Measuring the cortical correlation structure of spontaneous oscillatory activity with EEG and MEG

Active suicidal ideation associated with dysfunction in default mode network using resting-state EEG and functional MRI - Findings from the T-RAD Study

Suicide in youth and young adults is a serious public health problem. However, the biological mechanisms of suicidal ideation (SI) remain poorly understood. The primary goal of these analyses was to identify the connectome profile of suicidal ideation using resting state electroencephalography (EEG). We evaluated the neurocircuitry of SI in a sample of youths and young adults (aged 10-26 years, n = 111) with current or past diagnoses of either a depressive disorder or bipolar disorder who were enrolled in the Texas Resilience Against Depression Study (T-RAD). Neurocircuitry was analyzed using orthogonalized power envelope connectivity computed from resting state EEG. Suicidal ideation was assessed with the 3-item Suicidal Thoughts factor of the Concise Health Risk Tracking self-report scale. The statistical pipeline involved dimension reduction using principal component analysis, and the association of neuroimaging data with SI using regularized canonical correlation analysis. From the original 111 participants and the correlation matrix of 4950 EEG connectivity pairs in each band (alpha, beta, theta), dimension reduction generated 1305 EEG connectivity pairs in the theta band, 2337 EEG pairs in the alpha band, and 914 EEG connectivity pairs in the beta band. Overall, SI was consistently involved with dysfunction of the default mode network (DMN). This report provides preliminary evidence of DMN dysfunction associated with active suicidal ideation in adolescents. Using EEG using power envelopes to compute connectivity moves us closer to using neurocircuit dysfunction in the clinical setting to identify suicidal ideation.

An Electroencephalography Profile of Paroxysmal Kinesigenic Dyskinesia

Paroxysmal kinesigenic dyskinesia (PKD) is associated with a disturbance of neural circuit and network activities, while its neurophysiological characteristics have not been fully elucidated. This study utilized the high-density electroencephalogram (hd-EEG) signals to detect abnormal brain activity of PKD and provide a neural biomarker for its clinical diagnosis and PKD progression monitoring. The resting hd-EEGs are recorded from two independent datasets and then source-localized for measuring the oscillatory activities and function connectivity (FC) patterns of cortical and subcortical regions. The abnormal elevation of theta oscillation in wildly brain regions represents the most remarkable physiological feature for PKD and these changes returned to healthy control level in remission patients. Another remarkable feature of PKD is the decreased high-gamma FCs in non-remission patients. Subtype analyses report that increased theta oscillations may be related to the emotional factors of PKD, while the decreased high-gamma FCs are related to the motor symptoms. Finally, the authors established connectome-based predictive modelling and successfully identified the remission state in PKD patients in dataset 1 and dataset 2. The findings establish a clinically relevant electroencephalography profile of PKD and indicate that hd-EEG can provide robust neural biomarkers to evaluate the prognosis of PKD.

Temporal hyper-connectivity and frontal hypo-connectivity within gamma band in schizophrenia: A resting state EEG study

OBJECTIVE

The brain network serves as the physiological foundation for information processing of the brain. Many studies have reported abnormalities of gamma oscillations in Schizophrenia. The aim of this study was to investigate the gamma-band connectivity in Schizophrenia patients.

METHODS

We recorded the resting state electroencephalogram (EEG) for 15 schizophrenia patients with refractory auditory hallucinations and 14 healthy controls, with eyes open and closed. The brain network was constructed based on weighted phase lag index for gamma band. Whole scalp metrics (clustering coefficient, global efficiency and local efficiency) and local region metrics (degree and betweenness centrality) in the frontal and temporal lobes were computed. Correlation analyses between network metrics and symptom scales were examined to find associations with symptom severity.

RESULTS

Schizophrenia patients had larger global efficiency and local efficiency (p < 0.05) with eyes closed, probably representing greater brain activity and information exchange. For degree and betweenness centrality, schizophrenia patients showed an increase (p < 0.05) in the temporal lobe but a decrease (p < 0.05) in the frontal lobe with eyes closed and open, potentially account for the patients' symptoms such as hallucinations and thought disorders. Local efficiency and frontal lobe degree were positively and negatively correlated with the scales, respectively (both p < 0.05).

CONCLUSIONS

Altered connectivity of the resting state brain network has been revealed and may be associated with the core symptoms of schizophrenia. Our study provides promising evidence for the investigation of the pathological basis of Schizophrenia and could aid in objective diagnosis.

Eavesdropping on Tinnitus Using MEG: Lessons Learned and Future Perspectives

Tinnitus has been widely investigated in order to draw conclusions about the underlying causes and altered neural activity in various brain regions. Existing studies have based their work on different tinnitus frameworks, ranging from a more local perspective on the auditory cortex to the inclusion of broader networks and various approaches towards tinnitus perception and distress. Magnetoencephalography (MEG) provides a powerful tool for efficiently investigating tinnitus and aberrant neural activity both spatially and temporally. However, results are inconclusive, and studies are rarely mapped to theoretical frameworks. The purpose of this review was to firstly introduce MEG to interested researchers and secondly provide a synopsis of the current state. We divided recent tinnitus research in MEG into study designs using resting state measurements and studies implementing tone stimulation paradigms. The studies were categorized based on their theoretical foundation, and we outlined shortcomings as well as inconsistencies within the different approaches. Finally, we provided future perspectives on how to benefit more efficiently from the enormous potential of MEG. We suggested novel approaches from a theoretical, conceptual, and methodological point of view to allow future research to obtain a more comprehensive understanding of tinnitus and its underlying processes.

Testing the generalization of neural representations

Multivariate analysis methods are widely used in neuroscience to investigate the presence and structure of neural representations. Representational similarities across time or contexts are often investigated using pattern generalization, e.g. by training and testing multivariate decoders in different contexts, or by comparable pattern-based encoding methods. It is however unclear what conclusions can be validly drawn on the underlying neural representations when significant pattern generalization is found in mass signals such as LFP, EEG, MEG, or fMRI. Using simulations, we show how signal mixing and dependencies between measurements can drive significant pattern generalization even though the true underlying representations are orthogonal. We suggest that, using an accurate estimate of the expected pattern generalization given identical representations, it is nonetheless possible to test meaningful hypotheses about the generalization of neural representations. We offer such an estimate of the expected magnitude of pattern generalization and demonstrate how this measure can be used to assess the similarity and differences of neural representations across time and contexts.

Brain-wide network analysis of resting-state neuromagnetic data

The present study performed a brain-wide network analysis of resting-state magnetoencephalograms recorded from 53 healthy participants to visualize elaborate brain maps of phase- and amplitude-derived graph-theory metrics at different frequencies. To achieve this, we conducted a vertex-wise computation of threshold-independent graph metrics by combining proportional thresholding and a conjunction analysis and applied them to a correlation analysis of age and brain networks. Source power showed a frequency-dependent cortical distribution. Threshold-independent graph metrics derived from phase- and amplitude-based connectivity showed similar or different distributions depending on frequency. Vertex-wise age-brain correlation maps revealed that source power at the beta band and the amplitude-based degree at the alpha band changed with age in local regions. The present results indicate that a brain-wide analysis of neuromagnetic data has the potential to reveal neurophysiological network features in the human brain in a resting state.

Dissociation between phase and power correlation networks in the human brain is driven by co-occurrent bursts

Well-known haemodynamic resting-state networks are better mirrored in power correlation networks than phase coupling networks in electrophysiological data. However, what do these power correlation networks reflect? We address this long-outstanding question in neuroscience using rigorous mathematical analysis, biophysical simulations with ground truth and application of these mathematical concepts to empirical magnetoencephalography (MEG) data. Our mathematical derivations show that for two non-Gaussian electrophysiological signals, their power correlation depends on their coherence, cokurtosis and conjugate-coherence. Only coherence and cokurtosis contribute to power correlation networks in MEG data, but cokurtosis is less affected by artefactual signal leakage and better mirrors haemodynamic resting-state networks. Simulations and MEG data show that cokurtosis may reflect co-occurrent bursting events. Our findings shed light on the origin of the complementary nature of power correlation networks to phase coupling networks and suggests that the origin of resting-state networks is partly reflected in co-occurent bursts in neuronal activity.

Direct modulation index: A measure of phase amplitude coupling for neurophysiology data

Neural communication across different spatial and temporal scales is a topic of great interest in clinical and basic science. Phase-amplitude coupling (PAC) has attracted particular interest due to its functional role in a wide range of cognitive and motor functions. Here, we introduce a novel measure termed the direct modulation index (dMI). Based on the classical modulation index, dMI provides an estimate of PAC that is (1) bound to an absolute interval between 0 and +1, (2) resistant against noise, and (3) reliable even for small amounts of data. To highlight the properties of this newly-proposed measure, we evaluated dMI by comparing it to the classical modulation index, mean vector length, and phase-locking value using simulated data. We ascertained that dMI provides a more accurate estimate of PAC than the existing methods and that is resilient to varying noise levels and signal lengths. As such, dMI permits a reliable investigation of PAC, which may reveal insights crucial to our understanding of functional brain architecture in key contexts such as behaviour and cognition. A Python toolbox that implements dMI and other measures of PAC is freely available at https://github.com/neurophysiological-analysis/FiNN.

Multistage classification identifies altered cortical phase- and amplitude-coupling in Multiple Sclerosis

Distinguishing groups of subjects or experimental conditions in a high-dimensional feature space is a common goal in modern neuroimaging studies. Successful classification depends on the selection of relevant features as not every neuronal signal component or parameter is informative about the research question at hand. Here, we developed a novel unsupervised multistage analysis approach that combines dimensionality reduction, bootstrap aggregating and multivariate classification to select relevant neuronal features. We tested the approach by identifying changes of brain-wide electrophysiological coupling in Multiple Sclerosis. Multiple Sclerosis is a demyelinating disease of the central nervous system that can result in cognitive decline and physical disability. However, related changes in large-scale brain interactions remain poorly understood and corresponding non-invasive biomarkers are sparse. We thus compared brain-wide phase- and amplitude-coupling of frequency specific neuronal activity in relapsing-remitting Multiple Sclerosis patients (n = 17) and healthy controls (n = 17) using magnetoencephalography. Changes in this dataset included both, increased and decreased phase- and amplitude-coupling in wide-spread, bilateral neuronal networks across a broad range of frequencies. These changes allowed to successfully classify patients and controls with an accuracy of 84%. Furthermore, classification confidence predicted behavioral scores of disease severity. In sum, our results unravel systematic changes of large-scale phase- and amplitude coupling in Multiple Sclerosis. Furthermore, our results establish a new analysis approach to efficiently contrast high-dimensional neuroimaging data between experimental groups or conditions.

Modern views of machine learning for precision psychiatry

In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

Combined electrophysiological and morphological phenotypes in patients with genetic generalized epilepsy and their healthy siblings

OBJECTIVE

Genetic generalized epilepsy is characterized by aberrant neuronal dynamics and subtle structural alterations. We evaluated whether a combination of magnetic and electrical neuronal signals and cortical thickness would provide complementary information about network pathology in GGE. We also investigated if these imaging phenotypes were present in healthy siblings of the patients to test for genetic influence.

METHODS

In this cross-sectional study, we analyzed five minutes of resting-state data acquired using electroencephalography (EEG) and magnetoencephalography (MEG) in patients, their siblings, and controls, matched for age and sex. We computed source-reconstructed power and connectivity in six frequency bands (1-40 Hz) and cortical thickness (derived from magnetic resonance imaging (MRI)). Group differences were assessed using permutation analysis of linear models for each modality separately and jointly for all modalities using a non-parametric combination.

RESULTS

Patients with GGE (n = 23) had higher power than controls (n = 35) in all frequencies, with a more posterior focus in MEG than EEG. Connectivity was also increased, particularly in frontotemporal and central regions in theta (strongest in EEG) and low beta frequencies (strongest in MEG), which was eminent in the joint EEG/MEG analysis. EEG showed weaker connectivity differences in higher frequencies, possibly related to drug effects. The inclusion of cortical thickness reinforced group differences in connectivity and power. Siblings (n = 18) had functional and structural patterns intermediate between those of patients and controls.

SIGNIFICANCE

EEG detected increased connectivity and power in GGE similar to MEG, but with different spectral sensitivity, highlighting the importance of theta and beta oscillations. Cortical thickness reductions in GGE corresponded to functional imaging patterns. Our multimodal approach extends the understanding of the resting-state in GGE and points to genetic underpinnings of the imaging markers studied, providing new insights into the causes and consequences of epilepsy.

Dynamic reconfiguration of frequency-specific cortical coactivation patterns during psychedelic and anesthetized states induced by ketamine

Recent neuroimaging studies have demonstrated that spontaneous brain activity exhibits rich spatiotemporal structure that can be characterized as the exploration of a repertoire of spatially distributed patterns that recur over time. The repertoire of brain states may reflect the capacity for consciousness, since general anesthetics suppress and psychedelic drugs enhance such dynamics. However, the modulation of brain activity repertoire across varying states of consciousness has not yet been studied in a systematic and unified framework. As a unique drug that has both psychedelic and anesthetic properties depending on the dose, ketamine offers an opportunity to examine brain reconfiguration dynamics along a continuum of consciousness. Here we investigated the dynamic organization of cortical activity during wakefulness and during altered states of consciousness induced by different doses of ketamine. Through k-means clustering analysis of the envelope data of source-localized electroencephalographic (EEG) signals, we identified a set of recurring states that represent frequency-specific spatial coactivation patterns. We quantified the effect of ketamine on individual brain states in terms of fractional occupancy and transition probabilities and found that ketamine anesthesia tends to shift the configuration toward brain states with low spatial variability. Furthermore, by assessing the temporal dynamics of the occurrence and transitions of brain states, we showed that subanesthetic ketamine is associated with a richer repertoire, while anesthetic ketamine induces dynamic changes in brain state organization, with the repertoire richness evolving from a reduced level to one comparable to that of normal wakefulness before recovery of consciousness. These results provide a novel description of ketamine's modulation of the dynamic configuration of cortical activity and advance understanding of the neurophysiological mechanism of ketamine in terms of the spatial, temporal, and spectral structures of underlying whole-brain dynamics.

Microstates and power envelope hidden Markov modeling probe bursting brain activity at different timescales

State modeling of whole-brain electroencephalography (EEG) or magnetoencephalography (MEG) allows to investigate transient, recurring neurodynamical events. Two widely-used techniques are the microstate analysis of EEG signals and hidden Markov modeling (HMM) of MEG power envelopes. Both reportedly lead to similar state lifetimes on the 100 ms timescale, suggesting a common neural basis. To investigate whether microstates and power envelope HMM states describe the same neural dynamics, we used simultaneous MEG/EEG recordings at rest and compared the spatial signature and temporal activation dynamics of microstates and power envelope HMM states obtained separately from EEG and MEG. Results showed that microstates and power envelope HMM states differ both spatially and temporally. Microstates reflect sharp events of neural synchronization, whereas power envelope HMM states disclose network-level activity with 100–200 ms lifetimes. Further, MEG microstates do not correspond to the canonical EEG microstates but are better interpreted as split HMM states. On the other hand, both MEG and EEG HMM states involve the (de)activation of similar functional networks. Microstate analysis and power envelope HMM thus appear sensitive to neural events occurring over different spatial and temporal scales. As such, they represent complementary approaches to explore the fast, sub-second scale bursting electrophysiological dynamics in spontaneous human brain activity.

Cortical correlation structure of aperiodic neuronal population activity

Electrophysiological population signals contain oscillatory and non-oscillatory aperiodic (1/frequency-like) components. So far research has largely focused on oscillatory activity, and only recently, interest in aperiodic population activity has gained momentum. Accordingly, while the cortical correlation structure of oscillatory population activity has been characterized, little is known about the correlation of aperiodic neuronal activity. To address this, we investigated aperiodic neuronal population activity in the human brain using resting-state magnetoencephalography (MEG). We combined source-analysis, signal orthogonalization and irregular-resampling auto-spectral analysis (IRASA) to systematically characterize the cortical distribution and correlation of aperiodic neuronal activity. We found that aperiodic population activity is robustly correlated across the cortex and that this correlation is spatially well structured. Furthermore, we found that the cortical correlation structure of aperiodic activity is similar but distinct from the correlation structure of oscillatory neuronal activity. Anterior cortical regions showed the strongest differences between oscillatory and aperiodic correlation patterns. Our results suggest that correlations of aperiodic population activity serve as robust markers of cortical network interactions. Furthermore, our results show that aperiodic and oscillatory signal components provide non-redundant information about large-scale neuronal correlations. This may reflect at least partly distinct neuronal mechanisms underlying and reflected by oscillatory and aperiodic neuronal population activity.

Investigating the spectral features of the brain meso-scale structure at rest

Recent studies provide novel insights into the meso-scale organization of the brain, highlighting the co-occurrence of different structures: classic assortative (modular), disassortative, and core-periphery. However, the spectral properties of the brain meso-scale remain mostly unexplored. To fill this knowledge gap, we investigated how the meso-scale structure is organized across the frequency domain. We analyzed the resting state activity of healthy participants with source-localized high-density electroencephalography signals. Then, we inferred the community structure using weighted stochastic block-model (WSBM) to capture the landscape of meso-scale structures across the frequency domain. We found that different meso-scale modalities co-exist and are diversely organized over the frequency spectrum. Specifically, we found a core-periphery structure dominance, but we also highlighted a selective increase of disassortativity in the low frequency bands (<8 Hz), and of assortativity in the high frequency band (30-50 Hz). We further described other features of the meso-scale organization by identifying those brain regions which, at the same time, (a) exhibited the highest degree of assortativity, disassortativity, and core-peripheriness (i.e., participation) and (b) were consistently assigned to the same community, irrespective from the granularity imposed by WSBM (i.e., granularity-invariance). In conclusion, we observed that the brain spontaneous activity shows frequency-specific meso-scale organization, which may support spatially distributed and local information processing.

Dynamic large-scale connectivity of intrinsic cortical oscillations supports adaptive listening in challenging conditions

In multi-talker situations, individuals adapt behaviorally to this listening challenge mostly with ease, but how do brain neural networks shape this adaptation? We here establish a long-sought link between large-scale neural communications in electrophysiology and behavioral success in the control of attention in difficult listening situations. In an age-varying sample of N = 154 individuals, we find that connectivity between intrinsic neural oscillations extracted from source-reconstructed electroencephalography is regulated according to the listener's goal during a challenging dual-talker task. These dynamics occur as spatially organized modulations in power-envelope correlations of alpha and low-beta neural oscillations during approximately 2-s intervals most critical for listening behavior relative to resting-state baseline. First, left frontoparietal low-beta connectivity (16 to 24 Hz) increased during anticipation and processing of a spatial-attention cue before speech presentation. Second, posterior alpha connectivity (7 to 11 Hz) decreased during comprehension of competing speech, particularly around target-word presentation. Connectivity dynamics of these networks were predictive of individual differences in the speed and accuracy of target-word identification, respectively, but proved unconfounded by changes in neural oscillatory activity strength. Successful adaptation to a listening challenge thus latches onto two distinct yet complementary neural systems: a beta-tuned frontoparietal network enabling the flexible adaptation to attentive listening state and an alpha-tuned posterior network supporting attention to speech.

Tiagabine induced modulation of oscillatory connectivity and activity match PET-derived, canonical GABA-A receptor distributions

As the most abundant inhibitory neurotransmitter in the mammalian brain, γ-aminobutyric acid (GABA) plays a crucial role in shaping the frequency and amplitude of oscillations, which suggests a role for GABA in shaping the topography of functional connectivity and activity. This study explored the effects of pharmacologically blocking the reuptake of GABA (increasing local concentrations) using the GABA transporter 1 (GAT1) blocker, tiagabine (15 mg). In a placebo-controlled crossover design, we collected resting magnetoencephalography (MEG) recordings from 15 healthy individuals prior to, and at 1-, 3- and 5- hours post, administration of tiagabine and placebo. We quantified whole brain activity and functional connectivity in discrete frequency bands. Drug-by-session (2 × 4) analysis of variance in connectivity revealed interaction and main effects. Post-hoc permutation testing of each post-drug recording vs. respective pre-drug baseline revealed consistent reductions of a bilateral occipital network spanning theta, alpha and beta frequencies, across 1- 3- and 5- hour recordings following tiagabine only. The same analysis applied to activity revealed significant increases across frontal regions, coupled with reductions in posterior regions, across delta, theta, alpha and beta frequencies. Crucially, the spatial distribution of tiagabine-induced changes overlap with group-averaged maps of the distribution of GABA_A receptors, from flumazenil (FMZ-V_T) PET, demonstrating a link between GABA availability, GABA_A receptor distribution, and low-frequency network oscillations. Our results indicate that the relationship between PET receptor distributions and MEG effects warrants further exploration, since elucidating the nature of this relationship may uncover electrophysiologically-derived maps of oscillatory activity as sensitive, time-resolved, and targeted receptor-mapping tools for pharmacological imaging.

Predicting MEG resting-state functional connectivity from microstructural information

Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from 90 healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity, and a myelin measure (derived from multicomponent relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer time series in the delta, theta, alpha, and beta frequency bands. Nonnegative matrix factorization was performed to identify the components of the functional connectivity. Shortest path length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructure-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. This provides a methodology to understand functional mechanisms better. The shortest path length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Overall, different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.

Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography

The understanding and treatment of psychiatric disorders, which are known to be neurobiologically and clinically heterogeneous, could benefit from the data-driven identification of disease subtypes. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the default mode network. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets of patients with PTSD and MDD, and show that the subtypes are transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis.

Measuring functional connectivity with wearable MEG

Optically-pumped magnetometers (OPMs) offer the potential for a step change in magnetoencephalography (MEG) enabling wearable systems that provide improved data quality, accommodate any subject group, allow data capture during movement and potentially reduce cost. However, OPM-MEG is a nascent technology and, to realise its potential, it must be shown to facilitate key neuroscientific measurements, such as the characterisation of brain networks. Networks, and the connectivities that underlie them, have become a core area of neuroscientific investigation, and their importance is underscored by many demonstrations of their disruption in brain disorders. Consequently, a demonstration of network measurements using OPM-MEG would be a significant step forward. Here, we aimed to show that a wearable 50-channel OPM-MEG system enables characterisation of the electrophysiological connectome. To this end, we measured connectivity in the resting state and during a visuo-motor task, using both OPM-MEG and a state-of-the-art 275-channel cryogenic MEG device. Our results show that resting-state connectome matrices from OPM and cryogenic systems exhibit a high degree of similarity, with correlation values >70%. In addition, in task data, similar differences in connectivity between individuals (scanned multiple times) were observed in cryogenic and OPM-MEG data, again demonstrating the fidelity of the OPM-MEG device. This is the first demonstration of network connectivity measured using OPM-MEG, and results add weight to the argument that OPMs will ultimately supersede cryogenic sensors for MEG measurement.

Brain dysconnectivity relates to disability and cognitive impairment in multiple sclerosis

The pathophysiology of cognitive dysfunction in multiple sclerosis (MS) is still unclear. This magnetoencephalography (MEG) study investigates the impact of MS on brain resting-state functional connectivity (rsFC) and its relationship to disability and cognitive impairment. We investigated rsFC based on power envelope correlation within and between different frequency bands, in a large cohort of participants consisting of 99 MS patients and 47 healthy subjects. Correlations were investigated between rsFC and outcomes on disability, disease duration and 7 neuropsychological scores within each group, while stringently correcting for multiple comparisons and possible confounding factors. Specific dysconnections correlating with MS-induced physical disability and disease duration were found within the sensorimotor and language networks, respectively. Global network-level reductions in within- and cross-network rsFC were observed in the default-mode network. Healthy subjects and patients significantly differed in their scores on cognitive fatigue and verbal fluency. Healthy subjects and patients showed different correlation patterns between rsFC and cognitive fatigue or verbal fluency, both of which involved a shift in patients from the posterior default-mode network to the language network. Introducing electrophysiological rsFC in a regression model of verbal fluency and cognitive fatigue in MS patients significantly increased the explained variance compared to a regression limited to structural MRI markers (relative thalamic volume and lesion load). This MEG study demonstrates that MS induces distinct changes in the resting-state functional brain architecture that relate to disability, disease duration and specific cognitive functioning alterations. It highlights the potential value of electrophysiological intrinsic rsFC for monitoring the cognitive impairment in patients with MS.

Changes in electrophysiological static and dynamic human brain functional architecture from childhood to late adulthood

This magnetoencephalography study aimed at characterizing age-related changes in resting-state functional brain organization from mid-childhood to late adulthood. We investigated neuromagnetic brain activity at rest in 105 participants divided into three age groups: children (6-9 years), young adults (18-34 years) and healthy elders (53-78 years). The effects of age on static resting-state functional brain integration were assessed using band-limited power envelope correlation, whereas those on transient functional brain dynamics were disclosed using hidden Markov modeling of power envelope activity. Brain development from childhood to adulthood came with (1) a strengthening of functional integration within and between resting-state networks and (2) an increased temporal stability of transient (100-300 ms lifetime) and recurrent states of network activation or deactivation mainly encompassing lateral or medial associative neocortical areas. Healthy aging was characterized by decreased static resting-state functional integration and dynamic stability within the primary visual network. These results based on electrophysiological measurements free of neurovascular biases suggest that functional brain integration mainly evolves during brain development, with limited changes in healthy aging. These novel electrophysiological insights into human brain functional architecture across the lifespan pave the way for future clinical studies investigating how brain disorders affect brain development or healthy aging.

Electrophysiological network alterations in adults with copy number variants associated with high neurodevelopmental risk

Rare copy number variants associated with increased risk for neurodevelopmental and psychiatric disorders (referred to as ND-CNVs) are characterized by heterogeneous phenotypes thought to share a considerable degree of overlap. Altered neural integration has often been linked to psychopathology and is a candidate marker for potential convergent mechanisms through which ND-CNVs modify risk; however, the rarity of ND-CNVs means that few studies have assessed their neural correlates. Here, we used magnetoencephalography (MEG) to investigate resting-state oscillatory connectivity in a cohort of 42 adults with ND-CNVs, including deletions or duplications at 22q11.2, 15q11.2, 15q13.3, 16p11.2, 17q12, 1q21.1, 3q29, and 2p16.3, and 42 controls. We observed decreased connectivity between occipital, temporal, and parietal areas in participants with ND-CNVs. This pattern was common across genotypes and not exclusively characteristic of 22q11.2 deletions, which were present in a third of our cohort. Furthermore, a data-driven graph theory framework enabled us to successfully distinguish participants with ND-CNVs from unaffected controls using differences in node centrality and network segregation. Together, our results point to alterations in electrophysiological connectivity as a putative common mechanism through which genetic factors confer increased risk for neurodevelopmental and psychiatric disorders.

Frequency-dependent functional connectivity in resting state networks

Functional magnetic resonance imaging studies have documented the resting human brain to be functionally organized in multiple large‐scale networks, called resting‐state networks (RSNs). Other brain imaging techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG), have been used for investigating the electrophysiological basis of RSNs. To date, it is largely unclear how neural oscillations measured with EEG and MEG are related to functional connectivity in the resting state. In addition, it remains to be elucidated whether and how the observed neural oscillations are related to the spatial distribution of the network nodes over the cortex. To address these questions, we examined frequency‐dependent functional connectivity between the main nodes of several RSNs, spanning large part of the cortex. We estimated connectivity using band‐limited power correlations from high‐density EEG data collected in healthy participants. We observed that functional interactions within RSNs are characterized by a specific combination of neuronal oscillations in the alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–80 Hz) bands, which highly depend on the position of the network nodes. This finding may contribute to a better understanding of the mechanisms through which neural oscillations support functional connectivity in the brain.

Using structural connectivity to augment community structure in EEG functional connectivity

Recently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle from spurious FC introduced by volume conduction. Here, we investigate the relationship between white matter structural connectivity (SC) and large-scale network structure encoded in EEG-FC. We start by confirming that FC (power envelope correlations) is predicted by SC beyond the impact of Euclidean distance, in line with the assumption that SC mediates genuine FC. We then use information from white matter structural connectivity in order to smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions increases while FC between nonconnected regions remains unchanged, resulting in an increase in genuine, SC-mediated FC. We analyze the induced changes in FC, assessing the resemblance between EEG-FC and volume-conduction- free fMRI-FC, and find that smoothing increases resemblance in terms of overall correlation and community structure. This result suggests that our method boosts genuine FC, an outcome that is of interest for many EEG network neuroscience questions.

Association of Magnetoencephalographically Measured High-Frequency Oscillations in Visual Cortex With Circuit Dysfunctions in Local and Large-scale Networks During Emerging Psychosis

IMPORTANCE

Psychotic disorders are characterized by impairments in neural oscillations, but the nature of the deficit, the trajectory across illness stages, and functional relevance remain unclear.

OBJECTIVES

To examine whether changes in spectral power, phase locking, and functional connectivity in visual cortex are present during emerging psychosis and whether these abnormalities are associated with clinical outcomes.

DESIGN, SETTING, AND PARTICIPANTS

In this cross-sectional study, participants meeting clinical high-risk criteria for psychosis, participants with first-episode psychosis, participants with affective disorders and substance abuse, and a group of control participants were recruited. Participants underwent measurements with magnetoencephalography and magnetic resonance imaging. Data analysis was carried out between 2018 and 2019.

MAIN OUTCOMES AND MEASURES

Magnetoencephalographical activity was examined in the 1- to 90-Hz frequency range in combination with source reconstruction during a visual grating task. Event-related fields, power modulation, intertrial phase consistency, and connectivity measures in visual and frontal cortices were associated with neuropsychological scores, psychosocial functioning, and clinical symptoms as well as persistence of subthreshold psychotic symptoms at 12 months.

RESULTS

The study participants included those meeting clinical high-risk criteria for psychosis (n = 119; mean [SD] age, 22 [4.4] years; 32 men), 26 patients with first-episode psychosis (mean [SD] age, 24 [4.2] years; 16 men), 38 participants with affective disorders and substance abuse (mean [SD] age, 23 [4.7] years; 11 men), and 49 control participants (mean age [SD], 23 [3.6] years; 16 men). Clinical high-risk participants and patients with first-episode psychosis were characterized by reduced phase consistency of β/γ-band oscillations in visual cortex (d = 0.63/d = 0.93). Moreover, the first-episode psychosis group was also characterized by reduced occipital γ-band power (d = 1.14) and altered visual cortex connectivity (d = 0.74-0.84). Impaired fronto-occipital connectivity was present in both clinical high-risk participants (d = 0.54) and patients with first-episode psychosis (d = 0.84). Importantly, reductions in intertrial phase coherence predicted persistence of subthreshold psychosis in clinical high-risk participants (receiver operating characteristic area under curve = 0.728; 95% CI, 0.612-0.841; P = .001).

CONCLUSIONS AND RELEVANCE

High-frequency oscillations are impaired in the visual cortex during emerging psychosis and may be linked to behavioral and clinical impairments. Impaired phase consistency of γ-band oscillations was also associated with the persistence of subthreshold psychosis, suggesting that magnetoencephalographical measured neural oscillations could constitute a biomarker for clinical staging of emerging psychosis.

Frequency-Dependent Intrinsic Electrophysiological Functional Architecture of the Human Verbal Language Network

Functional magnetic resonance imaging (fMRI) allowed the spatial characterization of the resting-state verbal language network (vLN). While other resting-state networks (RSNs) were matched with their electrophysiological equivalents at rest and could be spectrally defined, such correspondence is lacking for the vLN. This magnetoencephalography (MEG) study aimed at defining the spatio-spectral characteristics of the neuromagnetic intrinsic functional architecture of the vLN. Neuromagnetic activity was recorded at rest in 100 right-handed healthy adults (age range: 18-41 years). Band-limited power envelope correlations were performed within and across frequency bands (θ, α, β, and low γ) from a seed region placed in the left Broca's area, using static orthogonalization as leakage correction. K-means clustering was used to segregate spatio-spectral clusters of resting-state functional connectivity (rsFC). Remarkably, unlike other RSNs, within-frequency long-range rsFC from the left Broca's area was not driven by one main carrying frequency but was characterized by a specific spatio-spectral pattern segregated along the ventral (predominantly θ and α) and dorsal (β and low-γ bands) vLN streams. In contrast, spatial patterns of cross-frequency vLN functional integration were spectrally more widespread and involved multiple frequency bands. Moreover, the static intrinsic functional architecture of the neuromagnetic human vLN involved clearly left-hemisphere-dominant vLN interactions as well as cross-network interactions with the executive control network and postero-medial nodes of the DMN. Overall, this study highlighted the involvement of multiple modes of within and cross-frequency power envelope couplings at the basis of long-range electrophysiological vLN functional integration. As such, it lays the foundation for future works aimed at understanding the pathophysiology of language-related disorders.

Magnetoencephalography resting state connectivity patterns as indicatives of surgical outcome in epilepsy patients

OBJECTIVE

Focal epilepsy is a disorder affecting several brain networks; however, epilepsy surgery usually targets a restricted region, the so-called epileptic focus. There is a growing interest in embedding resting state (RS) connectivity analysis into pre-surgical workup.

APPROACH

In this retrospective study, we analyzed Magnetoencephalography (MEG) long-range RS functional connectivity patterns in patients with drug-resistant focal epilepsy. MEG recorded prior to surgery from seven seizure-free (Engel Ia) and five non seizure-free (Engel III or IV) patients were analyzed (minimum 2-years post-surgical follow-up). MEG segments without any detectable epileptic activity were source localized using wavelet-based Maximum Entropy on the Mean method. Amplitude envelope correlation in the theta (4-8 Hz), alpha (8-13 Hz), and beta (13-26 Hz) bands were used for assessing connectivity.

MAIN RESULTS

For seizure-free patients, we found an isolated epileptic network characterized by weaker connections between the brain region where interictal epileptic discharges (IED) are generated and the rest of the cortex, when compared to connectivity between the corresponding contralateral homologous region and the rest of the cortex. Contrarily, non seizure-free patients exhibited a widespread RS epileptic network characterized by stronger connectivity between the IED generator and the rest of the cortex, in comparison to the contralateral region and the cortex. Differences between the two seizure outcome groups concerned mainly distant long-range connections and were found in the alpha-band.

SIGNIFICANCE

Importantly, these connectivity patterns suggest specific mechanisms describing the underlying organization of the epileptic network and were detectable at the individual patient level, supporting the prospect use of MEG connectivity patterns in epilepsy to predict post-surgical seizure outcome.

Genuine cross-frequency coupling networks in human resting-state electrophysiological recordings

Phase synchronization of neuronal oscillations in specific frequency bands coordinates anatomically distributed neuronal processing and communication. Typically, oscillations and synchronization take place concurrently in many distinct frequencies, which serve separate computational roles in cognitive functions. While within-frequency phase synchronization has been studied extensively, less is known about the mechanisms that govern neuronal processing distributed across frequencies and brain regions. Such integration of processing between frequencies could be achieved via cross-frequency coupling (CFC), either by phase–amplitude coupling (PAC) or by n:m-cross–frequency phase synchrony (CFS). So far, studies have mostly focused on local CFC in individual brain regions, whereas the presence and functional organization of CFC between brain areas have remained largely unknown. We posit that interareal CFC may be essential for large-scale coordination of neuronal activity and investigate here whether genuine CFC networks are present in human resting-state (RS) brain activity. To assess the functional organization of CFC networks, we identified brain-wide CFC networks at mesoscale resolution from stereoelectroencephalography (SEEG) and at macroscale resolution from source-reconstructed magnetoencephalography (MEG) data. We developed a novel, to our knowledge, graph-theoretical method to distinguish genuine CFC from spurious CFC that may arise from nonsinusoidal signals ubiquitous in neuronal activity. We show that genuine interareal CFC is present in human RS activity in both SEEG and MEG data. Both CFS and PAC networks coupled theta and alpha oscillations with higher frequencies in large-scale networks connecting anterior and posterior brain regions. CFS and PAC networks had distinct spectral patterns and opposing distribution of low- and high-frequency network hubs, implying that they constitute distinct CFC mechanisms. The strength of CFS networks was also predictive of cognitive performance in a separate neuropsychological assessment. In conclusion, these results provide evidence for interareal CFS and PAC being 2 distinct mechanisms for coupling oscillations across frequencies in large-scale brain networks.

Genuine interareal cross-frequency coupling (CFC) can be identified from human resting state activity using magnetoencephalography, stereoelectroencephalography, and novel network approaches. CFC couples slow theta and alpha oscillations to faster oscillations across brain regions.

Stabilization of a brain-computer interface via the alignment of low-dimensional spaces of neural activity

The instability of neural recordings can render clinical brain-computer interfaces (BCIs) uncontrollable. Here, we show that the alignment of low-dimensional neural manifolds (low-dimensional spaces that describe specific correlation patterns between neurons) can be used to stabilize neural activity, thereby maintaining BCI performance in the presence of recording instabilities. We evaluated the stabilizer with non-human primates during online cursor control via intracortical BCIs in the presence of severe and abrupt recording instabilities. The stabilized BCIs recovered proficient control under different instability conditions and across multiple days. The stabilizer does not require knowledge of user intent and can outperform supervised recalibration. It stabilized BCIs even when neural activity contained little information about the direction of cursor movement. The stabilizer may be applicable to other neural interfaces and may improve the clinical viability of BCIs.

Juvenile myoclonic epilepsy shows increased posterior theta, and reduced sensorimotor beta resting connectivity

•

We investigated whole brain source space connectivity in JME using across standard MEG frequency bands.

•

Connectivity was increased in posterior theta and alpha bands in JME, and decreased in sensorimotor beta band.

•

Our findings highlight altered interactions between posterior networks of arousal and attention and the motor system in JME.

Background

Widespread structural and functional brain network changes have been shown in Juvenile Myoclonic Epilepsy (JME) despite normal clinical neuroimaging. We sought to better define these changes using magnetoencephalography (MEG) and source space connectivity analysis for optimal neurophysiological and anatomical localisation.

Methods

We consecutively recruited 26 patients with JME who underwent resting state MEG recording, along with 26 age-and-sex matched controls. Whole brain connectivity was determined through correlation of Automated Anatomical Labelling (AAL) atlas source space MEG timeseries in conventional frequency bands of interest delta (1−4 Hz), theta (4−8 Hz), alpha (8−13 Hz), beta (13−30 Hz) and gamma (40−60 Hz). We used a Linearly Constrained Minimum Variance (LCMV) beamformer to extract voxel wise time series of ‘virtual sensors’ for the desired frequency bands, followed by connectivity analysis using correlation between frequency- and node-specific power fluctuations, for the voxel maxima in each AAL atlas label, correcting for noise, potentially spurious connections and multiple comparisons.

Results

We found increased connectivity in the theta band in posterior brain regions, surviving statistical correction for multiple comparisons (corrected p < 0.05), and decreased connectivity in the beta band in sensorimotor cortex, between right pre- and post- central gyrus (p < 0.05) in JME compared to controls.

Conclusions

Altered resting-state MEG connectivity in JME comprised increased connectivity in posterior theta – the frequency band associated with long range connections affecting attention and arousal - and decreased beta-band sensorimotor connectivity. These findings likely relate to altered regulation of the sensorimotor network and seizure prone states in JME.

Dissociated neuronal phase- and amplitude-coupling patterns in the human brain

Coupling of neuronal oscillations may reflect and facilitate the communication between neuronal populations. Two primary neuronal coupling modes have been described: phase-coupling and amplitude-coupling. Theoretically, both coupling modes are independent, but so far, their neuronal relationship remains unclear. Here, we combined MEG, source-reconstruction and simulations to systematically compare cortical amplitude-coupling and phase-coupling patterns in the human brain. Importantly, we took into account a critical bias of amplitude-coupling measures due to phase-coupling. We found differences between both coupling modes across a broad frequency range and most of the cortex. Furthermore, by combining empirical measurements and simulations we ruled out that these results were caused by methodological biases, but instead reflected genuine neuronal amplitude coupling. Our results show that cortical phase- and amplitude-coupling patterns are non-redundant, which may reflect at least partly distinct neuronal mechanisms. Furthermore, our findings highlight and clarify the compound nature of amplitude coupling measures.

An Electroencephalography Connectomic Profile of Posttraumatic Stress Disorder

OBJECTIVE

The authors sought to identify brain regions whose frequency-specific, orthogonalized resting-state EEG power envelope connectivity differs between combat veterans with posttraumatic stress disorder (PTSD) and healthy combat-exposed veterans, and to determine the behavioral correlates of connectomic differences.

METHODS

The authors first conducted a connectivity method validation study in healthy control subjects (N=36). They then conducted a two-site case-control study of veterans with and without PTSD who were deployed to Iraq and/or Afghanistan. Healthy individuals (N=95) and those meeting full or subthreshold criteria for PTSD (N=106) underwent 64-channel resting EEG (eyes open and closed), which was then source-localized and orthogonalized to mitigate effects of volume conduction. Correlation coefficients between band-limited source-space power envelopes of different regions of interest were then calculated and corrected for multiple comparisons. Post hoc correlations of connectomic abnormalities with clinical features and performance on cognitive tasks were conducted to investigate the relevance of the dysconnectivity findings.

RESULTS

Seventy-four brain region connections were significantly reduced in PTSD (all in the eyes-open condition and predominantly using the theta carrier frequency). Underconnectivity of the orbital and anterior middle frontal gyri were most prominent. Performance differences in the digit span task mapped onto connectivity between 25 of the 74 brain region pairs, including within-network connections in the dorsal attention, frontoparietal control, and ventral attention networks.

CONCLUSIONS

Robust PTSD-related abnormalities were evident in theta-band source-space orthogonalized power envelope connectivity, which furthermore related to cognitive deficits in these patients. These findings establish a clinically relevant connectomic profile of PTSD using a tool that facilitates the lower-cost clinical translation of network connectivity research.

Small-World Propensity Reveals the Frequency Specificity of Resting State Networks

Goal: Functional connectivity (FC) is an important indicator of the brain's state in different conditions, such as rest/task or health/pathology. Here we used high-density electroencephalography coupled to source reconstruction to assess frequency-specific changes of FC during resting state. Specifically, we computed the Small-World Propensity (SWP) index to characterize network small-world architecture across frequencies. Methods: We collected resting state data from healthy participants and built connectivity matrices maintaining the heterogeneity of connection strengths. For a subsample of participants, we also investigated whether the SWP captured FC changes after the execution of a working memory (WM) task. Results: We found that SWP demonstrated a selective increase in the alpha and low beta bands. Moreover, SWP was modulated by a cognitive task and showed increased values in the bands entrained by the WM task. Conclusions: SWP is a valid metric to characterize the frequency-specific behavior of resting state networks.

Brainstem Modulation of Large-Scale Intrinsic Cortical Activity Correlations

Brain activity fluctuates continuously, even in the absence of changes in sensory input or motor output. These intrinsic activity fluctuations are correlated across brain regions and are spatially organized in macroscale networks. Variations in the strength, topography, and topology of correlated activity occur over time, and unfold upon a backbone of long-range anatomical connections. Subcortical neuromodulatory systems send widespread ascending projections to the cortex, and are thus ideally situated to shape the temporal and spatial structure of intrinsic correlations. These systems are also the targets of the pharmacological treatment of major neurological and psychiatric disorders, such as Parkinson's disease, depression, and schizophrenia. Here, we review recent work that has investigated how neuromodulatory systems shape correlations of intrinsic fluctuations of large-scale cortical activity. We discuss studies in the human, monkey, and rodent brain, with a focus on non-invasive recordings of human brain activity. We provide a structured but selective overview of this work and distil a number of emerging principles. Future efforts to chart the effect of specific neuromodulators and, in particular, specific receptors, on intrinsic correlations may help identify shared or antagonistic principles between different neuromodulatory systems. Such principles can inform models of healthy brain function and may provide an important reference for understanding altered cortical dynamics that are evident in neurological and psychiatric disorders, potentially paving the way for mechanistically inspired biomarkers and individualized treatments of these disorders.

Synchrony, metastability, dynamic integration, and competition in the spontaneous functional connectivity of the human brain

The human brain is functionally organized into large-scale neural networks that are dynamically interconnected. Multiple short-lived states of resting-state functional connectivity (rsFC) identified transiently synchronized networks and cross-network integration. However, little is known about the way brain couplings covary as rsFC states wax and wane. In this magnetoencephalography study, we explore the synchronization structure among the spontaneous interactions of well-known resting-state networks (RSNs). To do so, we extracted modes of dynamic coupling that reflect rsFC synchrony and analyzed their spatio-temporal features. These modes identified transient, sporadic rsFC changes characterized by the widespread integration of RSNs across the brain, most prominently in the β band. This is in line with the metastable rsFC state model of resting-state dynamics, wherein our modes fit as state transition processes. Furthermore, the default-mode network (DMN) stood out as being structured into competitive cross-network couplings with widespread DMN-RSN interactions, especially among the β-band modes. These results substantiate the theory that the DMN is a core network enabling dynamic global brain integration in the β band.

Oscillatory hyperactivity and hyperconnectivity in young APOE-ɛ4 carriers and hypoconnectivity in Alzheimer's disease

We studied resting-state oscillatory connectivity using magnetoencephalography in healthy young humans (N = 183) genotyped for APOE-ɛ4, the greatest genetic risk for Alzheimer's disease (AD). Connectivity across frequencies, but most prevalent in alpha/beta, was increased in APOE-ɛ4 in a set of mostly right-hemisphere connections, including lateral parietal and precuneus regions of the Default Mode Network. Similar regions also demonstrated hyperactivity, but only in gamma (40-160 Hz). In a separate study of AD patients, hypoconnectivity was seen in an extended bilateral network that partially overlapped with the hyperconnected regions seen in young APOE-ɛ4 carriers. Using machine-learning, AD patients could be distinguished from elderly controls with reasonable sensitivity and specificity, while young APOE-e4 carriers could also be distinguished from their controls with above chance performance. These results support theories of initial hyperconnectivity driving eventual profound disconnection in AD and suggest that this is present decades before the onset of AD symptomology.

Effect of Cultural Priming on Social Behavior and EEG Correlates of Self-Processing

Humans are social beings and the self is inevitably conceptualized in terms of social environment. The degree to which the self is perceived as fundamentally similar or fundamentally different from other people is modulated by cultural stereotypes, such as collectivism and individualism. These stereotypes are not hardwired in our brains and individuals differ in the degree to which they adopt the attitudes that define their culture. Moreover, individuals can acquire multiple sets of cultural knowledge and, depending on the context, either individualistic or collectivistic cultural mindset could be activated. In this study, we used cultural priming techniques to activate either individualistic or collectivistic mindset and investigated the association between source-level EEG connectivity in the default mode network (DMN) and spontaneous self-related thoughts in the subsequent resting state. Afterward, participants performed a social interaction task, in which they were allowed to choose between friendly, avoidant, or aggressive behavior. After collectivism priming, self-related thoughts were associated with increased connectivity of DMN with the right temporoparietal junction (TPJ), which is involved in taking the perspective of others and is more active in representatives of collectivistic cultures, whereas after individualism priming they were associated with increased connectivity with the temporal pole, which is involved in self/other discrimination and is more active in representatives of individualistic cultures. Individual differences in the intensity of post-priming self-related thoughts and the strength of DMN-temporal pole connectivity predicted individual differences in behavior during the social interaction task, with individualistic mindset predisposing to more friendly and trustful social behavior.

Variability and stability of large-scale cortical oscillation patterns

Individual differences in brain organization exist at many spatiotemporal scales and underlie the diversity of human thought and behavior. Oscillatory neural activity is crucial for these processes, but how such rhythms are expressed across the cortex within and across individuals is poorly understood. We conducted a systematic characterization of brain-wide activity across frequency bands and oscillatory features during rest and task execution. We found that oscillatory profiles exhibit sizable group-level similarities, indicating the presence of common templates of oscillatory organization. Nonetheless, well-defined subject-specific network profiles were discernible beyond the structure shared across individuals. These individualized patterns were sufficiently stable to recognize individuals several months later. Moreover, network structure of rhythmic activity varied considerably across distinct oscillatory frequencies and features, indicating the existence of several parallel information processing streams embedded in distributed electrophysiological activity. These findings suggest that network similarity analyses may be useful for understanding the role of large-scale brain oscillations in physiology and behavior.

Neural oscillations are critical for the human brain’s ability to optimally respond to complex environmental input. However, relatively little is known about the network properties of these oscillatory rhythms. We used electroencephalography (EEG) to analyze large-scale brain wave patterns, focusing on multiple frequency bands and several key features of oscillatory communication. We show that networks defined in this manner are, in fact, distinct, suggesting that EEG activity encompasses multiple, parallel information processing streams. Remarkably, the same networks can be used to uniquely identify individuals over a period of approximately half a year, thus serving as neural fingerprints. These findings indicate that investigating oscillatory dynamics from a network perspective holds considerable promise as a tool to understand human cognition and behavior.

Seizure susceptibility and infraslow modulatory activity in the intracranial electroencephalogram

OBJECTIVE

Studies of infraslow amplitude modulations (<0.15 Hz) of band power time series suggest that these envelope correlations may form a basis for distant spatial coupling in the brain. In this study, we sought to determine how infraslow relationships are affected by antiepileptic drug (AED) taper, time of day, and seizure.

METHODS

We studied intracranial electroencephalographic (icEEG) data collected from 13 medically refractory adult epilepsy patients who underwent monitoring at Yale-New Haven Hospital. We estimated the magnitude-squared coherence (MSC) at <0.15 Hz of traditional EEG frequency band power time series for all electrode contact pairs to quantify infraslow envelope correlations between them. We studied, first, hour-long background icEEG epochs before and after AED taper to understand the effect of taper. Second, we analyzed the entire record for each patient to study the effect of time of day. Finally, for each patient, we reviewed the clinical record to find all seizures that were at least 6 hours removed from other seizures and analyzed infraslow envelope MSC before and after them.

RESULTS

Infraslow envelope MSC increased slightly, but significantly, after AED taper, and increased on average during the night and decreased during the day. It was also increased significantly in all frequency bands up to 3 hours preseizure and 1 hour postseizure as compared to background icEEG (61 seizures studied). These changes occurred for both daytime and nighttime seizures (28 daytime, 33 nighttime). Interestingly, there was significant spatial variability to these changes, with the seizure onset area peaking at 3 hours preseizure, then showing progressive desynchronization from 3 hours preseizure to 1 hour postseizure.

SIGNIFICANCE

Infraslow envelope analysis may be used to understand long-term changes over the course of icEEG monitoring, provide unique insight into interictal electrophysiological changes related to ictogenesis, and contribute to the development of novel seizure forecasting algorithms.

Task-positive and task-negative networks in major depressive disorder: A combined fMRI and EEG study

BACKGROUND

The study of intrinsic connectivity networks, i.e., sets of brain regions that show a high degree of interconnectedness even in the absence of a task, showed that major depressive disorder (MDD) patients demonstrate an increased connectivity within the default mode network (DMN), which is active in a resting state and is implicated in self-referential processing, and a decreased connectivity in task-positive networks (TPNs), which increase their activity in attention tasks. Cortical localization of this 'dominance' of the DMN over the TPN in MDD patients is not fully understood. Besides, this effect has been investigated using fMRI and its electrophysiological underpinning is not known.

METHOD

In this study, we tested the dominance hypothesis using seed-based connectivity analysis of resting-state fMRI and EEG data obtained in 41 MDD patients and 23 controls.

RESULTS

In MDD patients, as compared to controls, insula, pallidum/putamen, amygdala, and left dorso- and ventrolateral prefrontal cortex are more strongly connected with DMN than with TPN seeds. In EEG, all significant effects were obtained in the delta frequency band.

LIMITATIONS

fMRI and EEG data were not obtained simultaneously during the same session.

CONCLUSIONS

In MDD patients, major emotion and attention regulation circuits are more strongly connected with DMN than with TPN implying they are more prepared to respond to internally generated self-related thoughts than to environmental challenges.

Source-reconstruction of the sensorimotor network from resting-state macaque electrocorticography

The discovery of hemodynamic (BOLD-fMRI) resting-state networks (RSNs) has brought about a fundamental shift in our thinking about the role of intrinsic brain activity. The electrophysiological underpinnings of RSNs remain largely elusive and it has been shown only recently that electric cortical rhythms are organized into the same RSNs as hemodynamic signals. Most electrophysiological studies into RSNs use magnetoencephalography (MEG) or scalp electroencephalography (EEG), which limits the spatial resolution with which electrophysiological RSNs can be observed. Due to their close proximity to the cortical surface, electrocorticographic (ECoG) recordings can potentially provide a more detailed picture of the functional organization of resting-state cortical rhythms, albeit at the expense of spatial coverage. In this study we propose using source-space spatial independent component analysis (spatial ICA) for identifying generators of resting-state cortical rhythms as recorded with ECoG and for reconstructing their functional connectivity. Network structure is assessed by two kinds of connectivity measures: instantaneous correlations between band-limited amplitude envelopes and oscillatory phase-locking. By simulating rhythmic cortical generators, we find that the reconstruction of oscillatory phase-locking is more challenging than that of amplitude correlations, particularly for low signal-to-noise levels. Specifically, phase-lags can both be over- and underestimated, which troubles the interpretation of lag-based connectivity measures. We illustrate the methodology on somatosensory beta rhythms recorded from a macaque monkey using ECoG. The methodology decomposes the resting-state sensorimotor network into three cortical generators, distributed across primary somatosensory and primary and higher-order motor areas. The generators display significant and reproducible amplitude correlations and phase-locking values with non-zero lags. Our findings illustrate the level of spatial detail attainable with source-projected ECoG and motivates wider use of the methodology for studying resting-state as well as event-related cortical dynamics in macaque and human.

Ghost interactions in MEG/EEG source space: A note of caution on inter-areal coupling measures

When combined with source modeling, magneto- (MEG) and electroencephalography (EEG) can be used to study long-range interactions among cortical processes non-invasively. Estimation of such inter-areal connectivity is nevertheless hindered by instantaneous field spread and volume conduction, which artificially introduce linear correlations and impair source separability in cortical current estimates. To overcome the inflating effects of linear source mixing inherent to standard interaction measures, alternative phase- and amplitude-correlation based connectivity measures, such as imaginary coherence and orthogonalized amplitude correlation have been proposed. Being by definition insensitive to zero-lag correlations, these techniques have become increasingly popular in the identification of correlations that cannot be attributed to field spread or volume conduction. We show here, however, that while these measures are immune to the direct effects of linear mixing, they may still reveal large numbers of spurious false positive connections through field spread in the vicinity of true interactions. This fundamental problem affects both region-of-interest-based analyses and all-to-all connectome mappings. Most importantly, beyond defining and illustrating the problem of spurious, or "ghost" interactions, we provide a rigorous quantification of this effect through extensive simulations. Additionally, we further show that signal mixing also significantly limits the separability of neuronal phase and amplitude correlations. We conclude that spurious correlations must be carefully considered in connectivity analyses in MEG/EEG source space even when using measures that are immune to zero-lag correlations.

Cortical Statistical Correlation Tomography of EEG Resting State Networks

Resting state networks (RSNs) have been found in human brains during awake resting states. RSNs are composed of spatially distributed regions in which spontaneous activity fluctuations are temporally and dynamically correlated. A new computational framework for reconstructing RSNs with human EEG data has been developed in the present study. The proposed framework utilizes independent component analysis (ICA) on short-time Fourier transformed inverse source maps imaged from EEG data and statistical correlation analysis to generate cortical tomography of electrophysiological RSNs. The proposed framework was evaluated on three sets of resting-state EEG data obtained in the comparison of two conditions: (1) healthy controls with eyes closed and eyes open; (2) healthy controls and individuals with a balance disorder; (3) individuals with a balance disorder before and after receiving repetitive transcranial magnetic stimulation (rTMS) treatment. In these analyses, the same group of five RSNs with similar spatial and spectral patterns were successfully reconstructed by the proposed framework from each individual EEG dataset. These EEG RSN tomographic maps showed significant similarity with RSN templates derived from functional magnetic resonance imaging (fMRI). Furthermore, significant spatial and spectral differences of RSNs among compared conditions were observed in tomographic maps as well as their spectra, which were consistent with findings reported in the literature. Beyond the success of reconstructing EEG RSNs spatially on the cortical surface as in fMRI studies, this novel approach defines RSNs further with spectra, providing a new dimension in understanding and probing basic neural mechanisms of RSNs. The findings in patients' data further demonstrate its potential in identifying biomarkers for the diagnosis and treatment evaluation of neuropsychiatric disorders.