Localization of brain electrical activity via linearly constrained minimum variance spatial filtering

Computational modeling of ketamine-induced changes in gamma-band oscillations: The contribution of parvalbumin and somatostatin interneurons

Ketamine, an NMDA receptor (NMDA-R) antagonist, produces psychotomimetic effects when administered in sub-anesthetic dosages. While previous research suggests that Ketamine alters the excitation/inhibition (E/I)-balance in cortical microcircuits, the precise neural mechanisms by which Ketamine produces these effects are not well understood. We analyzed resting-state MEG data from n = 12 participants who were administered Ketamine to assess changes in gamma-band (30-90 Hz) power and the slope of the aperiodic power spectrum compared to placebo. In addition, correlations of these effects with gene-expression of GABAergic interneurons and NMDA-Rs subunits were analyzed. Finally, we compared Ketamine-induced spectral changes to the effects of systematically changing NMDA-R levels on pyramidal cells, and parvalbumin-, somatostatin- and vasoactive intestinal peptide-expressing interneurons in a computational model of cortical layer-2/3 to identify crucial sites of Ketamine action. Ketamine resulted in a flatter aperiodic slope and increased gamma-band power across brain regions, with pronounced effects in prefrontal and central areas. These effects were correlated with the spatial distribution of parvalbumin and GluN2D gene expression. Computational modeling revealed that reduced NMDA-R activity in parvalbumin or somatostatin interneurons could reproduce increased gamma-band power by increasing pyramidal neuron firing rate, but did not account for changes in the aperiodic slope. The results suggest that parvalbumin and somatostatin interneurons may underlie increased gamma-band power following Ketamine administration in healthy volunteers, while changes in the aperiodic component could not be recreated. These findings have implications for current models of E/I-balance, as well as for understanding the mechanisms underlying the circuit effects of Ketamine.

Greedy Optimization of Sensor Array Geometry for Magnetocardiographic Source Localization

OBJECTIVE

Until recently, magnetocardiography (MCG) studies were performed using SQUID systems, consisting of a planar array of sensors with uniform spacing. The introduction of optically-pumped magnetometers (OPMs) now enables the deployment of large, conformal arrays, in which the sensors can be mounted on a wearable vest at nearly any location. The objective of this study was to optimize the sensor array geometry of an OPM system for MCG imaging applications.

METHODS

We devised a new optimization criterion for spatial resolution based on sensitivity to localization error. We also implemented a greedy optimization technique to overcome the difficulty of combinatoric optimization over an extremely large number of possible sensor configurations. Simulations were performed to compare the localization accuracy of the optimized arrays to that of conventional arrays with a regular geometry over the front of the torso. The number of sensors and the signal-to-noise ratio were varied.

RESULTS

Optimization resulted in non-planar, irregular geometries biased toward the left half of the torso. Arrays optimized for posterior cardiac sources showed the best overall performance. The localization accuracy was shown to be significantly improved by optimization for a given number of sensors and signal-to-noise ratio.

CONCLUSION

The results of this study can serve as a guide for designing MCG arrays for a given number of sensors and/or determining the required number of sensors for a given level of performance.

SIGNIFICANCE

Sensor array optimization can improve the performance of OPM-based MCG imaging systems for applications, such as non-invasive localization of arrhythmogenic foci.

Multimodal MEG and Microstructure-MRI Investigations of the Human Hippocampal Scene Network

Although several studies have demonstrated that perceptual discrimination of complex scenes relies on an extended hippocampal posteromedial system, we currently have limited insight into the specific functional and structural properties of this system in humans. Here, combining electrophysiological (magnetoencephalography) and advanced microstructural (multishell diffusion magnetic resonance imaging; quantitative magnetization transfer) imaging in healthy human adults (30 females/10 males), we show that both theta power modulation of the hippocampus and fiber restriction/hindrance (reflecting axon packing/myelination) of the fornix (a major input/output pathway of the hippocampus) were independently related to scene, but not face, perceptual discrimination accuracy. Conversely, microstructural features of the inferior longitudinal fasciculus (a long-range occipitoanterotemporal tract) correlated with face, but not scene, perceptual discrimination accuracy. Our results provide new mechanistic insight into the neurocognitive systems underpinning complex scene discrimination, providing novel support for the idea of multiple processing streams within the human medial temporal lobe.

Oscillatory brain dynamics underlying affective face processing

Facial expressions are ubiquitous and highly reliable social cues. Decades of research has shown that affective faces undergo facilitated processing across a distributed brain network. However, few studies have examined the multispectral brain dynamics underlying affective face processing, which is surprising given the multiple brain regions and rapid temporal dynamics thought to be involved. Herein, we used magnetoencephalography to derive dynamic functional maps of angry, neutral, and happy face processing in healthy adults. We found stronger theta oscillations shortly after the onset of affective relative to neutral faces (0-250 ms), within distributed ventral visual and parietal cortices, and the anterior hippocampus. Early gamma oscillations (100-275 ms) were strongest for angry faces in the inferior parietal lobule, temporoparietal junction, and presupplementary motor cortex. Finally, beta oscillations (175-575 ms) were stronger for neutral relative to affective expressions in the middle occipital and fusiform cortex. These results are consistent with the literature in regard to the critical brain regions, and delineate a distributed network where multispectral oscillatory dynamics support affective face processing through the rapid merging of low-level visual inputs to interpret the emotional meaning of each facial expression.

Neocortical and Hippocampal Theta Oscillations Track Audiovisual Integration and Replay of Speech Memories

"Are you talkin' to me?!" If you ever watched the masterpiece "Taxi Driver" directed by Martin Scorsese, you certainly recall the monologue during which Travis Bickle rehearses an imaginary confrontation in front of a mirror. While remembering this scene, you recollect a myriad of speech features across visual and auditory senses with a smooth sensation of unified memory. The aim of this study was to investigate how the fine-grained synchrony between coinciding visual and auditory features impacts brain oscillations when forming multisensory speech memories. We developed a memory task presenting participants with short synchronous or asynchronous movie clips focused on the face of speakers in real interviews, all the while undergoing magnetoencephalography recording. In the synchronous condition, the natural alignment between visual and auditory onsets was kept intact. In the asynchronous condition, auditory onsets were delayed to present lip movements and speech sounds in antiphase specifically with respect to the theta oscillation synchronizing them in the original movie. Our results first showed that theta oscillations in the neocortex and hippocampus were modulated by the level of synchrony between lip movements and syllables during audiovisual speech perception. Second, theta asynchrony between the lip movements and auditory envelope during audiovisual speech perception reduced the accuracy of subsequent theta oscillation reinstatement during memory recollection. We conclude that neural theta oscillations play a pivotal role in both audiovisual integration and memory replay of speech.

Cannabis- and HIV-related perturbations to the cortical gamma dynamics supporting inhibitory processing

The main psychoactive component in cannabis-Δ9-tetrahydrocannabinol-is known to have anti-inflammatory properties and to alter gamma oscillations, pointing to its potential as a therapeutic agent for people with HIV (PWH). However, it remains unknown how cannabis use among PWH interacts with the neural circuitry underlying inhibitory processing. Herein, using a cross-sectional study design, we collected data from 108 cannabis users and non-users with and without HIV. Participants were interviewed regarding their substance use history and completed a paired-pulse somatosensory stimulation paradigm during magnetoencephalography (MEG). MEG data were imaged using a beamformer and peak voxel time series data were extracted to examine neural oscillations in response to the stimulation and the strength of spontaneous activity in the same tissue during the baseline period. Across all participants, we observed robust gamma oscillations following stimulation in the left primary somatosensory cortices, with responses to the second stimulation being strongly attenuated relative to the first, thus demonstrating somatosensory gating. PWH who used cannabis exhibited stronger oscillatory gamma activity compared with non-users with HIV, while the latter group also exhibited elevated spontaneous gamma activity relative to all other groups. Finally, we found that a longer duration of time since HIV diagnosis was associated with less efficient inhibitory processing among PWH who did not use cannabis, but not among PWH who regularly use cannabis. These findings provide new evidence that cannabis use may mitigate the harmful effects of HIV on oscillatory and spontaneous gamma activity serving inhibitory processing.

Association of spatiotemporal interaction of gamma oscillations with heart rate variability during response inhibition processing in patients with major depressive disorder: An MEG study

BACKGROUND

Impairment in response inhibition function is highly prevalent in patients with major depressive disorder (MDD), yet the spatiotemporal neural activity underlying response inhibition and its relationship with the autonomic nervous system (ANS) remains unclear.

METHODS

35 MDD participants and 35 healthy controls (HC) were included with magnetoencephalography (MEG) and electrocardiogram (ECG) data collecting during a go/no-go task. Heart rate variability (HRV) indices were calculated from the ECG data. Differences in functional connectivity (FC) of gamma oscillations (60-90 Hz) between 0-200 ms, 200-400 ms, and 400-600 ms in the two groups after no-go stimuli were analyzed, and the correlation between FC and HRV indices was examined.

RESULTS

The MDD group exhibited poorer task performance and lower HRV indices than the HC group. During the 200-400 ms period, compared to the HC group, the MDD group exhibited decreased FC between the left inferior frontal gyrus (opercular part) and right temporal pole (middle temporal gyrus) (t = 3.62, p < 0.05), and increased FC between the right superior frontal gyrus (orbital part) and right superior occipital gyrus (t = 3.68, p < 0.05). Additionally, a significant positive correlation was found between FC of the left inferior frontal gyrus (opercular part) and right middle temporal gyrus (temporal pole) and the HRV index RMSSD in the MDD group (r = 0.491, p < 0.05).

CONCLUSION

Abnormal spatiotemporal interactions in gamma oscillations related to response inhibition are observed in MDD patients and abnormal gamma oscillations showed task-dependent covariation with ANS indices, suggesting their potential interplay in MDD pathophysiology.

Electrophysiological Correlates of Lucid Dreaming: Sensor and Source Level Signatures

Lucid dreaming (LD) is a state of conscious awareness of the ongoing oneiric state, predominantly linked to REM sleep. Progress in understanding its neurobiological basis has been hindered by small sample sizes, diverse EEG setups, and artifacts like saccadic eye movements. To address these challenges in characterizing the electrophysiological correlates of LD, we introduced an adaptive multistage preprocessing pipeline, applied to human data (male and female) pooled across laboratories, allowing us to explore sensor- and source-level markers of LD. We observed that, while sensor-level differences between LD and nonlucid REM sleep were minimal, mixed-frequency analysis revealed broad low alpha to gamma power reductions during LD compared with wakefulness. Source-level analyses showed significant beta power (12-30 Hz) reductions in right central and parietal areas, including the temporoparietal junction, during LD. Moreover, functional connectivity in the alpha band (8-12 Hz) increased during LD compared with nonlucid REM sleep. During initial LD eye signaling compared with the baseline, source-level gamma1 power (30-36 Hz) increased in right temporo-occipital regions, including the right precuneus. Finally, functional connectivity analysis revealed increased interhemispheric and inter-regional gamma1 connectivity during LD, reflecting widespread network engagement. These results suggest that distinct source-level power and connectivity patterns characterize the dynamic neural processes underlying LD, including shifts in network communication and regional activation that may underlie the specific changes in perception, memory processing, self-awareness, and cognitive control. Taken together, these findings illuminate the electrophysiological correlates of LD, laying the groundwork for decoding the mechanisms of this intriguing state of consciousness.

Abstract choice representations during stable choice-response associations

An increasing body of evidence has demonstrated neural representations of choices independent of the motor actions used to report them - so-called abstract choices. However, it remains unclear whether such representations arise due to dynamic changes in choice-response associations or reflect a general property of decision-making. Here, we show that in the human brain, choices are represented abstractly even when choice-response associations remain stable over time. We recorded neural activity using magnetoencephalography while participants performed a motion discrimination task, with choice-response mappings held constant within blocks. We found neural information about participants' perceptual choices independent of both motor response and visual stimulus. Choice information increased during the stimulus and peaked after the response. Moreover, choice and response information showed distinct cortical distributions, with choice-related signals strongest in frontoparietal regions. Thus, abstract choice representations are not limited to dynamic or action-independent contexts and may be a general feature of decision-making.

The Inattentional Rhythm in Audition

The detection of temporally unpredictable visual targets depends on the preceding phase of alpha oscillations (∼7-12 Hz). In audition, however, such an effect seemed to be absent. Due to the transient nature of its input, the auditory system might be particularly vulnerable to information loss that occurs if relevant information coincides with the low-excitability phase of the oscillation. We therefore hypothesized that effects of oscillatory phase in audition will be restored if auditory events are made task irrelevant and information loss can be tolerated. To this end, we collected electroencephalography (EEG) data from 29 human participants (21F) while they detected pure tones at one sound frequency and ignored others. Confirming our hypothesis, we found that the neural response to task-irrelevant but not to task-relevant tones depends on the prestimulus phase of neural oscillations. Alpha oscillations modulated early stages of stimulus processing, whereas theta oscillations (∼3-7 Hz) affected later components, possibly related to distractor inhibition. We also found evidence that alpha oscillations alternate between sound frequencies during divided attention. Together, our results suggest that the efficacy of auditory oscillations depends on the context they operate in and demonstrate how they can be employed in a system that heavily relies on information unfolding over time.

Individual alpha frequency tACS reduces static functional connectivity across the default mode network

Introduction

Research on the influence of transcranial alternating current stimulation over alpha functional connectivity (FC) is scarce, even when it poses as a potential treatment for various diseases. This study aimed to investigate the effects of individual alpha frequency tACS (IAF-tACS) on FC within the default mode network (DMN) in healthy individuals, particularly following the triple network model.

Materials and methods

27 healthy participants were recruited, who underwent a 20-min IAF-tACS session over parieto-occipital areas and three magnetoencephalography (MEG) recordings: two pre-stimulation and one post-stimulation. Participants were randomly assigned to either the stimulation or sham group. Both dynamic FC (dFC) and static FC (sFC) were evaluated through the leakage corrected amplitude envelope correlation (AEC-c). Statistical analyses compared both Pre-Post FC ratio between groups through ratio t-tests and intragroup FC changes through repeated measures t-tests, with FDR correction applied to account for multiple comparisons. An additional analysis simulated the influence of the cortical folding on the effect of tACS over FC.

Results

IAF-tACS significantly decreased sFC in intra- and inter-DMN links in the stimulation group compared to the sham group, with a special influence over antero-posterior links between hubs of the DMN. Negative correlations were found between AEC-c sFC changes and power alterations in posterior DMN areas, suggesting a complex interaction between cortical folding and electric field direction. On the other hand, dFC increased in both sham and stimulation groups, and no between-group differences were found.

Conclusion

Against our initial hypothesis, IAF-tACS reduced sFC in the DMN, possibly due to phase disparities introduced by cortical gyrification. These findings suggest that tACS might modulate FC in a more complex manner than previously thought, highlighting the need for further research into the personalized application of neuromodulation techniques, as well as its potential therapeutic implications for conditions like Alzheimer's disease.

Phase-amplitude coupling within MEG data can identify eloquent cortex

Objective.Proper identification of eloquent cortices is essential to minimize post-surgical deficits in patients undergoing resection for epilepsy and tumors. Current methods are subjective, vary across centers, and require significant expertise, underscoring the need for more objective pre-surgical mapping. Phase-amplitude coupling (PAC), the interaction between the phase of low-frequency oscillations and the amplitude of high-frequency activity, has been implicated in task-induced brain activity and may serve as a biomarker for functional mapping. Our objective was to develop a novel PAC-based algorithm to non-invasively identify somatosensory eloquent cortex using magnetoencephalography (MEG) data in epilepsy patients.Approach.We analyzed somatosensory and spontaneous MEG recordings from 30 subjects with drug-resistant epilepsy. PAC was calculated on source-reconstructed data (5-12 Hz for low frequencies and 30-300 Hz for high frequencies), followed by rank-2 tensor decomposition. Density-based clustering compared active brain regions during somatosensory task and spontaneous data at a population level. We employed a linear mixed-effects model to quantify changes in PAC between somatosensory and resting-state data. We developed a patient-specific support vector machine (SVM) classifier to identify active brain regions based on PAC values during the somatosensory task.Main results.Five of six expected brain regions were active during left and right-sided stimulation (p=1.08×10-8, hypergeometric probability test). The mixed-effects model confirmed task-specific PAC in anatomically relevant brain regions (p < 0.01). The SVM classifier gave a specificity of 99.46% and a precision of 66.9%. These results demonstrate that the PAC algorithm reliably identifies somatosensory cortex activation at both individual and population levels with statistical significance.Significance.This study demonstrates the feasibility of using PAC as a non-invasive marker for identifying functionally relevant brain regions during somatosensory task in epilepsy patients. Future work will evaluate its applicability for mapping other eloquent cortices, including language, motor, and auditory areas.

Non-feature-specific elevated responses and feature-specific backward replay in human brain induced by visual sequence exposure

The ability of cortical circuits to adapt in response to experience is a fundamental property of the brain. After exposure to a moving dot sequence, flashing a dot as a cue at the starting point of the sequence can elicit successive elevated responses even in the absence of the sequence. These cue-triggered elevated responses have been shown to play a crucial role in predicting future events in dynamic environments. However, temporal sequences we are exposed to typically contain rich feature information. It remains unknown whether the elevated responses are feature-specific and, more crucially, how the brain organizes sequence information after exposure. To address these questions, participants were exposed to a predefined sequence of four motion directions for about 30 min, followed by the presentation of the start or end motion direction of the sequence as a cue. Surprisingly, we found that cue-triggered elevated responses were not specific to any motion direction. Interestingly, motion direction information was spontaneously reactivated, and the motion sequence was backward replayed in a time-compressed manner. These effects were observed even after brief exposure. Notably, no replay events were observed when the second or third motion direction of the sequence served as a cue. Further analyses revealed that activity in the medial temporal lobe (MTL) preceded the ripple power increase in visual cortex at the onset of replay, implying a coordinated relationship between the activities in the MTL and visual cortex. Together, these findings demonstrate that visual sequence exposure induces twofold brain plasticity that may simultaneously serve for different functional purposes. The non-feature-specific elevated responses may facilitate general processing of upcoming stimuli, whereas the feature-specific backward replay may underpin passive learning of visual sequences.

A Novel Candidate Neuromarker of Central Motor Dysfunction in Childhood Apraxia of Speech

Childhood apraxia of speech (CAS) is conceived as an impairment of the central motor system's ability to program multiple speech movements, resulting in inaccurate transitions between and relative timing across speech sounds. However, the extant neuroimaging evidence base is scant and inconclusive, and the neurophysiological origins of these motor planning problems remain highly underspecified. In the first magnetoencephalography study of this disorder, we measured brain activity from typically developing (TD) children (N = 19, 11 males, 8 females) and children with CAS (N = 7 males) during performance of a speech task designed to interrogate function of the speech areas of the primary sensorimotor cortex. Relative to their TD peers, our sample of children with CAS showed abnormal speech-related responses within the mu-band motor rhythm, and beamformer source reconstruction analyses specify a brain origin of this speech rhythm in the left cerebral hemisphere, within or near pre-Rolandic motor areas crucial for the planning and control of speech and oromotor movements. These results provide a new and specific candidate mechanism for the core praxic features of CAS; point to a novel and robust neurophysiological marker of typical and atypical expressive speech development; and support an emerging neuroscientific consensus which assigns a central role for programming and coordination of speech movements to the motor cortices of the precentral gyrus.

Oscillatory Coupling Between Thalamus, Cerebellum, and Motor Cortex in Essential Tremor

BACKGROUND

Essential tremor is hypothesized to emerge from synchronized oscillatory activity within the cerebello-thalamo-cortical circuit. However, this hypothesis has not yet been tested using local field potentials directly recorded from the thalamus alongside signals from both the cortex and cerebellum, leaving a gap in the understanding of essential tremor.

OBJECTIVES

To clarify the importance of cerebello-thalamo-cortical oscillatory coupling for essential tremor.

METHODS

We investigated oscillatory coupling between thalamic local field potentials and simultaneously recorded magnetoencephalography in 19 essential tremor patients with externalized deep brain stimulation electrodes. Brain activity was measured while patients repeatedly adopted a tremor-provoking posture and while pouring rice grains from one cup to another. In a whole-brain analysis of coherence between the ventral intermediate nucleus of the thalamus and cortex we contrasted epochs containing tremor and epochs lacking tremor.

RESULTS

Both postural and kinetic tremor were associated with an increase of thalamic power and thalamo-cortex coherence at individual tremor frequency in the bilateral cerebellum and primary sensorimotor cortex contralateral to tremor. These areas also exhibited an increase in corticomuscular coherence in the presence of tremor. The coupling of motor cortex to both thalamus and muscle correlated with tremor amplitude during postural tremor.

CONCLUSIONS

These results demonstrate that essential tremor is indeed associated with increased oscillatory coupling at tremor frequency within a cerebello-thalamo-cortical network, with coupling strength directly reflecting tremor severity. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Facilitating cognitive neuroscience research with 80-sensor optically pumped magnetometer magnetoencephalography (OPM-MEG)

Recent advancements in optically pumped magnetometer magnetoencephalography (OPM-MEG) make it a promising alternative to conventional SQUID-MEG systems. Nonetheless, as reported in the literature, current OPM-MEG systems are often constrained by a limited number of sampling points, which restricts their capability to match the full-head coverage offered by SQUID-MEG systems. Additionally, whether OPM-MEG can deliver results comparable to SQUID-MEG in practical cognitive neuroscience applications remains largely unexplored. In this study, we introduce a high-density, full-head coverage OPM-MEG system with 80 sensors and systematically compare the performance of OPM-MEG and SQUID-MEG, from sensor- to source-level analysis, across various classic cognitive tasks. Our results demonstrate that visual and auditory evoked fields captured using OPM-MEG align closely with those obtained from SQUID-MEG. Furthermore, steady-state visual evoked field and finger-tapping-induced beta power change recorded with OPM-MEG are accurately localized to corresponding brain regions, with activation centers highly congruent to those observed with SQUID-MEG. For resting-state recordings, the two modalities exhibit similar power distributions, functional connectomes, and microstate clusters. These findings indicate that the 80-sensor OPM-MEG system provides spatial and temporal characteristics comparable to those of traditional SQUID-MEG. Thus, our study offers empirical evidence supporting the efficacy of high-density OPM-MEG and suggests that OPM-MEG, with dense sampling capability, represents a compelling alternative to conventional SQUID-MEG, facilitating further exploration of human cognition.

GABAergic modulation of beta power enhances motor adaptation in frontotemporal lobar degeneration

INTRODUCTION

We examined how abnormal prefrontal neurophysiology and changes in gamma-aminobutyric acid-ergic (GABAergic) neurotransmission contribute to behavioral impairments in disorders associated with frontotemporal lobar degeneration (FTLD).

METHODS

We recorded magnetoencephalography during an adaptive visuomotor task from 11 people with behavioral-variant frontotemporal dementia, 11 with progressive supranuclear palsy, and 20 age-matched controls. We used tiagabine, a gamma-aminobutyric acid (GABA) re-uptake inhibitor, as a pharmacological probe to assess the role of GABA during motor-related beta power changes.

RESULTS

Task impairments were associated with diminished movement-related beta power. Tiagabine facilitated partial recovery of behavioral impairments and neurophysiology, moderated by executive function, such that the greatest improvements were seen in those with higher cognitive scores. The right prefrontal cortex was revealed as a key site of drug interaction.

DISCUSSION

Behavioral and neurophysiological deficits can be mitigated by enhancement of GABAergic neurotransmission. Clinical trials are warranted to test for enduring clinical benefits from this restorative-psychopharmacology strategy.

HIGHLIGHTS

Event-related beta power changes during movement can be altered by the GABA reuptake inhibitor, tiagabine. In people with behavioral-variant frontotemporal dementia and progressive supranuclear palsy, tiagabine enhanced beta modulation and concurrently improved task performance, dependent on baseline cognition, and diagnosis. The effects of the drug suggest a GABA-dependent beta-related mechanism that underlies adaptive motor control. Restoring selective deficits in neurotransmission is a potential means to improve behavioral symptoms in patients with dementia.

Brain-wide spatial mapping of oscillatory activity during naturalistic motor behavior

Understanding oscillatory neural activity associated with motor behavior is greatly contributing to the development of neuroprosthetic systems, robotic interfaces, and advanced neurorehabilitation techniques. Most current knowledge about movement-specific patterns of cortical activity is derived from laboratory experiments using highly standardized, repetitive, and often meaningless movements that are very distinct from natural motor behavior. This is characterized by frequent task switching, diverse kinematics, and endogenous motivation. Whether observed patterns of movement-related neural activity during standard laboratory tasks can be generalized to natural motor behavior is largely unknown. Here, we investigated the spatial, spectral, and temporal features of oscillatory neural activity associated with human motor control in a parkour of everyday movements. We replicated strong and significant decreases in the alpha/beta frequency range before movement onset and further show that this power decrease began about 2 s before movement initiation and reached a nadir around movement onset. In addition to the sustained event-related decrease in the alpha/beta range, we identified brief (4-5 cycles) increases in low-frequency activity (3-5 Hz) that either preceded or peaked at movement onset. These low-frequency increases exhibited much greater focality and lateralization compared with the wide-spread alpha/beta decrease. Together, our results provide a comprehensive account of brain rhythmic electric activity across spatial, spectral, and temporal scales in naturalistic motor behavior. Movement-preceding low-frequency activity has previously been identified as a promising brain stimulation target in patients with stroke. Detectability of low-frequency activity in naturalistic movements may enhance its utility as a target for on-demand brain stimulation in neurorehabilitation.NEW & NOTEWORTHY We here provide a comprehensive account of brain rhythmic electric activity across spatial, spectral, and temporal scales, associated with ecologically valid, freely chosen, auditory cued, and visually guided movements of either hand. New and noteworthy, the brain-wide topography of movement preceding, short-lasting increases in low-frequency activity (3-5 Hz), recently identified as a promising target for on-demand neurostimulation in stroke rehabilitation, is described and compared with classically studied sensorimotor rhythms.

Subthalamic stimulation evokes hyperdirect high beta interruption and cortical high gamma entrainment in Parkinson's disease

Compound network dynamics in beta and gamma bands determine the severity of bradykinesia in Parkinson's disease. We explored its subthalamic stimulation related changes parallel with improvement of complex hand movements. Thirty eight patients with Parkinson's disease treated with bilateral stimulation accomplished voluntary and traced spiral drawing with their more affected hand on a digital tablet. A 64 channel electroencephalography was recorded, low and high beta and gamma power was computed in subthalamic and motor cortical sources at four stimulation levels. Subthalamic cortical effective connectivity was calculated, and subnetwork models were created. Beta power decreased, and gamma power increased in sources ipsilateral to stimulation with increasing stimulation intensity. Networks comprising the primary motor cortex played a dominant role in predicting the improvement of voluntary drawing speed. Subthalamic stimulation diminished the hyperdirect high beta information processing and promoted the cortico cortical interactions of the primary motor cortex in the high gamma band.

Reducing Tinnitus via Inhibitory Influence of the Sensorimotor System on Auditory Cortical Activity

Tinnitus is the subjective perception of a sound without corresponding external acoustic stimuli. Research highlights the influence of the sensorimotor system on tinnitus perception. Associated neuronal processes, however, are insufficiently understood, and it remains unclear how and at which hierarchical level the sensorimotor system interacts with the tinnitus-processing auditory system. We therefore asked 23 patients suffering from chronic tinnitus (11 males) to perform specific exercises, aimed at relaxing or tensing the jaw area, which temporarily modulated tinnitus perception. Associated neuronal processes were assessed using magnetencephalography. Results show that chronic tinnitus patients experienced their tinnitus as weaker and less annoying after completion of relaxing compared with tensing exercises. Furthermore, (1) sensorimotor alpha power and alpha-band connectivity directed from the somatosensory to the auditory cortex increased and (2) gamma power in the auditory cortex reduced, which (3) related to reduced tinnitus annoyance perception on a trial-by-trial basis in the relaxed state. No effects were revealed for 23 control participants without tinnitus (six males) performing the same experiment. We conclude that the increase in directed alpha-band connectivity from the somatosensory to the auditory cortex most likely reflects the transmission of inhibition from the somatosensory to the auditory cortex during relaxation, where concurrently tinnitus-related gamma power reduces. We suggest that revealed neuronal processes are transferable to other tinnitus-modulating systems beyond the sensorimotor one that is involved in attentional or emotional tinnitus modulation and provides deeper mechanistic insights into how and through which channels phantom sound perception might be modulated on a neuronal level.

Investigating the effects of calibration errors on the spatial resolution of OPM-MEG beamformer imaging

The use of optically pumped magnetometers (OPMs) has provided a feasible, moveable and wearable alternative to superconducting detectors for magnetoencephalography (MEG) measurements. Recently, the widely used beamformer imaging technique has greatly improved spatial accuracy of MEG in the field of source reconstruction of neuroimaging. The spatial resolution of the source reconstruction using beamformer imaging technique was explored in the present study. The spatial accuracy of a beamformer reconstruction depends on accurate estimation of the data covariance matrix and lead field. In practical measurements, many sensor calibration errors including the gain error, crosstalk and angular error of the sensitive axis of OPMs due to for example, the low frequency magnetic field drift will distort the measured data as well as the forward model and thus reduce spatial resolution. The theory of OPM calibration errors was first provided based on the Bloch equations. The calibration errors are then quantified using the self-developed OPM array. And an analytical relationship between the Frobenius norm of the covariance matrix error and gain error, crosstalk was derived. The relationship between point-spread function (PSF) and the forward model error caused by the angular error of sensitive axis was analyzed. Finally, the effects of calibration errors on spatial resolution of OPM-MEG were investigated using simulations of two dipoles with orthogonal signals at the source level based on realistic head models. We find the presence of calibration errors will decrease the spatial resolution of beamformer reconstruction. And this decrease will become more severe as the signal-to-noise ratio increases.

Hemisphere- and condition-specific alpha oscillations support semantic and spatial cognition in aging

Neuroimaging studies have shown age-related alterations in brain structure and function supporting semantic knowledge, although the significance of these is not fully understood. Herein, we report novel temporal, spectral, and spatial information on age-related changes from the largest dynamic functional mapping study of semantic processing. Participants (N = 130, age range 21-87 years, Mage = 51.05, SD = 14.73, 68 females) performed a semantic judgement task during magnetoencephalography (MEG), and significant task-related oscillatory responses were projected into anatomical space using a beamformer. Voxel-wise linear mixed-effects models were performed to assess semantic-related neural oscillations, irrespective of and influenced by age. Mediation analyses were performed to assess if local oscillations mediated the relationship between age and reaction time. Whole-brain analyses revealed stronger left-lateralized alpha/beta oscillations in frontotemporal cortices during semantic trials and stronger right-lateralized alpha/beta responses in temporoparietal regions during length trials (all ps <.001). Older adults showed stronger left temporoparietal alpha and left frontal beta during semantic processing and stronger alpha in the right temporal cortex during the length condition (all ps <.001). Alpha oscillations further mediated the relationship between age and reaction time in a hemisphere- and condition-specific manner, whereby right temporal activity mediated the age-behavior relationship in the length but not semantic condition (Z = 2.01, p =.022), while left temporoparietal activity significantly mediated this relationship in the semantic but not length condition (Z =  - 2.41, p =.008). Altogether, our findings demonstrate accentuated oscillations in aging which are hemisphere- and condition-specific and support compensatory processing to aid in maintaining adequate behavioral performance, lending clear support to leading neuroscientific models of aging.

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.

Structured noise champagne: an empirical Bayesian algorithm for electromagnetic brain imaging with structured noise

Introduction

Electromagnetic brain imaging is the reconstruction of brain activity from non-invasive recordings of electroencephalography (EEG), magnetoencephalography (MEG), and also from invasive ones such as the intracranial recording of electrocorticography (ECoG), intracranial electroencephalography (iEEG), and stereo electroencephalography EEG (sEEG). These modalities are widely used techniques to study the function of the human brain. Efficient reconstruction of electrophysiological activity of neurons in the brain from EEG/MEG measurements is important for neuroscience research and clinical applications. An enduring challenge in this field is the accurate inference of brain signals of interest while accounting for all sources of noise that contribute to the sensor measurements. The statistical characteristic of the noise plays a crucial role in the success of the brain source recovery process, which can be formulated as a sparse regression problem.

Method

In this study, we assume that the dominant environment and biological sources of noise that have high spatial correlations in the sensors can be expressed as a structured noise model based on the variational Bayesian factor analysis. To the best of our knowledge, no existing algorithm has addressed the brain source estimation problem with such structured noise. We propose to apply a robust empirical Bayesian framework for iteratively estimating the brain source activity and the statistics of the structured noise. In particular, we perform inference of the variational Bayesian factor analysis (VBFA) noise model iteratively in conjunction with source reconstruction.

Results

To demonstrate the effectiveness of the proposed algorithm, we perform experiments on both simulated and real datasets. Our algorithm achieves superior performance as compared to several existing benchmark algorithms.

Discussion

A key aspect of our algorithm is that we do not require any additional baseline measurements to estimate the noise covariance from the sensor data under scenarios such as resting state analysis, and other use cases wherein a noise or artifactual source occurs only in the active period but not in the baseline period (e.g., neuro-modulatory stimulation artifacts and speech movements).

Reactivation of previous decisions repulsively biases sensory encoding but attractively biases decision-making

Automatic shaping of perception by past experiences is common in many cognitive functions, reflecting the exploitation of temporal regularities in environments. A striking example is serial dependence, i.e., current perception is biased by previous trials. However, the neural implementation of its operational circle in human brains remains unclear. In two experiments with electroencephalography (EEG)/magnetoencephalography (MEG) recordings and delayed-response tasks, we demonstrate a two-stage 'repulsive-then-attractive' past-present interaction mechanism underlying serial dependence. First, past-trial reports, instead of past stimuli, serve as a prior to be reactivated during both encoding and decision-making. Crucially, past reactivation interacts with current information processing in a two-stage manner: repelling and attracting the present during encoding and decision-making, and arising in the sensory cortex and prefrontal cortex, respectively. Finally, while the early stage occurs automatically, the late stage is modulated by task and predicts bias behavior. These findings might also illustrate general mechanisms of past-present influences in neural operations.

Association of a DASH diet and magnetoencephalography in dementia-free adults with different risk levels of Alzheimer's disease

This study explored how adherence to the DASH diet relates to electrophysiological measures in individuals at varying Alzheimer's disease (AD) risk due to family history (FH). There were 179 dementia-free subjects. DASH index was calculated, and participants were classified into different DASH adherence groups. Tertiles of relative alpha power in default mode network (DMN) regions were calculated. Multivariate logistic regression models were used to examine the association. Lower DASH adherence was associated with decreased odds of higher relative alpha power in the DMN, observed across the entire sample and specifically among those without a FH of AD. Logistic regression models indicated that participants with poorer DASH adherence had a reduced likelihood of elevated DMN alpha power, potentially influenced by vascular and amyloid-beta mechanisms. These findings underscore the dietary pattern's potential role in neural activity modulation, particularly in individuals not genetically predisposed to AD.

The Neural Oscillatory Basis of Perspective-Taking in Autistic and Non-Autistic Adolescents Using Magnetoencephalography

Taking another's perspective is a high-level mental skill underlying many aspects of social cognition. Perspective-taking is usually an embodied egocentric process whereby people mentally rotate themselves away from their physical location into the other's orientation. This is accompanied by increased theta-band (3-7 Hz) brain oscillations within a widespread fronto-parietal cortical network including the temporoparietal junction. Individuals with autism spectrum conditions (ASC) have been reported to experience challenges with high-level perspective-taking, particularly when adopting embodied strategies. To investigate the potential neurophysiological basis of these autism-related individual differences, we used magnetoencephalography in combination with a well-replicated perspective-taking paradigm in a group of 18 autistic and 17 age-matched non-autistic adolescents. Findings revealed that increasing the angle between self and other perspective resulted in prolonged reaction times for the autistic group during perspective-taking. This was accompanied by reduced theta power across a wide network of regions typically active during social cognitive tasks. On the other hand, the autistic group showed greater alpha power decreases in visual cortex compared with the non-autistic group across all perspective-taking conditions. These divergent theta and alpha power effects, coupled with steeper response time slopes, suggest that autistic individuals may rely more on alternative cognitive strategies, such as mental object rotation, rather than an egocentric embodied approach. Finally, no group differences were found when participants were asked to track, rather than take, another's viewpoint, suggesting that autism-related individual differences are specific to high-level perspective-taking.

Flexible Alternating Projection for Spatially Extended Brain Source Localization

OBJECTIVE

Understanding neural sources behind MEG and EEG signals is significant for basic and clinical neuroscience. Existing techniques, such as Recursively Applied and Projected Multiple Signal Classification (RAP-MUSIC) and Alternating Projection (AP), rely on limited current dipoles, representing focal sources with zero spatial extent. However, this oversimplifies realistic neural activity, which exhibits varying spatial extents.

METHODS

To address this, we enhanced the AP approach, creating FLEX-AP, capable of localizing discrete and extended sources. FLEX-AP simultaneously optimizes location and spatial extent of candidate sources.

RESULTS

FLEX-AP demonstrated superior performance, reducing localization errors compared to AP, RAP-MUSIC, and FLEX-RAP-MUSIC, particularly with extended sources. Moreover, FLEX-AP exhibited enhanced robustness against modeling errors in realistic scenarios. Applying FLEX-AP to MEG recordings of auditory responses validated its effectiveness, underscoring potential in advancing neuroscientific investigations.

CONCLUSION

FLEX-AP offers a robust, flexible framework for M/EEG source localization, overcoming limitations of simplistic zero-extent dipole models. By accurately estimating position and spatial extent of neural sources, FLEX-AP bridges the gap between theoretical models and realistic activity, demonstrating utility in simulated and real-world scenarios.

SIGNIFICANCE

FLEX-AP advances source imaging techniques with implications for basic neuroscience and clinical applications. Modeling extended sources precisely enables more accurate investigations of brain dynamics, potentially improving diagnostic and therapeutic approaches for neurological and psychiatric disorders.

The Role of the Dorsolateral Prefrontal Cortex in Ego Dissolution and Emotional Arousal During the Psychedelic State

Lysergic acid diethylamide (LSD) is a classic serotonergic psychedelic that induces a profoundly altered conscious state. In conjunction with psychological support, it is currently being explored as a treatment for generalized anxiety disorder and depression. The dorsolateral prefrontal cortex (DLPFC) is a brain region that is known to be involved in mood regulation and disorders; hypofunction in the left DLPFC is associated with depression. This study investigated the role of the DLPFC in the psycho-emotional effects of LSD with functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) data of healthy human participants during the acute LSD experience. In the fMRI data, we measured the correlation between changes in resting-state functional connectivity (RSFC) of the DLPFC and post-scan subjective ratings of positive mood, emotional arousal, and ego dissolution. We found significant, positive correlations between ego dissolution and functional connectivity between the left & right DLPFC, thalamus, and a higher-order visual area, the fusiform face area (FFA). Additionally, emotional arousal was significantly associated with increased connectivity between the right DLPFC, intraparietal sulcus (IPS), and the salience network (SN). A confirmational "reverse" analysis, in which the outputs of the original RSFC analysis were used as input seeds, substantiated the role of the right DLPFC and the aforementioned regions in both ego dissolution and emotional arousal. Subsequently, we measured the effects of LSD on directed functional connectivity in MEG data that was source-localized to the input and output regions of both the original and reverse analyses. The Granger causality (GC) analysis revealed that LSD increased information flow between two nodes of the 'ego dissolution network', the thalamus and the DLPFC, in the theta band, substantiating the hypothesis that disruptions in thalamic gating underlie the experience of ego dissolution. Overall, this multimodal study elucidates a role for the DLPFC in LSD-induced states of consciousness and sheds more light on the brain basis of ego dissolution.

Variations in neuronal cytoskeletal integrity affect directed communication in distributed networks during inhibitory control

To ensure goal-directed behavior in daily life, the use of inhibitory control is of great importance. The aim of this study is to shed light on the underlying neuronal mechanisms of inhibitory control and the relevance of cytoarchitectonic integrity in it. We combine sophisticated EEG analysis techniques assessing directed communication between brain structures with measurements of neurofilaments as an index of cytoarchitectonic integrity. We show that an extensive theta band activity related neural network with fronto-temporal, parietal, and occipital brain regions is active during response inhibition. Importantly, cytoarchitectonic integrity as measured using neurofilaments modulates nonlinear directional connectivity, particularly when complex reconfiguration of perceptual and action mapping is required. The study thus shows an inter-relation between different levels of biological functioning-the level of cytoarchitectonic integrity and neurophysiological directed communication-for inhibitory control and emphasizes the role of nonlinear brain connectivity in cognitive control.

Lateral frontopolar theta-band activity supports flexible switching between emotion regulation strategies

Flexible emotion regulation is essential for mental health and well-being. However, neurocognitive mechanisms supporting emotion regulation flexibility remain unclear. Lateral frontal pole (FPl) contributes to flexible behavior by monitoring the efficacy of alternative strategies. This preregistered study examines if FPl also supports flexible use of emotion regulation strategies. It focuses on pre-decision theta-band activity as a potential indicator of this adaptive process. Sixty-three participants performed an emotion regulation strategy-switching task, involving three phases: (1) implementing an instructed (reappraisal or distraction) strategy, (2) deciding whether to 'maintain' the current or 'switch' to the alternative strategy, and (3) implementing the chosen strategy. Results showed that switching is predicted by the reduced efficacy of an initial emotion regulation strategy (indexed with EMG corrugator activity) and is made in accordance with situational demands (stimulus reappraisal affordances). Critically, switching to an alternative emotion regulation strategy is associated with increased theta-band power in FPl around the time of the decision. These findings support the previously established role of FPl theta-band activity in monitoring counterfactual efficacy of alternative strategies. Crucially, they extend this notion to cognitive emotion regulation, thereby offering promising neural targets for stimulation-based therapies aimed at enhancing emotion regulation flexibility in affective psychopathologies.

Neural signatures of temporal anticipation in human cortex represent event probability density

Temporal prediction is a fundamental function of neural systems. Recent results show that humans anticipate future events by calculating probability density functions, rather than hazard rates. However, direct neural evidence for this hypothesized mechanism is lacking. We recorded neural activity using magnetoencephalography as participants anticipated auditory and visual events distributed in time. We show that temporal anticipation, measured as reaction times, approximates the event probability density function, but not hazard rate. Temporal anticipation manifests as spatiotemporally patterned activity in three anatomically and functionally distinct parieto-temporal and sensorimotor cortical areas. Each of these areas revealed a marked neural signature of anticipation: Prior to sensory cues, activity in a specific frequency range of neural oscillations, spanning alpha and beta ranges, encodes the event probability density function. These neural signals predicted reaction times to imminent sensory cues. These results demonstrate that supra-modal representations of probability density across cortex underlie the anticipation of future events.

Distinct Alpha Networks Modulate Different Aspects of Perceptual Decision-Making

Why do we sometimes perceive a faint stimulus but miss it at other times? One explanation is that conscious perception fluctuates with the brain's internal state, influencing how external stimuli are processed. Ongoing brain oscillations in the alpha band (8-13 Hz), thought to reflect neuronal excitability levels1-5 and play a role in functional inhibition6,7, have been shown as a key contributor to such perceptual variability8,9. Under high alpha conditions, faint stimuli are more likely to be missed8. Some studies suggested alpha oscillations modulate perceptual criterion ( c ) 10-14, shifting the threshold for interpreting sensory information; while others (including our prior work15) suggested alpha modulates sensitivityd ' 15-19, changing the precision of sensory encoding. Few studies observed modulations in both metrics, making these results appear mutually exclusive. Most studies have focused solely on overall alpha activity-whether within a region of interest or across the whole brain-and overlooked the coexistence of multiple distinct alpha networks20-26, which fluctuate in terms of predominance20,27,28 and adapt to behavioural demands29,30. Hence, it remained unclear whether different networks' contributions to perception vary with their momentary state. Here, aiming to characterize how different alpha networks influence perceptual decision-making, we analyzed magnetoencephalography (MEG) data recorded while participants performed a visual detection task with threshold-level stimuli. We found that while the visual alpha network modulates perceptual sensitivity, the sensorimotor alpha network modulates criterion in perceptual decision-making. These findings reconcile previous conflicting results and highlight the functional diversity of alpha networks in shaping perception.

Accelerated algorithms for source orientation detection and spatiotemporal LCMV beamforming in EEG source localization

This paper illustrates the development of two efficient source localization algorithms for electroencephalography (EEG) data, aimed at enhancing real-time brain signal reconstruction while addressing the computational challenges of traditional methods. Accurate EEG source localization is crucial for applications in cognitive neuroscience, neurorehabilitation, and brain-computer interfaces (BCIs). To make significant progress toward precise source orientation detection and improved signal reconstruction, we introduce the Accelerated Linear Constrained Minimum Variance (ALCMV) beamforming toolbox and the Accelerated Brain Source Orientation Detection (AORI) toolbox. The ALCMV algorithm speeds up EEG source reconstruction by utilizing recursive covariance matrix calculations, while AORI simplifies source orientation detection from three dimensions to one, reducing computational load by 66% compared to conventional methods. Using both simulated and real EEG data, we demonstrate that these algorithms maintain high accuracy, with orientation errors below 0.2% and signal reconstruction accuracy within 2%. These findings suggest that the proposed toolboxes represent a substantial advancement in the efficiency and speed of EEG source localization, making them well-suited for real-time neurotechnological applications.

Spatio-Temporal Decoding of the Navon Task Challenges Rigid Hemispheric Asymmetries in Global-Local Processing

Functional hemispheric asymmetries are considered a key factor in intra- and interindividual variability of global precedence effects. However, research in this area is permeated by a considerable number of inconsistent findings which may stem from significant methodological limitations. In pursuit of a more detailed model of global-local processing by combining both high temporal and spatial resolution, we employed Multivariate Pattern Analysis (MVPA) on Magnetoencephalography (MEG) recordings from 63 participants performing a divided visual field, divided attention Navon paradigm. The resulting decoding accuracies between various hierarchical letter forms and target levels were used to pinpoint potentially involved spatial networks and temporal processing sequences. Linear Discriminant Analysis (LDA) revealed temporal precedence of global over local letter form decoding accuracy peaks. Furthermore, searchlight analysis provided a nuanced spatial mapping that not only validated previously established core regions (lingual gyrus for local processing; inferior occipital gyrus for global processing) but also identified potential regions implicated in global-local integration. Yet, we observed substantial variation in lateralization patterns across our study sample, challenging the conventional assumption of right-hemispheric dominance for global and left-hemispheric dominance for local processing in the context of MVPA. Overall, our findings validate and broaden the scope of prior research by providing, for the first time, accurate temporal and spatial data on global-local processing from a single measurement. Moreover, we introduce interindividual variability in lateralization patterns as a potential factor contributing to past inconsistencies.

The impact of channel density, inverse solutions, connectivity metrics and calibration errors on OPM-MEG connectivity analysis: A simulation study

Magnetoencephalography (MEG) systems based on optically pumped magnetometers (OPMs) have rapidly developed in the fields of brain function, health, and disease. Functional connectivity analysis related to the resting-state has gained popularity as a field of research in recent years. Several studies have attempted to use OPM-based MEG (OPM-MEG) for brain network estimation research; however, the choice of source connectivity analysis pipeline may lead to outcome variability. Several methods and related parameters must be selected carefully at each step of the analysis. Therefore, this study assessed the effect of such analytical variability on the OPM-MEG connectivity analysis by conducting simulations. Synthetic MEG data corresponding to two default mode networks (DMN) with six or ten DMN regions were generated using the Gaussian Graphical Spectral (GGS) model. Six intersensor spacings were constructed, and six inverse algorithms and six functional connectivity measures were selected to assess their impact on the network reconstruction accuracy. Three potential sources of error - errors in the sensor gain, crosstalk, and angular errors of the sensitive axis of the OPM - were also assessed. Analytical variability with regard to the tested intersensor spacings, inverse solutions, and functional connectivity measures led to high result variability. Crosstalk exerted a significant impact on the accuracy, which may lead to network reconstruction failure. The accuracy improvement caused by an increase in the sensor density may be reduced by gain and angular errors. The minimum norm estimate (MNE) and weighted minimum norm estimate (wMNE) exhibited low robustness to sensor noise and calibration errors. Hence, a calibration workflow for accurate sensor parameters, such as the gain and direction of the sensitive axis, before commencing OPM-MEG measurement and a careful choice of different method combinations play crucial roles in ensuring that OPMs yield optimal results for functional connectivity analysis. A thorough framework for analyzing brain connectivity networks was provided herein.

An implemented predictive coding model of lexico-semantic processing explains the dynamics of univariate and multivariate activity within the left ventromedial temporal lobe during reading comprehension

During language comprehension, the larger neural response to unexpected versus expected inputs is often taken as evidence for predictive coding-a specific computational architecture and optimization algorithm proposed to approximate probabilistic inference in the brain. However, other predictive processing frameworks can also account for this effect, leaving the unique claims of predictive coding untested. In this study, we used MEG to examine both univariate and multivariate neural activity in response to expected and unexpected inputs during word-by-word reading comprehension. We further simulated this activity using an implemented predictive coding model that infers the meaning of words from their orthographic form. Consistent with previous findings, the univariate analysis showed that, between 300 and 500 ms, unexpected words produced a larger evoked response than expected words within a left ventromedial temporal region that supports the mapping of orthographic word-forms onto lexical and conceptual representations. Our model explained this larger evoked response as the enhanced lexico-semantic prediction error produced when prior top-down predictions failed to suppress activity within lexical and semantic "error units". Critically, our simulations showed that despite producing minimal prediction error, expected inputs nonetheless reinstated top-down predictions within the model's lexical and semantic "state" units. Two types of multivariate analyses provided evidence for this functional distinction between state and error units within the ventromedial temporal region. First, within each trial, the same individual voxels that produced a larger response to unexpected inputs between 300 and 500 ms produced unique temporal patterns to expected inputs that resembled the patterns produced within a pre-activation time window. Second, across trials, and again within the same 300-500 ms time window and left ventromedial temporal region, pairs of expected words produced spatial patterns that were more similar to one another than the spatial patterns produced by pairs of expected and unexpected words, regardless of specific item. Together, these findings provide compelling evidence that the left ventromedial temporal lobe employs predictive coding to infer the meaning of incoming words from their orthographic form during reading comprehension.

Debiased Estimation and Inference for Spatial-Temporal EEG/MEG Source Imaging

The development of accurate electroencephalography (EEG) and magnetoencephalography (MEG) source imaging algorithm is of great importance for functional brain research and non-invasive presurgical evaluation of epilepsy. In practice, the challenge arises from the fact that the number of measurement channels is far less than the number of candidate source locations, rendering the inverse problem ill-posed. A widely used approach is to introduce a regularization term into the objective function, which inevitably biased the estimated amplitudes towards zero, leading to an inaccurate estimation of the estimator's variance. This study proposes a novel debiased EEG/MEG source imaging (DeESI) algorithm for detecting sparse brain activities, which corrects the estimation bias in signal amplitude, dipole orientation and depth. The DeESI extends the idea of group Lasso by incorporating both the matrix Frobenius norm and the L1-norm, which guarantees the estimators are only sparse over sources while maintains smoothness in time and orientation. We also derived variance of the debiased estimators for standardization and hypothesis testing. A fast alternating direction method of multipliers (ADMM) algorithm is proposed for solving the matrix form optimization problem directly without the need for vectorization. The proposed algorithm is compared with eleven existing ESI methods using simulations and an open source EEG dataset whose stimulation locations are known precisely. The DeESI exhibits the best performance in peak localization and amplitude reconstruction.

Training the embodied self in its impermanence: meditators evidence neurophysiological markers of death acceptance

BACKGROUND

Human predictive capacity underlies its adaptive strength but also the potential for existential terror. Grounded in the predictive processing framework of brain function, we recently showed using a magnetoencephalogram visual mismatch-response (vMMR) paradigm that prediction-based self-specific neural mechanisms shield the self from existential threat-at the level of perception-by attributing death to the 'other' (nonself). Here we test the preregistered hypothesis that insight meditation grounded on mindful awareness is associated with a reduction in the brain's defensiveness toward mortality. In addition, we examine whether these neurophysiological markers of death-denial are associated with the phenomenology of meditative self-dissolution (embodied training in impermanence).

METHODS

Thirty-eight meditators pooled from a previous project investigating self-dissolution neurophenomenology underwent the vMMR task, as well as self-report measures of mental health, and afterlife beliefs. Results were associated with the previously-reported phenomenological dimensions of self-dissolution.

RESULTS

Meditators' brains responded to the coupling of death and self-stimuli in a manner indicating acceptance rather than denial, corresponding to increased self-reported well-being. Additionally, degree of death acceptance predicted positively valenced meditation-induced self-dissolution experiences, thus shedding light on possible mechanisms underlying wholesome vs. pathological disruptions to self-consciousness.

CONCLUSIONS

The findings provide empirical support for the hypothesis that the neural mechanisms underlying the human tendency to avoid death are not hard-wired but are amenable to mental training, one which is linked with meditating on the experience of the embodied self's impermanence. The results also highlight the importance of assessing and addressing mortality concerns when implementing psychopharmacological or contemplative interventions with the potential of inducing radical disruptions to self-consciousness.

Temporal autocorrelation is predictive of age-An extensive MEG time-series analysis

Understanding the evolving dynamics of the brain throughout life is pivotal for anticipating and evaluating individual health. While previous research has described age effects on spectral properties of neural signals, it remains unclear which ones are most indicative of age-related processes. This study addresses this gap by analyzing resting-state data obtained from magnetoencephalography (MEG) in 350 adults (18 to 88 y). We employed advanced time-series analysis at the brain region level and machine learning to predict age. While traditional spectral features achieved low to moderate accuracy, over a hundred time-series features proved superior. Notably, temporal autocorrelation (AC) emerged as the most robust predictor of age. Distinct patterns of AC within the visual and temporal cortex were most informative, offering a versatile measure of age-related signal changes for comprehensive health assessments based on brain activity.

Behavioral and neural effects of temporoparietal high-definition transcranial direct current stimulation in logopenic variant primary progressive aphasia: a preliminary study

Background

High-definition-tDCS (HD-tDCS) is a recent technology that allows for localized cortical stimulation, but has not yet been investigated as an augmentative therapy while targeting the left temporoparietal cortex in logopenic variant PPA (lvPPA). The changes in neuronal oscillatory patterns and resting-state functional connectivity in response to HD-tDCS also remains poorly understood.

Objective

We sought to investigate the effects of HD-tDCS with phonologic-based language training on language, cognition, and resting-state functional connectivity in lvPPA.

Methods

We used a double-blind, within-subject, sham-controlled crossover design with a 4-month between-treatment period in four participants with lvPPA. Participants completed language, cognitive assessments, and imaging with magnetoencephalography (MEG) and resting-state functional MRI (fMRI) prior to treatment with either anodal HD-tDCS or sham targeting the left supramarginal gyrus over 10 sessions. Language and cognitive assessments, MEG, and fMRI were repeated after the final session and at 2 months follow-up. Preliminary data on efficacy was evaluated based on relative changes from baseline in language and cognitive scores. Language measures included metrics derived from spontaneous speech from picture description. Changes in resting-state functional connectivity within the phonological network were analyzed using fMRI. Magnitudes of source-level evoked responses and hemispheric laterality indices from language task-based MEG were used to assess changes in cortical engagement induced by HD-tDCS.

Results

All four participants were retained across the 4-month between-treatment period, with satisfactory blinding of participants and investigators throughout the study. Anodal HD-tDCS was well tolerated with a side effect profile that did not extend past the immediate treatment period. No benefit of HD-tDCS over sham on language and cognitive measures was observed in this small sample. Functional imaging results using MEG and fMRI indicated an excitatory effect of anodal HD-tDCS compared to sham and suggested that greater temporoparietal activation and connectivity was positively associated with language outcomes.

Conclusion

Anodal HD-tDCS to the inferior parietal cortex combined with language training appears feasible and well tolerated in participants with lvPPA. Language outcomes may be explained by regression to the mean, and to a lesser degree, by ceiling effects and differences in baseline disease severity. The intervention has apparent temporoparietal correlates, and its clinical efficacy should be further studied in larger trials.

Clinical trial registration

ClinicalTrials.gov, Number NCT03805659.

Auditory Cognitive Impairment Reflects Source Localization of the P300 ERP Component in MBI Patients: The sLORETA Investigation

OBJECTIVES

This study aimed to investigate the differences between the source localization of the P300 event-related potential (ERP) component among the healthy and mild brain injury (MBI) patient population using standardized low-resolution electromagnetic tomography (sLORETA).

METHODS

Thirty-eight participants were divided into control (n = 19) and MBI (n = 19) groups. Control participants were normal, healthy people, and participants with MBI were assigned into two groups: MBI 1st Test (7 days after a road traffic accident (RTA)) and MBI 2nd Test (2-6 months after RTA with the same participants of the 1st Test group). The 128-ERP nets were used on the heads of the participants during the experiments. Under the auditory oddball paradigm, all participants silently counted the target tones, while ignoring the standard tones. This study used the sLORETA tool in the Net Station software for the source localization of the P300 ERP component. The Mann-Whitney U test was used to compare intensities between groups, while the Wilcoxon Signed-Rank test was applied for paired observations within groups.

RESULTS

Standard stimuli evoked P300 sources in the superior frontal gyrus (BA11) of the right frontal lobe in the control group, the superior temporal gyrus (BA38) of the right temporal lobe in the MBI 1st Test group, and the inferior frontal gyrus (BA47) of the left frontal lobe in the MBI 2nd Test group. Meanwhile, target stimuli evoked P300 sources at BA11 for all groups but in different gyrus: the superior frontal gyrus, orbital gyrus, and rectal gyrus in the control, MBI 1st Test, and MBI 2nd Test groups, respectively. In addition, there were significant differences in dipole intensities between and within groups among control and MBI patients in both standard and target stimuli.

CONCLUSION

P300 source localization was shifted presumably due to the auditory cognitive impairment, and the dipole intensities were significantly higher in the MBI group than in the control group, indicating that the MBI group compensated for both standard and target tone stimuli, reflected in the sLORETA investigation.

Neurophysiological profiles underlying action withholding and action discarding

Although inhibitory control is essential to goal-directed behavior, not all inhibition is the same: Previous research distinguished discarding an action plan from simply withholding it, suggesting separate neurophysiological mechanisms. This study tracks the neurophysiological signatures of both using time-frequency transformation and beamforming in n = 34 healthy individuals. We show that discarding an action plan reduces working memory load, with stronger initial theta band activity compared to withholding it. This oscillatory difference was localized in the (para-)hippocampus and anterior temporal lobe, likely reflecting the need to dissolve action plan features first to enable the following decrease of working memory load. Contrary, when exposed to the embedded stimulus, withholding was associated with higher theta, alpha, and beta band activity relative to discarding. This study advances our understanding of inhibition by revealing distinct neurophysiological mechanisms and functional neuroanatomical structures involved in withholding versus discarding an action.

Altered cortical network dynamics during observing and preparing action in patients with corticobasal syndrome

Corticobasal syndrome (CBS) is characterized not only by parkinsonism but also by higher-order cortical dysfunctions, such as apraxia. However, the electrophysiological mechanisms underlying these symptoms remain poorly understood. To explore the pathophysiology of CBS, we recorded magnetoencephalographic (MEG) data from 17 CBS patients and 20 age-matched controls during an observe-to-imitate task. This task involved observing a tool-use video (action observation), withholding movement upon a Go cue (movement preparation), and subsequently imitating the tool-use action. We analyzed spectral power modulations at the source level. During action observation, event-related beta power (13-30 Hz) suppression was weaker in CBS patients compared to controls. This reduction was evident bilaterally in superior parietal, primary motor, premotor and inferior frontal cortex. During movement preparation, beta power suppression was also reduced in CBS patients, correlating with longer reaction times. Immediately prior to movement onset, however, beta suppression was comparable between groups. Our findings suggest that action observation induces beta suppression, likely indicative of motor cortical disinhibition, which is impaired in CBS patients. This alteration may represent a neural correlate of disrupted visuo-motor mapping in CBS. The altered timing of beta suppression to the Go cue suggests deficits in learning the task's temporal structure rather than in movement initiation itself.

Source Imaging Method Based on Spatial Smoothing and Edge Sparsity (SISSES) and Its Application to OPM-MEG

Source estimation in magnetoencephalography (MEG) involves solving a highly ill-posed problem without a unique solution. Accurate estimation of the time course and spatial extent of the source is important for studying the mechanisms of brain activity and preoperative functional localization. Traditional methods tend to yield small-amplitude diffuse or large-amplitude focused source estimates. Recently, the structured sparsity-based source imaging algorithm has emerged as one of the most promising algorithms for improving source extent estimation. However, it suffers from a notable amplitude bias. To improve the spatiotemporal resolution of reconstructed sources, we propose a novel method called the source imaging method based on spatial smoothing and edge sparsity (SISSES). In this method, the temporal dynamics of sources are modeled using a set of temporal basis functions, and the spatial characteristics of the source are represented by a first-order Markov random field (MRF) model. In particular, sparse constraints are imposed on the MRF model residuals in the original and variation domains. Numerical simulations were conducted to validate the SISSES. The results demonstrate that SISSES outperforms benchmark methods for estimating the time course, location, and extent of patch sources. Additionally, auditory and median nerve stimulation experiments were performed using a 31-channel optically pumped magnetometer MEG system, and the SISSES was applied to the source imaging of these data. The results demonstrate that SISSES correctly identified the source regions in which brain responses occurred at different times, demonstrating its feasibility for various practical applications.

Pseudo-MRI Engine for MRI-Free Electromagnetic Source Imaging

Structural head MRIs are a crucial ingredient in MEG/EEG source imaging; they are used to define a realistically shaped volume conductor model, constrain the source space, and visualize the source estimates. However, individual MRIs are not always available, or they may be of insufficient quality for segmentation, leading to the use of a generic template MRI, matched MRI, or the application of a spherical conductor model. Such approaches deviate the model geometry from the true head structure and limit the accuracy of the forward solution. Here, we implemented an easy-to-use tool, pseudo-MRI engine, which utilizes the head-shape digitization acquired during a MEG/EEG measurement for warping an MRI template to fit the subject's head. To this end, the algorithm first removes outlier digitization points, densifies the point cloud by interpolation if needed, and finally warps the template MRI and its segmented surfaces to the individual head shape using the thin-plate-spline method. To validate the approach, we compared the geometry of segmented head surfaces, cortical surfaces, and canonical brain regions in the real and pseudo-MRIs of 25 subjects. We also tested the MEG source reconstruction accuracy with pseudo-MRIs against that obtained with the real MRIs from individual subjects with simulated and real MEG data. We found that the pseudo-MRI enables comparable source localization accuracy to the one obtained with the subject's real MRI. The study indicates that pseudo-MRI can replace the need for individual MRI scans in MEG/EEG source imaging for applications that do not require subcentimeter spatial accuracy.

Local and network changes after multichannel transcranial direct current stimulation using magnetoencephalography in patients with refractory epilepsy

OBJECTIVE

Non-invasive neuromodulation techniques, particularly transcranial direct current stimulation (tDCS), are promising for drug-resistant epilepsy (DRE), though the mechanisms of their efficacy remain unclear. This study aims to (i) investigate tDCS neurophysiological mechanisms using a personalized multichannel protocol with magnetoencephalography (MEG) and (ii) assess post-tDCS changes in brain connectivity, correlating them with clinical outcomes.

METHODS

Seventeen patients with focal DRE underwent three cycles of tDCS over five days, each consisting of 40-minute stimulations targeting the epileptogenic zone (EZ) identified via stereo-EEG. MEG was performed before and after sessions to assess functional connectivity (FC) and power spectral density (PSD),estimated at source level (beamforming).

RESULTS

Five of fourteen patients experienced a seizure frequency reduction > 50 %. Distinct PSD changes were seen across frequency bands, with reduced FC in responders and increased connectivity in non-responders (p < 0.05). No significant differences were observed between EZ network and non-involved networks. Responders also had higher baseline FC, suggesting it could predict clinical response to tDCS in DRE.

CONCLUSIONS

Personalized multichannel tDCS induces neurophysiological changes associated with seizure reduction in DRE.

SIGNIFICANCE

These results provide valuable insights into tDCS effects on epileptic brain networks, informing future clinical applications in epilepsy treatment.

Intermodal Consistency of Whole-Brain Connectivity and Signal Propagation Delays

Measuring propagation of perturbations across the human brain and their transmission delays is critical for network neuroscience, but it is a challenging problem that still requires advancement. Here, we compare results from a recently introduced, noninvasive technique of functional delays estimation from source-reconstructed electro/magnetoencephalography, to the corresponding findings from a large dataset of cortico-cortical evoked potentials estimated from intracerebral stimulations of patients suffering from pharmaco-resistant epilepsies. The two methods yield significantly similar probabilistic connectivity maps and signal propagation delays, in both cases characterized with Pearson correlations greater than 0.5 (when grouping by stimulated parcel is applied for delays). This similarity suggests a correspondence between the mechanisms underpinning the propagation of spontaneously generated scale-free perturbations (i.e., neuronal avalanches observed in resting state activity studied using magnetoencephalography) and the spreading of cortico-cortical evoked potentials. This manuscript provides evidence for the accuracy of the estimate of functional delays obtained noninvasively from reconstructed sources. Conversely, our findings show that estimates obtained from externally induced perturbations in patients capture physiological activities in healthy subjects. In conclusion, this manuscript constitutes a mutual validation between two modalities, broadening their scope of applicability and interpretation. Importantly, the capability to measure delays noninvasively (as per MEG) paves the way for the inclusion of functional delays in personalized large-scale brain models as well as in diagnostic and prognostic algorithms.

The polarity of high-definition transcranial direct current stimulation affects the planning and execution of movement sequences

Noninvasive brain stimulation of the primary motor cortex has been shown to alter therapeutic outcomes in stroke and other neurological conditions, but the precise mechanisms remain poorly understood. Determining the impact of such neurostimulation on the neural processing supporting motor control is a critical step toward further harnessing its therapeutic potential in multiple neurological conditions affecting the motor system. Herein, we leverage the excellent spatio-temporal precision of magnetoencephalographic (MEG) imaging to identify the spectral, spatial, and temporal effects of high-definition transcranial direct current stimulation (HD-tDCS) on the neural responses supporting motor control. Participants (N = 67) completed three HD-tDCS visits (anode, cathode, sham), with each involving 20 min of left primary motor cortex stimulation and performance of a simple/complex motor sequencing task during MEG. Whole-brain statistical analyses of beta oscillatory responses revealed stimulation-by-task interaction effects in the left primary motor cortex, right occipitotemporal, and the right dorsolateral prefrontal cortices. Broadly, anodal stimulation induced significantly stronger beta oscillatory responses in these regions during simple movement sequences, while neural responses to complex sequences were not affected by stimulation. En masse, these data suggest that the beta oscillations serving motor planning (i.e., pre-movement) are particularly sensitive to the polarity of noninvasive stimulation and that the impact varies based on the difficulty of the movement sequence.

The Impact of Selective Attention and Musical Training on the Cortical Speech Tracking in the Delta and Theta Frequency Bands

Oral communication regularly takes place amidst background noise, requiring the ability to selectively attend to a target speech stream. Musical training has been shown to be beneficial for this task. Regarding the underlying neural mechanisms, recent studies showed that the speech envelope is tracked by neural activity in auditory cortex, which plays a role in the neural processing of speech, including speech in noise. The neural tracking occurs predominantly in two frequency bands, the delta and the theta bands. However, much regarding the specifics of these neural responses, as well as their modulation through musical training, still remain unclear. Here, we investigated the delta- and theta-band cortical tracking of the speech envelope of target and distractor speech using magnetoencephalography (MEG) recordings. We thereby assessed both musicians and nonmusicians to explore potential differences between these groups. The cortical speech tracking was quantified through source-reconstructing the MEG data and subsequently relating the speech envelope in a certain frequency band to the MEG data using linear models. We thereby found the theta-band tracking to be dominated by early responses with comparable magnitudes for target and distractor speech, whereas the delta band tracking exhibited both earlier and later responses that were modulated by selective attention. Almost no significant differences emerged in the neural responses between musicians and nonmusicians. Our findings show that only the speech tracking in the delta but not in the theta band contributes to selective attention, but that this mechanism is essentially unaffected by musical training.