Adaptive Deep Brain Stimulation Research Articles

Bridging the Gap: Local Field Potentials Offer a Peek Into the Brain of a Person With Parkinson Disease.

Clinically available deep brain recordings in patients with Parkinson disease (PD) offer insights into disease mechanisms and create a pathway for personalized treatment strategies. This case illustrates the transformative potential of recordings of neuronal firing in the form of local field potentials (LFPs) by detailing a patient's clinical trajectory for 6 months after deep brain stimulation (DBS) surgery to treat their PD symptoms. LFPs, obtained easily in clinic with a tablet interface to measure and track brain rhythms across the disease course, enriched the patient's clinical picture. Specifically, strong beta peaks were captured at initial programming, and, as the beta peaks diminished over the course of optimizing settings, symptoms improved. These signals may also reveal insights into the neural dynamics of PD such as hypersynchrony in basal ganglia circuitry. Furthermore, the ability to record chronically may unlock new understanding of neuronal dysfunction in PD, possibly enabling future adaptive DBS. In conclusion, identification, tracking, and modulation of LFPs correlated with subjective and objective clinical improvement in the case presented. The use of neurophysiologic signals in the future may lead to therapeutic innovations for our patients with PD.

Auditory cues modulate the short timescale dynamics of STN activity during stepping in Parkinson's disease

BackgroundGait impairment has a major impact on quality of life in patients with Parkinson's disease (PD). It is believed that basal ganglia oscillatory activity at β frequencies (15–30 Hz) may contribute to gait impairment, but the precise dynamics of this oscillatory activity during gait remain unclear. Additionally, auditory cues are known to lead to improvements in gait kinematics in PD. If the neurophysiological mechanisms of this cueing effect were better understood they could be leveraged to treat gait impairments using adaptive Deep Brain Stimulation (aDBS) technologies. ObjectiveWe aimed to characterize the dynamics of subthalamic nucleus (STN) oscillatory activity during stepping movements in PD and to establish the neurophysiological mechanisms by which auditory cues modulate gait. MethodsWe studied STN local field potentials (LFPs) in eight PD patients while they performed stepping movements. Hidden Markov Models (HMMs) were used to discover transient states of spectral activity that occurred during stepping with and without auditory cues. ResultsThe occurrence of low and high β bursts was suppressed during and after auditory cues. This manifested as a decrease in their fractional occupancy and state lifetimes. Interestingly, α transients showed the opposite effect, with fractional occupancy and state lifetimes increasing during and after auditory cues. ConclusionsWe show that STN oscillatory activity in the α and β frequency bands are differentially modulated by gait-promoting oscillatory cues. These findings suggest that the enhancement of α rhythms may be an approach for ameliorating gait impairments in PD.

Quantifying local field potential dynamics with amplitude and frequency stability between ON and OFF medication and stimulation in Parkinson's disease

Neural oscillations are critical to understanding the synchronisation of neural activities and their relevance to neurological disorders. For instance, the amplitude of beta oscillations in the subthalamic nucleus has gained extensive attention, as it has been found to correlate with medication status and the therapeutic effects of continuous deep brain stimulation in people with Parkinson's disease. However, the frequency stability of subthalamic nucleus beta oscillations, which has been suggested to be associated with dopaminergic information in brain states, has not been well explored. Moreover, the administration of medicine can have inverse effects on changes in frequency and amplitude. In this study, we proposed a method based on the stationary wavelet transform to quantify the amplitude and frequency stability of subthalamic nucleus beta oscillations and evaluated the method using simulation and real data for Parkinson's disease patients. The results suggest that the amplitude and frequency stability quantification has enhanced sensitivity in distinguishing pathological conditions in Parkinson's disease patients. Our quantification shows the benefit of combining frequency stability information with amplitude and provides a new potential feedback signal for adaptive deep brain stimulation.

Local field potentials: Therapeutic implications for DBS in dystonia including adaptive DBS for dystonia

Local field potentials (LFPs) are signals generated primarily by the result of the synchronous discharge of post-synaptic potentials. Detection of LFPs from intracranial electrodes sited within the basal ganglia has led to the recognition that there are specific features of the LFP that could serve as neurophysiological biomarkers of different disease states. For example, in Parkinson’s disease peaks in the beta frequency range of the power spectrum are known to correlate with the degree of bradykinesia and rigidity, whereas low frequency, alpha-theta peaks are increasingly considered a biomarker of dystonic activity. The introduction of newer deep brain stimulation (DBS) devices capable of simultaneous stimulation and LFP detection creates opportunities to investigate new strategies for treating patients with dystonia. This review aims to summarise the LFP findings seen in patients with dystonia undergoing DBS with a particular focus on the potential implications for future clinical practice and the development of adaptive DBS systems.

Multi-night cortico-basal recordings reveal mechanisms of NREM slow-wave suppression and spontaneous awakenings in Parkinson's disease.

Sleep disturbance is a prevalent and disabling comorbidity in Parkinson's disease (PD). We performed multi-night (n = 57) at-home intracranial recordings from electrocorticography and subcortical electrodes using sensing-enabled Deep Brain Stimulation (DBS), paired with portable polysomnography in four PD participants and one with cervical dystonia (clinical trial: NCT03582891). Cortico-basal activity in delta increased and in beta decreased during NREM (N2 + N3) versus wakefulness in PD. DBS caused further elevation in cortical delta and decrease in alpha and low-beta compared to DBS OFF state. Our primary outcome demonstrated an inverse interaction between subcortical beta and cortical slow-wave during NREM. Our secondary outcome revealed subcortical beta increases prior to spontaneous awakenings in PD. We classified NREM vs. wakefulness with high accuracy in both traditional (30 s: 92.6 ± 1.7%) and rapid (5 s: 88.3 ± 2.1%) data epochs of intracranial signals. Our findings elucidate sleep neurophysiology and impacts of DBS on sleep in PD informing adaptive DBS for sleep dysfunction.

Coupling between beta band and high frequency oscillations as a clinically useful biomarker for DBS

Beta hypersynchrony was recently introduced into clinical practice in Parkinson’s disease (PD) to identify the best stimulation contacts and for adaptive deep brain stimulation (aDBS) sensing. However, many other oscillopathies accompany the disease, and beta power sensing may not be optimal for all patients. The aim of this work was to study the potential clinical usefulness of beta power phase-amplitude coupling (PAC) with high frequency oscillations (HFOs). Subthalamic nucleus (STN) local field potentials (LFPs) from externalized DBS electrodes were recorded and analyzed in PD patients (n = 19). Beta power and HFOs were evaluated in a resting-state condition; PAC was then studied and compared with the electrode contact positions, structural connectivity, and medication state. Beta-HFO PAC (mainly in the 200–500 Hz range) was observed in all subjects. PAC was detectable more specifically in the motor part of the STN compared to beta power and HFOs. Moreover, the presence of PAC better corresponds to the stimulation setup based on the clinical effect. PAC is also sensitive to the laterality of symptoms and dopaminergic therapy, where the greater PAC cluster reflects the more affected side and medication “off” state. Coupling between beta power and HFOs is known to be a correlate of the PD “off” state. Beta-HFO PAC seems to be more sensitive than beta power itself and could be more helpful in the selection of the best clinical stimulation contact and probably also as a potential future input signal for aDBS.

Proceedings of the 11th Annual Deep Brain Stimulation Think Tank: pushing the forefront of neuromodulation with functional network mapping, biomarkers for adaptive DBS, bioethical dilemmas, AI-guided neuromodulation, and translational advancements.

The Deep Brain Stimulation (DBS) Think Tank XI was held on August 9-11, 2023 in Gainesville, Florida with the theme of "Pushing the Forefront of Neuromodulation". The keynote speaker was Dr. Nico Dosenbach from Washington University in St. Louis, Missouri. He presented his research recently published in Nature inn a collaboration with Dr. Evan Gordon to identify and characterize the somato-cognitive action network (SCAN), which has redefined the motor homunculus and has led to new hypotheses about the integrative networks underpinning therapeutic DBS. The DBS Think Tank was founded in 2012 and provides an open platform where clinicians, engineers, and researchers (from industry and academia) can freely discuss current and emerging DBS technologies, as well as logistical and ethical issues facing the field. The group estimated that globally more than 263,000 DBS devices have been implanted for neurological and neuropsychiatric disorders. This year's meeting was focused on advances in the following areas: cutting-edge translational neuromodulation, cutting-edge physiology, advances in neuromodulation from Europe and Asia, neuroethical dilemmas, artificial intelligence and computational modeling, time scales in DBS for mood disorders, and advances in future neuromodulation devices.

Improving naturalistic neuroscience with patient engagement strategies.

The clinical implementation of chronic electrophysiology-driven adaptive deep brain stimulation (DBS) algorithms in movement disorders requires reliable representation of motor and non-motor symptoms in electrophysiological biomarkers, throughout normal life (naturalistic). To achieve this, there is the need for high-resolution and -quality chronic objective and subjective symptom monitoring in parallel to biomarker recordings. To realize these recordings, an active participation and engagement of the investigated patients is necessary. To date, there has been little research into patient engagement strategies for DBS patients or chronic electrophysiological recordings. We here present our concept and the first results of a patient engagement strategy for a chronic DBS study. After discussing the current state of literature, we present objectives, methodology and consequences of the patient engagement regarding study design, data acquisition, and study infrastructure. Nine patients with Parkinson's disease and their caregivers participated in the meeting, and their input led to changes to our study design. Especially, the patient input helped us designing study-set-up meetings and support structures. We believe that patient engagement increases compliance and study motivation through scientific empowerment of patients. While considering patient opinion on sensors or questionnaire questions may lead to more precise and reliable data acquisition, there was also a high demand for study support and engagement structures. Hence, we recommend the implementation of patient engagement in planning of chronic studies with complex designs, long recording durations or high demand for individual active study participation.

Single threshold adaptive deep brain stimulation in Parkinson's disease depends on parameter selection, movement state and controllability of subthalamic beta activity

BackgroundDeep brain stimulation (DBS) is an invasive treatment option for patients with Parkinson's disease. Recently, adaptive DBS (aDBS) systems have been developed, which adjust stimulation timing and amplitude in real-time. However, it is unknown how changes in parameters, movement states and the controllability of subthalamic beta activity affect aDBS performance. ObjectiveTo characterize how parameter choice, movement state and controllability interactively affect the electrophysiological and behavioral response to single threshold aDBS. MethodsWe recorded subthalamic local field potentials in 12 patients with Parkinson's disease receiving single threshold aDBS in the acute post-operative state. We investigated changes in two aDBS parameters: the onset time and the smoothing of real-time beta power. Electrophysiological patterns and motor performance were assessed while patients were at rest and during a simple motor task. We further studied the impact of controllability on aDBS performance by comparing patients with and without beta power modulation during continuous stimulation. ResultsOur findings reveal that changes in the onset time control the extent of beta power suppression achievable with single threshold adaptive stimulation during rest. Behavioral data indicate that only specific parameter combinations yield a beneficial effect of single threshold aDBS. During movement, action induced beta power suppression reduces the responsivity of the closed loop algorithm. We further demonstrate that controllability of beta power is a prerequisite for effective parameter dependent modulation of subthalamic beta activity. ConclusionOur results highlight the interaction between single threshold aDBS parameter selection, movement state and controllability in driving subthalamic beta activity and motor performance. By this means, we identify directions for the further development of closed-loop DBS algorithms.

A localized pallidal physiomarker in Meige syndrome.

Oscillatory patterns in local field potentials (LFPs) have been recognized as disease-specific physiomarkers, particularly in the context of Parkinson's disease and cervical dystonia. This characteristic oscillatory feature is currently employed in adaptive deep brain stimulation (aDBS). However, for other types of dystonia, especially Meige syndrome, a distinct physiomarker of this nature is yet to be identified. Local field potentials were recorded during microelectrode-guided deep brain stimulation surgery from 28 patients with primary Meige syndrome. Before surgery, the severity of patients' motor syndrome were assessed using the Burke-Fahn-Marsden Dystonia Rating Scale-Motor (BFMDRS-M). An instantaneous oscillation detection method was employed to identify true narrowband oscillations. Subsequently, a linear mixed effects model was utilized to examine the relationship between oscillatory activities (including power amplitude and burst duration) and symptom severity. The focal peaks of "oscillatory activities" detected were predominantly concentrated in the narrow theta band (4-8 Hz), constituting 81.5% of the total detected oscillations in all recording sites near active DBS contacts in the globus pallidus internus (GPi). The linear mixed effects model revealed a positive correlation between the theta burst duration and the severity of preoperative motor impairment, but no correlation with postoperative motor scores. Additionally, there was no significant lateralization effect observed between the left and right GPi. Our findings suggest that the exaggerated narrowband theta activity (mainly the burst duration) in the GPi is predictive of dystonia symptom severity and may be used as a physiomarker for optimized DBS target during surgery and adaptive DBS for the treatment of Meige syndrome.

At home adaptive dual target deep brain stimulation in Parkinson's disease with proportional control.

Continuous deep brain stimulation (cDBS) of the subthalamic nucleus (STN) or globus pallidus is an effective treatment for the motor symptoms of Parkinson's disease. The relative benefit of one region over the other is of great interest but cannot usually be compared in the same patient. Simultaneous DBS of both regions may synergistically increase the therapeutic benefit. Continuous DBS is limited by a lack of responsiveness to dynamic, fluctuating symptoms intrinsic to the disease. Adaptive DBS (aDBS) adjusts stimulation in response to biomarkers to improve efficacy, side effects, and efficiency. We combined bilateral DBS of both STN and globus pallidus (dual target DBS) in a prospective within-participant, clinical trial in six patients with Parkinson's disease (n = 6, 55-65 years, n = 2 females). Dual target cDBS was tested for Parkinson's disease symptom control annually over 2 years, measured by motor rating scales, on time without dyskinesia, and medication reduction. Random amplitude experiments probed system dynamics to estimate parameters for aDBS. We then implemented proportional-plus-integral aDBS using a novel distributed (off-implant) architecture. In the home setting, we collected tremor and dyskinesia scores as well as individualized β and DBS amplitudes. Dual target cDBS reduced motor symptoms as measured by Unified Parkinson's Disease Rating Scale (UPDRS) to a greater degree than either region alone (P < 0.05, linear mixed model) in the cohort. The amplitude of β-oscillations in the STN correlated to the speed of hand grasp movements for five of six participants (P < 0.05, Pearson correlation). Random amplitude experiments provided insight into temporal windowing to avoid stimulation artefacts and demonstrated a correlation between STN β amplitude and DBS amplitude. Proportional plus integral control of aDBS reduced average power, while preserving UPDRS III scores in the clinic (P = 0.28, Wilcoxon signed rank), and tremor and dyskinesia scores during blinded testing at home (n = 3, P > 0.05, Wilcoxon ranked sum). In the home setting, DBS power reductions were slight but significant. Dual target cDBS may offer an improvement in treatment of motor symptoms of Parkinson's disease over DBS of either the STN or globus pallidus alone. When combined with proportional plus integral aDBS, stimulation power may be reduced, while preserving the increased benefit of dual target DBS.

Towards automated sleep-stage classification for adaptive deep brain stimulation targeting sleep in patients with Parkinson’s disease

Sleep dysfunction affects over 90% of Parkinson’s disease patients. Recently, subthalamic nucleus deep brain stimulation has shown promise for alleviating sleep dysfunction. We previously showed that a single-layer neural network could classify sleep stages from local field potential recordings in Parkinson’s disease patients. However, it was unable to categorise non-rapid eye movement into its different sub-stages. Here we employ a larger hidden layer network architecture to distinguish the substages of non-rapid eye movement with reasonable accuracy, up to 88% for the lightest substage and 92% for deeper substages. Using Shapley attribution analysis on local field potential frequency bands, we show that low gamma and high beta are more important to model decisions than other frequency bands. These results suggest that the proposed neural network-based classifier can be employed for deep brain stimulation treatment in commercially available devices with lower local field potential sampling frequencies.

Oscillatory vs. non-oscillatory subthalamic beta activity in Parkinson's disease.

Parkinson's disease is characterized by exaggerated beta activity (13-35Hz) in cortico-basal ganglia motor loops. Beta activity includes both periodic fluctuations (i.e. oscillatory activity) and aperiodic fluctuations reflecting spiking activity and excitation/inhibition balance (i.e. non-oscillatory activity). However, the relative contribution, dopamine dependency and clinical correlations of oscillatory vs. non-oscillatory beta activity remain unclear. We recorded, modelled and analysed subthalamic local field potentials in parkinsonian patients at rest while off or on medication. Autoregressive modelling with additive 1/f noise clarified the relationships between measures of beta activity in the time domain (i.e. amplitude and duration of beta bursts) or in the frequency domain (i.e. power and sharpness of the spectral peak) and oscillatory vs. non-oscillatory activity: burst duration and spectral sharpness are specifically sensitive to oscillatory activity, whereas burst amplitude and spectral power are ambiguously sensitive to both oscillatory and non-oscillatory activity. Our experimental data confirmed the model predictions and assumptions. We subsequently analysed the effect of levodopa, obtaining strong-to-extreme Bayesian evidence that oscillatory beta activity is reduced in patients on vs. off medication, with moderate evidence for absence of modulation of the non-oscillatory component. Finally, specifically the oscillatory component of beta activity correlated with the rate of motor progression of the disease. Methodologically, these results provide an integrative understanding of beta-based biomarkers relevant for adaptive deep brain stimulation. Biologically, they suggest that primarily the oscillatory component of subthalamic beta activity is dopamine dependent and may play a role not only in the pathophysiology but also in the progression of Parkinson's disease. KEY POINTS: Betaactivity in Parkinson's disease includes both true periodic fluctuations (i.e. oscillatory activity) and aperiodic fluctuationsreflecting spiking activity and synaptic balance (i.e. non-oscillatory activity). Therelative contribution, dopamine dependency and clinical correlations of oscillatory vs.non-oscillatory beta activity remain unclear. Burstduration and spectral sharpness are specifically sensitive to oscillatoryactivity, while burst amplitude and spectral power are ambiguously sensitive toboth oscillatory and non-oscillatory activity. Only theoscillatory component of subthalamic beta activity is dopamine-dependent. Stronger beta oscillatory activity correlates with faster motor progression of the disease.

Unraveling the complexities of programming neural adaptive deep brain stimulation in Parkinson's disease.

Over the past three decades, deep brain stimulation (DBS) for Parkinson's disease (PD) has been applied in a continuous open loop fashion, unresponsive to changes in a given patient's state or symptoms over the course of a day. Advances in recent neurostimulator technology enable the possibility for closed loop adaptive DBS (aDBS) for PD as a treatment option in the near future in which stimulation adjusts in a demand-based manner. Although aDBS offers great clinical potential for treatment of motor symptoms, it also brings with it the need for better understanding how to implement it in order to maximize its benefits. In this perspective, we outline considerations for programing several key parameters for aDBS based on our experience across several aDBS-capable research neurostimulators. At its core, aDBS hinges on successful identification of relevant biomarkers that can be measured reliably in real-time working in cohesion with a control policy that governs stimulation adaption. However, auxiliary parameters such as the window in which stimulation is allowed to adapt, as well as the rate it changes, can be just as impactful on performance and vary depending on the control policy and patient. A standardize protocol for programming aDBS will be crucial to ensuring its effective application in clinical practice.

Open Hardware Implementation of Real-Time Phase and Amplitude Estimation for Neurophysiologic Signals.

Adaptive deep brain stimulation (aDBS) is a promising concept in the field of DBS that consists of delivering electrical stimulation in response to specific events. Dynamic adaptivity arises when stimulation targets dynamically changing states, which often calls for a reliable and fast causal estimation of the phase and amplitude of the signals. Here, we present an open-hardware implementation that exploits the concepts of resonators and Hilbert filters embedded in an open-hardware platform. To emulate real-world scenarios, we built a hardware setup that included a system to replay and process different types of physiological signals and test the accuracy of the instantaneous phase and amplitude estimates. The results show that the system can provide a precise and reliable estimation of the phase even in the challenging scenario of dealing with high-frequency oscillations (~250 Hz) in real-time. The framework might be adopted in neuromodulation studies to quickly test biomarkers in clinical and preclinical settings, supporting the advancement of aDBS.

Parkinsonian rest tremor can be distinguished from voluntary hand movements based on subthalamic and cortical activity

ObjectiveTo distinguish Parkinsonian rest tremor and different voluntary hand movements by analyzing brain activity. MethodsWe re-analyzed magnetoencephalography and local field potential recordings from the subthalamic nucleus of six patients with Parkinson’s disease. Data were obtained after withdrawal from dopaminergic medication (Med Off) and after administration of levodopa (Med On). Using gradient-boosted tree learning, we classified epochs as tremor, fist-clenching, forearm extension or tremor-free rest. ResultsSubthalamic activity alone was insufficient for distinguishing the four different motor states (balanced accuracy mean: 38%, std: 7%). The combination of cortical and subthalamic features, in contrast, allowed for a much more accurate classification (balanced accuracy mean: 75%, std: 17%). Adding a single cortical area improved balanced accuracy by 17% on average, as compared to classification based on subthalamic activity alone. In most patients, the most informative cortical areas were sensorimotor cortical regions. Decoding performance was similar in Med On and Med Off. ConclusionsElectrophysiological recordings allow for distinguishing several motor states, provided that cortical signals are monitored in addition to subthalamic activity. SignificanceBy combining cortical recordings, subcortical recordings and machine learning, adaptive deep brain stimulation systems might be able to detect tremor specifically and to respond adequately to several motor states.

Deep brain stimulation from past to future: research trends and global outcomes with bibliometric analysis during 1980-2022

Aims: We aimed to summarize the intellectual structure of the deep brain stimulation (DBS), to reveal the global productivity, to identify and map the latest trends by analysing the social and structural relationships between the different research components of scientific articles published on DBS.&#x0D; Methods: 5939 articles on DBS published during 1980 and 2022 were analysed utilized various statistical approaches. Network visualization maps were created to reveal trend topics, citation analysis, and international collaborations. Spearman's correlation analysis was used for correlation investigations. The exponential smoothing predictor was used to determine the article productivity trend.&#x0D; Results: The most prolific author on DBS was Okun, Michael S. (209 articles) and the most productive institution was the University of Toronto (n=283). The top 3 productive countries were United States of America (n=2371, 39.9%), Germany (910, 15.3%), and United Kingdom (550, 9.2%). From past to present, the most studied topics were Parkinson's disease, subthalamic nucleus DBS, dystonia, globus pallidus, essential tremor, movement disorders, thalamus, functional neurosurgery, neuromodulation, depression, obsessive compulsive disorder, basal ganglia.&#x0D; Conclusion: The primary trend topics that have been studied more in recent years are tractography, freezing of gait, Parkinson’s disease, Parkinson&amp;#8217, Parkinson#apos, autonomy, self, machine learning, non-motor symptoms, functional connectivity, globus pallidus interna, volume of tissue activated, adaptive deep brain stimulation, beta oscillations, medial forebrain bundle, and local field potential. The secondery trend topics were optogenetics, pediatric, frameless, closed-loop DBS, refractory epilepsy, satellite broadcasting, asleep DBS, optimization, biomarker, directional Leeds, nucleus bassals of meynert, personality, authenticity, and anterior nucleus of thalamus.

Local field potential and clinical symptoms of Parkinson’s disease patients implanted with adaptive deep brain stimulation

Local field potential and clinical symptoms of Parkinson’s disease patients implanted with adaptive deep brain stimulation

Adaptive Deep Brain Stimulation for sleep stage targeting in Parkinson’s disease

BackgroundSleep dysfunction is disabling in people with Parkinson’s disease and is linked to worse motor and non-motor outcomes. Sleep-specific adaptive Deep Brain Stimulation has the potential to target pathophysiologies of sleep. ObjectiveDevelop an adaptive Deep Brain Stimulation algorithm that modulates stimulation parameters in response to intracranially classified sleep stages. MethodsWe performed at-home, multi-night intracranial electrocorticography and polysomnogram recordings to train personalized linear classifiers for discriminating the N3 NREM sleep stage. Classifiers were embedded into investigational Deep Brain Stimulators for N3 specific adaptive DBS. ResultsWe report high specificity of embedded, autonomous, intracranial electrocorticography N3 sleep stage classification across two participants and provide proof-of-principle of successful sleep stage specific adaptive Deep Brain Stimulation. ConclusionMulti-night cortico-basal recordings and sleep specific adaptive Deep Brain Stimulation provide an experimental framework to investigate sleep pathophysiology and mechanistic interactions with stimulation, towards the development of therapeutic neurostimulation paradigms directly targeting sleep dysfunction.

Task-Related Reorganization of Cognitive Network in Parkinson's Disease Using Electrophysiology.

Cognitive deficits in Parkinson's disease (PD) patients are well described, however, their underlying neural mechanisms as assessed by electrophysiology are not clear. To reveal specific neural network alterations during the performance of cognitive tasks in PD patients using electroencephalography (EEG). Ninety participants, 60 PD patients and 30 controls underwent EEG recording while performing a GO/NOGO task. Source localization of 16 regions of interest known to play a pivotal role in GO/NOGO task was performed to assess power density and connectivity within this cognitive network. The connectivity matrices were evaluated using a graph-theory approach that included measures of cluster-coefficient, degree, and global-efficiency. A mixed-model analysis, corrected for age and levodopa equivalent daily dose was performed to examine neural changes between PD patients and controls. PD patients performed worse in the GO/NOGO task (P < 0.001). The power density was higher in δ and θ bands, but lower in α and β bands in PD patients compared to controls (interaction group × band: P < 0.001), indicating a general slowness within the network. Patients had more connections within the network (P < 0.034) than controls and these were used for graph-theory analysis. Differences between groups in graph-theory measures were found only in cluster-coefficient, which was higher in PD compared to controls (interaction group × band: P < 0.001). Cognitive deficits in PD are underlined by alterations at the brain network level, including higher δ and θ activity, lower α and β activity, increased connectivity, and segregated network organization. These findings may have important implications on future adaptive deep brain stimulation. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.