Biology subjects

Neumann, W.-J.

Publications and source records attributed to Neumann, W.-J..

At least 19 recordsLinked to original sources

bioRxiv2026

Device-embedded accelerometry complements neural signals for tracking parkinsonian motor states

Adaptive deep brain stimulation (aDBS) relies on physiological biomarkers to infer motor state and guide therapeutic stimulation in Parkinsons disease. However, neural biomarkers may themselves be altered by stimulation, potentially limiting their utility for closed-loop control. We address this limitation by testing whether DBS device-embedded accelerometers can accurately track Parkinsonian motor state across stimulation conditions. We analysed over 1,900 hours of chronic recordings of subthalamic nucleus (STN), sensorimotor cortical and device-embedded accelerometry signals acquired before and during continuous STN stimulation, alongside continuous wearable assessments of bradykinesia and dyskinesia. Across stimulation conditions, accelerometry-derived features robustly tracked motor symptom severity and outperformed neural features for symptom decoding. Mechanistically, total STN beta power - a widely used biomarker for aDBS - proved less informative because it conflates periodic and aperiodic neural processes with opposing relationships to motor state. Under active stimulation, periodic beta activity showed reduced coupling to symptom severity, whereas STN aperiodic activity, cortical periodic activity and cortico-subthalamic coherence remained comparatively stable. Together, these findings demonstrate that neural and behavioural biomarkers exhibit differential robustness during deep brain stimulation and identify device-embedded accelerometry as a robust behavioural biomarker of motor state, motivating its use in next-generation adaptive DBS systems.

bioengineering↗

bioRxiv2026

A cardiac pulse signal affects local field potentials recorded from deep brain stimulation electrodes across clinical targets

ObjectiveMany deep brain stimulation (DBS) systems sense local field potentials (LFPs) for patient monitoring or closed-loop therapy (CL-DBS). LFPs can be impacted by artifacts, including a recently discovered cardiac non-electrocardiographic pulse signal that can be visually masked by commercial device filters. We aimed to establish its prevalence across patient groups and brain areas, and to investigate its spectral impact. MethodsWe performed a cross-sectional analysis of LFPs recorded from the cranially mounted Picostim from the pedunculopontine nucleus in multiple systems atrophy patients, periacqueductal gray and sensory thalamus in chronic pain patients, and the centromedian thalamic nucleus (CMT) in paediatric epilepsy patients. For comparison, we analyse externalised recordings from the subthalamic nucleus in Parkinsons disease patients. The PulsAr algorithm was developed to detect and extract pulsatile signals, and we characterised contamination level and spectral content. ResultsThough not visually obvious in CMT, the pulsatile signal was algorithmically detected in all targets, with 33% of LFPs across targets classed as contaminated. Pulse signal power was similar across targets and may have been masked by higher endogenous activity in CMT. While its dominant frequencies were in the heartbeat range, the signal had spectral content extending up to >10Hz. ConclusionsA heart pulse signal affects LFP recordings from DBS leads across brain regions and patient groups. While masked by some device filters, spectral content can extend into higher (clinically relevant) frequencies. Researchers and clinicians should exercise caution when sensing lower LFP frequencies, especially for automated control of therapy in CL-DBS. HighlightsO_LIHistorically, electrocardiographic artifacts have been a major source of artifact affecting deep brain stimulation recordings, however pulsatile artifact is less well described. C_LIO_LIA cardiac pulse signal affects local field potentials recorded from deep brain stimulation electrodes across clinical targets. C_LIO_LIWe introduce an ECG-independent algorithm that detects and extracts this pulsatile signal. C_LIO_LIThe heart pulse signal looks like the intracranial pressure waveform and affects spectral frequencies above the heart rate range up to >10Hz. C_LIO_LIClinicians should incorporate screening and procedures to ensure accurate biomarker detection for clinical decision making. C_LI

bioengineering↗

bioRxiv2025

Change in motor state equilibrium explains prokinetic effect of apomorphine on locomotion in experimental Parkinsonism

Gait impairments remain a major therapeutic challenge in Parkinsons disease (PD). Apomorphine is gaining renewed clinical attention with the expanding use of pump infusion systems. Yet, the specific role of apomorphine on the neural regulation of gait has remained poorly characterized, limiting its targeted use for symptom-specific therapy in PD. Here, we examined the neurobehavioral effects of apomorphine on runway locomotion in the unilateral 6-hydroxydopamine (6-OHDA) rat model. Therapeutic drug doses significantly increased total walking distance, related to reduced akinesia and prolonged gait episodes. Conversely, 3D kinematic analysis revealed reduced limb velocities under medication. At the neural level, therapy doses selectively enhanced cortical high-gamma rhythms without substantially altering beta or low-gamma activity. Instead, beta and low-gamma oscillations were consistently suppressed during motor activity in both medication ON and OFF conditions. Neurobehavioral correlations showed that transitions into gait were facilitated by reductions in beta and low-gamma activity, whereas transitions to akinesia were primarily suppressed when high-gamma activity was elevated. Our findings suggest that modulating cortical activity can aid ameliorating gait deficits in PD. We further propose that the complex therapy effects of apomorphine are best explained by a shift in motor-state equilibrium that is defined by the transitions of akinesia, stationary movements and gait. Together, these insights establish a mechanistic framework to guide the development of targeted gait therapies in PD.

neuroscience↗

bioRxiv2025

Dynamics of cortico-subthalamic neuronal patterns during dyskinesia in Parkinsons disease

Parkinson patients suffer from levodopa-induced dyskinesia, which occur adversely to chronic dopaminergic treatment. These abnormal involuntary movements can only partly be actively suppressed and affect quality of life. A lowered motor inhibition during hyperdopaminergic states, associated with structural and plasticity changes in the cortico-basal-ganglia motor network, is hypothesized to enable dyskinesia. Oscillatory cortico-subthalamic patterns associated with dyskinesia are proposed as adaptive neuromodulation biomarkers, but their dependence on behavioral states such as dyskinetic movement presence or suppression remains unknown. We studied cortico-subthalamic oscillations in 22 Parkinsons patients during dyskinesia-evoking protocols. We clinically differentiated between non-dyskinetic and dyskinetic periods, and defined movement presence with kinematics, leading to four behavioral states containing rest, voluntary movements, movement suppression during dyskinesia, and dyskinetic movements. Elevated subthalamic theta-activity and attenuated beta-activity was found during both dyskinetic movement suppression and execution, while cortico-subthalamic gamma-activity only increased during dyskinetic movement execution. Subthalamic spectral changes significantly predicted dyskinesia presence, and movement presence significantly affected the predictive performance. A movement-aware classifier enhanced dyskinesia detection based on movement-depending biomarkers containing cortical oscillations and gamma-bands. We propose movement execution during dyskinesia to be a distinct behavioral and neural microstate within a dopamine-depending dyskinetic macrostate, that can enhance dyskinesia classification for adaptive neuromodulation.

neuroscience↗

bioRxiv2025

Uncovering the Neural Fingerprint of Akinetic States in a Parkinsons Disease Rodent Model

Gait deficits present an unresolved therapeutic challenge in Parkinsons Disease. At the behavioral level, symptoms exhibit heterogeneity, including bradykinesia and hypokinesia during cyclical limb movement, as well as sudden interruptions in the gait sequence, also known as freezing of gait. The neural activities that drive these various deficits remain largely unknown. Here, we investigated the neural correlates of gait sequence interruptions with deep neurobehavioral phenotyping. For this, we transformed kinematic trajectories and cortical oscillations into continuous time series of multimodal feature vectors. Next, we applied machine learning, combining low-dimensional embedding with supervised classification, to identify cortical oscillation features that drive gait deficits. In a rodent Parkinsons disease model, our approach revealed that gait, akinesia, and stationary movements occupy prominently different regions in the low-dimensional embedding space. Among the predominant features separating the states, we found Hjorth complexity and mobility to modulate with the onset of akinetic episodes. Additionally, we validated our analysis approach in two Parkinson patients with freezing of gait, where neural features in STN recordings partially reflected the findings from ECoG measurements in rodents. The presented neurobehavioral phenotyping approach is translational and can easily generalize to the analysis of other complex movement disorders. Together, our results highlight specific features of neural oscillations as potential biomarkers that may support the development of adaptive closed-loop algorithms for gait therapy in PD.

neuroscience↗

bioRxiv2025

Differential modulation of movement speed with state-dependent deep brain stimulation in Parkinson's disease

Subthalamic deep brain stimulation (STN-DBS) provides unprecedented spatiotemporal precision for the treatment of Parkinsons disease (PD), allowing for direct real-time state-specific adjustments. Inspired by findings from optogenetic stimulation in mice, we hypothesized that STN-DBS effects on movement speed depend on ongoing movement kinematics that patients exhibit during stimulation. To investigate this hypothesis, we implemented a motor state-dependent closed-loop neurostimulation algorithm, adapting DBS burst delivery to ongoing movement speed in 24 PD patients. We found a stronger anti-bradykinetic effect, raising movement speed to the level of healthy controls, when STN-DBS was applied during fast but not slow movements, while only stimulating 5% of overall movement time. To study underlying brain circuits and neurophysiological mechanisms, we investigated the behavioral effects with MRI connectomics and motor cortex electrocorticography. Finally, we demonstrate that machine learning-based brain signal decoding can be used to predict continuous movement speed for fully embedded state-dependent closed-loop algorithms. Our findings provide novel insights into the state-dependency of invasive neuromodulation, which could inspire advanced state-dependent neurostimulation algorithms for brain disorders.

neuroscience↗

bioRxiv2025

Beyond beta: Aperiodic broadband power reflects Parkinson's disease severity - a multicenter study

Parkinsons disease is linked to increased beta oscillations in the subthalamic nucleus, which correlate with motor symptoms. However, findings across studies have varied. Our standardized analysis of multicenter datasets shows that small sample sizes contributed to these discrepancies--a challenge we address by pooling datasets into one large cohort (n=119). Moving beyond beta power, we disentangled spectral components reflecting distinct neural processes. Combining aperiodic offset, low beta, and low gamma oscillations explained significantly more variance in symptom severity than beta alone. Moreover, interhemispheric within-patient analyses showed that, unlike beta oscillations, aperiodic broadband power-likely reflecting spiking activity-was increased in the more affected hemisphere. These findings identify aperiodic broadband power as a potential biomarker for adaptive deep brain stimulation and provide novel insights into the relationship between subthalamic hyperactivity and motor symptoms in human Parkinsons disease.

neuroscience↗

bioRxiv2024

Shared pathway-specific network mechanisms of dopamine and deep brain stimulation for the treatment of Parkinson's disease

Deep brain stimulation is a brain circuit intervention that can modulate distinct neural pathways for the alleviation of neurological symptoms in patients with brain disorders. In Parkinsons disease, subthalamic deep brain stimulation clinically mimics the effect of dopaminergic drug treatment, but the shared pathway mechanisms on cortex - basal ganglia networks are unknown. To address this critical knowledge gap, we combined fully invasive neural multisite recordings in patients undergoing deep brain stimulation surgery with normative MRI-based whole-brain connectomics. Our findings demonstrate that dopamine and stimulation exert distinct mesoscale effects through modulation of local neural population activity. In contrast, at the macroscale, stimulation mimics dopamine in its suppression of excessive interregional network synchrony associated with indirect and hyperdirect cortex - basal ganglia pathways. Our results provide a better understanding of the circuit mechanisms of dopamine and deep brain stimulation, laying the foundation for advanced closed-loop neurostimulation therapies.

neuroscience↗

bioRxiv2024

Cortical beta oscillations map to shared brain networks modulated by dopamine

Brain rhythms can facilitate neural communication for the maintenance of brain function. Beta rhythms (13-35 Hz) have been proposed to serve multiple domains of human ability, including motor control, cognition, memory and ewmotion, but the overarching organisational principles remain unknown. To uncover the circuit architecture of beta oscillations, we leverage normative brain data, analysing over 30 hours of invasive brain signals from 1772 channels from cortical areas in epilepsy patients, to demonstrate that beta is the most distributed cortical brain rhythm. Next, we identify a shared brain network from beta dominant areas with deeper brain structures, like the basal ganglia, by mapping parametrised oscillatory peaks to whole-brain functional and structural MRI connectomes. Finally, we show that these networks share significant overlap with dopamine uptake as indicated by positron emission tomography. Our study suggests that beta oscillations emerge in cortico-subcortical brain networks that are modulated by dopamine. It provides the foundation for a unifying circuit-based conceptualisation of the functional role of beta activity beyond the motor domain and may inspire an extended investigation of beta activity as a feedback signal for closed-loop neurotherapies for dopaminergic disorders.

neuroscience↗

bioRxiv2023

Dopamine and neuromodulation shorten the latency from motor intention to execution in Parkinson's disease

The ability to initiate volitional action is fundamental to human behaviour. Loss of dopaminergic neurons in Parkinsons disease is associated with impaired action initiation, also termed akinesia. Both dopamine and subthalamic deep brain stimulation (DBS) can alleviate akinesia, but the underlying mechanisms are unknown. An important question is whether dopamine and DBS facilitate de novo build-up of neural dynamics for motor execution or accelerate existing cortical movement initiation signals through shared modulatory circuit effects. Answering these questions can provide the foundation for new closed-loop neurotherapies with adaptive DBS, but the objectification of neural processing delays prior to performance of volitional action remains a significant challenge. To overcome this challenge, we studied readiness potentials and trained brain signal decoders on invasive neurophysiology signals in 25 DBS patients (12 female) with Parkinsons disease during performance of self-initiated movements. Combined sensorimotor cortex electrocorticography (ECoG) and subthalamic local field potential (LFP) recordings were performed OFF therapy (N=22), ON dopaminergic medication (N=18) and ON subthalamic deep brain stimulation (N=8). This allowed us to compare their therapeutic effects on neural latencies between the earliest cortical representation of movement intention as decoded by linear discriminant analysis classifiers and onset of muscle activation recorded with electromyography (EMG). In the hypodopaminergic OFF state, we observed long latencies between motor intention and motor execution for readiness potentials and machine learning classifications. Both, dopamine and DBS significantly shortened these latencies, hinting towards a shared therapeutic mechanism for alleviation of akinesia. To investigate this further, we analysed directional cortico-subthalamic oscillatory communication with multivariate granger causality. Strikingly, we found that both therapies independently shifted cortico-subthalamic oscillatory information flow from antikinetic beta (13-35 Hz) to prokinetic theta (4-10 Hz) rhythms, which was correlated with latencies in motor execution. Our study reveals a shared brain network modulation pattern of dopamine and DBS that may underlie the acceleration of neural dynamics for augmentation of movement initiation in Parkinsons disease. Instead of producing or increasing preparatory brain signals, both therapies modulate oscillatory communication. These insights provide a link between the pathophysiology of akinesia and its therapeutic alleviation with oscillatory network changes in other non-motor and motor domains, e.g. related to hyperkinesia or effort and reward perception. In the future, our study may inspire the development of clinical brain computer interfaces based on brain signal decoders to provide temporally precise support for action initiation in patients with brain disorders.

neuroscience↗

bioRxiv2022

A comparison of methods to suppress electrocardiographic artifacts in local field potential recordings

ObjectiveSensing-enabled neurostimulators for deep brain stimulation (DBS) therapy record neural activity directly from the stimulating electrodes in the form of local field potentials (LFPs). However, these LFPs are often contaminated with electrocardiographic (ECG) artifacts that impede the detection of physiomarkers for adaptive DBS research. This study systematically compared the ability of different ECG suppression methods to recover disease-specific electrical brain activity from ECG-contaminated LFPs. ApproachThree ECG suppression methods were evaluated: (1) QRS interpolation of the Perceive toolbox, (2) four variants of a template subtraction method, and (3) sixteen variants of a singular value decomposition (SVD) method. The performance of these methods was examined using LFPs recorded with the Medtronic PerceptTM PC system from the subthalamic nucleus in nine patients with Parkinsons disease while stimulation was turned off ("OFF-DBS"; anode disconnected) and while stimulation was turned on at 0 mA ("ON-DBS 0 mA"; anode connected). In addition, ECG-contaminated LFPs were simulated by scaling a co-recorded external ECG signal and adding it to the OFF-DBS LFPs. Main ResultsECG artifacts were present in 10 out of 18 ON-DBS 0 mA recordings. All ECG suppression methods were able to drastically reduce the percent difference of beta band (13 - 35 Hz) spectral power and at least partly recover the beta peak and beta burst dynamics. Using predetermined R-peaks improved the performance of the ECG suppression methods. Lengthening the time window around the R-peaks resulted in stronger reduction in artifact-induced beta band power but at an increased risk of flattening the beta peak and loss of beta burst dynamics. SignificanceThe SVD method formed the preferred trade-off between artifact cleaning and signal loss, as long as its parameter settings (time window around the R-peaks; number of components) are adequately chosen.

neuroscience↗

bioRxiv2022

Subthalamic beta bursts correlate with dopamine-dependent motor symptoms in 106 Parkinson's patients

Pathologically increased beta power has been described as a biomarker for Parkinsons disease (PD) and related to prolonged bursts of subthalamic beta synchronization. Here, we investigate the association between subthalamic beta dynamics and motor impairment in a cohort of 106 Parkinsons patients in the ON- and OFF-medication state, suing two different methods of beta burst determination. We report a frequency-specific correlation of low beta power and burst duration with motor impairment OFF dopaminergic medication. Furthermore, reduction of power and burst duration correlated significantly with symptom alleviation through dopaminergic medication. Importantly, qualitatively similar results were yielded with two different methods of beta burst definition. Our findings validate the robustness of previous results on pathological changes in subcortical oscillations both in the frequency-as well as in the time-domain in the largest cohort of PD patients to date with important implications for next-generation adaptive deep brain stimulation control algorithms.

neuroscience↗

bioRxiv2021

A spatio-spectral approach for movement decoding from cortical and subcortical recordings in patients with Parkinson's disease

The application of machine learning to intracranial signal analysis has the potential to revolutionize deep brain stimulation (DBS) by personalizing therapy to dynamic brain states, specific to symptoms and behaviors. Most decoding pipelines for movement decoding in the context of adaptive DBS are based on single channel frequency domain features, neglecting spatial information available in multichannel recordings. Such features are extracted either from DBS lead recordings in the subcortical target and/or from electrocorticography (ECoG). To optimize the simultaneous use of both types of signals, we developed a supervised online-compatible movement decoding pipeline based on multichannel and multiple site recordings. We found that adding spatial information to the model has the potential to improve decoding. In addition, we demonstrate movement decoding from spatio-spectral features derived from cortical and subcortical oscillations. We demonstrate between-patients variability of the spatial neural maps and its relationship to feature decoding performance. This application of spatial filters to decode movement from combined cortical and subcortical recordings is an important step in developing machine learning approaches for intelligent DBS systems.

neuroscience↗

bioRxiv2021

Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson's disease

Brain signal decoding promises significant advances in the development of clinical brain computer interfaces (BCI). In Parkinsons disease (PD), first bidirectional BCI implants for adaptive deep brain stimulation (DBS) are now available. Brain signal decoding can extend the clinical utility of adaptive DBS but the impact of neural source, computational methods and PD pathophysiology on decoding performance are unknown. This represents an unmet need for the development of future neurotechnology. To address this, we developed an invasive brain-signal decoding approach based on intraoperative sensorimotor electrocorticography (ECoG) and subthalamic LFP to predict grip-force, a representative movement decoding application, in 11 PD patients undergoing DBS. We demonstrate that ECoG is superior to subthalamic LFP for accurate grip-force decoding. Gradient boosted decision trees (XGBOOST) outperformed other model architectures. ECoG based decoding performance negatively correlated with motor impairment, which could be attributed to subthalamic beta bursts in the motor preparation and movement period. This highlights the impact of PD pathophysiology on the neural capacity to encode movement kinematics. Finally, we developed a connectomic analysis that could predict grip-force decoding performance of individual ECoG channels across patients by using their connectomic fingerprints. Our study provides a neurophysiological and computational framework for invasive brain signal decoding to aid the development of an individualized precision-medicine approach to intelligent adaptive DBS. Significance StatementNeurotechnology will revolutionize the treatment of neurological and psychiatric patients, promising novel treatment avenues for previously intractable brain disorders. However, optimal surgical and computational approaches and their interactions with neurological disorders are unknown. How can recent advances in machine learning and connectomics aid the precision and performance of invasive brain signal decoding strategies? Do the brain disorders treated with such approaches have impact on decoding performance? We propose a real time compatible advanced machine learning pipeline for invasively recorded brain signals in Parkinsons disease (PD) patients. We report optimal movement decoding strategies with respect to signal source, model architecture and connectomic fingerprint and demonstrate that PD pathophysiology significantly and negatively impacts movement decoding. Our study has broad impacts for the development of smart brain implants for the treatment of PD and other brain disorders.

neuroscience↗

bioRxiv2021

The sensitivity of ECG contamination to surgical implantation site in adaptive closed-loop neurostimulation systems

BackgroundBrain sensing devices are approved today for Parkinsons, essential tremor, and epilepsy therapies. Clinical decisions for implants are often influenced by the premise that patients will benefit from using sensing technology. However, artifacts, such as ECG contamination, can render such treatments unreliable. Therefore, clinicians need to understand how surgical decisions may affect artifact probability. ObjectivesInvestigate neural signal contamination with ECG activity in sensing enabled neurostimulation systems, and in particular clinical choices such as implant location that impact signal fidelity. MethodsElectric field modelling and empirical signals from 85 patients were used to investigate the relationship between implant location and ECG contamination.a ResultsThe impact on neural recordings depends on the difference between ECG signal and noise floor of the electrophysiological recording. Empirically, we demonstrate that severe ECG contamination was more than 3.2x higher in left-sided subclavicular implants (48.3%), when compared to right-sided implants (15.3%). Cranial implants did not show ECG contamination. ConclusionsGiven the relative frequency of corrupted neural signals, we conclude that implant location will impact the ability of brain sensing devices to be used for "closed-loop" algorithms. Clinical adjustments such as implant location can significantly affect signal integrity and need consideration. HighlightsO_LIChronic embedded brain sensing promises algorithm-based neurostimulation C_LIO_LIAlgorithms for closed-loop stimulation can be impaired by artifacts C_LIO_LIThe relationship of implant location to cardiac dipole has relevant impact on neural signal fidelity; simple models can provide guidance on the sensitivity C_LIO_LIECG artifacts are present in up to 50% of neural signals from left subclavicular DBS systems C_LIO_LIImplanting DBS in a right subclavicular location significantly reduces frequency of ECG artifacts C_LIO_LICranial-mounted implants are relatively immune to artifacts C_LI

neuroscience↗

bioRxiv2020

Neural signatures of pathological hyperdirect pathway activity in Parkinson's disease

Parkinsons disease (PD) is characterised by the emergence of pathological patterns of oscillatory synchronisation across the cortico-basal-ganglia circuit. The relationship between anatomical connectivity and oscillatory synchronisation within this system remains poorly understood. We address this by integrating evidence from invasive electrophysiology, magnetoencephalography, tractography and computational modelling in patients. Coupling between supplementary motor area (SMA) and subthalamic nucleus (STN) within the high beta frequency (21-30 Hz) range correlated with fibre tract densities between these two structures. Additionally within the STN, non-linear waveform features suggestive of cortical synchronisation correlated with cortico-STN fibre densities. Finally, computational modelling revealed that exaggerated hyperdirect cortical inputs to the STN in the upper beta frequency range can provoke the generation of widespread pathological synchrony at lower beta (13-20 Hz) frequencies. These observations reveal a spectral signature of the hyperdirect pathway at high beta frequencies and provide evidence for its pathophysiological role in oscillatory network dysfunction in PD. One sentence summarySignatures of the hyperdirect pathway and its likely role in pathological network disruption in Parkinsons disease are identified.

neuroscience↗

bioRxiv2019

Modulating the human functional connectome using deep brain stimulation

Neuroimaging has seen a paradigm shift from a formal description of local activity patterns toward studying distributed brain networks. The recently defined framework of the human connectome allows to globally analyse parts of the brain and their interconnections. Deep brain stimulation (DBS) is an invasive therapy for patients with severe movement disorders aiming to retune abnormal brain network activity by local high frequency stimulation of the basal ganglia. Beyond clinical utility, DBS represents a powerful research platform to study functional connectomics and the modulation of distributed brain networks in the human brain. We acquired resting-state functional MRI in twenty Parkinsons disease (PD) patients with subthalamic DBS switched ON and OFF. An age-matched control cohort of fifteen subjects was acquired from an open data repository. DBS lead placement in the subthalamic nucleus (STN) was localized using a state-of-the art pipeline that involved brain shift correction, multispectral image registration and use of a precise subcortical atlas. Based on a realistic 3D model of the electrode and surrounding anatomy, the amount of local impact of DBS was estimated using a finite element method approach. On a global level, average connectivity increases and decreases throughout the brain were estimated by contrasting ON and OFF DBS scans on a voxel-wise graph comprising eight thousand nodes. Local impact of DBS on the sensorimotor STN explained half the variance in global connectivity increases within the sensorimotor network (R = 0.711, p < 0.001). Moreover, local impact of DBS on the motor STN could explain the degree of how much voxel-wise average brain connectivity normalized toward healthy controls (R = 0.713, p < 0.001). Finally, a network based statistics analysis revealed that DBS attenuated specific couplings that are known to be pathological in PD. Namely, coupling between motor thalamus and sensorimotor cortex was increased and striatal coupling with cerebellum, external pallidum and STN was decreased by DBS. Our results show that rs-fMRI may be acquired in DBS ON and OFF conditions on clinical MRI hardware and that data is useful to gain additional insight into how DBS modulates the functional connectome of the human brain. We demonstrate that effective DBS increases overall connectivity in the motor network, normalizes the network profile toward healthy controls and specifically strengthens thalamo-cortical connectivity while reducing striatal control over basal ganglia and cerebellar structures.

neuroscience↗

bioRxiv2018

Cortico-basal-ganglia communication: Temporally structured activity for selective motor control

Despite the hard-wired structural connectivity of neural pathways, neural circuits allow context-dependent reactions to sensory cues by triggering the desired movement. Cortico-basal-ganglia circuits seem particularly important for flexible motor control as this is impaired in Parkinsons disease (PD). We analysed subthalamic nucleus (STN) spike and cortical ECoG activity from PD patients performing a visually-cued hand grip task. Fast reaction times were preceded by enhanced STN spike-to-cortical gamma phase coupling irrespective of firing rate changes, suggesting a role of gamma coupling in motor preparation. STN spike timing was offset by half a cycle when comparing ipsilateral with contralateral movements. Additionally, cortical high-frequency activity increased more steeply within each gamma cycle at the sites that showed the strongest coupling with STN spikes. Cortico-basal-ganglia gamma coupling may thus help shape neural activity to facilitate selective motor control. The observation that this effect occurs independent of changes in mean firing rate has far-reaching implications.\n\nHighlightsO_LIFast RTs were preceded by enhanced STN spike-to-cortical gamma phase coupling\nC_LIO_LISTN spike probability was significantly modulated relative to the gamma cycle\nC_LIO_LIDuring ipsilateral movement, spikes were more likely at the opposite part of the cycle\nC_LIO_LISTN output may thus help shape cortical gamma for selective motor control\nC_LI

neuroscience↗