Functional connectivity maps of theta/alpha and beta coherence within the subthalamic nucleus region

Subthalamic nucleus dynamics during executive functioning: Insights from local field potentials in Parkinson's disease

This study explores the involvement of the subthalamic nucleus (STN) in executive functions, particularly cognitive flexibility, in Parkinson's disease (PD) patients. Utilizing a computerized Wisconsin Card Sorting Task (WCST) and local field potential (LFP) recordings from implanted deep brain stimulation (DBS) electrodes, we investigated task-specific neural dynamics. Behavioural results demonstrated increased error rates and prolonged response times in trials requiring set-shifting and rule induction via cross-temporal information integration. Electrophysiological analyses revealed integration-specific LFP modulations, including enhanced theta-band activity linked to conflict monitoring and cognitive control during high-demand trials, and beta-band suppression associated with motor inhibition and task disengagement. These findings underscore the STN's integrative role in non-motor domains, supporting its function in cross-temporal information integration for cognitive control. The results also highlight the utility of the WCST for assessing multiple executive processes and the potential of LFP-based biomarkers to refine DBS programming. Despite the relatively small sample size, this study provides novel insights into the oscillatory dynamics of the STN, emphasizing its broader role in decision-making and executive control. Future research should expand the understanding of the STN's contributions across cognitive domains.

Spike-phase coupling of subthalamic neurons to posterior perisylvian cortex predicts speech sound accuracy

Speech provides a rich context for understanding how cortical interactions with the basal ganglia contribute to unique human behaviors, but opportunities for direct human intracranial recordings across cortical-basal ganglia networks are rare. Here we have recorded electrocorticographic signals in the cortex synchronously with single units in the basal ganglia during awake neurosurgeries where participants spoke syllable repetitions. We have discovered that individual subthalamic nucleus (STN) neurons have transient (200 ms) spike-phase coupling (SPC) events with multiple cortical regions. The spike timing of STN neurons is locked to the phase of theta-alpha oscillations in the supramarginal and posterior superior temporal gyrus during speech planning and production. Speech sound errors occur when this STN-cortical interaction is delayed. Our results suggest that timely interactions between the STN and the posterior perisylvian cortex support auditory-motor coordinate transformation or phonological working memory during speech planning. These findings establish a framework for understanding cortical-basal ganglia interaction in other human behaviors, and additionally indicate that firing-rate based models are insufficient for explaining basal ganglia circuit behavior.

The Deep Brain Stimulation Response Network in Parkinson's Disease Operates in the High Beta Band

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) improves motor symptoms in patients with Parkinson's disease. Using functional MRI, optimal DBS response networks have been characterized. However, neural activity associated with Parkinsonian symptoms is magnitudes faster than what can be resolved by this method. While both spatial and temporal domains of these networks appear critical, no single study has yet investigated both domains simultaneously. Here, we aim to close this gap using subthalamic local field potentials that were concurrently recorded alongside whole-brain magnetoencephalography in a multi-center cohort of patients that underwent STN-DBS for the treatment of Parkinson's disease (N = 100 hemispheres). In every cortical vertex, cortico-subthalamic coupling was correlated with stimulation outcomes. This network spatially resembled fMRI-based findings (R = 0.40, P = 0.039) and explained significant amounts of variance in clinical outcomes (β std = 0.30, P = 0.002), while theta-alpha and low beta coupling did not show significant associations with DBS response (theta-alpha: β std = -0.02, P = 0.805; low beta: β std = -0.08, P = 0.426). The 'optimal' high beta coupling map was robust when subjected to various cross-validation designs (10-fold cross-validation: R = 0.29, P = 0.009; split-half design: R = 0.31, P = 0.026) and was able to predict outcomes across DBS centers (R = 0.74; P (1) = 8.9e-5). We identified a DBS response network that i) resembles the previously defined MRI network and ii) operates in the high-beta band. Maximal connectivity to this network was associated with optimal DBS outcomes and was able to cross-predict clinical improvements across DBS surgeons and centers.

Neurophysiological gradient in the Parkinsonian subthalamic nucleus as a marker for motor symptoms and apathy

Sensing-based deep brain stimulation should optimally consider both the motor and neuropsychiatric domain to maximize quality of life of Parkinson's disease (PD) patients. Here we characterize the neurophysiological properties of the subthalamic nucleus (STN) in 69 PD patients using a newly established neurophysiological gradient metric and contextualize it with motor symptoms and apathy. We could evidence a STN power gradient that holds most of the spectral information between 5 and 30 Hz spanning along the dorsal-ventral axis. It shows elevated power in the sub-beta range (8-12 Hz) toward the ventral STN, and elevated dorsal beta power (16-24 Hz) indicative for the hemispheres contralateral to the more affected hemi-body side. The rigidity response to DBS was highest dorsally on the axis. Importantly, apathetic symptoms can be related to reduced ventral alpha power. In conclusion, the STN spectral gradient may inform about the motor and neuropsychiatric domain, supporting integrative closed-loop strategies.

Statistical Multiscore Functional Atlas Creation for Image-Guided Deep Brain Stimulation

Deep brain stimulation is increasingly performed for patients who suffer from drug-resistant movement disorders. It still remains challenging to determine the optimal electrode contact location to obtain the optimal surgical outcome and simultaneously minimize adverse effects. This paper proposes to construct a new statistical functional atlas to guide electrode contact targeting during deep brain stimulation. The construction of the atlas consists of four main steps: 1) multimodal image segmentation and registration, 2) activation volume modeling, 3) computing and combining multiple functional scores, and 4) generation of multiscore functional atlas. Based on these steps, the statistical functional atlas is created by integrating anatomical information analysis with multiple clinical scores that postoperatively characterize stimulation efficacy (e.g., motor symptom) and adverse effect. We evaluated the created atlas on 40 subthalamic nucleus stimulated parkinsonian patient datasets. The experimental results show that the reproducibility of the created statistical functional atlas was more than 75% in the cross validation. In addition, the motor, neuropsychological, and health scores can be reproduced up to 77%, 82%, and 78%. Compared to the actually implanted electrode position, the atlas predicted and the manually planned electrode position errors were 2.89 mm and 2.38 mm, respectively. The constructed multiscore atlas provides an automatic and accurate electrode targeting strategy that potentially outperforms manually planned approaches.

Low-beta versus high-beta band cortico-subcortical coherence in movement inhibition and expectation

Beta band oscillations in the sensorimotor cortex and subcortical structures, such as the subthalamic nucleus (STN) and internal pallidum (GPi), are closely linked to motor control. Recent research suggests that low-beta (14.5-23.5 Hz) and high-beta (23.5-35 Hz) cortico-STN coherence arise through distinct networks, possibly reflecting indirect and hyperdirect pathways. In this study, we sought to probe whether low- and high-beta coherence also exhibit different functional roles in facilitating and inhibiting movement. Twenty patients with Parkinson's disease who had deep brain stimulation electrodes implanted in either STN or GPi performed a classical go/nogo task while undergoing simultaneous magnetoencephalography and local field potentials recordings. Subjects' expectations were manipulated by presenting go- and nogo-trials with varying probabilities. We identified a lateral source in the sensorimotor cortex for low-beta coherence, as well as a medial source near the supplementary motor area for high-beta coherence. Task-related coherence time courses for these two sources revealed that low-beta coherence was more strongly implicated than high-beta coherence in the performance of go-trials. Accordingly, average pre-stimulus low-beta but not high-beta coherence or spectral power correlated with overall reaction time across subjects. High-beta coherence during unexpected nogo-trials was higher compared to expected nogo-trials at a relatively long latency of 3 s after stimulus presentation. Neither low- nor high-beta coherence showed a significant correlation with patients' symptom severity at baseline assessment. While low-beta cortico-subcortical coherence appears to be related to motor output, the role of high-beta coherence requires further investigation.

Applications of OPM-MEG for translational neuroscience: a perspective

Magnetoencephalography (MEG) allows the non-invasive measurement of brain activity at millisecond precision combined with localization of the underlying generators. So far, MEG-systems consisted of superconducting quantum interference devices (SQUIDS), which suffer from several limitations. Recent technological advances, however, have enabled the development of novel MEG-systems based on optically pumped magnetometers (OPMs), offering several advantages over conventional SQUID-MEG systems. Considering potential improvements in the measurement of neuronal signals as well as reduced operating costs, the application of OPM-MEG systems for clinical neuroscience and diagnostic settings is highly promising. Here we provide an overview of the current state-of-the art of OPM-MEG and its unique potential for translational neuroscience. First, we discuss the technological features of OPMs and benchmark OPM-MEG against SQUID-MEG and electroencephalography (EEG), followed by a summary of pioneering studies of OPMs in healthy populations. Key applications of OPM-MEG for the investigation of psychiatric and neurological conditions are then reviewed. Specifically, we suggest novel applications of OPM-MEG for the identification of biomarkers and circuit deficits in schizophrenia, dementias, movement disorders, epilepsy, and neurodevelopmental syndromes (autism spectrum disorder and attention deficit hyperactivity disorder). Finally, we give an outlook of OPM-MEG for translational neuroscience with a focus on remaining methodological and technical challenges.

Exploring the electrophysiology of Parkinson's disease with magnetoencephalography and deep brain recordings

Aberrant information processing in the basal ganglia and connected cortical areas are key to many neurological movement disorders such as Parkinson's disease. Investigating the electrophysiology of this system is difficult in humans because non-invasive methods, such as electroencephalography or magnetoencephalography, have limited sensitivity to deep brain areas. Recordings from electrodes implanted for therapeutic deep brain stimulation, in contrast, provide clear deep brain signals but are not suited for studying cortical activity. Therefore, we combine magnetoencephalography and local field potential recordings from deep brain stimulation electrodes in individuals with Parkinson's disease. Here, we make these data available, inviting a broader scientific community to explore the dynamics of neural activity in the subthalamic nucleus and its functional connectivity to cortex. The dataset encompasses resting-state recordings, plus two motor tasks: static forearm extension and self-paced repetitive fist clenching. Most patients were recorded both in the medicated and the unmedicated state. Along with the raw data, we provide metadata on channels, events and scripts for pre-processing to help interested researchers get started.

Structural Brain Connectivity Guided Optimal Contact Selection for Deep Brain Stimulation of the Subthalamic Nucleus

BACKGROUND

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective therapy in ameliorating the motor symptoms of Parkinson disease. However, postoperative optimal contact selection is crucial for achieving the best outcome of deep brain stimulation of the subthalamic nucleus surgery, but the process is currently a trial-and-error and time-consuming procedure that relies heavily on surgeons' clinical experience.

METHODS

In this study, we propose a structural brain connectivity guided optimal contact selection method for deep brain stimulation of the subthalamic nucleus. Firstly, we reconstruct the DBS electrode location and estimate the stimulation range using volume of tissue activated from each DBS contact. Then, we extract the structural connectivity features by concatenating fractional anisotropy and the number of streamlines features of activated regions and the whole brain regions. Finally, we use a convolutional neural network with convolutional block attention module to identify the structural connectivity features for the optimal contact selection.

RESULTS

We review the data of 800 contacts from 100 patients with Parkinson disease for the experiment. The proposed method achieves promising results, with the average accuracy of 97.63%, average precision of 94.50%, average recall of 94.46%, and average specificity of 98.18%, respectively. Our method can provide the suggestion for optimal contact selection.

CONCLUSIONS

Our proposed method can improve the efficiency and accuracy of DBS optimal contact selection, reduce the dependence on surgeons' experience, and has the potential to facilitate the development of advanced DBS technology.

Spike-phase coupling of subthalamic neurons to posterior opercular cortex predicts speech sound accuracy

Speech provides a rich context for understanding how cortical interactions with the basal ganglia contribute to unique human behaviors, but opportunities for direct intracranial recordings across cortical-basal ganglia networks are rare. We recorded electrocorticographic signals in the cortex synchronously with single units in the basal ganglia during awake neurosurgeries where subjects spoke syllable repetitions. We discovered that individual STN neurons have transient (200ms) spike-phase coupling (SPC) events with multiple cortical regions. The spike timing of STN neurons was coordinated with the phase of theta-alpha oscillations in the posterior supramarginal and superior temporal gyrus during speech planning and production. Speech sound errors occurred when this STN-cortical interaction was delayed. Our results suggest that the STN supports mechanisms of speech planning and auditory-sensorimotor integration during speech production that are required to achieve high fidelity of the phonological and articulatory representation of the target phoneme. These findings establish a framework for understanding cortical-basal ganglia interaction in other human behaviors, and additionally indicate that firing-rate based models are insufficient for explaining basal ganglia circuit behavior.

Motor network gamma oscillations in chronic home recordings predict dyskinesia in Parkinson's disease

In Parkinson's disease, imbalances between 'antikinetic' and 'prokinetic' patterns of neuronal oscillatory activity are related to motor dysfunction. Invasive brain recordings from the motor network have suggested that medical or surgical therapy can promote a prokinetic state by inducing narrowband gamma rhythms (65-90 Hz). Excessive narrowband gamma in the motor cortex promotes dyskinesia in rodent models, but the relationship between narrowband gamma and dyskinesia in humans has not been well established. To assess this relationship, we used a sensing-enabled deep brain stimulator system, attached to both motor cortex and basal ganglia (subthalamic or pallidal) leads, paired with wearable devices that continuously tracked motor signs in the contralateral upper limbs. We recorded 984 h of multisite field potentials in 30 hemispheres of 16 subjects with Parkinson's disease (2/16 female, mean age 57 ± 12 years) while at home on usual antiparkinsonian medications. Recordings were done 2-4 weeks after implantation, prior to starting therapeutic stimulation. Narrowband gamma was detected in the precentral gyrus, subthalamic nucleus or both structures on at least one side of 92% of subjects with a clinical history of dyskinesia. Narrowband gamma was not detected in the globus pallidus. Narrowband gamma spectral power in both structures co-fluctuated similarly with contralateral wearable dyskinesia scores (mean correlation coefficient of ρ = 0.48 with a range of 0.12-0.82 for cortex, ρ = 0.53 with a range of 0.5-0.77 for subthalamic nucleus). Stratification analysis showed the correlations were not driven by outlier values, and narrowband gamma could distinguish 'on' periods with dyskinesia from 'on' periods without dyskinesia. Time lag comparisons confirmed that gamma oscillations herald dyskinesia onset without a time lag in either structure when using 2-min epochs. A linear model incorporating the three oscillatory bands (beta, theta/alpha and narrowband gamma) increased the predictive power of dyskinesia for several subject hemispheres. We further identified spectrally distinct oscillations in the low gamma range (40-60 Hz) in three subjects, but the relationship of low gamma oscillations to dyskinesia was variable. Our findings support the hypothesis that excessive oscillatory activity at 65-90 Hz in the motor network tracks with dyskinesia similarly across both structures, without a detectable time lag. This rhythm may serve as a promising control signal for closed-loop deep brain stimulation using either cortical or subthalamic detection.

Mapping Subcortico-Cortical Coupling-A Comparison of Thalamic and Subthalamic Oscillations

BACKGROUND

The ventral intermediate nucleus of the thalamus (VIM) is an effective target for deep brain stimulation in tremor patients. Despite its therapeutic importance, its oscillatory coupling to cortical areas has rarely been investigated in humans.

OBJECTIVES

The objective of this study was to identify the cortical areas coupled to the VIM in patients with essential tremor.

METHODS

We combined resting-state magnetoencephalography with local field potential recordings from the VIM of 19 essential tremor patients. Whole-brain maps of VIM-cortex coherence in several frequency bands were constructed using beamforming and compared with corresponding maps of subthalamic nucleus (STN) coherence based on data from 19 patients with Parkinson's disease. In addition, we computed spectral Granger causality.

RESULTS

The topographies of VIM-cortex and STN-cortex coherence were very similar overall but differed quantitatively. Both nuclei were coupled to the ipsilateral sensorimotor cortex in the high-beta band; to the sensorimotor cortex, brainstem, and cerebellum in the low-beta band; and to the temporal cortex, brainstem, and cerebellum in the alpha band. High-beta coherence to sensorimotor cortex was stronger for the STN (P = 0.014), whereas low-beta coherence to the brainstem was stronger for the VIM (P = 0.017). Although the STN was driven by cortical activity in the high-beta band, the VIM led the sensorimotor cortex in the alpha band.

CONCLUSIONS

Thalamo-cortical coupling is spatially and spectrally organized. The overall similar topographies of VIM-cortex and STN-cortex coherence suggest that functional connections are not necessarily unique to one subcortical structure but might reflect larger frequency-specific networks involving VIM and STN to a different degree. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Eltoprazine modulated gamma oscillations on ameliorating L-dopa-induced dyskinesia in rats

AIM

Parkinson's disease (PD) is a pervasive neurodegenerative disease, and levodopa (L-dopa) is its preferred treatment. The pathophysiological mechanism of levodopa-induced dyskinesia (LID), the most common complication of long-term L-dopa administration, remains obscure. Accumulated evidence suggests that the dopaminergic as well as non-dopaminergic systems contribute to LID development. As a 5-hydroxytryptamine 1A/1B receptor agonist, eltoprazine ameliorates dyskinesia, although little is known about its electrophysiological mechanism. The aim of this study was to investigate the cumulative effects of chronic L-dopa administration and the potential mechanism of eltoprazine's amelioration of dyskinesia at the electrophysiological level in rats.

METHODS

Neural electrophysiological analysis techniques were conducted on the acquired local field potential (LFP) data from primary motor cortex (M1) and dorsolateral striatum (DLS) during different pathological states to obtain the information of power spectrum density, theta-gamma phase-amplitude coupling (PAC), and functional connectivity. Behavior tests and AIMs scoring were performed to verify PD model establishment and evaluate LID severity.

RESULTS

We detected exaggerated gamma activities in the dyskinetic state, with different features and impacts in distinct regions. Gamma oscillations in M1 were narrowband manner, whereas that in DLS had a broadband appearance. Striatal exaggerated theta-gamma PAC in the LID state contributed to broadband gamma oscillation, and aperiodic-corrected cortical beta power correlated robustly with aperiodic-corrected gamma power in M1. M1-DLS coherence and phase-locking values (PLVs) in the gamma band were enhanced following L-dopa administration. Eltoprazine intervention reduced gamma oscillations, theta-gamma PAC in the DLS, and coherence and PLVs in the gamma band to alleviate dyskinesia.

CONCLUSION

Excessive cortical gamma oscillation is a compelling clinical indicator of dyskinesia. The detection of enhanced PAC and functional connectivity of gamma-band oscillation can be used to guide and optimize deep brain stimulation parameters. Eltoprazine has potential clinical application for dyskinesia.

Uncovering the neurophysiology of mood, motivation and behavioral symptoms in Parkinson's disease through intracranial recordings

Neuropsychiatric mood and motivation symptoms (depression, anxiety, apathy, impulse control disorders) in Parkinson's disease (PD) are highly disabling, difficult to treat and exacerbated by current medications and deep brain stimulation therapies. High-resolution intracranial recording techniques have the potential to undercover the network dysfunction and cognitive processes that drive these symptoms, towards a principled re-tuning of circuits. We highlight intracranial recording as a valuable tool for mapping and desegregating neural networks and their contribution to mood, motivation and behavioral symptoms, via the ability to dissect multiplexed overlapping spatial and temporal neural components. This technique can be powerfully combined with behavioral paradigms and emerging computational techniques to model underlying latent behavioral states. We review the literature of intracranial recording studies investigating mood, motivation and behavioral symptomatology with reference to 1) emotional processing, 2) executive control 3) subjective valuation (reward & cost evaluation) 4) motor control and 5) learning and updating. This reveals associations between different frequency specific network activities and underlying cognitive processes of reward decision making and action control. If validated, these signals represent potential computational biomarkers of motivational and behavioural states and could lead to principled therapy development for mood, motivation and behavioral symptoms in PD.

Sensorimotor integration is affected by acute whole-body vibration: a coherence study

Introduction: Several whole-body vibration (WBV) effects on performance have been related to potential changes in the neural drive, motor unit firing rate, and sensorimotor integration. In the present paper, motor unit coherence analysis was performed to detect the source of neural modulation based on the frequency domain. Methods: Thirteen men [25 ± 2.1 years; Body Mass Index (BMI) = 23.9 ± 1.3 kg m2; maximal voluntary force (MVF): 324.36 ± 41.26 N] performed sustained contractions of the Tibialis Anterior (TA) at 10%MVF before and after acute WBV. The vibrating stimulus was applied barefoot through a platform to target the TA. High-Density surface Electromyography (HDsEMG) was used to record the myoelectrical activity of TA to evaluate coherence from motor unit cumulative spike-trains (CSTs). Results: Mean coherence showed a significant decrease in the alpha and low-beta bandwidths (alpha: from 0.143 ± 0.129 to 0.132 ± 0.129, p = 0.035; low-beta: from 0.117 ± 0.039 to 0.086 ± 0.03, p = 0.0001), whereas no significant changes were found in the other ones (p > 0.05). The discharge rate (DR) and the Force Covariance (CovF%) were not significantly affected by acute WBV exposure (p > 0.05). Discussion: According to the significant effects found in alpha and low-beta bandwidths, which reflect sensorimotor integration parameters, accompanied by no differences in the DR and CovF%, the present results underlined that possible neural mechanisms at the base of the previously reported performance enhancements following acute WBV are likely based on sensorimotor integration rather than direct neural drive modulation.

Localized estimation of electromagnetic sources underlying event-related fields using recurrent neural networks

Objective. To use a recurrent neural network (RNN) to reconstruct neural activity responsible for generating noninvasively measured electromagnetic signals.Approach. Output weights of an RNN were fixed as the lead field matrix from volumetric source space computed using the boundary element method with co-registered structural magnetic resonance images and magnetoencephalography (MEG). Initially, the network was trained to minimise mean-squared-error loss between its outputs and MEG signals, causing activations in the penultimate layer to converge towards putative neural source activations. Subsequently, L1 regularisation was applied to the final hidden layer, and the model was fine-tuned, causing it to favour more focused activations. Estimated source signals were then obtained from the outputs of the last hidden layer. We developed and validated this approach with simulations before applying it to real MEG data, comparing performance with beamformers, minimum-norm estimate, and mixed-norm estimate source reconstruction methods.Main results. The proposed RNN method had higher output signal-to-noise ratios and comparable correlation and error between estimated and simulated sources. Reconstructed MEG signals were also equal or superior to the other methods regarding their similarity to ground-truth. When applied to MEG data recorded during an auditory roving oddball experiment, source signals estimated with the RNN were generally biophysically plausible and consistent with expectations from the literature.Significance. This work builds on recent developments of RNNs for modelling event-related neural responses by incorporating biophysical constraints from the forward model, thus taking a significant step towards greater biological realism and introducing the possibility of exploring how input manipulations may influence localised neural activity.

Oscillatory beta dynamics inform biomarker-driven treatment optimization for Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder characterized by loss of dopaminergic neurons and dysregulation of the basal ganglia. Cardinal motor symptoms include bradykinesia, rigidity, and tremor. Deep brain stimulation (DBS) of select subcortical nuclei is standard of care for medication-refractory PD. Conventional open-loop DBS delivers continuous stimulation with fixed parameters that do not account for a patient's dynamic activity state or medication cycle. In comparison, closed-loop DBS, or adaptive DBS (aDBS), adjusts stimulation based on biomarker feedback that correlates with clinical state. Recent work has identified several neurophysiological biomarkers in local field potential recordings from PD patients, the most promising of which are 1) elevated beta (∼13-30 Hz) power in the subthalamic nucleus (STN), 2) increased beta synchrony throughout basal ganglia-thalamocortical circuits, notably observed as coupling between the STN beta phase and cortical broadband gamma (∼50-200 Hz) amplitude, and 3) prolonged beta bursts in the STN and cortex. In this review, we highlight relevant frequency and time domain features of STN beta measured in PD patients and summarize how spectral beta power, oscillatory beta synchrony, phase-amplitude coupling, and temporal beta bursting inform PD pathology, neurosurgical targeting, and DBS therapy. We then review how STN beta dynamics inform predictive, biomarker-driven aDBS approaches for optimizing PD treatment. We therefore provide clinically useful and actionable insight that can be applied toward aDBS implementation for PD.

Magnetoencephalography to measure the effect of contact point-specific deep brain stimulation in Parkinson's disease: A proof of concept study

BACKGROUND

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for disabling fluctuations in motor symptoms in Parkinson's disease (PD) patients. However, iterative exploration of all individual contact points (four in each STN) by the clinician for optimal clinical effects may take months.

OBJECTIVE

In this proof of concept study we explored whether magnetoencephalography (MEG) has the potential to noninvasively measure the effects of changing the active contact point of STN-DBS on spectral power and functional connectivity in PD patients, with the ultimate aim to aid in the process of selecting the optimal contact point, and perhaps reduce the time to achieve optimal stimulation settings.

METHODS

The study included 30 PD patients who had undergone bilateral DBS of the STN. MEG was recorded during stimulation of each of the eight contact points separately (four on each side). Each stimulation position was projected on a vector running through the longitudinal axis of the STN, leading to one scalar value indicating a more dorsolateral or ventromedial contact point position. Using linear mixed models, the stimulation positions were correlated with band-specific absolute spectral power and functional connectivity of i) the motor cortex ipsilateral tot the stimulated side, ii) the whole brain.

RESULTS

At group level, more dorsolateral stimulation was associated with lower low-beta absolute band power in the ipsilateral motor cortex (p = .019). More ventromedial stimulation was associated with higher whole-brain absolute delta (p = .001) and theta (p = .005) power, as well as higher whole-brain theta band functional connectivity (p = .040). At the level of the individual patient, switching the active contact point caused significant changes in spectral power, but the results were highly variable.

CONCLUSIONS

We demonstrate for the first time that stimulation of the dorsolateral (motor) STN in PD patients is associated with lower low-beta power values in the motor cortex. Furthermore, our group-level data show that the location of the active contact point correlates with whole-brain brain activity and connectivity. As results in individual patients were quite variable, it remains unclear if MEG is useful in the selection of the optimal DBS contact point.

Insights and opportunities for deep brain stimulation as a brain circuit intervention

Deep brain stimulation (DBS) is an effective treatment and has provided unique insights into the dynamic circuit architecture of brain disorders. This Review illustrates our current understanding of the pathophysiology of movement disorders and their underlying brain circuits that are modulated with DBS. It proposes principles of pathological network synchronization patterns like beta activity (13-35 Hz) in Parkinson's disease. We describe alterations from microscale including local synaptic activity via modulation of mesoscale hypersynchronization to changes in whole-brain macroscale connectivity. Finally, an outlook on advances for clinical innovations in next-generation neurotechnology is provided: from preoperative connectomic targeting to feedback controlled closed-loop adaptive DBS as individualized network-specific brain circuit interventions.

Basal ganglia functional connectivity network analysis does not support the 'noisy signal' hypothesis of Parkinson's disease

The 'noisy signal' hypothesis of basal ganglia dysfunction in Parkinson's disease (PD) suggests that major motor symptoms of the disorder are caused by the development of abnormal basal ganglia activity patterns resulting in the propagation of 'noisy' signals to target systems. While such abnormal activity patterns might be useful biomarkers for the development of therapeutic interventions, correlation between specific changes in activity and PD symptoms has been inconsistently demonstrated, and raises questions concerning the accuracy of the hypothesis. Here, we tested this hypothesis by considering three nodes of the basal ganglia network, the subthalamus, globus pallidus interna, and cortex during self-paced and cued movements in patients with PD. Interactions between these regions were analyzed using measures that assess both linear and non-linear relationships. Marked changes in the network are observed with dopamine state. Specifically, we detected functional disconnection of the basal ganglia from the cortex and higher network variability in untreated PD, but various patterns of directed functional connectivity with lower network variability in treated PD. When we examine the system output, significant correlation is observed between variability in the cortico-basal ganglia network and muscle activity variability but only in the treated state. Rather than supporting a role of the basal ganglia in the transmission of noisy signals in patients with PD, these findings suggest that cortico-basal ganglia network interactions by fault or design, in the treated Parkinsonian state, are actually associated with improved cortical network output variability.