of levodopa on electroencephalographic biomarkers of the parkinsonian state.

The objective of this study was to evaluate proposed electroencephalographic (EEG) biomarkers of Parkinson's disease (PD) and test their correlation with motor impairment in a new, well-characterized cohort of PD patients and controls. Sixty-four-channel EEG was recorded from 14 patients with rigid-akinetic PD with minimal tremor and from 14 age-matched healthy controls at rest and during voluntary movement. Patients were tested off and on medication during a single session. Recordings were analyzed for phase-amplitude coupling over sensorimotor cortex and for pairwise coherence from all electrode pairs in the recording montage (distributed coherence). Phase-amplitude coupling and distributed coherence were found to be elevated Off compared with On levodopa, and their reduction was correlated with motor improvement. In the Off medication state, phase-amplitude coupling was greater in sensorimotor contacts contralateral to the most affected body part and reduced by voluntary movement. We conclude that phase-amplitude coupling and distributed coherence are cortical biomarkers of the parkinsonian state that are detectable noninvasively and may be useful as objective aids for management of dopaminergic therapy. Several analytic methods may be used for noninvasive measurement of abnormal brain synchronization in PD. Calculation of phase-amplitude coupling requires only a single electrode over motor cortex. NEW & NOTEWORTHY Several EEG biomarkers of the parkinsonian state have been proposed that are related to abnormal cortical synchronization. We report several new findings in this study: correlations of EEG markers of synchronization with specific motor signs of Parkinson's disease (PD), and demonstration that one of the EEG markers, phase-amplitude coupling, is more elevated over the more clinically affected brain hemisphere. These findings underscore the potential utility of scalp EEG for objective, noninvasive monitoring of medication state in PD.

Gamma Oscillations in the Hyperkinetic State Detected with Chronic Human Brain Recordings in Parkinson's Disease.

UNLABELLED: Hyperkinetic states are common in human movement disorders, but their neural basis remains uncertain. One such condition is dyskinesia, a serious adverse effect of medical and surgical treatment for Parkinson's disease (PD). To study this, we used a novel, totally implanted, bidirectional neural interface to obtain multisite long-term recordings. We focus our analysis on two patients with PD who experienced frequent dyskinesia and studied them both at rest and during voluntary movement. We show that dyskinesia is associated with a narrowband gamma oscillation in motor cortex between 60 and 90 Hz, a similar, though weaker, oscillation in subthalamic nucleus, and strong phase coherence between the two. Dyskinesia-related oscillations are minimally affected by voluntary movement. When dyskinesia persists during therapeutic deep brain stimulation (DBS), the peak frequency of this signal shifts to half the stimulation frequency. These findings suggest a circuit-level mechanism for the generation of dyskinesia as well as a promising control signal for closed-loop DBS. SIGNIFICANCE STATEMENT: Oscillations in brain networks link functionally related brain areas to accomplish thought and action, but this mechanism may be altered or exaggerated by disease states. Invasive recording using implanted electrodes provides a degree of spatial and temporal resolution that is ideal for analysis of network oscillations. Here we used a novel, totally implanted, bidirectional neural interface for chronic multisite brain recordings in humans with Parkinson's disease. We characterized an oscillation between cortex and subcortical modulators that is associated with a serious adverse effect of therapy for Parkinson's disease: dyskinesia. The work shows how a perturbation in oscillatory dynamics might lead to a state of excessive movement and also suggests a possible biomarker for feedback-controlled neurostimulation to treat hyperkinetic disorders.

Motor System Interactions in the Beta Band Decrease during Loss of Consciousness.

Communication between brain areas and how they are influenced by changes in consciousness are not fully understood. One hypothesis is that brain areas communicate via oscillatory processes, utilizing network-specific frequency bands, that can be measured with metrics that reflect between-region interactions, such as coherence and phase amplitude coupling (PAC). To evaluate this hypothesis and understand how these interactions are modulated by state changes, we analyzed electrophysiological recordings in humans at different nodes of one well-studied brain network: the basal ganglia-thalamocortical loops of the motor system during loss of consciousness induced by anesthesia. We recorded simultaneous electrocorticography over primary motor cortex (M1) with local field potentials from subcortical motor regions (either basal ganglia or thalamus) in 15 movement disorder patients during anesthesia (propofol) induction as a part of their surgery for deep brain stimulation. We observed reduced coherence and PAC between M1 and the subcortical nuclei, which was specific to the beta band (∼18-24 Hz). The fact that this pattern occurs selectively in beta underscores the importance of this frequency band in the motor system and supports the idea that oscillatory interactions at specific frequencies are related to the capacity for normal brain function and behavior.

Electrocorticography reveals beta desynchronization in the basal ganglia-cortical loop during rest tremor in Parkinson's disease.

The pathophysiology of rest tremor in Parkinson's disease (PD) is not well understood, and its severity does not correlate with the severity of other cardinal signs of PD. We hypothesized that tremor-related oscillatory activity in the basal-ganglia-thalamocortical loop might serve as a compensatory mechanism for the excessive beta band synchronization associated with the parkinsonian state. We recorded electrocorticography (ECoG) from the sensorimotor cortex and local field potentials (LFP) from the subthalamic nucleus (STN) in patients undergoing lead implantation for deep brain stimulation (DBS). We analyzed differences in measures of network synchronization during epochs of spontaneous rest tremor, versus epochs without rest tremor, occurring in the same subjects. The presence of tremor was associated with reduced beta power in the cortex and STN. Cortico-cortical coherence and phase-amplitude coupling (PAC) decreased during rest tremor, as did basal ganglia-cortical coherence in the same frequency band. Cortical broadband gamma power was not increased by tremor onset, in contrast to the movement-related gamma increase typically observed at the onset of voluntary movement. These findings suggest that the cortical representation of rest tremor is distinct from that of voluntary movement, and support a model in which tremor acts to decrease beta band synchronization within the basal ganglia-cortical loop.

Elevated synchrony in Parkinson disease detected with electroencephalography.

OBJECTIVE: Parkinson disease (PD) can be difficult to diagnose and treat. Development of a biomarker for PD would reduce these challenges by providing an objective measure of disease. Emerging theories suggest PD is characterized by excessive synchronization in the beta frequency band (∼20Hz) throughout basal ganglia-thalamocortical loops. Recently we showed with invasive electrocorticography that one robust measure of this synchronization is the coupling of beta phase to broadband gamma amplitude (ie, phase-amplitude coupling [PAC]). Other recent work suggests that high-frequency activity is detectable at the scalp using electroencephalography (EEG). Motivated by these findings, we tested whether beta-gamma PAC over sensorimotor cortex, recorded noninvasively with EEG, differs between PD patients off and on medications, and healthy control subjects. METHODS: Resting EEG was compared from 15 PD patients and 16 healthy control subjects. PD patients were tested on and off medications on different days, in a counterbalanced order. For each data set we calculated PAC and compared results across groups. RESULTS: PAC was elevated in the patients off medications compared to on medications (p = 0.008) and for patients off medications compared to controls (p = 0.009). INTERPRETATION: Elevated PAC is detectable using scalp EEG in PD patients off medications compared to on medications, and compared to healthy controls. This suggests that EEG PAC may provide a noninvasive biomarker of the parkinsonian state. This biomarker could be used as a control signal for closed-loop control of deep brain stimulation devices, for adjustment of dopaminergic treatment, and also has the potential to aid in diagnosis.

Intracranial electroencephalography reveals different temporal profiles for dorsal- and ventro-lateral prefrontal cortex in preparing to stop action.

Preparing to stop an inappropriate action requires keeping in mind the task goal and using this to influence the action control system. We tested the hypothesis that different subregions of prefrontal cortex show different temporal profiles consistent with dissociable contributions to preparing-to-stop, with dorsolateral prefrontal cortex (DLPFC) representing the task goal and ventrolateral prefrontal cortex (VLPFC) implementing action control. Five human subjects were studied using electrocorticography recorded from subdural grids over right lateral frontal cortex. On each trial, a task cue instructed the subject whether stopping might be needed or not (Maybe Stop [MS] or No Stop [NS]), followed by a go cue, and on some MS trials, a subsequent stop signal. We focused on go trials, comparing MS with NS. In the DLPFC, most subjects had an increase in high gamma activity following the task cue and the go cue. In contrast, in the VLPFC, all subjects had activity after the go cue near the time of the motor response on MS trials, related to behavioral slowing, and significantly later than the DLPFC activity. These different temporal profiles suggest that DLPFC and VLPFC could have dissociable roles, with DLPFC representing task goals and VLPFC implementing action control.

Prepared and reactive inhibition in smokers and non-smokers.

INTRODUCTION: Models of addiction have identified deficits in inhibitory control, or the ability to inhibit inappropriate or unwanted behaviors, as one factor in the development and maintenance of addictive behaviors. Current literature supports disruption of the prefrontal circuits that mediate reactive inhibitory control processes (i.e., inhibition in response to sudden, unplanned changes in environmental demands) in substance use disorders. However, the relationship between disorders of addiction, such as nicotine dependence, and planned inhibitory processes (i.e., inhibition that occurs after advance warning) is unclear. The goal of the present study was to examine the extent to which reactive and planned inhibitory processes are differentially disrupted in nicotine dependent individuals. METHOD: We employed an internet-based novel stop signal task wherein participants were instructed to stop a continuous movement at either a predictable or unpredictable time. This task explicitly separated planned and reactive inhibitory processes and assessed group differences in task performance between smokers (N = 281) and non-smokers (N = 164). The smoker group was defined as any participant that identified as a smoker and reported an average daily nicotine consumption of at least 2 mg. The non-smoker group was defined as any participant that identified as a non-smoker and had not been a former smoker that quit within the last year. The smoker group also completed a questionnaire regarding smoking behaviors which included the Fägerstrom Test of Nicotine Dependence (FTND). We used these data to assess the continuous relation between planned stopping, unplanned stopping, and smoking behaviors. RESULTS: We found significant differences in stop times for both reactive and planned stopping between groups as well as within the smoker group. Additionally, in the smoker group, dependence as measured by the FTND was associated with longer stop times on planned stop trials. Surprisingly, greater daily average consumption of nicotine was related to faster stopping for both trial types. CONCLUSION: These results indicate the relevance of measuring both reactive and planned inhibitory processes for elucidating the relationship between nicotine addiction and mechanisms of inhibitory control.

Stereo-EEG recordings extend known distributions of canonical movement-related oscillations.

Objective.Previous electrophysiological research has characterized canonical oscillatory patterns associated with movement mostly from recordings of primary sensorimotor cortex. Less work has attempted to decode movement based on electrophysiological recordings from a broader array of brain areas such as those sampled by stereoelectroencephalography (sEEG), especially in humans. We aimed to identify and characterize different movement-related oscillations across a relatively broad sampling of brain areas in humans and if they extended beyond brain areas previously associated with movement.Approach.We used a linear support vector machine to decode time-frequency spectrograms time-locked to movement, and we validated our results with cluster permutation testing and common spatial pattern decoding.Main results.We were able to accurately classify sEEG spectrograms during a keypress movement task versus the inter-trial interval. Specifically, we found these previously-described patterns: beta (13-30 Hz) desynchronization, beta synchronization (rebound), pre-movement alpha (8-15 Hz) modulation, a post-movement broadband gamma (60-90 Hz) increase and an event-related potential. These oscillatory patterns were newly observed in a wide range of brain areas accessible with sEEG that are not accessible with other electrophysiology recording methods. For example, the presence of beta desynchronization in the frontal lobe was more widespread than previously described, extending outside primary and secondary motor cortices.Significance.Our classification revealed prominent time-frequency patterns which were also observed in previous studies that used non-invasive electroencephalography and electrocorticography, but here we identified these patterns in brain regions that had not yet been associated with movement. This provides new evidence for the anatomical extent of the system of putative motor networks that exhibit each of these oscillatory patterns.

Roles for the pre-supplementary motor area and the right inferior frontal gyrus in stopping action: electrophysiological responses and functional and structural connectivity.

Both the pre-supplementary motor area (preSMA) and the right inferior frontal gyrus (rIFG) are important for stopping action outright. These regions are also engaged when preparing to stop. We aimed to elucidate the roles of these regions by harnessing the high spatio-temporal resolution of electrocorticography (ECoG), and by using a task that engages both preparing to stop and stopping outright. First, we validated the task using fMRI in 16 healthy control participants to confirm that both the preSMA and the rIFG were active. Next, we studied a rare patient with intracranial grid coverage of both these regions, using macrostimulation, diffusion tractography, cortico-cortical evoked potentials (CCEPs) and task-based ECoG. Macrostimulation of the preSMA induced behavioral motor arrest. Diffusion tractography revealed a structural connection between the preSMA and rIFG. CCEP analysis showed that stimulation of the preSMA evoked strong local field potentials within 30 ms in rIFG. During the task, when preparing to stop, there was increased high gamma amplitude (~70-250 Hz) in both regions, with preSMA preceding rIFG by ~750 ms. For outright stopping there was also a high gamma amplitude increase in both regions, again with preSMA preceding rIFG. Further, at the time of stopping, there was an increase in beta band activity (~16 Hz) in both regions, with significantly stronger inter-regional coherence for successful vs. unsuccessful stop trials. The results complement earlier reports of a structural/functional action control network between the preSMA and rIFG. They go further by revealing between-region timing differences in the high gamma band when preparing to stop and stopping outright. They also reveal strong between-region coherence in the beta band when stopping is successful. Implications for theories of action control are discussed.

Reduced sensorimotor beta dynamics could represent a "slowed movement state" in healthy individuals.

It is well established that the amplitude of beta oscillations (∼13-30 Hz)-recorded over the sensorimotor cortex-distinctly change throughout movement. Specifically, a movement-related beta decrease (MRBD) occurs before and during movement, and a post-movement beta rebound (PMBR) follows. We investigated how the magnitude of the MRBD and PMBR vary when participants are put in an experimentally induced slow versus fast movement state. Individuals performed a task with blocks that elicited longer reaction times (RTs) and shorter RTs (SLOW and FAST blocks, respectively) while scalp-electroencephalography (EEG) was recorded. The timing of an upcoming movement was also modulated to create blocks with certain and uncertain response timing (FIXED and VARIED blocks, respectively). We found that beta modulation was reduced in SLOW blocks compared to FAST blocks (i.e., a less negative MRBD and less positive PMBR). For the movement certainty manipulation, we saw mixed behavioral and EEG results. Our primary finding of reduced beta modulation during an experimentally induced "slowed movement state" aligns with previous work showing reduced movement-related beta activity in patients with Parkinson's disease.

Chronic Sensing of Subthalamic Local Field Potentials: Comparison of First and Second Generation Implantable Bidirectional Systems Within a Single Subject.

BACKGROUND: Many adaptative deep brain stimulation (DBS) paradigms rely upon the ability to sense neural signatures of specific clinical signs or symptoms in order to modulate therapeutic stimulation. In first-generation bidirectional neurostimulators, the ability to sense neural signals during active stimulation was often limited by artifact. Newer devices, with improved design specifications for sensing, have recently been developed and are now clinically available. OBJECTIVE: To compare the sensing capabilities of the first-generation Medtronic PC + S and second-generation Percept PC neurostimulators within a single patient. METHODS: A 42-year-old man with Parkinson's disease was initially implanted with left STN DBS leads connected to a PC + S implantable pulse generator. Four years later, the PC + S was replaced with the Percept PC. Local field potential (LFP) signals were recorded, both with stimulation OFF and ON, at multiple timepoints with each device and compared. Offline processing of time series data included artifact removal using digital filtering and template subtraction, before subsequent spectral analysis. With Percept PC, embedded processing of spectral power within a narrow frequency band was also utilized. RESULTS: In the absence of stimulation, both devices demonstrated a peak in the beta range (approximately 20 Hz), which was stable throughout the 4-year period. Similar to previous reports, recordings with the PC + S during active stimulation demonstrated significant stimulation artifact, limiting the ability to recover meaningful LFP signal. In contrast, the Percept PC, using the same electrodes and stimulation settings, produced time series data during stimulation with spectral analysis revealing a peak in the beta-band. Online analysis by the Percept demonstrated a reduction in beta-band activity with increasing stimulation amplitude. CONCLUSION: This report highlights recent advances in implantable neurostimulator technology for DBS, demonstrating improvements in sensing capabilities during active stimulation between first- and second-generation devices. The ability to reliably sense during stimulation is an important step toward both the clinical implementation of adaptive algorithms and the further investigation into the neurophysiology underlying movement disorders.

pd-parser: A tool for Matching Photodiode Deflection Events to Time-Stamped Events.

Precise synchronization of events displayed on a monitor to recordings of time series data is critical for applications such as vision or psychophysics research. To achieve this, researchers often use a photodiode to convert the luminance on a monitor over time into a voltage time course, which is what is recorded. pd-parser matches photodiode deflection events to time-stamped events; it is particularly useful when the photodiode signal is corrupted or there is drift between the clock of the computer controlling the monitor and the data acquisition computer clock.

Characteristics of Waveform Shape in Parkinson's Disease Detected with Scalp Electroencephalography.

Neural activity in the ß frequency range (13-30 Hz) is excessively synchronized in Parkinson's disease (PD). Previous work using invasive intracranial recordings and non-invasive scalp electroencephalography (EEG) has shown that correlations between ß phase and broad-band γ (>50 Hz) amplitude [i.e., phase amplitude coupling (PAC)] are elevated in PD, perhaps a reflection of this synchrony. Recently, it has also been shown, in invasive human recordings, that non-sinusoidal features of ß oscillation shape also characterize PD. Here, we show that these features of ß waveform shape also distinguish PD patients on and off medication using non-invasive recordings in a dataset of 15 PD patients with resting scalp EEG. Specifically, ß oscillations over sensorimotor electrodes in PD patients off medication had greater sharpness asymmetry and steepness asymmetry than on medication (sign rank, p < 0.02, corrected). We also showed that ß oscillations over sensorimotor cortex most often had a canonical shape, and that using this prototypical shape as an inclusion criteria increased the effect size of our findings. Together, our findings suggest that novel ways of measuring ß synchrony that incorporate waveform shape could improve detection of PD pathophysiology in non-invasive recordings. Moreover, they motivate the consideration of waveform shape in future EEG studies.

A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task.

Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis.

Chronic deep brain stimulation normalizes scalp EEG activity in isolated dystonia.

OBJECTIVE: To investigate cortical activity using scalp EEG in patients with isolated dystonia treated with chronic deep brain stimulation (DBS), on and off stimulation. METHODS: We analyzed 64-channel scalp EEG in 12 isolated dystonia patients treated with chronic DBS (7 generalized, 5 cervical/segmental; 7 globus pallidus (GP), 5 subthalamic nucleus (STN)), and 20 healthy age-matched controls. Recordings during rest and movement task, and clinical motor scores, were collected with DBS-on and during a 90-min DBS washout. RESULTS: Resting state alpha power in the dominant (or contralateral to more dystonic side) motor cortex channel during DBS was comparable to healthy controls, but it increased when DBS was stopped. Resting state and movement-related alpha coherence between bilateral motor cortex channels was increased off DBS. CONCLUSIONS: Chronic DBS reduces exaggerated alpha oscillations and interhemispheric alpha coherence in the motor cortex of patients with isolated dystonia. SIGNIFICANCE: These findings complement related studies in Parkinson's disease and support the view that network desynchronization is a prominent mechanism of DBS in movement disorders.

Cortical gamma oscillations in isolated dystonia.

We describe a novel electrophysiologic signal from the motor cortex of patients with generalized dystonia - a discrete gamma-band oscillation induced by movement and associated with emergence of dystonia. This was observed using both invasive and non-invasive methods. This phenomenon is similar to the gamma oscillation reported in parkinsonian dyskinesia.

Adaptive deep brain stimulation for Parkinson's disease using motor cortex sensing.

OBJECTIVE: Contemporary deep brain stimulation (DBS) for Parkinson's disease is delivered continuously, and adjustments based on patient's changing symptoms must be made manually by a trained clinician. Patients may be subjected to energy intensive settings at times when they are not needed, possibly resulting in stimulation-induced adverse effects, such as dyskinesia. One solution is 'adaptive' DBS, in which stimulation is modified in real time based on neural signals that co-vary with the severity of motor signs or of stimulation-induced adverse effects. Here we show the feasibility of adaptive DBS using a fully implanted neural prosthesis. APPROACH: We demonstrate adaptive deep brain stimulation in two patients with Parkinson's disease using a fully implanted neural prosthesis that is enabled to utilize brain sensing to control stimulation amplitude (Activa PC + S). We used a cortical narrowband gamma (60-90 Hz) oscillation related to dyskinesia to decrease stimulation voltage when gamma oscillatory activity is high (indicating dyskinesia) and increase stimulation voltage when it is low. MAIN RESULTS: We demonstrate the feasibility of 'adaptive deep brain stimulation' in two patients with Parkinson's disease. In short term in-clinic testing, energy savings were substantial (38%-45%), and therapeutic efficacy was maintained. SIGNIFICANCE: This is the first demonstration of adaptive DBS in Parkinson's disease using a fully implanted device and neural sensing. Our approach is distinct from other strategies utilizing basal ganglia signals for feedback control.