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Background Objective Methods Results Conclusion Abnormal rapid eye movement (REM) sleep, including REM sleep behavior disorder (RBD) and reduced REM sleep, is common in Parkinson's disease (PD), highlighting the importance of further study on REM sleep. However, the biomarkers of REM disturbances remain unknown, leading to the lack of REM‐specific neuromodulation interventions.This study aims to investigate the neurophysiological biomarkers of REM disturbance in parkinsonian patients.Ten PD patients implanted with bilateral subthalamic nucleus‐deep brain stimulation (STN‐DBS) were included in this study, of whom 4 were diagnosed with RBD. Sleep monitoring was conducted 1 month after surgery. Subthalamic local field potentials (LFP) were recorded through sensing‐enabled DBS. The neurophysiological features of subthalamic LFP during phasic and tonic microstates of REM sleep and their correlation with REM sleep fragmentation and RBD were analyzed.Differences in subthalamic γ oscillation between phasic and tonic REM correlated positively with the severity of REM sleep fragmentation. Patients with RBD also exhibited stronger γ oscillations during REM sleep compared with non‐RBD patients, and both increased β and γ were found before the onset of RBD episodes. Stimulation changes in simulated γ‐triggered feedback modulation followed more closely with phasic REM density, whereas an opposite trend was found in simulated β‐triggered feedback modulation.Excess subthalamic γ oscillations may contribute to REM instability and RBD, suggesting that γ oscillation could serve as a feedback signal for adaptive DBS for REM sleep disorders. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Intra- and perioperatively recorded local field potential (LFP) activity of the nucleus subthalamicus (STN) has been suggested to guide contact selection in patients undergoing deep brain stimulation (DBS) for Parkinson's disease (PD). Despite the invention of sensing capacities in chronically implanted devices, a comprehensible algorithm that enables contact selection using such recordings is still lacking. We evaluated a fully automated algorithm that uses the weighted average of bipolar recordings to determine effective monopolar contacts based on elevated activity in the beta band. LFPs from 14 hemispheres in seven PD patients with newly implanted directional DBS leads of the STN were recorded. First, the algorithm determined the stimulation level with the highest beta activity. Based on the prior determined level, the directional contact with the highest beta activity was chosen in the second step. The mean clinical efficacy of the contacts chosen using the algorithm did not statistically differ from the mean clinical efficacy of standard contact selection as performed in clinical routine. All recording sites were projected into MNI standard space to investigate the feasibility of the algorithm with respect to the anatomical boundaries of the STN. We conclude that the proposed algorithm is a first step towards LFP-based contact selection in STN-DBS for PD using chronically implanted devices. [ABSTRACT FROM AUTHOR]

Intra- and perioperatively recorded local field potential (LFP) activity of the nucleus subthalamicus (STN) has been suggested to guide contact selection in patients undergoing deep brain stimulation (DBS) for Parkinson’s disease (PD). Despite the invention of sensing capacities in chronically implanted devices, a comprehensible algorithm that enables contact selection using such recordings is still lacking. We evaluated a fully automated algorithm that uses the weighted average of bipolar recordings to determine effective monopolar contacts based on elevated activity in the beta band. LFPs from 14 hemispheres in seven PD patients with newly implanted directional DBS leads of the STN were recorded. First, the algorithm determined the stimulation level with the highest beta activity. Based on the prior determined level, the directional contact with the highest beta activity was chosen in the second step. The mean clinical efficacy of the contacts chosen using the algorithm did not statistically differ from the mean clinical efficacy of standard contact selection as performed in clinical routine. All recording sites were projected into MNI standard space to investigate the feasibility of the algorithm with respect to the anatomical boundaries of the STN. We conclude that the proposed algorithm is a first step towards LFP-based contact selection in STN-DBS for PD using chronically implanted devices.

Sleep disturbances, specifically decreases in total sleep time and sleep efficiency as well as increased sleep onset latency and wakefulness after sleep onset, are highly prevalent in patients with Parkinson's disease (PD). Impairment of sleep significantly and adversely impacts several comorbidities in this patient population, including cognition, mood, and quality of life. Sleep disturbances and other non-motor symptoms of PD have come to the fore as the effectiveness of advanced therapies such as deep brain stimulation (DBS) optimally manage the motor symptoms. Although some studies have suggested that DBS provides benefit for sleep disturbances in PD, the mechanisms by which this might occur, as well as the optimal stimulation parameters for treating sleep dysfunction, remain unknown. In patients treated with DBS, electrophysiologic recording from the stimulating electrode, in the form of local field potentials (LFPs), has led to the identification of several findings associated with both motor and non-motor symptoms including sleep. For example, beta frequency (13–30 Hz) oscillations are associated with worsened bradykinesia while awake and decrease during non-rapid eye movement sleep. LFP investigation of sleep has largely focused on the subthalamic nucleus (STN), though corresponding oscillatory activity has been found in the globus pallidus internus (GPi) and thalamus as well. LFPs are increasingly being recognized as a potential biomarker for sleep states in PD, which may allow for closed-loop optimization of DBS parameters to treat sleep disturbances in this population. In this review, we discuss the relationship between LFP oscillations in STN and the sleep architecture of PD patients, current trends in utilizing DBS to treat sleep disturbance, and future directions for research. In particular, we highlight the capability of novel technologies to capture and record LFP data in vivo, while patients continue therapeutic stimulation for motor symptoms. These technological advances may soon allow for real-time adaptive stimulation to treat sleep disturbances.

Basal ganglia dysfunction is implicated in movement disorders including Parkinson Disease, dystonia, and choreiform disorders. Contradicting standard “rate models” of basal ganglia-thalamic interactions, internal pallidotomy improves both hypo- and hyper-kinetic movement disorders. This “paradox of stereotaxic surgery” was recognized shortly after rate models were developed, and is underscored by the outcomes of deep brain stimulation (DBS) for movement disorders. Despite strong evidence that DBS activates local axons, the clinical effects of lesions and DBS are nearly identical. These observations argue against standard models in which GABAergic basal ganglia output gates thalamic activity, and raise the question of how lesions and stimulation can have similar effects. These paradoxes may be resolved by considering thalamocortical loops as primary drivers of motor output. Rather than suppressing or releasing cortex via motor thalamus, the basal ganglia may modulate the timing of thalamic perturbations to cortical activity. Motor cortex exhibits rotational dynamics during movement, allowing the same thalamocortical perturbation to affect motor output differently depending on its timing with respect to the rotational cycle. We review classic and recent studies of basal ganglia, thalamic, and cortical physiology to propose a revised model of basal ganglia-thalamocortical function with implications for basic physiology and neuromodulation.

Sleep disturbances, specifically decreases in total sleep time and sleep efficiency as well as increased sleep onset latency and wakefulness after sleep onset, are highly prevalent in patients with Parkinson's disease (PD). Impairment of sleep significantly and adversely impacts several comorbidities in this patient population, including cognition, mood, and quality of life. Sleep disturbances and other non-motor symptoms of PD have come to the fore as the effectiveness of advanced therapies such as deep brain stimulation (DBS) optimally manage the motor symptoms. Although some studies have suggested that DBS provides benefit for sleep disturbances in PD, the mechanisms by which this might occur, as well as the optimal stimulation parameters for treating sleep dysfunction, remain unknown. In patients treated with DBS, electrophysiologic recording from the stimulating electrode, in the form of local field potentials (LFPs), has led to the identification of several findings associated with both motor and non-motor symptoms including sleep. For example, beta frequency (13–30 Hz) oscillations are associated with worsened bradykinesia while awake and decrease during non-rapid eye movement sleep. LFP investigation of sleep has largely focused on the subthalamic nucleus (STN), though corresponding oscillatory activity has been found in the globus pallidus internus (GPi) and thalamus as well. LFPs are increasingly being recognized as a potential biomarker for sleep states in PD, which may allow for closed-loop optimization of DBS parameters to treat sleep disturbances in this population. In this review, we discuss the relationship between LFP oscillations in STN and the sleep architecture of PD patients, current trends in utilizing DBS to treat sleep disturbance, and future directions for research. In particular, we highlight the capability of novel technologies to capture and record LFP data in vivo , while patients continue therapeutic stimulation for motor symptoms. These technological advances may soon allow for real-time adaptive stimulation to treat sleep disturbances. [ABSTRACT FROM AUTHOR]

Basal ganglia dysfunction is implicated in movement disorders including Parkinson Disease, dystonia, and choreiform disorders. Contradicting standard "rate models" of basal ganglia-thalamic interactions, internal pallidotomy improves both hypo- and hyper-kinetic movement disorders. This "paradox of stereotaxic surgery" was recognized shortly after rate models were developed, and is underscored by the outcomes of deep brain stimulation (DBS) for movement disorders. Despite strong evidence that DBS activates local axons, the clinical effects of lesions and DBS are nearly identical. These observations argue against standard models in which GABAergic basal ganglia output gates thalamic activity, and raise the question of how lesions and stimulation can have similar effects. These paradoxes may be resolved by considering thalamocortical loops as primary drivers of motor output. Rather than suppressing or releasing cortex via motor thalamus, the basal ganglia may modulate the timing of thalamic perturbations to cortical activity. Motor cortex exhibits rotational dynamics during movement, allowing the same thalamocortical perturbation to affect motor output differently depending on its timing with respect to the rotational cycle. We review classic and recent studies of basal ganglia, thalamic, and cortical physiology to propose a revised model of basal ganglia-thalamocortical function with implications for basic physiology and neuromodulation. [ABSTRACT FROM AUTHOR]

Deep brain stimulation (DBS) has been clinically useful in the treatment of PD at all stages, especially in those patients with motor symptoms only partly controlled by dopaminergic drugs, such as severe rest tremor or off-period dystonia, and motor fluctuations. Parkinson's disease, brain stimulation, sleep disturbance, local field potential (LFP), transcranial direct current stimulation (tDCS), repetitive transcranial magnetic stimulation (rTMS), deep brain stimulation (DBS) Keywords: Parkinson's disease; brain stimulation; local field potential (LFP); transcranial direct current stimulation (tDCS); repetitive transcranial magnetic stimulation (rTMS); sleep disturbance; deep brain stimulation (DBS) EN Parkinson's disease brain stimulation local field potential (LFP) transcranial direct current stimulation (tDCS) repetitive transcranial magnetic stimulation (rTMS) sleep disturbance deep brain stimulation (DBS) 1 3 3 06/22/22 20220620 NES 220620 Parkinson's disease (PD) is the second most prevalent neurodegenerative disease, substantially impacting quality of life and economic burden. [Extracted from the article]

Subthalamic local field potentials in the beta band are considered as potential biomarkers for closed-loop deep brain stimulation. To investigate the subthalamic beta band peak amplitudes in a Parkinson's disease patient over an extended period of time by using a novel and commercially available neurostimulator with permanent sensing capability. We recorded local field potentials of the subthalamic nucleus using the Medtronic Percept™ implantable neurostimulator at rest and during physical activity (gait) with and in response to deep brain stimulation. We found a double-peaked beta activity on both sides. Increasing stimulation and physical activity resulted in a decreased beta band amplitude, but was accompanied by the appearance of a second, and previously unrecognized peak at 13 Hz in the right hemisphere. Our results will support the investigation of distinct different peaks in the beta band and their relevance and usefulness as closed-loop biomarkers. [ABSTRACT FROM AUTHOR]

• TWNK mutation patients presenting with generalized dystonia who underwent deep brain stimulation are reported. • Local field potential is more closely related to the clinical syndrome than specific disease. • Deep brain stimulation can inhibit the TWNK mutation patients' pathological oscillations. We focused on a rare gene mutation causing dystonia in two siblings who received globus pallidus internus deep brain stimulation (GPi-DBS). The aim was to characterize the relationship between neuronal activity patterns and clinical syndromes. Whole exome sequencing was applied to identify the TWNK (previous symbol C10orf2) mutation; Two siblings with TWNK mutation presented as generalized dystonia with rigidity and bradykinesia; four other sporadic generalized dystonia patients underwent GPi-DBS and local field potentials (LFPs) were recorded. Oscillatory activities were illustrated with power spectra and temporal dynamics measured by the Lempel-Ziv complexity (LZC). Normalized power spectra of GPi LFPs differed between patients with TWNK mutation and dystonia over the low beta bands. Patients with TWNK mutation had higher low beta power (15–27 Hz, unpaired t-test, corrected P < 0.0022) and lower LZC (15–27 Hz, unpaired t-test, P < 0.01) than other patients with generalized dystonia. On the other hand, the TWNK mutation patients showed decreased low frequency and beta oscillation in the GPi after DBS, as well as improved movement performance. The LFPs were different in TWNK mutation dystonia siblings than other patients with generalized dystonia, which indicate the abnormal LFPs were related to symptoms rather than specific disease. In addition, the inhibited effect on oscillations also provided a potential evidence for DBS treatment on rare movement disorders. This study could potentially aid in the future development of adaptive DBS via rare disease LFPs comparison. [ABSTRACT FROM AUTHOR]

Deep brain stimulation (DBS) is an established treatment for patients with Parkinson’s disease (PD). Sleep disorders are common complications of PD and affected by subthalamic DBS treatment. To achieve more precise neuromodulation, chronicsleepmonitoringand closed-loop DBS toward sleep–wake cycles could potentially be utilized. Local field potential (LFP) signals that are sensed by the DBS electrode could be processed as primary feedback signals. This is the first study to systematically investigate the sleep-stage classification based on LFPs in subthalamic nucleus (STN). With our newly developed recording and transmission system, STN-LFPs were collected from 12 PD patients during wakefulness and nocturnal polysomnography sleep monitoring at one month after DBS implantation. Automatic sleep-stage classificationmodels were built with robust and interpretable machine learning methods (support vector machine and decision tree). The accuracy, sensitivity, selectivity, and specificity of the classification reached high values (above90% at most measures) at group and individual levels. Features extracted in alpha (8–13 Hz), beta (13–35 Hz), and gamma (35–50 Hz) bandswere found to contribute the most to the classification. These results will directly guide the engineering development of implantable sleepmonitoring and closed-loopDBS and pave the way for a better understanding of the STN-LFP sleep patterns. [ABSTRACT FROM AUTHOR]

Neuromodulation-based interventions continue to be evaluated across an array of appetitive disorders but broader implementation of these approaches remains limited due to variable treatment outcomes. We hypothesize that individual variation in treatment outcomes may be linked to differences in the networks underlying these disorders. Here, Sprague-Dawley rats received deep brain stimulation separately within each nucleus accumbens (NAc) sub-region (core and shell) using a within-animal crossover design in a rat model of binge eating. Significant reductions in binge size were observed with stimulation of either target but with significant variation in effectiveness across individuals. When features of local field potentials (LFPs) recorded from the NAc were used to classify the pre-defined stimulation outcomes (response or non-response) from each rat using a machine-learning approach (lasso), stimulation outcomes could be classified with greater accuracy than expected by chance (effect sizes: core = 1.13, shell = 1.05). Further, these LFP features could be used to identify the best stimulation target for each animal (core vs. shell) with an effect size = 0.96. These data suggest that individual differences in underlying network activity may relate to the variable outcomes of circuit based interventions, and measures of network activity could have the potential to individually guide the selection of an optimal stimulation target to improve overall treatment response rates.

Neuromodulation-based interventions continue to be evaluated across an array of appetitive disorders but broader implementation of these approaches remains limited due to variable treatment outcomes. We hypothesize that individual variation in treatment outcomes may be linked to differences in the networks underlying these disorders. Here, Sprague-Dawley rats received deep brain stimulation separately within each nucleus accumbens (NAc) sub-region (core and shell) using a within-animal crossover design in a rat model of binge eating. Significant reductions in binge size were observed with stimulation of either target but with significant variation in effectiveness across individuals. When features of local field potentials (LFPs) recorded from the NAc were used to classify the pre-defined stimulation outcomes (response or non-response) from each rat using a machine-learning approach (lasso), stimulation outcomes could be classified with greater accuracy than expected by chance (effect sizes: core = 1.13, shell = 1.05). Further, these LFP features could be used to identify the best stimulation target for each animal (core vs. shell) with an effect size = 0.96. These data suggest that individual differences in underlying network activity may relate to the variable outcomes of circuit based interventions, and measures of network activity could have the potential to individually guide the selection of an optimal stimulation target to improve overall treatment response rates. [ABSTRACT FROM AUTHOR]

This paper presents a signal processing method for the electrophysiology simultaneously recorded during deep brain stimulation (DBS) as a research tool. Regarding the local field potential (LFP) signals recorded during stimulation, a novel method was proposed for removal of stimulation artifacts caused by the much stronger stimulating pulse compared to typical LFP. This artifact suppression method was tested and evaluated in an in vitro situation. The results indicate that the stimulation artifacts are well suppressed by this method. Secondly, this method was tested in vivo in Parkinson’s disease (PD) patients. It was used to process the LFP signals recorded intraoperatively from PD patients to preliminarily explore the quantitative dependencies of beta band synchronization variations in the subthalamic nucleus (STNs) on the applied DBS parameters, including stimulation voltage, frequency and pulse width. The results confirm that DBS therapy can suppress excessive beta frequency activity and that the degree of attenuation increases with increasing DBS voltage within a range of 1–3 V and increasing DBS frequency within a range of 60–120 Hz. The proposed artifact suppression method provides technical support for exploring the direct effect of electrical stimulation on the brain activities. [ABSTRACT FROM PUBLISHER]

Suppression of pathologically altered activity in the beta-band has previously been suggested as a biomarker for feedback-based neurostimulation in subthalamic deep brain stimulation (STN-DBS) for Parkinson's Disease (PD). To assess the utility of beta-band suppression as a tool for contact selection in STN-DBS for PD. A sample of seven PD patients (13 hemispheres) with newly implanted directional DBS leads of the STN were recorded during a standardized monopolar contact review (MPR). Recordings were received from contact pairs adjacent to the stimulation contact. The degree of beta-band suppression for each investigated contact was then correlated to the respective clinical results. Additionally, we have implemented a cumulative ROC analysis, to test the predictive value of beta-band suppression on the clinical efficacy of the respective contacts. Stimulation ramping led to frequency-specific changes in the beta-band, while lower frequencies remained unaffected. Most importantly, our results showed that the degree of low beta-band suppression from baseline activity (stimulation off) served as a predictor for clinical efficacy of the respective stimulation contact. In contrast suppression of high beta-band activity yielded no predictive power. The degree of low beta-band suppression can serve as a time-saving, objective tool for contact selection in STN-DBS. • Stimulation ramping does not affect low frequencies. • Low and high beta-band activity are suppressed with active stimulation. • Only suppression of low beta-band activity serves as a predictor for contact efficacy. [ABSTRACT FROM AUTHOR]

Background Deep Brain Stimulation (DBS) is an effective treatment for several neurological and psychiatric disorders. In order to gain insights into the therapeutic mechanisms of DBS and to advance future therapies a better understanding of the effects of DBS on large-scale brain networks is required. New method In this paper, we describe an experimental protocol and analysis pipeline for simultaneously performing DBS and intracranial local field potential (LFP) recordings at a target brain region during concurrent magnetoencephalography (MEG) measurement. Firstly we describe a phantom setup that allowed us to precisely characterise the MEG artefacts that occurred during DBS at clinical settings. Results Using the phantom recordings we demonstrate that with MEG beamforming it is possible to recover oscillatory activity synchronised to a reference channel, despite the presence of high amplitude artefacts evoked by DBS. Finally, we highlight the applicability of these methods by illustrating in a single patient with Parkinson's disease (PD), that changes in cortical-subthalamic nucleus coupling can be induced by DBS. Comparison with existing approaches To our knowledge this paper provides the first technical description of a recording and analysis pipeline for combining simultaneous cortical recordings using MEG, with intracranial LFP recordings of a target brain nucleus during DBS. [ABSTRACT FROM AUTHOR]

Current strategies for optimizing deep brain stimulation (DBS) therapy involve multiple postoperative visits. During each visit, stimulation parameters are adjusted until desired therapeutic effects are achieved and adverse effects are minimized. However, the efficacy of these therapeutic parameters may decline with time due at least in part to disease progression, interactions between the host environment and the electrode, and lead migration. As such, development of closed-loop control systems that can respond to changing neurochemical environments, tailoring DBS therapy to individual patients, is paramount for improving the therapeutic efficacy of DBS.Evidence obtained using electrophysiology and imaging techniques in both animals and humans suggests that DBS works by modulating neural network activity. Recently, animal studies have shown that stimulation-evoked changes in neurotransmitter release that mirror normal physiology are associated with the therapeutic benefits of DBS. Therefore, to fully understand the neurophysiology of DBS and optimize its efficacy, it may be necessary to look beyond conventional electrophysiological analyses and characterize the neurochemical effects of therapeutic and non-therapeutic stimulation. By combining electrochemical monitoring and mathematical modeling techniques, we can potentially replace the trial-and-error process used in clinical programming with deterministic approaches that help attain optimal and stable neurochemical profiles. In this manuscript, we summarize the current understanding of electrophysiological and electrochemical processing for control of neuromodulation therapies. Additionally, we describe a proof-of-principle closed-loop controller that characterizes DBS-evoked dopamine changes to adjust stimulation parameters in a rodent model of DBS. The work described herein represents the initial steps toward achieving a smart neuroprosthetic system for treatment of neurologic and psychiatric disorders.

Parkinson is a neurodegenerative disease, in which tremor is the main symptom. This paper investigates the use of different classification methods to identify tremors experienced by Parkinsonian patients. Some previous research has focussed tremor analysis on external body signals (e.g., electromyography, accelerometer signals, etc.). Our advantage is that we have access to sub-cortical data, which facilitates the applicability of the obtained results into real medical devices since we are dealing with brain signals directly. Local field potentials (LFP) were recorded in the subthalamic nucleus of 7 Parkinsonian patients through the implanted electrodes of a deep brain stimulation (DBS) device prior to its internalization. Measured LFP signals were preprocessed by means of splinting, down sampling, filtering, normalization and rectification. Then, feature extraction was conducted through a multi-level decomposition via a wavelet transform. Finally, artificial intelligence techniques were applied to feature selection, clustering of tremor types, and tremor detection. The key contribution of this paper is to present initial results which indicate, to a high degree of certainty, that there appear to be two distinct subgroups of patients within the group-1 of patients according to the Consensus Statement of the Movement Disorder Society on Tremor. Such results may well lead to different resultant treatments for the patients involved, depending on how their tremor has been classified. Moreover, we propose a new approach for demand driven stimulation, in which tremor detection is also based on the subtype of tremor the patient has. Applying this knowledge to the tremor detection problem, it can be concluded that the results improve when patient clustering is applied prior to detection. [ABSTRACT FROM AUTHOR]

Subthalamic nucleus (STN) local field potentials (LFP) are neural signals that have been shown to reveal motor and language behavior, as well as pathological parkinsonian states. We use a research-grade implantable neurostimulator (INS) with data collection capabilities to record STN-LFP outside the operating room to determine the reliability of the signals over time and assess their dynamics with respect to behavior and dopaminergic medication. Seven subjects were implanted with the recording augmented deep brain stimulation (DBS) system, and bilateral STN-LFP recordings were collected in the clinic over twelve months. Subjects were cued to perform voluntary motor and language behaviors in on and off medication states. The STN-LFP recorded with the INS demonstrated behavior-modulated desynchronization of beta frequency (13–30 Hz) and synchronization of low gamma frequency (35–70 Hz) oscillations. Dopaminergic medication did not diminish the relative beta frequency oscillatory desynchronization with movement. However, movement-related gamma frequency oscillatory synchronization was only observed in the medication on state. We observed significant inter-subject variability, but observed consistent STN-LFP activity across recording systems and over a one-year period for each subject. These findings demonstrate that an INS system can provide robust STN-LFP recordings in ambulatory patients, allowing for these signals to be recorded in settings that better represent natural environments in which patients are in a variety of medication states.

Abstract: Objective: Oscillatory activity in the beta band is increased in the subthalamic nucleus (STN) of Parkinson's disease (PD) patients. Rigidity and bradykinesia are associated with the low-beta component (13–20Hz) but the neurophysiological correlate of freezing of gait in PD has not been ascertained. Methods: We evaluated the power and coherence of the low- and high-beta bands in the STN and cortex (EEG) of PD patients with (p-FOG) (n=14) or without freezing of gait (n-FOG) (n=8) in whom electrodes for chronic stimulation in the STN had been implanted for treatment with deep brain stimulation. Results: p-FOG patients showed higher power in the high-beta band (F=11.6, p=0.002) that was significantly reduced after l-dopa administration along with suppression of FOG (F=4.6, p=0.042). High-beta cortico-STN coherence was maximal for midline cortical EEG electrodes, whereas the low-beta band was maximal for lateral electrodes (χ 2 =20.60, p<0.0001). Conclusions: The association between freezing of gait, high-beta STN oscillations and cortico-STN coherence suggests that this oscillatory activity might interfere in the frontal cortex–basal ganglia networks, thereby participating in the pathophysiology of FOG in PD. [Copyright &y& Elsevier]

Abstract: Intracerebral recordings of neuronal activity in patients undergoing deep brain stimulation have revealed characteristic movement-related desynchronization at frequencies <30Hz and increased activity in the gamma band (~30–100Hz) in the basal ganglia and thalamus. Thalamic gamma activity is also found during arousal. Here, we explore oscillatory gamma band activity recorded from the ventralis intermedius nucleus of the thalamus during motor performance in a Go/noGo task in 10 patients with essential tremor after implantation of deep brain stimulation electrodes. We show that movement-related gamma activity is lateralized to the nucleus contralateral to the moved side similar to previous findings in the globus pallidus internus and the subthalamic nucleus. The onset of contralateral gamma band synchronization following imperative Go cues is positively correlated with reaction time. Remarkably, baseline levels of gamma activity shortly before the Go cue correlated with the reaction times. Here, faster responses occurred in patients with higher levels of pre-cue gamma activity. Our findings support the role of gamma activity as a physiological prokinetic activity in the motor system. Moreover, we suggest that subtle fluctuations in pre-cue gamma band activity may have an impact on task performance and may index arousal-related states. [Copyright &y& Elsevier]

Abstract: In the normal brain beta band oscillatory activity has been associated with retaining of ongoing motor activities. In Parkinson's disease, enhanced beta band oscillatory activity is displayed across the cortico-basal ganglia pathway and is one of the prominent neurophysiological phenomena associated with the disorder. Intraoperative and postoperative recordings of neural activity in patients undergoing stereotactic neurosurgery combined with studies in animal models of parkinsonism have led to the accumulation of complementary data regarding these oscillations. In this review we address some of the key issues facing researchers in the field. These issues encompass existing agreements as well as open debates in modern studies of beta band oscillations, including their defining characteristics, links to clinical symptoms and the functional properties of their formation and effects on behavior. We address these questions by comparing and contrasting the results of neurophysiological observations in human patients, MPTP primate model and 6-OHDA rat model with conceptual and computational models of the normal and parkinsonian basal ganglia. Defining a unifying scheme of beta band oscillations and their relation to neurophysiological, functional and clinical phenomena will enable future targeting of these oscillations for both diagnosis and treatment of Parkinson's disease. [Copyright &y& Elsevier]

Abstract: Investigations of local field potentials of the subthalamic nucleus of patients with Parkinson’s disease have provided evidence for pathologically exaggerated oscillatory beta-band activity (13–30Hz) which is amenable to physiological modulation by, e.g., voluntary movement. Previous functional magnetic resonance imaging studies in healthy controls have provided evidence for an increase of subthalamic nucleus blood-oxygenation-level-dependant signal in incremental force generation tasks. However, the modulation of neuronal activity by force generation and its relationship to peripheral feedback remain to be elucidated. We hypothesised that beta-band activity in the subthalamic nucleus is modulated by incremental force generation. Subthalamic nucleus local field potentials were recorded intraoperatively in 13 patients with Parkinson’s disease (37 recording sites) during rest and five incremental isometric force generation conditions of the arm with applied loads of 0–400g (in 100-g increments). Repeated measures analysis of variance (ANOVA) revealed a modulation of local field potential (LFP) power in the upper beta-band (in 24–30Hz; F (3.042) =4.693, p =0.036) and the gamma-band (in 70–76Hz; F (4) =4.116, p =0.036). Granger-causality was computed with the squared partial directed coherence and showed no significant modulation during incremental isometric force generation. Our findings indicate that the upper beta- and gamma-band power of subthalamic nucleus local field potentials are modulated by the physiological task of force generation in patients with Parkinson’s disease. This modulation seems to be not an effect of a modulation of peripheral feedback. [Copyright &y& Elsevier]

Background: The consequences of chronic alcohol dependence cause important health and economic burdens worldwide. Relapse rates after standard treatment (medication and psychotherapy) are high. There is evidence from in vivo investigations and from studies in patients that the brain's reward system is critically involved in the development and maintenance of addictive behavior, suggesting that modification of this system could significantly improve the prognosis of addictive patients. Motivated by an accidental observation, we used the nucleus accumbens (NAc), which has a central position in the dopaminergic reward system for deep brain stimulation (DBS) of alcohol addiction. Methods: We report our first experiences with NAc DBS for alcohol dependence and review the literature addressing the mechanisms leading to addiction. Results: Five patients were treated off-label with bilateral NAc DBS for severe alcohol addiction (average follow-up 38 months). All patients experienced significant and ongoing improvement of craving. Two patients remained completely abstinent for more than 4 years. NAc stimulation was tolerated without permanent side effects. Simultaneous recording of local field potentials from the target area and surface electroencephalography while patients performed neuropsychological tasks gave a hint on the pivotal role of the NAc in processing alcohol-related cues. Conclusions: To our knowledge, the data presented here reflect the first attempt to treat alcohol-addicted patients with NAc DBS. Electrical NAc stimulation probably counterbalances the effect of drug-related stimuli triggering involuntarily drug-seeking behavior. Meanwhile, two prospective clinical studies using randomized, double-blind, and crossover stimulation protocols for DBS are underway to corroborate these preliminary results. [Copyright &y& Elsevier]