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The mechanisms behind Alzheimer's disease are not yet fully described, and changes in the electrophysiology of patients across the continuum of the disease could help to understand them. In this work, we study the power spectral distribution of a set of 129 individuals from the Connectomics of Brian Aging and Dementia project. From this sample, we acquired task-free data, with eyes closed, and estimated the power spectral distribution in source space. We compared the spectral profiles of three groups of individuals: 70 healthy controls, 27 patients with amnestic MCI, and 32 individuals showing cognitive impairment without subjective complaints (IWOC). The results showed a slowing of the brain activity in the aMCI patients, when compared to both the healthy controls and the IWOC individuals. These differences appeared both as a decrease in power for high frequency oscillations and an increase in power in alpha oscillations. The slowing of the spectrum was significant mainly in parietal and medial frontal areas. We were able to validate the slowing of the brain activity in individuals with aMCI, appearing in our sample in areas related to the default mode network. However, this pattern did not appear in the IWOC individuals, suggesting that their condition is not part of the AD continuum. This work raises interesting questions about this group of individuals, and the underlying brain mechanisms behind their cognitive impairment.

The amplitude envelope of speech is crucial for accurate comprehension. Considered a key stage in speech processing, the phase of neural activity in the theta-delta bands (1-10 Hz) tracks the phase of the speech amplitude envelope during listening. However, the mechanisms underlying this envelope representation have been heavily debated. A dominant model posits that envelope tracking reflects entrainment of endogenous low-frequency oscillations to the speech envelope. Alternatively, envelope tracking reflects a series of evoked responses to acoustic landmarks within the envelope. It has proven challenging to distinguish these two mechanisms. To address this, we recorded MEG while participants (n= 12, 6 female) listened to natural speech, and compared the neural phase patterns to the predictions of two computational models: an oscillatory entrainment model and a model of evoked responses to peaks in the rate of envelope change. Critically, we also presented speech at slowed rates, where the spectro-temporal predictions of the two models diverge. Our analyses revealed transient theta phase-locking in regular speech, as predicted by both models. However, for slow speech, we found transient theta and delta phase-locking, a pattern that was fully compatible with the evoked response model but could not be explained by the oscillatory entrainment model. Furthermore, encoding of acoustic edge magnitudes was invariant to contextual speech rate, demonstrating speech rate normalization of acoustic edge representations. Together, our results suggest that neural phase-locking to the speech envelope is more likely to reflect discrete representation of transient information rather than oscillatory entrainment.SIGNIFICANCE STATEMENTThis study probes a highly debated topic in speech perception: the neural mechanisms underlying the cortical representation of the temporal envelope of speech. It is well established that the slow intensity profile of the speech signal, its envelope, elicits a robust brain response that “tracks” these envelope fluctuations. The oscillatory entrainment model posits that envelope tracking reflects phase alignment of endogenous neural oscillations. Here the authors provide evidence for a distinct mechanism. They show that neural speech envelope tracking arises from transient evoked neural responses to rapid increases in the speech envelope. Explicit computational modeling provides direct and compelling evidence that evoked responses are the primary mechanism underlying cortical speech envelope representations, with no evidence for oscillatory entrainment.

First published: 27 February 2023 The coordination between the theta phase (3–7 Hz) and gamma power (25–35 Hz) oscillations (namely theta-gamma phase-amplitude coupling, PAC) in the auditory cortex has been proposed as an essential neural mechanism involved in speech processing. However, it has not been established how this mechanism is related to the efficiency with which a listener processes speech. Speech processing in a non-native language offers a useful opportunity to evaluate if theta-gamma PAC is modulated by the challenges imposed by the reception of speech input in a non-native language. The present study investigates how auditory theta-gamma PAC (recorded with magnetoencephalography) is modulated in both native and non-native speech reception. Participants were Spanish native (L1) speakers studying Basque (L2) at three different levels: beginner (Grade 1), intermediate (Grade 2), and advanced (Grade 3). We found that during L2 speech processing (i) theta-gamma PAC was more highly coordinated for intelligible compared to unintelligible speech; (ii) this coupling was modulated by proficiency in Basque being lower for beginners, higher for intermediate, and highest for advanced speakers (no difference observed in Spanish); (iii) gamma power did not differ between languages and groups. These findings highlight how the coordinated theta-gamma oscillatory activity is tightly related to speech comprehension: the stronger this coordination is, the more the comprehension system will proficiently parse the incoming speech input. This research is supported by the Basque Government through the BERC 2022-2025 program, the Spanish State Research Agency through BCBL Severo Ochoa excellence accreditation CEX2020- 001010-S, the Spanish Ministry of Science, Innovation and Universities (IJC2020-042886-I to M.L.; PID2021-122918OB-I00 to M.C; RTI2018-096311-B-I00 and PCI2022-135031-2 to N.M.;) and the IKUR #neural2speech project

Little is known about the pathogenesis underlying cognitive impairment in major depressive disorder (MDD). We aimed to explore the mechanisms of cognitive impairments among patients with MDD by investigating the dynamics of overlapping brain sub-networks.Forty unmedicated patients with MDD and 28 healthy controls (HC) were enrolled in this study. Cognitive function was measured using the Chinese versions of MATRICS Consensus Cognitive Battery (MCCB). All participants were scanned using a whole-head resting-state magnetoencephalography (MEG) machine. The dynamism of neural sub-networks was analyzed based on the detection of overlapping communities in five frequency bands of oscillatory brain signals.MDD demonstrated poorer cognitive performance in six domains compared to HC. The difference in community detection (functional integration mode) in MDD was frequency-dependent. MDD showed significantly decreased community dynamics in all frequency bands compared to HC. Specifically, differences in the visual network (VN) and default mode network (DMN) were detected in all frequency bands, differences in the cognitive control network (CCN) were detected in the alpha2 and beta frequency bands, and differences in the bilateral limbic network (BLN) were only detected in the beta frequency band. Moreover, community dynamics in the alpha2 frequency band were positively correlated with verbal learning and reasoning problem solving abilities in MDD.Our study found that decreasing in the dynamics of overlapping sub-networks may differ by frequency bands. The aberrant dynamics of overlapping neural sub-networks revealed by frequency-specific MEG signals may provide new information on the mechanism of cognitive impairments that result from MDD.

Unipolar depression (UD) and bipolar depression (BD) showed convergent and divergent cognitive impairments. Neural oscillations are linked to the foundational cognitive processes. We aimed to investigate the underpinning spectral neuronal power patterns by magnetoencephalography (MEG), which combinates high spatial and temporal resolution. We hypothesized that patients with UD and BD exhibit common and distinct patterns, which may contribute to their cognitive impairments.Group cognitive tests were performed. Eyes closed resting-state MEG data were collected from 61 UD, 55 BD, and 52 healthy controls (HC). Nonparametric cluster-based permutation tests were performed to deal with the multiple comparison problem on channel-frequency MEG data. Correlation analysis of cognitive dysfunction scores and MEG oscillation were conducted by Spearman or partial correlation analysis.Wisconsin Card Sorting Test showed similar cognitive impairment in patients with UD and BD. Moreover, patients with BD exhibited extensive cognitive deficits in verbal executive functions and visuospatial processing. Compare to HC, both patients with UD and BD showed increased frontal-central beta power while high gamma power was decreased in UD groups during the resting-state. The significant correlations between cognitive function and average beta power were observed.Patients with BD had more cognitive impairments on different dimensions than those with UD, involving disrupted beta power modulations. Our investigation provides a better understanding of the neuroelectrophysiological process underlying cognitive impairments in patients with UD and BD.

Introduction: The human brain shows modest traits of sexual dimorphism, with the female brain, on average, 10% smaller than the male brain. These differences do not imply a lowered cognitive performance, but suggest a more optimal brain organization in women. Here we evaluate the patterns of functional connectivity (FC) in women and men from the Connectomics of Brain Aging and Dementia sample. Methods: We used phase locking values to calculate FC from the magnetoencephalography time series in a sample of 138 old adults (87 females and 51 males). We compared the FC patterns between sexes, with the intention of detecting regions with different levels of connectivity. Results: We found a frontal cluster, involving anterior cingulate and the medial frontal lobe, where women showed higher FC values than men. Involved connections included the following: (1) medial parietal areas, such as posterior cingulate cortices and precunei; (2) right insula; and (3) medium cingulate and paracingulate cortices. Moreover, these differences persisted when considering only cognitively intact individuals, but not when considering only cognitively impaired individuals. Discussion: Increased anteroposterior FC has been identified as a biomarker for increased risk of developing cognitive impairment or dementia. In our study, cognitively intact women showed higher levels of FC than their male counterparts. This result suggests that neurodegenerative processes could be taking place in these women, but the changes are undetected by current diagnosis tools. FC, as measured here, might be valuable for early identification of this neurodegeneration.

Crowding refers to impaired object recognition of peripheral visual targets caused by nearby flankers. It has been shown that the response to a word was faster when it was preceded by a semantically related than unrelated crowded prime, demonstrating that semantic priming survives crowding. This study examines neural correlates of semantic priming under visual crowding using magnetoencephalography with four conditions: prime (isolated, crowded) x prime-target relationship (related, unrelated). Participants judged whether the target was a word or a nonword. We found significant differences in θ activity at the left inferior frontal gyrus (IFG) for both isolated and crowded primes when comparing the unrelated and related conditions, although the activation was delayed with the crowded prime compared to the isolated prime. The locations within the IFG were also different: theta-band activation was at BA 45 in the isolated condition and at BA 47 in the crowded condition. Phase-locking-value analysis revealed that bilateral IFG was more synchronized with unrelated prime-target pairs than related pairs regardless of whether the primes were isolated or crowded, indicating the recruitment of the right hemisphere when the prime-target semantic relationship was remote. Finally, the distinct waveform patterns found in the isolated and crowded conditions from both the source localization and PLV analysis suggest different neural mechanisms for processing semantic information with isolated primes versus crowded primes.

Recent empirical findings suggest that altered neural synchronization, which is hypothesized to be associated with an imbalance of excitatory (E) and inhibitory (I) neuronal activities, may underlie a core pathophysiological mechanism in patients with schizophrenia. The auditory steady-state response (ASSR) examined by electroencephalography (EEG) and magnetoencephalography (MEG) has been proposed as a potential biomarker for evaluating altered neural synchronization in schizophrenia. For this review, we performed a comprehensive literature search for papers published between 1999 and 2021 examining ASSRs in patients with schizophrenia. Almost all EEG-ASSR studies reported gamma-band ASSR reductions, especially to 40-Hz stimuli both in power and/or phase synchronization in chronic and first-episode schizophrenia. In addition, similar to EEG-ASSR findings, MEG-ASSR deficits to 80-Hz stimuli (high gamma) have been reported in patients with schizophrenia. Moreover, the 40-Hz ASSR is likely to be a predictor of the onset of schizophrenia. Notably, increased spontaneous (or ongoing) broadband (30-100 Hz) gamma power has been reported during ASSR tasks, which resembles the increased spontaneous gamma activity reported in animal models of E/I imbalance. Further research on ASSRs and evoked and spontaneous gamma oscillations is expected to elucidate the pathophysiology of schizophrenia with translational implications.

Neural oscillations often occur as transient bursts with variable amplitude and frequency dynamics. Quantifying these effects is important for understanding brain-behaviour relationships, especially in continuous datasets. To robustly measure bursts, rhythmical periods of oscillatory activity must be separated from arrhythmical background 1/f activity, which is ubiquitous in electrophysiological recordings. The Better OSCillation (BOSC) framework achieves this by defining a power threshold above the estimated background 1/f activity, combined with a duration threshold. Here we introduce a modification to this approach called fBOSC, which uses a spectral parametrisation tool to accurately model background 1/f activity in neural data. fBOSC (which is openly available as a MATLAB toolbox) is robust to power spectra with oscillatory peaks and can also model non-linear spectra. Through a series of simulations, we show that fBOSC more accurately models the 1/f power spectrum compared with existing methods. fBOSC was especially beneficial where power spectra contained a 'knee' below ~.5-10 Hz, which is typical in neural data. We also found that, unlike other methods, fBOSC was unaffected by oscillatory peaks in the neural power spectrum. Moreover, by robustly modelling background 1/f activity, the sensitivity for detecting oscillatory bursts was standardised across frequencies (e.g., theta- and alpha-bands). Finally, using openly available resting state magnetoencephalography and intracranial electrophysiology datasets, we demonstrate the application of fBOSC for oscillatory burst detection in the theta-band. These simulations and empirical analyses highlight the value of fBOSC in detecting oscillatory bursts, including in datasets that are long and continuous with no distinct experimental trials.

One devastating outcome of major depressive disorder (MDD) is high suicidality, especially for patients with suicide attempt (SA). Evidence indicated that SA may be strongly associated with inhibitory control deficits. We hypothesized that the inhibition function deficits of patient with SA might be underpinned by abnormal neuronal oscillations.Our study recruited 111 subjects including 74 patients and 37 controls, who performed a GO/NOGO task during magnetoencephalography recording. Time-frequency-representations and phase-amplitude-coupling were measured for the brain circuits involved in the inhibitory function. Phase-slope-indexes were calculated between regions to determine the direction of power flow.Significant increased reaction time and decreased judgment accuracy were observed in SA group. During the perception stage of GO task (approximately 125 ms), SA group manifested elevated alpha power in ventral prefrontal cortex (VPFC) and attenuated beta power in dorsal anterior cingulate (dACC) compared with other groups (p 0.01). In the processing stage of NOGO task (approximately 300 ms), they showed decreased beta power in VPFC and increased alpha power in dACC (p 0.01). Alpha-beta decoupling during both tasks was observed in SA group. Furthermore, the decoupling from VPFC to dACC under NOGO tasks was significantly correlated with suicide risk level.The number of participants was relatively small, and psychological elements were not involved in current study.Dysregulated oscillatory activities of dACC and VPFC suggested deficits in execution and inhibition functions triggering high suicide risks. The alpha-beta decoupling from VPFC to dACC could be served as a neuro-electrophysiological biomarker for identifying potential suicide risk.

To clinically validate the connectivity-based magnetoencephalography (MEG) analyses to identify seizure onset zone (SOZ) with comparing to equivalent current dipole (ECD).The ECD cluster was quantitatively analyzed by calculating the centroid of the cluster and maximum distance (the largest distance between all dipoles). The "primary hub" was determined by the highest eigencentrality. The distribution of nodes in the top 5% of eigenvector centrality values was quantified by generating the convex hull between each node.Thirty-one patients who underwent MEG, stereotactic-EEG, and focal surgery were included. The primary hub was significantly closer to the sEEG-defined SOZ compared to ECD (p = 0.009). The seizure freedom positive and negative predictive values of complete ECD cluster and primary hub resections did not significantly differ, although complete resection of the primary hub showed slightly better negative predictive value (ECD: 50.0% NPV, hub: 64.7% NPV). Both quantitative ECD and functional connectivity analyses suggested that spatially restricted dipole distributions and higher connectivity in a smaller region correlate with better seizure outcomes.Our findings suggest that MEG network analysis could be a valuable complement to the ECD methods.The results of this study are an important step towards using non-invasive neurophysiologic recordings to accurately define the epileptic network.

Magnetoencephalography (MEG) is a useful tool for clinically evaluating the localization of interictal spikes. Neurophysiologists visually identify spikes from the MEG waveforms and estimate the equivalent current dipoles (ECD). However, presently, these analyses are manually performed by neurophysiologists and are time-consuming. Another problem is that spike identification from MEG waveforms largely depends on neurophysiologists' skills and experiences. These problems cause poor cost-effectiveness in clinical MEG examination. To overcome these problems, we fully automated spike identification and ECD estimation using a deep learning approach fully automated AI-based MEG interictal epileptiform discharge identification and ECD estimation (FAMED). We applied a semantic segmentation method, which is an image processing technique, to identify the appropriate times between spike onset and peak and to select appropriate sensors for ECD estimation. FAMED was trained and evaluated using clinical MEG data acquired from 375 patients. FAMED training was performed in two stages: in the first stage, a classification network was learned, and in the second stage, a segmentation network that extended the classification network was learned. The classification network had a mean AUC of 0.9868 (10-fold patient-wise cross-validation); the sensitivity and specificity were 0.7952 and 0.9971, respectively. The median distance between the ECDs estimated by the neurophysiologists and those using FAMED was 0.63 cm. Thus, the performance of FAMED is comparable to that of neurophysiologists, and it can contribute to the efficiency and consistency of MEG ECD analysis.

Background and Hypothesis Prior research has shown that patients with schizophrenia (SZ) show disruption in brain network connectivity that is thought to underlie their cognitive and psychotic symptoms. However, most studies examining functional network disruption in schizophrenia have focused on the temporally correlated coupling of the strength of network connections. Here, we move beyond correlative metrics to assay causal computations of connectivity changes in directed neural information flow, assayed from a neural source to a target in SZ. Study Design This study describes a whole-brain magnetoencephalography-imaging approach to examine causal computations of connectivity changes in directed neural information flow between brain regions during resting states, quantified by phase-transfer entropy (PTE) metrics, assayed from a neural source to an endpoint, in 21 SZ compared with 21 healthy controls (HC), and associations with cognitive and clinical psychotic symptoms in SZ. Study Results We found that SZ showed significant disruption in information flow in alpha (8–12 Hz) and beta (12–30 Hz) frequencies, compared to HC. Reduced information flow in alpha frequencies from the precuneus to the medio-ventral occipital cortex was associated with more severe clinical psychopathology (ie, positive psychotic symptoms), while reduced information flow between insula and middle temporal gyrus was associated with worsening cognitive symptoms. Conclusions The present findings highlight the importance of delineating dysfunction in neural information flow in specific oscillatory frequencies between distinct regions that underlie the cognitive and psychotic symptoms in SZ, and provide potential neural biomarkers that could lead to innovations in future neuromodulation treatment development.

To assess the utility of interictal magnetic and electric source imaging (MSI and ESI) using dipole clustering in magnetic resonance imaging (MRI)-negative patients with drug resistant epilepsy (DRE).We localized spikes in low-density (LD-EEG) and high-density (HD-EEG) electroencephalography as well as magnetoencephalography (MEG) recordings using dipoles from 11 pediatric patients. We computed each dipole's level of clustering and used it to discriminate between clustered and scattered dipoles. For each dipole, we computed the distance from seizure onset zone (SOZ) and irritative zone (IZ) defined by intracranial EEG. Finally, we assessed whether dipoles proximity to resection was predictive of outcome.LD-EEG had lower clusterness compared to HD-EEG and MEG (p 0.05). For all modalities, clustered dipoles showed higher proximity to SOZ and IZ than scattered (p 0.001). Resection percentage was higher in optimal vs. suboptimal outcome patients (p 0.001); their proximity to resection was correlated to outcome (p 0.001). No difference in resection percentage was seen for scattered dipoles between groups.MSI and ESI dipole clustering helps to localize the SOZ and IZ and facilitate the prognostic assessment of MRI-negative patients with DRE.Assessing the MSI and ESI clustering allows recognizing epileptogenic areas whose removal is associated with optimal outcome.

Magnetoencephalography with optically pumped magnometers (OPM-MEG) is an emerging and novel, cost-effective wearable system that can simultaneously record neuronal activity with high temporal resolution ("when" neuronal activity occurs) and spatial resolution ("where" neuronal activity occurs). This paper will first outline recent methodological advances in OPM-MEG compared to conventional superconducting quantum interference device (SQUID)-MEG before discussing how OPM-MEG can become a valuable and noninvasive clinical support tool in epilepsy surgery evaluation. Although OPM-MEG and SQUID-MEG share similar data features, OPM-MEG is a wearable design that fits children and adults, and it is also robust to head motion within a magnetically shielded room. This means that OPM-MEG can potentially extend the application of MEG into the neurobiology of severe childhood epilepsies with intellectual disabilities (e.g., epileptic encephalopathies) without sedation. It is worth noting that most OPM-MEG sensors are heated, which may become an issue with large OPM sensor arrays (OPM-MEG currently has fewer sensors than SQUID-MEG). Future implementation of triaxial sensors may alleviate the need for large OPM sensor arrays. OPM-MEG designs allowing both awake and sleep recording are essential for potential long-term epilepsy monitoring.

Background Magnetoencephalography (MEG) is an established method used to detect and localize focal interictal epileptiform discharges (IEDs). Current MEG systems house hundreds of cryogenic sensors in a rigid, one-size-fits-all helmet, which results in several limitations, particularly in children. Purpose To determine if on-scalp MEG based on optically pumped magnetometers (OPMs) alleviates the main limitations of cryogenic MEG. Materials and Methods In this prospective single-center study conducted in a tertiary university teaching hospital, participants underwent cryogenic (102 magnetometers, 204 planar gradiometers) and on-scalp (32 OPMs) MEG. The two modalities for the detection and localization of IEDs were compared. The

Magnetoencephalography (MEG) measures human brain function via assessment of the magnetic fields generated by electrical activity in neurons. Despite providing high-quality spatiotemporal maps of electrophysiological activity, current MEG instrumentation is limited by cumbersome field sensing technologies, resulting in major barriers to utility. Here, we review a new generation of MEG technology that is beginning to lift many of these barriers. By exploiting quantum sensors, known as optically pumped magnetometers (OPMs), 'OPM-MEG' has the potential to dramatically outperform the current state of the art, promising enhanced data quality (better sensitivity and spatial resolution), adaptability to any head size/shape (from babies to adults), motion robustness (participants can move freely during scanning), and a less complex imaging platform (without reliance on cryogenics). We discuss the current state of this emerging technique and describe its far-reaching implications for neuroscience.

Background: Motor control requires a reciprocal volley between somatosensory and motor systems, with somatosensory feedback being essential for the online updating of motor commands to achieve behavioral outcomes. However, this dynamic interplay among sensorimotor brain systems serving motor control remains poorly understood. Methods: To address this, we designed a novel somatosensory entrainment-movement task, which 25 adults completed during magnetoencephalography (MEG). Specifically, participants completed a quasi-paced finger-tapping paradigm while subthreshold electrical stimulation was applied to the right median nerve at a sensorimotor-relevant frequency (15 Hz) and during a second condition where no electrical stimulation was applied. The MEG data were transformed into the time-frequency domain and imaged by using a beamformer to evaluate the effect of somatosensory feedback (i.e., entrainment) on movement-related oscillations and motor performance at the single trial level. Results: Our results indicated spectrally specific reductions in movement-related oscillatory power (i.e., theta, gamma) during 15 Hz stimulation in the contralateral motor cortex during motor execution. In addition, we observed robust cross-frequency coupling within the motor cortex and further, stronger theta-gamma coupling was predictive of faster reaction times, irrespective of condition (i.e., stim vs. no stim). Finally, in the presence of electrical stimulation, cross-frequency coupling of movement-related oscillations was reduced, and the stronger the entrained neuronal populations (i.e., increased oscillatory power) were before movement onset, the weaker the inherent theta-gamma coupling became in the motor cortex. Discussion: This novel exogenous manipulation paradigm provides key insights on how the somatosensory system modulates the motor cortical oscillations required for volitional movement in the normative sensorimotor system.

Psychiatric disorders encompass complex aberrations of cognition and affect and are among the most debilitating and poorly understood of any medical condition. Current treatments rely primarily on interventions that target brain function (drugs) or learning processes (psychotherapy). A mechanistic understanding of how these interventions mediate their therapeutic effects remains elusive. From the early 1990s, non-invasive functional neuroimaging, coupled with parallel developments in the cognitive neurosciences, seemed to signal a new era of neurobiologically grounded diagnosis and treatment in psychiatry. Yet, despite three decades of intense neuroimaging research, we still lack a neurobiological account for any psychiatric condition. Likewise, functional neuroimaging plays no role in clinical decision making. Here, we offer a critical commentary on this impasse and suggest how the field might fare better and deliver impactful neurobiological insights.

At rest, 8 to 12 Hz alpha rhythms are the dominant rhythm in the brain, with a common peak alpha frequency (PAF = the frequency at which alpha generators show maximum power) observed across brain regions. Although a common PAF across brain regions should result in high between-region connectivity, especially connectivity measures assessing the phase-similarity between alpha generators, high inter-regional alpha connectivity has not been observed. The present study was conducted as an initial step toward identifying mechanisms that allow brain regions to maintain functional independence in the presence of a common PAF.MEG data were obtained from 16 healthy control male adults (mean age = 24 years; range 21 to 30 years). A task requiring participants to alternate between a 10 s eyes-closed condition and a 5 s eyes-open condition was used to drive parietal-occipital alpha generators, with the 10 s eyes-closed condition eliciting large-amplitude alpha activity and thus providing alpha measures with good signal-to-noise ratio for source localization. Alpha source-space measures were obtained using Vector-based Spatial-Temporal Analysis using L1-minimum-norm. In each participant, the four strongest parietal-occipital alpha generators were identified. Connectivity between sources was assessed via a measure of phase-based connectivity called inter-site phase clustering (ISPC).Intra-class correlations (ICC) showed very high similarity in the average PAF (=computed using all eyes-closed data) between the four alpha sources (ICC single measure = 0.88, p 0.001). Despite a common average PAF, across participants, significant ISPC was often observed no more than that expected by chance. Examination of the alpha time course data indicated that low ISPC was often due to instantaneous changes in alpha phase (phase slips). ISPC analyses removing data with phase slips indicated that low ISPC was also due to slight continuous changes in the alpha frequency, with frequency drift more likely in non-significant than significant ISPC trials.The present exploratory effort suggested two processes underlying the lack of observed inter-regional alpha phase coherence that may help maintain regional functional independence even in the presence of a common PAF. In particular, although the alpha generators were observed to oscillate at the same rate on average, across time each alpha generator oscillated a little slower or faster, and about every one and a half seconds an alpha generator abruptly lost the beat. Because of this, functional independence among alpha generators (and thus brain regions) was the rule rather than the exception. Studies replicating these processes that allow brain regions to maintain functional independence, using different source localization methods and in different conditions (e.g., a true resting state), are warranted.Using source localization to measure parietal-occipital alpha generator activity, two properties that limit between-region alpha functional connectivity are proposed. In particular, a model of alpha generator activity is offered where via transient phase slips occurring approximately every 1.5 s, as well as slight non-stationarity in the alpha frequency, brain regions retain a common alpha frequency while also maintaining regional identity and presumably functionality. Findings also suggest novel markers for use in studies examining changes in alpha activity across maturation as well as in studies examining alpha activity in patient populations where alpha abnormalities have been reported.

The pathogenesis of depression is complex, and the current means of medical diagnosis is single. Patients with severe depression may even have great physical pain and suicidal tendencies. Magnetoencephalography (MEG) has the characteristics of ultrahigh spatiotemporal resolution and safety. It is a good medical means for the diagnosis of depression. In this paper, multivariate transfer entropy algorithm is used to study MEG of depression. In this paper, the subjects are divided into the same brain region and the multichannel combination between different brain regions, and the multivariate transfer entropy of patients with depression and healthy controls under different EEG signal frequency bands is calculated. Finally, the significant difference between the two groups of experimental samples is verified by the results of independent sample t-test. The experimental results show that for the same combination of brain channels, the multivariate transfer entropy in the depression group is generally lower than that in the healthy control group, and the difference is the best in γ frequency band and the largest in the frontal region.

Mild traumatic brain (mTBI) injury is often associated with long-term cognitive and behavioral complications, including an increased risk of memory impairment. Current research challenges include a lack of cross-modal convergence regarding the underlying neural-behavioral mechanisms of mTBI, which hinders therapeutics and outcome management for this frequently under-treated and vulnerable population. We used multi-modality imaging methods including magnetoencephalography (MEG) and diffusion tensor imaging (DTI) to investigate brain-behavior impairment in mTBI related to working memory. A total of 41 participants were recruited, including 23 patients with a first-time mTBI imaged within 3 months of injury (all male, age = 29.9, SD = 6.9), and 18 control participants (all male, age = 27.3, SD = 5.3). Whole-brain statistics revealed spatially concomitant functional-structural disruptions in brain-behavior interactions in working memory in the mTBI group compared with the control group. These disruptions are located in the hippocampal-prefrontal region and, additionally, in the amygdala (measured by MEG neural activation and DTI measures of fractional anisotropy in relation to working memory performance; p .05, two-way ANCOVA, nonparametric permutations, corrected). Impaired brain-behavior connections found in the hippocampal-prefrontal and amygdala circuits indicate brain dysregulation of memory, which may leave mTBI patients vulnerable to increased environmental demands exerting memory resources, leading to related cognitive and emotional psychopathologies. The findings yield clinical implications and highlight a need for early rehabilitation after mTBI, including attention- and sensory-based behavioral exercises.

Previous research showed that repetitive sensory stimulation entrains neural oscillations at the stimulation rate, facilitates long-term potentiation like perceptual learning, and improves behavioural performance. For example, short-time repetitive tactile stimulation improved tactile acuity measured with two-point or spatial orientation discrimination tests. The behavioural gain was maximal for a stimulation rate of 20 Hz, the same frequency at which repetitive somatosensory stimulation elicits a steady-state response with maximum amplitude. The current study investigated whether sensory stimulation must be strictly periodic to induce perceptual learning and whether the 20-Hz steady-state response plays a crucial role in the neural mechanisms of perceptual learning. In a crossover-designed experiment, young, healthy adults received sensory stimulation to the fingertip on three subsequent days. The stimulation was either periodic or temporally randomized (aperiodic) with the same number of stimuli. Tactile acuity was assessed with a grating orientation discrimination task, and brain activity was measured with magnetoencephalography (MEG). Stimulus type-by-session interactions were found for behavioural and brain data. Tactile acuity improved more after a session with aperiodic than periodic stimulation. Beta-band 20-Hz steady-state responses were localized in the primary somatosensory cortex contralateral to the stimulated finger and had larger amplitudes after periodic than aperiodic stimulation. Both stimulus types also elicited gamma oscillations, which increased in amplitude more with aperiodic than periodic stimulation. Sensory stimuli caused a phase reset of sensorimotor beta oscillations phase-coupled to alpha oscillations. The system of stimulus-related oscillations was discussed as underlying temporal processing. Learning may result from facilitating the temporal code. More pronounced behavioural gain with aperiodic than periodic stimulation suggests beneficial effects of temporal stimulus variability for perceptual learning.

The adolescent brain undergoes tremendous structural and functional changes throughout puberty. Previous research has demonstrated that pubertal hormones can modulate sexually dimorphic changes in cortical development, as well as age-related maturation of the neural activity underlying cognitive processes. However, the precise impact of pubertal hormones on these functional changes in the developing human brain remains poorly understood. In the current study, we quantified the neural oscillatory activity serving visuospatial processing using magnetoencephalography, and utilized measures of dehydroepiandrosterone (DHEA) as an index of development during the transition from childhood to adolescence (i.e., puberty). Within a sample of typically developing youth (ages 9-15), a novel association between pubertal DHEA and theta oscillatory activity indicated that less mature children exhibited stronger neural responses in higher-order prefrontal cortices during the visuospatial task. Theta coherence between bilateral prefrontal regions also increased with increasing DHEA, such that network-level theta activity became more distributed with more maturity. Additionally, significant DHEA-by-sex interactions in the gamma range were centered on cortical regions relevant for attention processing. These findings suggest that pubertal DHEA may modulate the development of neural oscillatory activity serving visuospatial processing and attention functions during the pubertal period.

The current study investigated the oscillatory brain activity of PTSD patients during directed and imaginal exposure to the traumatic memory using magnetoencephalography (MEG), in a paradigm resembling exposure therapy.Brain activity of healthy trauma-exposed controls and PTSD participants was measured with MEG as they listened to individualized trauma narratives as well as to a neutral narrative and as they imagined the narrative in detail. Source localization analysis by frequency bands was conducted in order to map neural generators of oscillatory activity.Elicitation of traumatic memories resulted in a distinct neural pattern in PTSD patients compared to healthy trauma-exposed individuals. In response to trauma scripts PTSD patients showed increases in high-gamma band power in visual areas, increased frontal and temporal theta as well as prefrontal alpha and medial temporal beta power relative to neutral scripts.Results suggest that when recollecting and imagining traumatic memories PTSD patients attempt to engage control or inhibition mechanisms. However, these are either not successfully recruited or inefficient leading to heightened responses and recollection.Investigating the oscillatory neural dynamics of PTSD patients can help us better understand the processes underlying trauma re-experiencing.

The posteromedial cortex (PMC) is a major hub of the brain's default mode network, and is implicated in a broad range of internally driven cognitions, including visuospatial working memory. However, its precise contribution to these cognitive processes remains unclear. Using MEG, we measured PMC activity in healthy human participants (young adults of both sexes) while they performed a visuospatial working memory task. Multivariate pattern classification analyses revealed stimulus-related information during encoding and retrieval in a set of a priori defined cortical ROIs, including prefrontal, occipital, and ventrotemporal cortices, in addition to PMC. We measured the extent to which this stimulus information was exchanged between areas in an information flow analysis, measuring Granger-causal relationships between areas over time. Consistent with the visual nature of the task, information from occipital cortex shaped other regions across most epochs. However, the PMC shaped object representations in occipital and prefrontal cortices during visuospatial working memory, influencing occipital cortex during retrieval and PFC across all task epochs. Our findings are consistent with a proposed role for the PMC in the representation of internal content, including remembered information, and in the comparison of external stimuli with remembered material. SIGNIFICANCE STATEMENT The human brain operates as a collection of highly interconnected regions. Mapping the function of this interconnectivity, as well as the specializations within different regions, is central to understanding the neural processes underlying cognition. The posteromedial cortex (PMC) is a highly connected cortical region, implicated in visuospatial working memory, although its precise contribution remains unclear. We measured the activity of PMC during a visuospatial working memory task, testing how different regions represented the stimuli, and whether these representations were driven by other cortical regions. We found that PMC influenced stimulus information in other regions across all task phases, suggesting that PMC plays a key role in shaping stimulus representations during visuospatial working memory.

Aim: The aim of the present study is to investigate the relationship between both functional connectivity and brain networks with cognitive decline, in patients with Parkinson's disease (PD). Introduction: PD phenotype is not limited to motor impairment but, rather, a wide range of non-motor disturbances can occur, with cognitive impairment being one of the most common. However, how the large-scale organization of brain activity differs in cognitively impaired patients, as opposed to cognitively preserved ones, remains poorly understood. Methods: Starting from source-reconstructed resting-state magnetoencephalography data, we applied the phase linearity measurement (PLM) to estimate functional connectivity, globally and between brain areas, in PD patients with and without cognitive impairment (respectively PD-CI and PD-NC), as compared with healthy subjects (HS). Further, using graph analysis, we characterized the alterations in brain network topology and related these, as well as the functional connectivity, to cognitive performance. Results: We found reduced global and nodal PLM in several temporal (fusiform gyrus, Heschl's gyrus, and inferior temporal gyrus), parietal (postcentral gyrus), and occipital (lingual gyrus) areas within the left hemisphere, in the gamma band, in PD-CI patients, as compared with PD-NC and HS. With regard to the global topological features, PD-CI patients, as compared with HS and PD-NC patients, showed differences in multi-frequencies bands (delta, alpha, gamma) in the Leaf fraction, Tree hierarchy (Th) (both higher in PD-CI), and Diameter (lower in PD-CI). Finally, we found statistically significant correlations between the Montreal Cognitive Assessment test and both the Diameter in delta band and the Th in the alpha band. Conclusion: Our work points to specific large-scale rearrangements that occur selectively in cognitively compromised PD patients and are correlated to cognitive impairment. Impact statement In this article, we want to test the hypothesis that the cognitive decline observed in Parkinson's disease (PD) patients may be related to specific changes of both functional connectivity and brain network topology. Specifically, starting from magnetoencephalography signals and by applying the phase linearity measurement (PLM), a connectivity metric that measures the synchronization between brain regions, we were able to highlight differences in the global and nodal PLM values in PD patients with cognitive impairment as compared with both cognitively unimpaired patients and healthy subjects. Further, using graph analysis, we analyzed alterations in brain network topology that were related to cognitive functioning.

Objective: Absence seizures are the prototypic primarily generalized seizures, but there is incomplete understanding regarding their generation and maintenance. A core network for absence seizures has been defined, including focal cortical and thalamic regions that have frequency-dependent interactions. The purpose of this study was to investigate within-frequency coupling and cross-frequency coupling (CFC) during human absence seizures, to identify key regions (hubs) within the absence network that contribute to propagation and maintenance. Methods: Thirteen children with new-onset and untreated childhood absence epilepsy had over 60 typical absence seizures during both electroencephalography-functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) recordings. The spatial map of the ictal network was defined using fMRI and used as prior information for MEG connectivity. A multilayer network approach was used to investigate within-frequency coupling and CFC for canonical frequency bands. A rigorous null-modeling approach was used to determine connections outside the noise floor. Results: Strong coupling between beta and gamma frequencies, within the left frontal cortex, and between the left frontal and right parietal regions was observed. There was also strong connectivity between left frontal and right parietal nodes within the gamma band. Multilayer versatility analysis identified a cluster of network hubs in the left frontal region. Interpretation: Cortical regions commonly identified as being critical for absence seizure generation (frontal cortex, precuneus) have strong CFC and within-frequency coupling between beta and gamma bands. As nonpharmacologic treatments, such as neuromodulation, become available for generalized epilepsies, detailed mechanistic understanding of how "diffuse" seizures are generated and maintained will be necessary to provide optimal outcomes.

Alcohol intoxication impairs inhibitory control, resulting in disinhibited, impulsive behavior. The anterior cingulate cortex (ACC) plays an essential role in a range of executive functions and is sensitive to the effects of alcohol, which contributes to the top-down cognitive dysregulation. This study used a multimodal approach to examine the acute effects of alcohol on the neural underpinnings of inhibitory control, inhibition failures, and neurobehavioral optimization as reflected in trial-to-trial dynamics of post-error adjustments.Adult social drinkers served as their own controls by participating in the Go/NoGo task during acute alcohol and placebo conditions in a multi-session, counterbalanced design. Distributed source modeling of the magnetoencephalographic signal was combined with structural magnetic resonance imaging to characterize the spatio-temporal dynamics of inhibitory control in the time-frequency domain.Successful response inhibition (NoGo) elicited right-lateralized event-related theta power (4 to 7 Hz). Errors elicited a short-latency increase in theta power in the dorsal (dACC), followed by activity in the rostral (rACC), which may underlie an affective "oh, no!" orienting response to errors. Error-related theta in the dACC was associated with subsequent activity of the motor areas on the first post-error trial, suggesting the occurrence of post-error output adjustments. Importantly, a gradual increase of the dACC theta across post-error trials closely tracked improvements in accuracy under placebo, which may reflect cognitive control engagement to optimize response accuracy. In contrast, alcohol increased NoGo commission errors, dysregulated theta during correct NoGo withholding, and abolished the post-error theta enhancement of cognitive control.Confirming the sensitivity of frontal theta to inhibitory control and error monitoring, the results support functional and temporal dissociation along the dorso-rostral axis of the ACC and the deleterious effects of alcohol on the frontal circuitry subserving top-down regulation. Over time, alcohol-induced disinhibition may give rise to compulsive drinking and contribute to alcohol misuse.

How do attentional networks influence conscious perception? To answer this question, we used magnetoencephalography (MEG) in human participants, and assessed the effects of spatially nonpredictive or predictive supra-threshold peripheral cues on the conscious perception of near-threshold Gabors. Three main results emerged. (1) As compared with invalid cues, both nonpredictive and predictive valid cues increased conscious detection. Yet, only predictive cues shifted the response criterion towards a more liberal decision (i.e., willingness to report the presence of a target under conditions of greater perceptual uncertainty) and affected target contrast leading to 50% detections. (2) Conscious perception following valid predictive cues was associated to enhanced activity in frontoparietal networks. These responses were lateralized to the left hemisphere during attentional orienting, and to the right hemisphere during target processing. The involvement of frontoparietal networks occurred earlier in valid than in invalid trials, a possible neural marker of the cost of re-orienting attention. (3) When detected targets were preceded by invalid predictive cues, and thus reorienting to the target was required, neural responses occurred in left hemisphere temporo-occipital regions during attentional orienting, and in right hemisphere anterior insular and temporo-occipital regions during target processing. These results confirm and specify the role of frontoparietal networks in modulating conscious processing, and detail how invalid orienting of spatial attention disrupts conscious processing.Significance StatementDo we need to pay attention to external objects in order to become aware of them? Characterizing the spatiotemporal dynamics of attentional effects on visual perception is critical to understand how humans process and select relevant information. Participants detected near-threshold visual targets preceded by supra-threshold spatial cues with varying degrees of predictivity, while their brain activity was recorded using magnetoencephalography. Results demonstrated that valid predictive cues biased participants’ conscious perception through an early recruitment of frontoparietal regions, and that attentional costs associated to invalid predictive cues were related to activation of the right hemisphere ventral network. This work characterizes the neural dynamics associated with the cost of attentional reorienting on conscious processing.

Neuronal oscillations are linked to symptoms of Parkinson's disease. This relation can be exploited for optimizing deep brain stimulation (DBS), e.g. by informing a device or human about the optimal location, time and intensity of stimulation. Whether oscillations predict individual DBS outcome is not clear so far.To predict motor symptom improvement from subthalamic power and subthalamo-cortical coherence.We applied machine learning techniques to simultaneously recorded magnetoencephalography and local field potential data from 36 patients with Parkinson's disease. Gradient-boosted tree learning was applied in combination with feature importance analysis to generate and understand out-of-sample predictions.A few features sufficed for making accurate predictions. A model operating on five coherence features, for example, achieved correlations of r 0.8 between actual and predicted outcomes. Coherence comprised more information in less features than subthalamic power, although in general their information content was comparable. Both signals predicted akinesia/rigidity reduction best. The most important local feature was subthalamic high-beta power (20-35 Hz). The most important connectivity features were subthalamo-parietal coherence in the very high frequency band (200 Hz) and subthalamo-parietal coherence in low-gamma band (36-60 Hz). Successful prediction was not due to the model inferring distance to target or symptom severity from neuronal oscillations.This study demonstrates for the first time that neuronal oscillations are predictive of DBS outcome. Coherence between subthalamic and parietal oscillations are particularly informative. These results highlight the clinical relevance of inter-areal synchrony in basal ganglia-cortex loops and might facilitate further improvements of DBS in the future.

Magnetoencephalography (MEG) is a neurophysiological technique that measures the magnetic fields associated with neuronal activity in the brain. It is closely related but distinct from its counterpart electroencephalography (EEG). The first MEG was recorded more than 50 years ago and has technologically evolved over this time. It is now well established in clinical practice particularly in the field of epilepsy surgery and functional brain mapping. However, underutilization and misunderstanding of the clinical applications of MEG is a challenge to more widespread use of this technology. A fundamental understanding of the neurophysiology and physics of MEG is discussed in this article as well as practical issues related to implementation, analysis, and clinical applications. The future of MEG and some potential clinical applications are briefly reviewed.

Schizophrenia (SZ) is characterized by psychotic symptoms and cognitive impairment, and is hypothesized to be a 'dysconnection' syndrome due to abnormal neural network formation. Although numerous studies have helped elucidate the pathophysiology of SZ, many aspects of the mechanism underlying psychotic symptoms remain unknown. This study used graph theory analysis to evaluate the characteristics of the resting-state network (RSN) in terms of microscale and macroscale indices, and to identify candidates as potential biomarkers of SZ. Specifically, we discriminated topological characteristics in the frequency domain and investigated them in the context of psychotic symptoms in patients with SZ.We performed graph theory analysis of electrophysiological RSN data using magnetoencephalography to compare topological characteristics represented by microscale (degree centrality and clustering coefficient) and macroscale (global efficiency, local efficiency, and small-worldness) indices in 29 patients with SZ and 38 healthy controls. In addition, we investigated the aberrant topological characteristics of the RSN in patients with SZ and their relationship with SZ symptoms.SZ was associated with a decreased clustering coefficient, local efficiency, and small-worldness, especially in the high beta band. In addition, macroscale changes in the low beta band are closely associated with negative symptoms.The local networks of patients with SZ may disintegrate at both the microscale and macroscale levels, mainly in the beta band. Adopting an electrophysiological perspective of SZ as a failure to form local networks in the beta band will provide deeper insights into the pathophysiology of SZ as a 'dysconnection' syndrome.

The dynamic description of neural networks has attracted the attention of researchers for dynamic networks may carry more information compared with resting-state networks. As a non-invasive electrophysiological data with high temporal and spatial resolution, magnetoencephalogram (MEG) can provide rich information for the analysis of dynamic functional brain networks. In this review, the development of MEG brain network was summarized. Several analysis methods such as sliding window, Hidden Markov model, and time-frequency based methods used in MEG dynamic brain network studies were discussed. Finally, the current research about multi-modal brain network analysis and their applications with MEG neurophysiology, which are prospected to be one of the research directions in the future, were concluded.

The excellent temporal resolution and advanced spatial resolution of magnetoencephalography (MEG) makes it an excellent tool to study the neural dynamics underlying cognitive processes in the developing brain. Nonetheless, a number of challenges exist when using MEG to image infant populations. There is a persistent belief that collecting MEG data with infants presents a number of limitations and challenges that are difficult to overcome. Due to this notion, many researchers either avoid conducting infant MEG research or believe that, in order to collect high-quality data, they must impose limiting restrictions on the infant or the experimental paradigm. In this article, we discuss the various challenges unique to imaging awake infants and young children with MEG, and share general best-practice guidelines and recommendations for data collection, acquisition, preprocessing, and analysis. The current article is focused on methodology that allows investigators to test the sensory, perceptual, and cognitive capacities of awake and moving infants. We believe that such methodology opens the pathway for using MEG to provide mechanistic explanations for the complex behavior observed in awake, sentient, and dynamically interacting infants, thus addressing core topics in developmental cognitive neuroscience.

Electrophysiological power spectra typically consist of two components: An aperiodic part usually following an 1/f power law $$P\propto 1/{f}^{\beta }$$ P ∝ 1 / f β and periodic components appearing as spectral peaks. While the investigation of the periodic parts, commonly referred to as neural oscillations, has received considerable attention, the study of the aperiodic part has only recently gained more interest. The periodic part is usually quantified by center frequencies, powers, and bandwidths, while the aperiodic part is parameterized by the y-intercept and the 1/f exponent $$\beta$$ β . For investigation of either part, however, it is essential to separate the two components. In this article, we scrutinize two frequently used methods, FOOOF (Fitting Oscillations & One-Over-F) and IRASA (Irregular Resampling Auto-Spectral Analysis), that are commonly used to separate the periodic from the aperiodic component. We evaluate these methods using diverse spectra obtained with electroencephalography (EEG), magnetoencephalography (MEG), and local field potential (LFP) recordings relating to three independent research datasets. Each method and each dataset poses distinct challenges for the extraction of both spectral parts. The specific spectral features hindering the periodic and aperiodic separation are highlighted by simulations of power spectra emphasizing these features. Through comparison with the simulation parameters defined a priori, the parameterization error of each method is quantified. Based on the real and simulated power spectra, we evaluate the advantages of both methods, discuss common challenges, note which spectral features impede the separation, assess the computational costs, and propose recommendations on how to use them.

Objective: Interictal epileptiform discharges (IEDs) constitute a diagnostic signature of epilepsy. These events reflect epileptogenic hypersynchronization. Previous studies indicated that IEDs arise from slow neuronal activation accompanied by metabolic and hemodynamic changes. These might induce cortical inhibition followed hypersynchronization at IED onset. As cortical inhibition is mediated by low-frequency oscillations, we aimed to analyze the role of low-frequency oscillations prior the IED using magnetencephalography (MEG). Methods: Low-frequency (1–8 Hz) oscillations pre-IED ([-1000 milliseconds (ms), IED onset]) were analyzed using MEG in 14 focal epilepsy patients (median age = 23 years, range = 7–46 age). Occurrence of local pre-IED oscillations was analyzed using Beamformer Dynamical Imaging of Coherent Sources (DICS) and event-related desynchronization/synchronization (ERD-ERS) maps constructed using cluster-based permutation tests. The development of pre-IED oscillations was characterized using Hilbert transformation. Results: All patients exhibited statistically significant increase in delta (1–4 Hz) and/or theta (4–8 Hz) oscillations pre-IED compared to baseline [-2000 ms, −1000 ms]. Furthermore, all patients exhibited low-frequency power increase up to IED onset. Conclusions: We demonstrated consistently occurring, low-frequency oscillations prior to IED onset. Significance: As low-frequency activity mediates cortical inhibition, our study demonstrates that a focal inhibition precedes hypersynchronization at IED onset.

Precise and accurate implantation of stereo-electroencephalography (SEEG) electrodes is critical for the localization of the seizure onset zone (SOZ), which plays a leading role in the prognosis of resective epilepsy surgery. Magnetoencephalography (MEG) is a noninvasive technique which can delineate the epilepsy focus by visualizing interictal spikes into dipole clusters. MEG may provide supporting information for guiding SEEG electrode implantation and improve the long-term outcomes of epilepsy surgery. In this study, we evaluated the accuracy of MEG in determining the SOZ.We retrospectively analyzed patients with refractory epilepsy who underwent MEG examination and SEEG implantation before resective epilepsy surgery in the Shanghai Ruijin Hospital. The SEEG plan was designed according to the dipole clusters and the resections were operated according to the SEEG recordings. We investigated the relationships of the pattern of MEG dipole clusters and SEEG sampling to the final resective surgery prognosis.We included 42 patients with a postoperative follow-up of at least 2 years (mean 34.1 months). Eighteen (42%) patients who showed concordant localization between MEG and SEEG evaluation had a higher probability of seizure-free outcome (p=0.046, χMEG dipole cluster helps SEEG implantation in localizing the SOZ for better long-term epilepsy surgery outcome. The MEG results can play a role as prognostic predictors of epilepsy surgery.

IntroductionThe cognitive and psychotic symptoms in patients with schizophrenia (SZ) are thought to result from disrupted brain network connectivity.MethodsWe capitalize on the high spatiotemporal resolution of magnetoencephalography imaging (MEG) to record spontaneous neuronal activity in resting state networks in 21 SZ compared with 21 healthy controls (HC).ResultsWe found that SZ showed significant global disrupted functional connectivity in delta-theta (2–8 Hz), alpha (8–12 Hz), and beta (12–30 Hz) frequencies, compared to HC. Disrupted global connectivity in alpha frequencies with bilateral frontal cortices was associated with more severe clinical psychopathology (i.e., positive psychotic symptoms). Specifically, aberrant connectivity in beta frequencies between the left primary auditory cortex and cerebellum, was linked to greater hallucination severity in SZ. Disrupted connectivity in delta-theta frequencies between the medial frontal and left inferior frontal cortex was associated with impaired cognition.DiscussionThe multivariate techniques employed in the present study highlight the importance of applying our source reconstruction techniques which leverage the high spatial localization abilities of MEG for estimating neural source activity using beamforming methods such as SAM (synthetic aperture morphometry) to reconstruct the source of brain activity, together with functional connectivity assessments, assayed with imaginary coherence metrics, to delineate how neurophysiological dysconnectivity in specific oscillatory frequencies between distinct regions underlie the cognitive and psychotic symptoms in SZ. The present findings employ powerful techniques in spatial and time-frequency domains to provide potential neural biomarkers underlying neuronal network dysconnectivity in SZ that will inform the development of innovations in future neuromodulation treatment development.

Deep neural networks (DNNs) are promising models of the cortical computations supporting human object recognition. However, despite their ability to explain a significant portion of variance in neural data, the agreement between models and brain representational dynamics is far from perfect. We address this issue by asking which representational features are currently unaccounted for in neural timeseries data, estimated for multiple areas of the ventral stream via source-reconstructed magnetoencephalography (MEG) data acquired in human participants (9 females, 6 males) during object viewing. We focus on the ability of visuo-semantic models, consisting of human-generated labels of object features and categories, to explain variance beyond the explanatory power of DNNs alone. We report a gradual reversal in the relative importance of DNN versus visuo-semantic features as ventral-stream object rep-resentations unfold over space and time. While lower-level visual areas are better explained by DNN features, especially during the early phase of the response (< 128 ms after stimulus onset), higher-level cortical dynamics are best accounted for by visuo-semantic features during a later time window (starting 146 ms after stimulus onset). Among the visuo-semantic features, object parts and basic categories drive the advantage over DNNs. These results show that a significant component of the variance unexplained by DNNs in higher-level cortical dynamics is structured, and can be explained by readily nameable aspects of the objects. We conclude that current DNNs fail to fully capture dynamic representations in higher-level human visual cortex and suggest a path toward more accurate models of ventral stream computations.SIGNIFICANCE STATEMENTWhen we view objects such as faces and cars in our visual environment, their neural representations dynamically unfold over time at a millisecond scale. These dynamics reflect the cortical computations that support fast and robust object recognition. Deep neural networks (DNNs) have emerged as a promising framework for modeling these computations but cannot yet fully account for the neural dynamics. Using magnetoencephalography data acquired in human observers during object viewing, we show that readily nameable aspects of objects, such as “eye”, “wheel”, and “face”, can account for variance in the neural dynamics over and above DNNs. These findings suggest that DNNs and humans may in part rely on different object features for visual recognition and provide guidelines for model improvement.

MEG is a neuroimaging technique often used to study changes in the brain’s functional organization. In order to assess changes in functional organization with respect to learning processes or pathology, the localization of the cortical representations of interest must be as accurate as possible. The present study aims to investigate the accuracy of the localization of representations in the primary somatosensory cortex using a classical paradigm to study changes in the brain’s functional organization. We will apply tactile, non-painful stimuli to both corners of the lips and thumbs to estimate the localization of the respective representation in S1. The retest reliability between two measurements (measurements 1 and 2) on the same day and at an interval of one week (measurements 3 and 4) will be considered to assess the reproducibility and stability of the dipole localization. We consider reliability on the same day and on two different measurement days. The comparison of two measurements on the same day (measurement 1 vs. measurement 2 and measurement 3 vs. measurement 4) indicates localization errors due to the methodology itself, e.g., due to slight variations in the stimulation position or the digitization of the head shape. The additional comparison of the measurements at one-week intervals (measurement 1 vs. measurement 3 and measurement 2 vs. measurement 4) allows us to observe a change over time and the stability of the representation over time.

Gliomas are a rare form of primary brain tumor, and the rate of morbidity and mortality is very high (Ho et al., 2014). Glioma patients often suffer from cognitive deficits, particularly in Executive Functioning (EF), which can decrease their quality of life (Caramanna et al., 2021; van Kessel et al., 2017; Weyer-Jamora et al., 2021). EF refers to a set of cognitive processes that are important in the cognitive control of behaviors such as attention, decision-making and working memory. EF deficits exist before surgery, and although studies have shown that tumor resection may be helpful in alleviating such problems, research has demonstrated varying trajectories in EF changes in patients that have undergone tumor resection, with some noting improving as well as deteriorating EF after surgery (Lemaitre et al., 2021; Ng et al., 2019; Noll et al., 2021; Sinha et al., 2020; Talacchi et al., 2011). Research has shown that gliomas are often localized in the frontal regions of the brain, residing in the white matter under the frontal cortices (Larjavaara et al., 2007, Wang et al., 2020). These brain regions have been shown to play an important role in the regulation of EF. Importantly, gliomas seem to directly integrate into their neural environment. The area in the vicinity of the tumor, the peritumoral area, has been shown to be hyperactive (Venkatesh et al., 2019, Numan et al., 2021). Investigations into the relationship between gliomas and neuronal activity in rodents, have revealed a direct link between the two, whereby synaptic and electric integration of gliomas into the neuronal networks occurs, which in turn promotes glioma progression (Buckingham et al., 2011; Campbell et al., 2012; Gibson et al., 2014; Venkatesh et al., 2015; Venkatesh et al., 2017; Venkatesh et al., 2019). Studies in rodents have further revealed that increased neuronal activity promotes glioma proliferation and growth (Gibson et al., 2014; Venkatesh et al., 2015). Various molecular mechanisms underlying this phenomenon have been researched, with some studies focusing on neuroligin-3 (NLGN3), whereby stimulation of neurons causes the cleavage and secretion of NLGN3, stimulating glioma growth (Venkatesh et al., 2015; Venkatesh et al., 2017). Other studies have shown similar mechanisms to occur through the secretion of brain derived neurotrophic factor (BDNF), glutamate and dopamine, all of which seem to promote tumor growth (Xiong et al., 2013; Dolma et al., 2016; Ishiuchi et al., 2002; Ishiuchi et al., 2007). Conversely, gliomas are also able to stimulate neuronal activity by forming synapses with healthy neurons through glutamate release (Buckingham et al., 2011; Campbell et al., 2012). Overall, such studies have shown an intricate bidirectional relationship between neurons and gliomas which impacts tumor growth. Further studies, in glioma patients, have also linked tumor progression and brain activity, as measured with magnetoencephalography (MEG), which may contribute to the progression of the disease. In patients, it has been shown that activity, as measured with magnetoencephalography (MEG), is pathologically heightened in comparison to healthy controls (HCs), not only around the tumor but throughout the entire brain (Numan et al., 2022; Zimmermann et al., 2023) Lower levels of oscillatory brain activity were correlated with lower levels of NLGN3 and with longer progression free survival (PFS) in glioma patients (Derks et al., 2018). Similarly, higher levels of broadband power, a proxy for neural activity, postoperatively, predicted shorter PFS and overall survival (OS) in this patient group and were shown to serve as an additional prognostic marker (Belgers et al., 2020). Further supporting the concept of glioma induced neuronal circuits remodeling, are studies that have observed altered electrophysiological properties throughout the brain of patients with glioma, when comparing to HCs. Distinct electrophysiological patterns have been detected, characterized by an increase in slow wavelengths such as higher delta band activity in comparison to HCs (Aabedi et al., 2022; Baayen et al., 2003; Bosma et al., 2008; De Jongh et al., 2003; Oshino et al., 2007). Such alterations have also been correlated with differences in cognition, such as language (Wolthuis et al., 2022). Moreover, recent studies using magnetoencephalography (MEG) demonstrated differences in network clustering (Derks et al., 2021) and connectivity in the brain as compared to HCs (Bartolomei et al., 2006a; Bartolomei et al., 2006b). These findings in turn have been associated with cognitive functioning (Bosma et al., 2008; Bosma et al., 2009; van Dellen et al., 2012; Derks et al., 2019). A recent study by van Lingen et al., (2023) investigated the link between alterations in multilayer network topology in the frontoparietal network (FPN), a network that plays an important role in facilitating EF, and changes in EF in glioma patients from the pre-operative state to a one-year follow up (FU). Results showed that lower set shifting correlated with a decrease in integration. Although studies, such as the previously mentioned one, have investigated how gliomas may affect cognitive functioning in patients, most of the current research has focused on linking alterations in neuronal networks and EF. However, neuronal activity has yet to be investigated with regard to its relationship to cognitive functioning in glioma patients. This is of particular interest due to the previously illustrated findings that demonstrated the integration of the tumor into its neural surroundings and the finding that activity is heightened throughout the brain in these patients in comparison to controls (Numan et al., 2021; Zimmermann et al., 2023). Additionally, it is unknown how activity changes from pre- to post-tumor resection in such patients. This study will therefore aim to bridge these gaps of knowledge by building upon previous work conducted by van Lingen et al., (2023). In addition to analyzing the relationship between activity and EF in patients, this study aims to explore the same in healthy individuals. Patient studies have found that greater brain activity in the default mode network (DMN) is associated with better cognitive flexibility (Sambataro et al., 2010). Furthermore, Zou et al. (2012) discovered that intrinsic resting-state brain activity was significantly correlated with working memory task performance. In addition to such research, brain stimulation, through techniques such as transcranial alternating current stimulation (tACS) and transcranial random noise stimulation (TRNS), have been shown to enhance long-term memory and other cognitive functions (Grover et al., 2022; Qi et al., 2021; Snowball et al., 2023). Although these findings suggest that activity may be related to cognitive performance, the association between EF and activity in the FPN in healthy individuals is unknown. This study therefore intends to investigate the relationship between activity in the FPN and EF in glioma patients and HCs. As brain activity has been shown to impact various aspects of glioma pathology, the question remains how this may relate to cognition.

We explore the stability and dynamic properties of a hierarchical, linearized, and analytic spectral graph model for neural oscillations that integrates the structural wiring of the brain. Previously, we have shown that this model can accurately capture the frequency spectra and the spatial patterns of the alpha and beta frequency bands obtained from magnetoencephalography recordings without regionally varying parameters. Here, we show that this macroscopic model based on long-range excitatory connections exhibits dynamic oscillations with a frequency in the alpha band even without any oscillations implemented at the mesoscopic level. We show that depending on the parameters, the model can exhibit combinations of damped oscillations, limit cycles, or unstable oscillations. We determined bounds on model parameters that ensure stability of the oscillations simulated by the model. Finally, we estimated time-varying model parameters to capture the temporal fluctuations in magnetoencephalography activity. We show that a dynamic spectral graph modeling framework with a parsimonious set of biophysically interpretable model parameters can thereby be employed to capture oscillatory fluctuations observed in electrophysiological data in various brain states and diseases.

Predictive coding accounts of brain functions profoundly influence current approaches to perceptual synthesis. However, a fundamental paradox has emerged, that may be very relevant for understanding hallucinations, psychosis, or cognitive inflexibility: in some situations, surprise or prediction error-related responses can decrease when predicted, and yet, they can increase when we know they are predictable. This paradox is resolved by recognizing that brain responses reflect precision-weighted prediction error. This presses us to disambiguate the contributions of precision and prediction error in electrophysiology. To meet this challenge for the first time, we appeal to a methodology that couples an original experimental paradigm with fine dynamic modeling. We examined brain responses in healthy human participants (

Background Traumatic brain injury (TBI) can precipitate new-onset psychiatric symptoms or worsen existing psychiatric conditions. To elucidate specific mechanisms for this interaction, neuroimaging is often used to study both psychiatric conditions and TBI. This systematic review aims to synthesize the existing literature of neuroimaging findings among patients with anxiety after TBI. Methods We conducted a Preferred Reporting Items for Systematic Review and Meta-Analyses–compliant literature search via PubMed (MEDLINE), PsychINFO, EMBASE, and Scopus databases before May, 2019. We included studies that clearly defined TBI, measured syndromic anxiety as a primary outcome, and statistically analyzed the relationship between neuroimaging findings and anxiety symptoms. Results A total of 5982 articles were retrieved from the systematic search, of which 65 studied anxiety and 13 met eligibility criteria. These studies were published between 2004 and 2017, collectively analyzing 764 participants comprised of 470 patients with TBI and 294 non-TBI controls. Imaging modalities used included magnetic resonance imaging, functional magnetic resonance imaging, diffusion tensor imaging, electroencephalogram, magnetic resonance spectrometry, and magnetoencephalography. Eight of 13 studies presented at least one significant finding and together reflect a complex set of changes that lead to anxiety in the setting of TBI. The left cingulate gyrus in particular was found to be significant in 2 studies using different imaging modalities. Two studies also revealed perturbances in functional connectivity within the default mode network. Conclusions This is the first systemic review of neuroimaging changes associated with anxiety after TBI, which implicated multiple brain structures and circuits, such as the default mode network. Future research with consistent, rigorous measurements of TBI and syndromic anxiety, as well as attention to control groups, previous TBIs, and time interval between TBI and neuroimaging, are warranted. By understanding neuroimaging correlates of psychiatric symptoms, this work could inform future post-TBI screening and surveillance, preventative efforts, and early interventions to improve neuropsychiatric outcomes.

For interpretation of electroencephalography (EEG) and magnetoencephalography (MEG) data, multiple solutions of the respective forward problems are needed. In this paper, we assess performance of the mixed-hybrid finite element method (MHFEM) applied to EEG and MEG modeling. The method provides an approximate potential and induced currents and results in a system with a positive semi-definite matrix. The system thus can be solved with a variety of standard methods (e.g. the preconditioned conjugate gradient method). The induced currents satisfy discrete charge conservation law making the method conservative. We studied its performance on unstructured tetrahedral grids for a layered spherical head model as well as a realistic head model. We also compared its accuracy versus the conventional nodal finite element method (P1 FEM). To avoid modeling singular sources, we completed our computations with a subtraction approach; the derived expression for the MEG response different from earlier published and involves integration of finite quantities only. We conclude that although the MHFEM is more computationally demanding than the P1 FEM, its use is justified for EEG and MEG modeling on low-resolution head models where P1 FEM loses accuracy.

Semantic processing is the ability to discern and maintain conceptual relationships among words and objects. While the neural circuits serving semantic representation and controlled retrieval are well established, the neuronal dynamics underlying these processes are poorly understood. Herein, we examined 25 healthy young adults who completed a semantic relation word-matching task during magnetoencephalography (MEG). MEG data were examined in the time–frequency domain and significant oscillatory responses were imaged using a beamformer. Whole-brain statistical analyses were conducted to compare semantic-related to length-related neural oscillatory responses. Time series were extracted to visualize the dynamics and were linked to task performance using structural equation modeling. The results indicated that participants had significantly longer reaction times in semantic compared to length trials. Robust MEG responses in the theta (3–6 Hz), alpha (10–16 Hz), and gamma (64–76 Hz and 64–94 Hz) bands were observed in parieto-occipital and frontal cortices. Whole-brain analyses revealed stronger alpha oscillations in a left-lateralized network during semantically related relative to length trials. Importantly, stronger alpha oscillations in the left superior temporal gyrus during semantic trials predicted faster responses. These data reinforce existing literature and add novel temporal evidence supporting the executive role of the semantic control network in behavior.

Background Atypical sensory behavior disrupts behavioral adaptation in children with autism spectrum disorder (ASD); however, neural correlates of sensory dysfunction using magnetoencephalography (MEG) remain unclear. Method We used MEG to measure the cortical activation elicited by visual (uni)/audiovisual (multisensory) movies in 46 children (7–14 years) were included in final analysis: 13 boys with atypical audiovisual behavior in ASD (AAV+), 10 without this condition, and 23 age-matched typically developing boys. Results The AAV+ group demonstrated an increase in the cortical activation in the bilateral insula in response to unisensory movies and in the left occipital, right superior temporal sulcus (rSTS), and temporal regions to multisensory movies. These increased responses were correlated with severity of the sensory impairment. Increased theta-low gamma oscillations were observed in the rSTS in AAV+. Conclusion The findings suggest that AAV is attributed to atypical neural networks centered on the rSTS.

Objective ranscranial direct current stimulation (tDCS) is a non-invasive neuro-modulation technique that delivers current through the scalp by a pair of patch electrodes (2-Patch). This study proposes a new multi-channel tDCS (mc-tDCS) optimization method, the distributed constrained maximum intensity (D-CMI) approach. For targeting the P20/N20 somatosensory source at Brodmann area 3b, an integrated combined magnetoencephalography (MEG) and electroencephalography (EEG) source analysis is used with individualized skull conductivity calibrated realistic head modeling. Methods Simulated electric fields (EF) for our new D-CMI method and the already known maximum intensity (MI), alternating direction method of multipliers (ADMM) and 2-Patch methods were produced and compared for the individualized P20/N20 somatosensory target for 10 subjects. Results D-CMI and MI showed highest intensities parallel to the P20/N20 target compared to ADMM and 2-Patch, with ADMM achieving highest focality. D-CMI showed a slight reduction in intensity compared to MI while reducing side effects and skin level sensations by current distribution over multiple stimulation electrodes. Conclusion Individualized D-CMI montages are preferred for our follow up somatosensory experiment to provide a good balance between high current intensities at the target and reduced side effects and skin sensations. Significance An integrated combined MEG and EEG source analysis with D-CMI montages for mc-tDCS stimulation potentially can improve control, reproducibility and reduce sensitivity differences between sham and real stimulations.