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.

Modern neurophysiological recordings are performed using multichannel sensor arrays that are able to record activity in an increasingly high number of channels numbering in the 100s to 1000s. Often, underlying lower-dimensional patterns of activity are responsible for the observed dynamics, but these representations are difficult to reliably identify using existing methods that attempt to summarize multivariate relationships in a post hoc manner from univariate analyses or using current blind source separation methods. While such methods can reveal appealing patterns of activity, determining the number of components to include, assessing their statistical significance, and interpreting them requires extensive manual intervention and subjective judgment in practice. These difficulties with component selection and interpretation occur in large part because these methods lack a generative model for the underlying spatio-temporal dynamics. Here, we describe a novel component analysis method anchored by a generative model where each source is described by a bio-physically inspired state-space representation. The parameters governing this representation readily capture the oscillatory temporal dynamics of the components, so we refer to it as oscillation component analysis. These parameters - the oscillatory properties, the component mixing weights at the sensors, and the number of oscillations - all are inferred in a data-driven fashion within a Bayesian framework employing an instance of the expectation maximization algorithm. We analyze high-dimensional electroencephalography and magnetoencephalography recordings from human studies to illustrate the potential utility of this method for neuroscience data.

Source analysis of magnetoencephalography (MEG) data requires the computation of the magnetic fields induced by current sources in the brain. This so-called MEG forward problem includes an accurate estimation of the volume conduction effects in the human head. Here, we introduce the Cut finite element method (CutFEM) for the MEG forward problem. CutFEM\'s meshing process imposes fewer restrictions on tissue anatomy than tetrahedral meshes while being able to mesh curved geometries contrary to hexahedral meshing. To evaluate the new approach, we compare CutFEM with a boundary element method (BEM) that distinguishes three tissue compartments and a 6-compartment hexahedral FEM in an n = 19 group study of somatosensory evoked fields (SEF). The neural generators of the 20 ms post-stimulus SEF components (M20) are reconstructed using both an unregularized and a regularized inversion approach. Changing the forward model resulted in reconstruction differences of about 1 centimeter in location and considerable differences in orientation. The tested 6-compartment FEM approaches significantly increase the goodness of fit to the measured data compared with the 3-compartment BEM. They also demonstrate higher quasi-radial contributions for sources below the gyral crowns. Furthermore, CutFEM improves source separability compared with both other approaches. We conclude that head models with 6 compartments rather than 3 and the new CutFEM approach are valuable additions to MEG source reconstruction, in particular for sources that are predominantly radial.

UNASSIGNED: People who regularly use cannabis exhibit altered brain dynamics during cognitive control tasks, though the impact of regular cannabis use on the neural dynamics serving motor control remains less understood.
UNASSIGNED: We sought to investigate how regular cannabis use modulates the neural dynamics serving motor control.
UNASSIGNED: Thirty-four people who regularly use cannabis (cannabis+) and 33 nonusers (cannabis-) underwent structured interviews about their substance use history and performed the Eriksen flanker task to map the neural dynamics serving motor control during high-density magnetoencephalography (MEG). The resulting neural data were transformed into the time-frequency domain to examine oscillatory activity and were imaged using a beamforming approach.
UNASSIGNED: MEG sensor-level analyses revealed robust beta (16-24 Hz) and gamma oscillations (66-74 Hz) during motor planning and execution, which were imaged using a beamformer. Both responses peaked in the left primary motor cortex and voxel time series were extracted to evaluate the spontaneous and oscillatory dynamics. Our key findings indicated that the cannabis+ group exhibited weaker spontaneous gamma activity in the left primary motor cortex relative to the cannabis- group, which scaled with cannabis use and behavioral metrics. Interestingly, regular cannabis use was not associated with differences in oscillatory beta and gamma activity, and there were no group differences in spontaneous beta activity.
UNASSIGNED: Our findings suggest that regular cannabis use is associated with suppressed spontaneous gamma activity in the left primary motor cortex, which scales with the degree of cannabis use disorder symptomatology and is coupled to behavioral task performance.

Evidence accumulates that the cerebellum\'s role in the brain is not restricted to motor functions. Rather, cerebellar activity seems to be crucial for a variety of tasks that rely on precise event timing and prediction. Due to its complex structure and importance in communication, human speech requires a particularly precise and predictive coordination of neural processes to be successfully comprehended. Recent studies proposed that the cerebellum is indeed a major contributor to speech processing, but how this contribution is achieved mechanistically remains poorly understood. The current study aimed to reveal a mechanism underlying cortico-cerebellar coordination and demonstrate its speech-specificity. In a reanalysis of magnetoencephalography data, we found that activity in the cerebellum aligned to rhythmic sequences of noise-vocoded speech, irrespective of its intelligibility. We then tested whether these \"entrained\" responses persist, and how they interact with other brain regions, when a rhythmic stimulus stopped and temporal predictions had to be updated. We found that only intelligible speech produced sustained rhythmic responses in the cerebellum. During this \"entrainment echo,\" but not during rhythmic speech itself, cerebellar activity was coupled with that in the left inferior frontal gyrus, and specifically at rates corresponding to the preceding stimulus rhythm. This finding represents evidence for specific cerebellum-driven temporal predictions in speech processing and their relay to cortical regions.

A neural signature of serial dependence has been found, which mirrors the attractive bias of visual information seen in behavioral experiments.

Responsive neurostimulation (RNS) is a treatment option for patients with refractory epilepsy when surgical resection is not possible due to overlap of the irritative zone and eloquent cortex. Presurgical evaluations for RNS placement typically rely on invasive methods. This study investigated the potential of transcranial magnetic stimulation (TMS) and magnetoencephalography (MEG) to provide key presurgical information non-invasively. We hypothesized that these non-invasive methods may assist in optimizing RNS placement by providing useful information for seizure localization by MEG and eloquent cortex mapping by TMS. A retrospective chart review identified nine patients who underwent RNS placement (mean age = 20.4 years [SD = 5.6], two-thirds were female). Characterization of the irritative zone using MEG was successful in eight of nine patients. Non-invasive mapping of relevant eloquent cortex was attempted in all patients. TMS was successful in eight of nine patients, and MEG was successful in two of six patients. Importantly, patients mapped with non-invasive modalities experienced an average seizure reduction of 77 % at their most recent clinic visit, compared to 75 % seizure reduction in those with invasive evaluations, indicating appropriate RNS placement. These data demonstrate that TMS and MEG can provide key information for RNS and may be feasible alternatives to invasive methods for assisting in decision making regarding RNS placement. Non-invasive methods for determining RNS placement have a high rate of success when data from multiple non-invasive modalities converge and can inform more accurate placement of intracranial electrodes prior to RNS placement or mitigate their need.

Neurophysiological signals, comprised of both periodic (e.g., oscillatory) and aperiodic (e.g., non-oscillatory) activity, undergo complex developmental changes between childhood and adulthood. With much of the existing literature primarily focused on the periodic features of brain function, our understanding of aperiodic signals is still in its infancy. Here, we are the first to examine age-related changes in periodic (peak frequency and power) and aperiodic (slope and offset) activity using optically pumped magnetometers (OPMs), a new, wearable magnetoencephalography (MEG) technology that is particularly well-suited for studying development. We examined age-related changes in these spectral features in a sample (N=65) of toddlers (1-3 years), children (4-5 years), young adults (20-26 years), and adults (27-38 years). Consistent with the extant literature, we found significant age-related decreases in the aperiodic slope and offset, and changes in peak frequency and power that were frequency-specific; we are the first to show that the effect sizes of these changes also varied across brain regions. This work not only adds to the growing body of work highlighting the advantages of using OPMs, especially for studying development, but also contributes novel information regarding the variation of neurophysiological changes with age across the brain.

Microstates are transient scalp configurations of brain activity measured by electroencephalography (EEG). The application of microstate analysis in magnetoencephalography (MEG) data remains challenging. In one MEG dataset (N = 113), we aimed to identify MEG microstates at rest, explore their brain sources, and relate them to changes in brain activity during open-eyes (ROE) or closed-eyes resting state (RCE) and an auditory Mismatch Negativity (MMN) task. In another dataset of simultaneously recorded EEG-MEG data (N = 21), we investigated the association between MEG and EEG microstates. Six MEG microstates (mMS) provided the best clustering of resting-state activity, each linked to different brain sources: mMS 1-2: left/right occipito-parietal; mMS 3: fronto-temporal; mMS 4: centro-medial; mMS 5-6: left/right fronto-parietal. Increases in occipital alpha power in RCE relative to ROE correlated with greater mMS 1-2 time coverage (τbs < 0.20, ps > .002), while the lateralization of deviance detection in MMN was associated with mMS 5-6 time coverage (τbs < 0.16, ps > .012). No temporal correlation was found between EEG and MEG microstates (ps > .05), despite some overlap in brain sources and global explained variance between mMS 2-3 and EEG microstates B-C (rs > 0.60, ps < .002). Hence, the MEG signal can be decomposed into microstates, but mMS brain activity clustering captures phenomena different from EEG microstates. Source reconstruction and task-related modulations link mMS to large-scale networks and localized activities. Thus, mMSs offer insights into brain dynamics and task-specific processes, complementing EEG microstates in studying physiological and dysfunctional brain activity.

Music is a non-verbal human language, built on logical, hierarchical structures, that offers excellent opportunities to explore how the brain processes complex spatiotemporal auditory sequences. Using the high temporal resolution of magnetoencephalography, we investigated the unfolding brain dynamics of 70 participants during the recognition of previously memorized musical sequences compared to novel sequences matched in terms of entropy and information content. Measures of both whole-brain activity and functional connectivity revealed a widespread brain network underlying the recognition of the memorized auditory sequences, which comprised primary auditory cortex, superior temporal gyrus, insula, frontal operculum, cingulate gyrus, orbitofrontal cortex, basal ganglia, thalamus, and hippocampus. Furthermore, while the auditory cortex responded mainly to the first tones of the sequences, the activity of higher-order brain areas such as the cingulate gyrus, frontal operculum, hippocampus, and orbitofrontal cortex largely increased over time during the recognition of the memorized versus novel musical sequences. In conclusion, using a wide range of analytical techniques spanning from decoding to functional connectivity and building on previous works, our study provided new insights into the spatiotemporal whole-brain mechanisms for conscious recognition of auditory sequences.