Data-driven MEG analysis to extract fMRI resting-state networks.

The electrophysiological basis of resting-state networks (RSN) is still under debate. In particular, no principled mechanism has been determined that is capable of explaining all RSN equally well. While magnetoencephalography (MEG) and electroencephalography are the methods of choice to determine the electrophysiological basis of RSN, no standard analysis pipeline of RSN yet exists. In this article, we compare the two main existing data-driven analysis strategies for extracting RSNs from MEG data and introduce a third approach. The first approach uses phase-amplitude coupling to determine the RSN. The second approach extracts RSN through an independent component analysis of the Hilbert envelope in different frequency bands, while the third new approach uses a singular value decomposition instead. To evaluate these approaches, we compare the MEG-RSN to the functional magnetic resonance imaging (fMRI)-RSN from the same subjects. Overall, it was possible to extract RSN with MEG using all three techniques, which matched the group-specific fMRI-RSN. Interestingly the new approach based on SVD yielded significantly higher correspondence to five out of seven fMRI-RSN than the two existing approaches. Importantly, with this approach, all networks-except for the visual network-had the highest correspondence to the fMRI networks within one frequency band. Thereby we provide further insights into the electrophysiological underpinnings of the fMRI-RSNs. This knowledge will be important for the analysis of the electrophysiological connectome.

Impact of data processing varieties on DCM estimates of effective connectivity from task-fMRI.

Effective connectivity (EC) refers to directional or causal influences between interacting neuronal populations or brain regions and can be estimated from functional magnetic resonance imaging (fMRI) data via dynamic causal modeling (DCM). In contrast to functional connectivity, the impact of data processing varieties on DCM estimates of task-evoked EC has hardly ever been addressed. We therefore investigated how task-evoked EC is affected by choices made for data processing. In particular, we considered the impact of global signal regression (GSR), block/event-related design of the general linear model (GLM) used for the first-level task-evoked fMRI analysis, type of activation contrast, and significance thresholding approach. Using DCM, we estimated individual and group-averaged task-evoked EC within a brain network related to spatial conflict processing for all the parameters considered and compared the differences in task-evoked EC between any two data processing conditions via between-group parametric empirical Bayes (PEB) analysis and Bayesian data comparison (BDC). We observed strongly varying patterns of the group-averaged EC depending on the data processing choices. In particular, task-evoked EC and parameter certainty were strongly impacted by GLM design and type of activation contrast as revealed by PEB and BDC, respectively, whereas they were little affected by GSR and the type of significance thresholding. The event-related GLM design appears to be more sensitive to task-evoked modulations of EC, but provides model parameters with lower certainty than the block-based design, while the latter is more sensitive to the type of activation contrast than is the event-related design. Our results demonstrate that applying different reasonable data processing choices can substantially alter task-evoked EC as estimated by DCM. Such choices should be made with care and, whenever possible, varied across parallel analyses to evaluate their impact and identify potential convergence for robust outcomes.

Cross-Frequency Brain Network Dynamics Support Pitch Change Detection.

Processing auditory sequences involves multiple brain networks and is crucial to complex perception associated with music appreciation and speech comprehension. We used time-resolved cortical imaging in a pitch change detection task to detail the underlying nature of human brain network activity, at the rapid time scales of neurophysiology. In response to tone sequence presentation to the participants, we observed slow inter-regional signaling at the pace of tone presentations (2-4 Hz) that was directed from auditory cortex toward both inferior frontal and motor cortices. Symmetrically, motor cortex manifested directed influence onto auditory and inferior frontal cortices via bursts of faster (15-35 Hz) activity. These bursts occurred precisely at the expected latencies of each tone in a sequence. This expression of interdependency between slow/fast neurophysiological activity yielded a form of local cross-frequency phase-amplitude coupling in auditory cortex, which strength varied dynamically and peaked when pitch changes were anticipated. We clarified the mechanistic relevance of these observations in relation to behavior by including a group of individuals afflicted by congenital amusia, as a model of altered function in processing sound sequences. In amusia, we found a depression of inter-regional slow signaling toward motor and inferior frontal cortices, and a chronic overexpression of slow/fast phase-amplitude coupling in auditory cortex. These observations are compatible with a misalignment between the respective neurophysiological mechanisms of stimulus encoding and internal predictive signaling, which was absent in controls. In summary, our study provides a functional and mechanistic account of neurophysiological activity for predictive, sequential timing of auditory inputs.SIGNIFICANCE STATEMENT Auditory sequences are processed by extensive brain networks, involving multiple systems. In particular, fronto-temporal brain connections participate in the encoding of sequential auditory events, but so far, their study was limited to static depictions. This study details the nature of oscillatory brain activity involved in these inter-regional interactions in human participants. It demonstrates how directed, polyrhythmic oscillatory interactions between auditory and motor cortical regions provide a functional account for predictive timing of incoming items in an auditory sequence. In addition, we show the functional relevance of these observations in relation to behavior, with data from both normal hearing participants and a rare cohort of individuals afflicted by congenital amusia, which we considered here as a model of altered function in processing sound sequences.

Slowing of Frontal ß Oscillations in Atypical Parkinsonism.

BACKGROUND: Diagnosis of atypical parkinsonian syndromes (APS) mostly relies on clinical presentation as well as structural and molecular brain imaging. Whether parkinsonian syndromes are distinguishable based on neuronal oscillations has not been investigated so far. OBJECTIVE: The aim was to identify spectral properties specific to atypical parkinsonism. METHODS: We measured resting-state magnetoencephalography in 14 patients with corticobasal syndrome (CBS), 16 patients with progressive supranuclear palsy (PSP), 33 patients with idiopathic Parkinson's disease, and 24 healthy controls. We compared spectral power as well as amplitude and frequency of power peaks between groups. RESULTS: Atypical parkinsonism was associated with spectral slowing, distinguishing both CBS and PSP from Parkinson's disease (PD) and age-matched healthy controls. Patients with atypical parkinsonism showed a shift in ß peaks (13-30 Hz) toward lower frequencies in frontal areas bilaterally. A concomitant increase in θ/α power relative to controls was observed in both APS and PD. CONCLUSION: Spectral slowing occurs in atypical parkinsonism, affecting frontal ß oscillations in particular. Spectral slowing with a different topography has previously been observed in other neurodegenerative disorders, such as Alzheimer's disease, suggesting that spectral slowing might be an electrophysiological marker of neurodegeneration. As such, it might support differential diagnosis of parkinsonian syndromes in the future. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Local Field Potentials Predict Motor Performance in Deep Brain Stimulation for Parkinson's Disease.

BACKGROUND: Deep brain stimulation (DBS) is an effective treatment option for patients with Parkinson's disease (PD). However, clinical programming remains challenging with segmented electrodes. OBJECTIVE: Using novel sensing-enabled neurostimulators, we investigated local field potentials (LFPs) and their modulation by DBS to assess whether electrophysiological biomarkers may facilitate clinical programming in chronically implanted patients. METHODS: Sixteen patients (31 hemispheres) with PD implanted with segmented electrodes in the subthalamic nucleus and a sensing-enabled neurostimulator were included in this study. Recordings were conducted 3 months after DBS surgery following overnight withdrawal of dopaminergic medication. LFPs were acquired while stimulation was turned OFF and during a monopolar review of both directional and ring contacts. Directional beta power and stimulation-induced beta power suppression were computed. Motor performance, as assessed by a pronation-supination task, clinical programming and electrode placement were correlated to directional beta power and stimulation-induced beta power suppression. RESULTS: Better motor performance was associated with stronger beta power suppression at higher stimulation amplitudes. Across directional contacts, differences in directional beta power and the extent of stimulation-induced beta power suppression predicted motor performance. However, within individual hemispheres, beta power suppression was superior to directional beta power in selecting the contact with the best motor performance. Contacts clinically activated for chronic stimulation were associated with stronger beta power suppression than non-activated contacts. CONCLUSIONS: Our results suggest that stimulation-induced ß power suppression is superior to directional ß power in selecting the clinically most effective contact. In sum, electrophysiological biomarkers may guide programming of directional DBS systems in PD patients. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

A First Methodological Development and Validation of ReTap: An Open-Source UPDRS Finger Tapping Assessment Tool Based on Accelerometer-Data.

Bradykinesia is a cardinal hallmark of Parkinson's disease (PD). Improvement in bradykinesia is an important signature of effective treatment. Finger tapping is commonly used to index bradykinesia, albeit these approaches largely rely on subjective clinical evaluations. Moreover, recently developed automated bradykinesia scoring tools are proprietary and are not suitable for capturing intraday symptom fluctuation. We assessed finger tapping (i.e., Unified Parkinson's Disease Rating Scale (UPDRS) item 3.4) in 37 people with Parkinson's disease (PwP) during routine treatment follow ups and analyzed their 350 sessions of 10-s tapping using index finger accelerometry. Herein, we developed and validated ReTap, an open-source tool for the automated prediction of finger tapping scores. ReTap successfully detected tapping blocks in over 94% of cases and extracted clinically relevant kinematic features per tap. Importantly, based on the kinematic features, ReTap predicted expert-rated UPDRS scores significantly better than chance in a hold out validation sample (n = 102). Moreover, ReTap-predicted UPDRS scores correlated positively with expert ratings in over 70% of the individual subjects in the holdout dataset. ReTap has the potential to provide accessible and reliable finger tapping scores, either in the clinic or at home, and may contribute to open-source and detailed analyses of bradykinesia.

Cortical network formation based on subthalamic beta bursts in Parkinson's disease.

Recent evidence suggests that beta bursts in subthalamic nucleus (STN) play an important role in Parkinsonian pathophysiology. We studied the spatio-temporal relationship between STN beta bursts and cortical activity in 26 Parkinson's disease (PD) patients undergoing deep brain stimulation (DBS) surgery. Postoperatively, we simultaneously recorded STN local field potentials (LFP) from externalized DBS leads and cortical activity using whole-brain magnetoencephalography. Event-related magnetic fields (ERF) were averaged time-locked to STN beta bursts and subjected to source localization. Our results demonstrate that ERF exhibiting activity significantly different from baseline activity were localized within areas functionally related to associative, limbic, and motor systems as well as regions pertinent for visual and language processing. Our data suggest that STN beta bursts are involved in network formation between STN and cortex. This interaction is in line with the idea of parallel processing within the basal ganglia-cortex loop, specifically within the functional subsystems of the STN (i.e., associative, limbic, motor, and the related cortical areas). ERFs within visual and language-related cortical areas indicate involvement of beta bursts in STN-cortex networks beyond the associative, limbic, and motor loops. In sum, our results highlight the involvement of STN beta bursts in the formation of multiple STN - cortex loops in patients with PD.

EEG and MEG primers for tracking DBS network effects.

Deep brain stimulation (DBS) is an effective treatment method for a range of neurological and psychiatric disorders. It involves implantation of stimulating electrodes in a precisely guided fashion into subcortical structures and, at a later stage, chronic stimulation of these structures with an implantable pulse generator. While the DBS surgery makes it possible to both record brain activity and stimulate parts of the brain that are difficult to reach with non-invasive techniques, electroencephalography (EEG) and magnetoencephalography (MEG) provide complementary information from other brain areas, which can be used to characterize brain networks targeted through DBS. This requires, however, the careful consideration of different types of artifacts in the data acquisition and the subsequent analyses. Here, we review both the technical issues associated with EEG/MEG recordings in DBS patients and the experimental findings to date. One major line of research is simultaneous recording of local field potentials (LFPs) from DBS targets and EEG/MEG. These studies revealed a set of cortico-subcortical coherent networks functioning at distinguishable physiological frequencies. Specific network responses were linked to clinical state, task or stimulation parameters. Another experimental approach is mapping of DBS-targeted networks in chronically implanted patients by recording EEG/MEG responses during stimulation. One can track responses evoked by single stimulation pulses or bursts as well as brain state shifts caused by DBS. These studies have the potential to provide biomarkers for network responses that can be adapted to guide stereotactic implantation or optimization of stimulation parameters. This is especially important for diseases where the clinical effect of DBS is delayed or develops slowly over time. The same biomarkers could also potentially be utilized for the online control of DBS network effects in the new generation of closed-loop stimulators that are currently entering clinical use. Through future studies, the use of network biomarkers may facilitate the integration of circuit physiology into clinical decision making.

The comparative performance of DBS artefact rejection methods for MEG recordings.

Deep brain stimulation (DBS) can be a very efficient treatment option for movement disorders and psychiatric diseases. To better understand DBS mechanisms, brain activity can be recorded using magnetoencephalography (MEG) with the stimulator turned on. However, DBS produces large artefacts compromising MEG data quality due to both the applied current and the movement of wires connecting the stimulator with the electrode. To filter out these artefacts, several methods to suppress the DBS artefact have been proposed in the literature. A comparative study evaluating each method's effectiveness, however, is missing so far. In this study, we evaluate the performance of four artefact rejection methods on MEG data from phantom recordings with DBS acquired with an Elekta Neuromag and a CTF system: (i) Hampel-filter, (ii) spectral signal space projection (S3P), (iii) independent component analysis with mutual information (ICA-MI), and (iv) temporal signal space separation (tSSS). In the sensor space, the largest increase in signal-to-noise (SNR) ratio was achieved by ICA-MI, while the best correspondence in terms of source activations was obtained by tSSS. LCMV beamforming alone was not sufficient to suppress the DBS-induced artefacts.

Spontaneous network activity <35 ​Hz accounts for variability in stimulus-induced gamma responses.

Gamma activity is thought to serve several cognitive processes, including attention and memory. Even for the simplest stimulus, the occurrence of gamma activity is highly variable, both within and between individuals. The sources of this variability, however, are largely unknown. In this paper, we address one possible cause: the cross-frequency influence of spontaneous, whole-brain network activity on visual stimulus processing. By applying Hidden Markov modelling to MEG data, we reveal that the trial-averaged gamma response to a moving grating depends on the individual network dynamics, inferred from slower brain activity (<35 â€‹Hz) in the absence of stimulation (resting-state and task baseline). In addition, we demonstrate that modulations of network activity in task baseline influence the gamma response on the level of trials. In summary, our results reveal a cross-frequency and cross-session association between gamma responses induced by visual stimulation and spontaneous network activity. These findings underline the dependency of visual stimulus processing on the individual, functional network architecture.

Characterization of information processing in the subthalamic area of Parkinson's patients.

Despite advances in symptomatic treatment options the pathomechanism of idiopathic Parkinson's disease (PD) remains poorly understood. Animal studies from recent years suggest pathological information processing in the basal ganglia network to be responsible for major movement deficits observed in patients, which, according to the information lesion hypothesis, might also explain the mechanism of action of deep brain stimulation (DBS). Using novel measures from information theory we characterize the information content, storage and transfer of intraoperatively recorded local field potentials (LFP) from the subthalamic area of n â€‹= â€‹19 PD patients undergoing surgery for implantation of electrodes for deep brain stimulation. In agreement with recent animal studies we demonstrate a significant positive correlation of subthalamic information content and movement deficits (ρ â€‹> â€‹0.48). Analysis of information storage reveals a larger processing memory in the zona incerta (ZI) than in the subthalamic nucleus (STN). We discuss possible implications for the efficiency of high frequency DBS. Further, we estimate the information transfer between forearm muscles and ZI/STN. Here, we show that the bidirectional information flow with respect to the STN is larger compared to the ZI. In contrast to the STN, however, the bidirectional information flow in the ZI is modulated, namely increased, by movement. The results of our study may help to understand the mechanism of action of deep brain stimulation and further explain recent studies claiming efficiency of ZI stimulation for certain motor symptoms.

Recovery of Impaired Endogenous Pain Modulation by Dopaminergic Medication in Parkinson's Disease.

BACKGROUND: Of patients with Parkinson's disease (PD), 30% to 85% report pain. However, mechanisms underlying this pain remain unclear. In line with known neuroanatomical impairments, we hypothesized that pain in PD is caused by alterations in emotional-motivational as opposed to sensory-discriminative pain processing and that dopamine recovers the capacity for endogenous emotional-motivational pain modulation in patients with PD. METHODS: A total of 20 patients with PD played a random reward paradigm with painful heat stimuli in addition to assessments of pain sensitivity once with and once without levodopa. RESULTS: Levodopa increased endogenous pain inhibition in terms of perceived pain intensity and un/pleasantness compared with a medication off state. Higher clinical pain was associated with higher increases in pain inhibition. Levodopa did not affect heat pain threshold, tolerance, or temporal summation. CONCLUSION: Patients with PD seem to be predominately impaired in emotional-motivational as opposed to sensory-discriminative pain processing. A differential understanding of pain in PD is urgently needed because effective treatment strategies are lacking. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Imaging of neural oscillations with embedded inferential and group prevalence statistics.

Magnetoencephalography and electroencephalography (MEG, EEG) are essential techniques for studying distributed signal dynamics in the human brain. In particular, the functional role of neural oscillations remains to be clarified. For that reason, imaging methods need to identify distinct brain regions that concurrently generate oscillatory activity, with adequate separation in space and time. Yet, spatial smearing and inhomogeneous signal-to-noise are challenging factors to source reconstruction from external sensor data. The detection of weak sources in the presence of stronger regional activity nearby is a typical complication of MEG/EEG source imaging. We propose a novel, hypothesis-driven source reconstruction approach to address these methodological challenges. The imaging with embedded statistics (iES) method is a subspace scanning technique that constrains the mapping problem to the actual experimental design. A major benefit is that, regardless of signal strength, the contributions from all oscillatory sources, which activity is consistent with the tested hypothesis, are equalized in the statistical maps produced. We present extensive evaluations of iES on group MEG data, for mapping 1) induced oscillations using experimental contrasts, 2) ongoing narrow-band oscillations in the resting-state, 3) co-modulation of brain-wide oscillatory power with a seed region, and 4) co-modulation of oscillatory power with peripheral signals (pupil dilation). Along the way, we demonstrate several advantages of iES over standard source imaging approaches. These include the detection of oscillatory coupling without rejection of zero-phase coupling, and detection of ongoing oscillations in deeper brain regions, where signal-to-noise conditions are unfavorable. We also show that iES provides a separate evaluation of oscillatory synchronization and desynchronization in experimental contrasts, which has important statistical advantages. The flexibility of iES allows it to be adjusted to many experimental questions in systems neuroscience.

The brain's resting-state activity is shaped by synchronized cross-frequency coupling of neural oscillations.

Functional imaging of the resting brain consistently reveals broad motifs of correlated blood oxygen level dependent (BOLD) activity that engages cerebral regions from distinct functional systems. Yet, the neurophysiological processes underlying these organized, large-scale fluctuations remain to be uncovered. Using magnetoencephalography (MEG) imaging during rest in 12 healthy subjects we analyze the resting state networks and their underlying neurophysiology. We first demonstrate non-invasively that cortical occurrences of high-frequency oscillatory activity are conditioned to the phase of slower spontaneous fluctuations in neural ensembles. We further show that resting-state networks emerge from synchronized phase-amplitude coupling across the brain. Overall, these findings suggest a unified principle of local-to-global neural signaling for long-range brain communication.

Dopamine replacement modulates oscillatory coupling between premotor and motor cortical areas in Parkinson's disease.

Efficient neural communication between premotor and motor cortical areas is critical for manual motor control. Here, we used high-density electroencephalography to study cortical connectivity in patients with Parkinson's disease (PD) and age-matched healthy controls while they performed repetitive movements of the right index finger at maximal repetition rate. Multiple source beamformer analysis and dynamic causal modeling were used to assess oscillatory coupling between the lateral premotor cortex (lPM), supplementary motor area (SMA), and primary motor cortex (M1) in the contralateral hemisphere. Elderly healthy controls showed task-related modulation in connections from lPM to SMA and M1, mainly within the γ-band (>30 Hz). Nonmedicated PD patients also showed task-related γ-γ coupling from lPM to M1, but γ coupling from lPM to SMA was absent. Levodopa reinstated physiological γ-γ coupling from lPM to SMA and significantly strengthened coupling in the feedback connection from M1 to lPM expressed as ß-ß as well as θ-ß coupling. Enhancement in cross-frequency θ-ß coupling from M1 to lPM was correlated with levodopa-induced improvement in motor function. The results show that PD is associated with an altered neural communication between premotor and motor cortical areas, which can be modulated by dopamine replacement.

Targeted reinforcement of neural oscillatory activity with real-time neuroimaging feedback.

Biofeedback and brain-computer interfacing using EEG has been receiving continuous and increasing interest. However, the limited spatial resolution of low-density scalp recordings is a roadblock to the unequivocal monitoring and targeting of neuroanatomical regions and physiological signaling. This latter aspect is pivotal to the actual efficiency of neurofeedback procedures, which are expected to engage the modulation of well-identified components of neural activity within and between predetermined brain regions. Our group has previously contributed to demonstrate the principles of real-time magnetoencephalography (MEG) source imaging. Here we show how the technique was further developed to provide healthy subjects with region-specific neurofeedback to modulate successfully predetermined components of their brain activity in targeted brain regions. Overall, our results positively indicate that neurofeedback based on time-resolved MEG imaging has the potential to become an innovative therapeutic approach in neurology and neuropsychiatry.

Levodopa reinstates connectivity from prefrontal to premotor cortex during externally paced movement in Parkinson's disease.

Dopamine deficiency affects functional integration of activity in distributed neural regions. It has been suggested that lack of dopamine induces disruption of neural interactions between prefrontal and premotor areas, which might underlie impairment of motor control observed in patients with Parkinson's disease (PD). In this study we recorded cortical activity with high-density electroencephalography in 11 patients with PD as a pathological model of dopamine deficiency, and 13 healthy control subjects. Participants performed repetitive extension-flexion movements of their right index finger, which were externally paced at a rate of 0.5 Hz. This required participants to align their movement velocity to the slow external pace. Patients were studied after at least 12-hour withdrawal of dopaminergic medication (OFF state) and after intake of the dopamine precursor levodopa (ON state) in order to examine oscillatory coupling between prefrontal and premotor areas during respectively low and high levels of dopamine. In 10 patients and 12 control participants multiple source beamformer analysis yielded task-related activation of a contralateral cortical network comprising prefrontal cortex (PFC), lateral premotor cortex (lPM), supplementary motor area (SMA) and primary motor cortex (M1). Dynamic causal modelling was used to characterize task-related oscillatory coupling between prefrontal and premotor cortical areas. Healthy participants showed task-induced coupling from PFC to SMA, which was modulated within the γ-band. In the OFF state, PD patients did not express any frequency-specific coupling between prefrontal and premotor areas. Application of levodopa reinstated task-related coupling from PFC to SMA, which was expressed as high-ß-γ coupling. Additionally, strong within-frequency γ-coupling as well as cross-frequency θ-γ coupling was observed from PFC to lPM. Enhancement of this cross-frequency θ-γ coupling after application of levodopa was positively correlated with individual improvement in motor function. The results demonstrate that dopamine deficiency impairs the ability to establish oscillatory coupling between prefrontal and premotor areas during an externally paced motor task. Application of extrinsic dopamine in PD patients reinstates physiological prefrontal-premotor coupling and additionally induces within- and cross-frequency coupling from prefrontal to premotor areas, which is not expressed in healthy participants.

Subthalamic stimulation modulates self-estimation of patients with Parkinson's disease and induces risk-seeking behaviour.

Patients with Parkinson's disease with deep brain stimulation in the subthalamic nucleus postoperatively often display higher impulsivity and therefore may experience difficulties in social interactions. Here, we examined social interactions of patients with Parkinson's disease with and without deep brain stimulation in the subthalamic nucleus in competitive situations. We hypothesized altered self-estimation and risk-seeking behaviour in this patient group induced by deep brain stimulation in the subthalamic nucleus. To test the hypothesis, an experimental setting was used in which participants performed a calculation task and chose their preferred compensation. Based on their actual calculation performance, more patients with Parkinson's disease with deep brain stimulation chose a competitive tournament compensation. Assuming rational behaviour, this self-selection pattern reflects increased risk tolerance. Since patients who performed in the lowest quartile chose the tournament option, the data suggest that deep brain stimulation in the subthalamic nucleus results in a loss of the correct reference frame against which patients with Parkinson's disease evaluate their performance. The stimulation-induced combination of overestimation of their own performance, increased risk-taking, and preference for competitive environments despite poor performance is likely to impact considerably on the patients' social and work life.

Time-resolved quantification of fine hand movements as a proxy for evaluating bradykinesia-induced motor dysfunction.

Bradykinesia is a behavioral manifestation that contributes to functional dependencies in later life. However, the current state of bradykinesia indexing primarily relies on subjective, time-averaged categorizations of motor deficits, which often yield poor reliability. Herein, we used time-resolved analyses of accelerometer recordings during standardized movements, data-driven factor analyses, and linear mixed effects models (LMEs) to quantitatively characterize general, task- and therapy-specific indices of motor impairment in people with Parkinson's disease (PwP) currently undergoing treatment for bradykinesia. Our results demonstrate that single-trial, accelerometer-based features of finger-tapping and rotational hand movements were significantly modulated by divergent therapeutic regimens. Further, these features corresponded well to current gold standards for symptom monitoring, with more precise predictive capacities of bradykinesia-specific declines achieved when considering kinematic features from diverse movement types together, rather than in isolation. Herein, we report data-driven, sample-specific kinematic profiles of diverse movement types along a continuous spectrum of motor impairment, which importantly, preserves the temporal scale for which biomechanical fluctuations in motor deficits evolve in humans. Therefore, this approach may prove useful for tracking bradykinesia-induced motor decline in aging populations the future.

Modulation of DBS-induced cortical responses and movement by the directionality and magnitude of current administered.

Subthalamic deep brain stimulation (STN-DBS) is an effective therapy for alleviating motor symptoms in people with Parkinson's disease (PwP), although some may not receive optimal clinical benefits. One potential mechanism of STN-DBS involves antidromic activation of the hyperdirect pathway (HDP), thus suppressing cortical beta synchrony to improve motor function, albeit the precise mechanisms underlying optimal DBS parameters are not well understood. To address this, 18 PwP with STN-DBS completed a 2 Hz monopolar stimulation of the left STN during MEG. MEG data were imaged in the time-frequency domain using minimum norm estimation. Peak vertex time series data were extracted to interrogate the directional specificity and magnitude of DBS current on evoked and induced cortical responses and accelerometer metrics of finger tapping using linear mixed-effects models and mediation analyses. We observed increases in evoked responses (HDP ~ 3-10 ms) and synchronization of beta oscillatory power (14-30 Hz, 10-100 ms) following DBS pulse onset in the primary sensorimotor cortex (SM1), supplementary motor area (SMA) and middle frontal gyrus (MFG) ipsilateral to the site of stimulation. DBS parameters significantly modulated neural and behavioral outcomes, with clinically effective contacts eliciting significant increases in medium-latency evoked responses, reductions in induced SM1 beta power, and better movement profiles compared to suboptimal contacts, often regardless of the magnitude of current applied. Finally, HDP-related improvements in motor function were mediated by the degree of SM1 beta suppression in a setting-dependent manner. Together, these data suggest that DBS-evoked brain-behavior dynamics are influenced by the level of beta power in key hubs of the basal ganglia-cortical loop, and this effect is exacerbated by the clinical efficacy of DBS parameters. Such data provides novel mechanistic and clinical insight, which may prove useful for characterizing DBS programming strategies to optimize motor symptom improvement in the future.