Adding dynamics to the Human Connectome Project with MEG

Source localization comparison and combination of OPM-MEG and fMRI to detect sensorimotor cortex responses

BACKGROUND AND OBJECTIVES

The exploration of various neuroimaging techniques have become focal points within the field of neuroscience research. Magnetoencephalography based on optically pumped magnetometers (OPM-MEG) has shown significant potential to be the next generation of functional neuroimaging with the advantages of high signal intensity and flexible sensor arrangement. In this study, we constructed a 31-channel OPM-MEG system and performed a preliminary comparison of the temporal and spatial relationship between magnetic responses measured by OPM-MEG and blood-oxygen-level-dependent signals detected by functional magnetic resonance imaging (fMRI) during a grasping task.

METHODS

For OPM-MEG, the β-band (15-30 Hz) oscillatory activities can be reliably detected across multiple subjects and multiple session runs. To effectively localize the inhibitory oscillatory activities, a source power-spectrum ratio-based imaging method was proposed. This approach was compared with conventional source imaging methods, such as minimum norm-type and beamformer methods, and was applied in OPM-MEG source analysis. Subsequently, the spatial and temporal responses at the source-level between OPM-MEG and fMRI were analyzed.

RESULTS

The effectiveness of the proposed method was confirmed through simulations compared to benchmark methods. Our demonstration revealed an average spatial separation of 10.57 ± 4.41 mm between the localization results of OPM-MEG and fMRI across four subjects. Furthermore, the fMRI-constrained OPM-MEG localization results indicated a more focused imaging extent.

CONCLUSIONS

Taken together, the performance exhibited by OPM-MEG positions it as a potential instrument for functional surgery assessment.

Characterization of Second-Order Mixing Effects in Reconstructed Cross-Spectra of Random Neural Fields

Functional connectivity in electroencephalography (EEG) and magnetoencephalography (MEG) data is commonly assessed by using measures that are insensitive to instantaneously interacting sources and as such would not give rise to false positive interactions caused by instantaneous mixing of true source signals (first-order mixing). Recent studies, however, have drawn attention to the fact that such measures are still susceptible to instantaneous mixing from lagged sources (i.e. second-order mixing) and that this can lead to a large number of false positive interactions. In this study we relate first- and second-order mixing effects on the cross-spectra of reconstructed source activity to the properties of the resolution operators that are used for the reconstruction. We derive two identities that relate first- and second-order mixing effects to the transformation properties of measurement and source configurations and exploit them to establish several basic properties of signal mixing. First, we provide a characterization of the configurations that are maximally and minimally sensitive to second-order mixing. It turns out that second-order mixing effects are maximal when the measurement locations are far apart and the sources coincide with the measurement locations. Second, we provide a description of second-order mixing effects in the vicinity of the measurement locations in terms of the local geometry of the point-spread functions of the resolution operator. Third, we derive a version of Lagrange's identity for cross-talk functions that establishes the existence of a trade-off between the magnitude of first- and second-order mixing effects. It also shows that, whereas the magnitude of first-order mixing is determined by the inner product of cross-talk functions, the magnitude of second-order mixing is determined by a generalized cross-product of cross-talk functions (the wedge product) which leads to an intuitive geometric understanding of the trade-off. All results are derived within the general framework of random neural fields on cortical manifolds.

A ventromedial visual cortical 'Where' stream to the human hippocampus for spatial scenes revealed with magnetoencephalography

The primate including the human hippocampus implicated in episodic memory and navigation represents a spatial view, very different from the place representations in rodents. To understand this system in humans, and the computations performed, the pathway for this spatial view information to reach the hippocampus was analysed in humans. Whole-brain effective connectivity was measured with magnetoencephalography between 30 visual cortical regions and 150 other cortical regions using the HCP-MMP1 atlas in 21 participants while performing a 0-back scene memory task. In a ventromedial visual stream, V1-V4 connect to the ProStriate region where the retrosplenial scene area is located. The ProStriate region has connectivity to ventromedial visual regions VMV1-3 and VVC. These ventromedial regions connect to the medial parahippocampal region PHA1-3, which, with the VMV regions, include the parahippocampal scene area. The medial parahippocampal regions have effective connectivity to the entorhinal cortex, perirhinal cortex, and hippocampus. In contrast, when viewing faces, the effective connectivity was more through a ventrolateral visual cortical stream via the fusiform face cortex to the inferior temporal visual cortex regions TE2p and TE2a. A ventromedial visual cortical 'Where' stream to the hippocampus for spatial scenes was supported by diffusion topography in 171 HCP participants at 7 T.

Volume-optimal persistence homological scaffolds of hemodynamic networks covary with MEG theta-alpha aperiodic dynamics

Higher-order properties of functional magnetic resonance imaging (fMRI) induced connectivity have been shown to unravel many exclusive topological and dynamical insights beyond pairwise interactions. Nonetheless, whether these fMRI-induced higher-order properties play a role in disentangling other neuroimaging modalities' insights remains largely unexplored and poorly understood. In this work, by analyzing fMRI data from the Human Connectome Project Young Adult dataset using persistent homology, we discovered that the volume-optimal persistence homological scaffolds of fMRI-based functional connectomes exhibited conservative topological reconfigurations from the resting state to attentional task-positive state. Specifically, while reflecting the extent to which each cortical region contributed to functional cycles following different cognitive demands, these reconfigurations were constrained such that the spatial distribution of cavities in the connectome is relatively conserved. Most importantly, such level of contributions covaried with powers of aperiodic activities mostly within the theta-alpha (4-12 Hz) band measured by magnetoencephalography (MEG). This comprehensive result suggests that fMRI-induced hemodynamics and MEG theta-alpha aperiodic activities are governed by the same functional constraints specific to each cortical morpho-structure. Methodologically, our work paves the way toward an innovative computing paradigm in multimodal neuroimaging topological learning. The code for our analyses is provided in https://github.com/ngcaonghi/scaffold_noise.

Cortical gene expression architecture links healthy neurodevelopment to the imaging, transcriptomics and genetics of autism and schizophrenia

Human brain organization involves the coordinated expression of thousands of genes. For example, the first principal component (C1) of cortical transcription identifies a hierarchy from sensorimotor to association regions. In this study, optimized processing of the Allen Human Brain Atlas revealed two new components of cortical gene expression architecture, C2 and C3, which are distinctively enriched for neuronal, metabolic and immune processes, specific cell types and cytoarchitectonics, and genetic variants associated with intelligence. Using additional datasets (PsychENCODE, Allen Cell Atlas and BrainSpan), we found that C1-C3 represent generalizable transcriptional programs that are coordinated within cells and differentially phased during fetal and postnatal development. Autism spectrum disorder and schizophrenia were specifically associated with C1/C2 and C3, respectively, across neuroimaging, differential expression and genome-wide association studies. Evidence converged especially in support of C3 as a normative transcriptional program for adolescent brain development, which can lead to atypical supragranular cortical connectivity in people at high genetic risk for schizophrenia.

Encoding Manual Dexterity through Modulation of Intrinsic α Band Connectivity

The human hand possesses both consolidated motor skills and remarkable flexibility in adapting to ongoing task demands. However, the underlying mechanisms by which the brain balances stability and flexibility remain unknown. In the absence of external input or behavior, spontaneous (intrinsic) brain connectivity is thought to represent a prior of stored memories. In this study, we investigated how manual dexterity modulates spontaneous functional connectivity in the motor cortex during hand movement. Using magnetoencephalography, in 47 human participants (both sexes), we examined connectivity modulations in the α and β frequency bands at rest and during two motor tasks (i.e., finger tapping or toe squeezing). The flexibility and stability of such modulations allowed us to identify two groups of participants with different levels of performance (high and low performers) on the nine-hole peg test, a test of manual dexterity. In the α band, participants with higher manual dexterity showed distributed decreases of connectivity, specifically in the motor cortex, increased segregation, and reduced nodal centrality. Participants with lower manual dexterity showed an opposite pattern. Notably, these patterns from the brain to behavior are mirrored by results from behavior to the brain. Indeed, when participants were divided using the median split of the dexterity score, we found the same connectivity patterns. In summary, this experiment shows that a long-term motor skill-manual dexterity-influences the way the motor systems respond during movements.

Bayesian reduced rank regression models generalizable neural fingerprints that differentiate between individuals in magnetoencephalography data

Recent magnetoencephalography (MEG) studies have reported that functional connectivity (FC) and power spectra can be used as neural fingerprints in differentiating individuals. Such studies have mainly used correlations between measurement sessions to distinguish individuals from each other. However, it has remained unclear whether such correlations might reflect a more generalizable principle of individually distinctive brain patterns. Here, we evaluated a machine-learning based approach, termed latent-noise Bayesian reduced rank regression (BRRR) as a means of modelling individual differences in the resting-state MEG data of the Human Connectome Project (HCP), using FC and power spectra as neural features. First, we verified that BRRR could model and reproduce the differences between metrics that correlation-based fingerprinting yields. We trained BRRR models to distinguish individuals based on data from one measurement and used the models to identify subsequent measurement sessions of those same individuals. The best performing BRRR models, using only 20 spatiospectral components, were able to identify subjects across measurement sessions with over 90% accuracy, approaching the highest correlation-based accuracies. Using cross-validation, we then determined whether that BRRR model could generalize to unseen subjects, successfully classifying the measurement sessions of novel individuals with over 80% accuracy. The results demonstrate that individual neurofunctional differences can be reliably extracted from MEG data with a low-dimensional predictive model and that the model is able to classify novel subjects.

Dorsal brain activity reflects the severity of menopausal symptoms

OBJECTIVE

The severity of menopausal symptoms, despite being triggered by hormonal imbalance, does not directly correspond to hormone levels in the blood; thus, the level of unpleasantness is assessed using subjective questionnaires in clinical practice. To provide better treatments, alternative objective assessments have been anticipated to support medical interviews and subjective assessments. This study aimed to develop a new objective measurement for assessing unpleasantness.

METHODS

Fourteen participants with menopausal symptoms and two age-matched participants who visited our outpatient section were enrolled. Resting-state brain activity was measured using magnetoencephalography. The level of unpleasantness of menopausal symptoms was measured using the Kupperman Kohnenki Shogai Index. The blood level of follicle-stimulating hormone and luteinizing hormone were also measured. Correlation analyses were performed between the oscillatory power of brain activity, index score, and hormone levels.

RESULTS

The level of unpleasantness of menopausal symptoms was positively correlated with high-frequency oscillatory powers in the parietal and bordering cortices (alpha; P = 0.016, beta; P = 0.015, low gamma; P = 0.010). The follicle-stimulating hormone blood level was correlated with high-frequency oscillatory powers in the dorsal part of the cortex (beta; P = 0.008, beta; P = 0.005, low gamma; P = 0.017), whereas luteinizing hormone blood level was not correlated.

CONCLUSION

Resting-state brain activity can serve as an objective measurement of unpleasantness associated with menopausal symptoms, which aids the selection of appropriate treatment and monitors its outcome.

Two what, two where, visual cortical streams in humans

ROLLS, E. T. Two What, Two Where, Visual Cortical Streams in Humans. NEUROSCI BIOBEHAV REV 2024. Recent cortical connectivity investigations lead to new concepts about 'What' and 'Where' visual cortical streams in humans, and how they connect to other cortical systems. A ventrolateral 'What' visual stream leads to the inferior temporal visual cortex for object and face identity, and provides 'What' information to the hippocampal episodic memory system, the anterior temporal lobe semantic system, and the orbitofrontal cortex emotion system. A superior temporal sulcus (STS) 'What' visual stream utilising connectivity from the temporal and parietal visual cortex responds to moving objects and faces, and face expression, and connects to the orbitofrontal cortex for emotion and social behaviour. A ventromedial 'Where' visual stream builds feature combinations for scenes, and provides 'Where' inputs via the parahippocampal scene area to the hippocampal episodic memory system that are also useful for landmark-based navigation. The dorsal 'Where' visual pathway to the parietal cortex provides for actions in space, but also provides coordinate transforms to provide inputs to the parahippocampal scene area for self-motion update of locations in scenes in the dark or when the view is obscured.

Human connectome topology directs cortical traveling waves and shapes frequency gradients

Traveling waves and neural oscillation frequency gradients are pervasive in the human cortex. While the direction of traveling waves has been linked to brain function and dysfunction, the factors that determine this direction remain elusive. We hypothesized that structural connectivity instrength gradients - defined as the gradually varying sum of incoming connection strengths across the cortex - could shape both traveling wave direction and frequency gradients. We confirm the presence of instrength gradients in the human connectome across diverse cohorts and parcellations. Using a cortical network model, we demonstrate how these instrength gradients direct traveling waves and shape frequency gradients. Our model fits resting-state MEG functional connectivity best in a regime where instrength-directed traveling waves and frequency gradients emerge. We further show how structural subnetworks of the human connectome generate opposing wave directions and frequency gradients observed in the alpha and beta bands. Our findings suggest that structural connectivity instrength gradients affect both traveling wave direction and frequency gradients.

CiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics

We present CiftiStorm, an electrophysiological source imaging (ESI) pipeline incorporating recently developed methods to improve forward and inverse solutions. The CiftiStorm pipeline produces Human Connectome Project (HCP) and megconnectome-compliant outputs from dataset inputs with varying degrees of spatial resolution. The input data can range from low-sensor-density electroencephalogram (EEG) or magnetoencephalogram (MEG) recordings without structural magnetic resonance imaging (sMRI) to high-density EEG/MEG recordings with an HCP multimodal sMRI compliant protocol. CiftiStorm introduces a numerical quality control of the lead field and geometrical corrections to the head and source models for forward modeling. For the inverse modeling, we present a Bayesian estimation of the cross-spectrum of sources based on multiple priors. We facilitate ESI in the T1w/FSAverage32k high-resolution space obtained from individual sMRI. We validate this feature by comparing CiftiStorm outputs for EEG and MRI data from the Cuban Human Brain Mapping Project (CHBMP) acquired with technologies a decade before the HCP MEG and MRI standardized dataset.

A bicoherence approach to analyze multi-dimensional cross-frequency coupling in EEG/MEG data

We introduce a blockwise generalisation of the Antisymmetric Cross-Bicoherence (ACB), a statistical method based on bispectral analysis. The Multi-dimensional ACB (MACB) is an approach that aims at detecting quadratic lagged phase-interactions between vector time series in the frequency domain. Such a coupling can be empirically observed in functional neuroimaging data, e.g., in electro/magnetoencephalographic signals. MACB is invariant under orthogonal trasformations of the data, which makes it independent, e.g., on the choice of the physical coordinate system in the neuro-electromagnetic inverse procedure. In extensive synthetic experiments, we prove that MACB performance is significantly better than that obtained by ACB. Specifically, the shorter the data length, or the higher the dimension of the single data space, the larger the difference between the two methods.

Higher-order functional connectivity analysis of resting-state functional magnetic resonance imaging data using multivariate cumulants

Blood-level oxygenation-dependent (BOLD) functional magnetic resonance imaging (fMRI) is the most common modality to study functional connectivity in the human brain. Most research to date has focused on connectivity between pairs of brain regions. However, attention has recently turned towards connectivity involving more than two regions, that is, higher-order connectivity. It is not yet clear how higher-order connectivity can best be quantified. The measures that are currently in use cannot distinguish between pairwise (i.e., second-order) and higher-order connectivity. We show that genuine higher-order connectivity can be quantified by using multivariate cumulants. We explore the use of multivariate cumulants for quantifying higher-order connectivity and the performance of block bootstrapping for statistical inference. In particular, we formulate a generative model for fMRI signals exhibiting higher-order connectivity and use it to assess bias, standard errors, and detection probabilities. Application to resting-state fMRI data from the Human Connectome Project demonstrates that spontaneous fMRI signals are organized into higher-order networks that are distinct from second-order resting-state networks. Application to a clinical cohort of patients with multiple sclerosis further demonstrates that cumulants can be used to classify disease groups and explain behavioral variability. Hence, we present a novel framework to reliably estimate genuine higher-order connectivity in fMRI data which can be used for constructing hyperedges, and finally, which can readily be applied to fMRI data from populations with neuropsychiatric disease or cognitive neuroscientific experiments.

Does the brain behave like a (complex) network? I. Dynamics

Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network structure does not entail that the brain actually works as a network. Asking whether the brain behaves as a network means asking whether network properties count. From the viewpoint of neurophysiology and, possibly, of brain physics, the most substantial issues a network structure may be instrumental in addressing relate to the influence of network properties on brain dynamics and to whether these properties ultimately explain some aspects of brain function. Here, we address the dynamical implications of complex network, examining which aspects and scales of brain activity may be understood to genuinely behave as a network. To do so, we first define the meaning of networkness, and analyse some of its implications. We then examine ways in which brain anatomy and dynamics can be endowed with a network structure and discuss possible ways in which network structure may be shown to represent a genuine organisational principle of brain activity, rather than just a convenient description of its anatomy and dynamics.

A recurrent Hopfield network for estimating meso-scale effective connectivity in MEG

The architecture of communication within the brain, represented by the human connectome, has gained a paramount role in the neuroscience community. Several features of this communication, e.g., the frequency content, spatial topology, and temporal dynamics are currently well established. However, identifying generative models providing the underlying patterns of inhibition/excitation is very challenging. To address this issue, we present a novel generative model to estimate large-scale effective connectivity from MEG. The dynamic evolution of this model is determined by a recurrent Hopfield neural network with asymmetric connections, and thus denoted Recurrent Hopfield Mass Model (RHoMM). Since RHoMM must be applied to binary neurons, it is suitable for analyzing Band Limited Power (BLP) dynamics following a binarization process. We trained RHoMM to predict the MEG dynamics through a gradient descent minimization and we validated it in two steps. First, we showed a significant agreement between the similarity of the effective connectivity patterns and that of the interregional BLP correlation, demonstrating RHoMM's ability to capture individual variability of BLP dynamics. Second, we showed that the simulated BLP correlation connectomes, obtained from RHoMM evolutions of BLP, preserved some important topological features, e.g, the centrality of the real data, assuring the reliability of RHoMM. Compared to other biophysical models, RHoMM is based on recurrent Hopfield neural networks, thus, it has the advantage of being data-driven, less demanding in terms of hyperparameters and scalable to encompass large-scale system interactions. These features are promising for investigating the dynamics of inhibition/excitation at different spatial scales.

Oscillatory characteristics of resting-state magnetoencephalography reflect pathological and symptomatic conditions of cognitive impairment

Background

Dementia and mild cognitive impairment are characterised by symptoms of cognitive decline, which are typically assessed using neuropsychological assessments (NPAs), such as the Mini-Mental State Examination (MMSE) and Frontal Assessment Battery (FAB). Magnetoencephalography (MEG) is a novel clinical assessment technique that measures brain activities (summarised as oscillatory parameters), which are associated with symptoms of cognitive impairment. However, the relevance of MEG and regional cerebral blood flow (rCBF) data obtained using single-photon emission computed tomography (SPECT) has not been examined using clinical datasets. Therefore, this study aimed to investigate the relationships among MEG oscillatory parameters, clinically validated biomarkers computed from rCBF, and NPAs using outpatient data retrieved from hospital records.

Methods

Clinical data from 64 individuals with mixed pathological backgrounds were retrieved and analysed. MEG oscillatory parameters, including relative power (RP) from delta to high gamma bands, mean frequency, individual alpha frequency, and Shannon's spectral entropy, were computed for each cortical region. For SPECT data, three pathological parameters-'severity', 'extent', and 'ratio'-were computed using an easy z-score imaging system (eZIS). As for NPAs, the MMSE and FAB scores were retrieved.

Results

MEG oscillatory parameters were correlated with eZIS parameters. The eZIS parameters associated with Alzheimer's disease pathology were reflected in theta power augmentation and slower shift of the alpha peak. Moreover, MEG oscillatory parameters were found to reflect NPAs. Global slowing and loss of diversity in neural oscillatory components correlated with MMSE and FAB scores, whereas the associations between eZIS parameters and NPAs were sparse.

Conclusion

MEG oscillatory parameters correlated with both SPECT (i.e. eZIS) parameters and NPAs, supporting the clinical validity of MEG oscillatory parameters as pathological and symptomatic indicators. The findings indicate that various components of MEG oscillatory characteristics can provide valuable pathological and symptomatic information, making MEG data a rich resource for clinical examinations of patients with cognitive impairments. SPECT (i.e. eZIS) parameters showed no correlations with NPAs. The results contributed to a better understanding of the characteristics of electrophysiological and pathological examinations for patients with cognitive impairments, which will help to facilitate their co-use in clinical application, thereby improving patient care.

Precision data-driven modeling of cortical dynamics reveals idiosyncratic mechanisms underlying canonical oscillations

Task-free brain activity affords unique insight into the functional structure of brain network dynamics and is a strong marker of individual differences. In this work, we present an algorithmic optimization framework that makes it possible to directly invert and parameterize brain-wide dynamical-systems models involving hundreds of interacting brain areas, from single-subject time-series recordings. This technique provides a powerful neurocomputational tool for interrogating mechanisms underlying individual brain dynamics ("precision brain models") and making quantitative predictions. We extensively validate the models' performance in forecasting future brain activity and predicting individual variability in key M/EEG markers. Lastly, we demonstrate the power of our technique in resolving individual differences in the generation of alpha and beta-frequency oscillations. We characterize subjects based upon model attractor topology and a dynamical-systems mechanism by which these topologies generate individual variation in the expression of alpha vs. beta rhythms. We trace these phenomena back to global variation in excitation-inhibition balance, highlighting the explanatory power of our framework in generating mechanistic insights.

Connectivity-based Meta-Bands: A new approach for automatic frequency band identification in connectivity analyses

The majority of electroencephalographic (EEG) and magnetoencephalographic (MEG) studies filter and analyse neural signals in specific frequency ranges, known as "canonical" frequency bands. However, this segmentation, is not exempt from limitations, mainly due to the lack of adaptation to the neural idiosyncrasies of each individual. In this study, we introduce a new data-driven method to automatically identify frequency ranges based on the topological similarity of the frequency-dependent functional neural network. The resting-state neural activity of 195 cognitively healthy subjects from three different databases (MEG: 123 subjects; EEG1: 27 subjects; EEG2: 45 subjects) was analysed. In a first step, MEG and EEG signals were filtered with a narrow-band filter bank (1 Hz bandwidth) from 1 to 70 Hz with a 0.5 Hz step. Next, the connectivity in each of these filtered signals was estimated using the orthogonalized version of the amplitude envelope correlation to obtain the frequency-dependent functional neural network. Finally, a community detection algorithm was used to identify communities in the frequency domain showing a similar network topology. We have called this approach the "Connectivity-based Meta-Bands" (CMB) algorithm. Additionally, two types of synthetic signals were used to configure the hyper-parameters of the CMB algorithm. We observed that the classical approaches to band segmentation are partially aligned with the underlying network topologies at group level for the MEG signals, but they are missing individual idiosyncrasies that may be biasing previous studies, as revealed by our methodology. On the other hand, the sensitivity of EEG signals to reflect this underlying frequency-dependent network structure is limited, revealing a simpler frequency parcellation, not aligned with that defined by the "canonical" frequency bands. To the best of our knowledge, this is the first study that proposes an unsupervised band segmentation method based on the topological similarity of functional neural network across frequencies. This methodology fully accounts for subject-specific patterns, providing more robust and personalized analyses, and paving the way for new studies focused on exploring the frequency-dependent structure of brain connectivity.

Hierarchical organization of the human ventral visual streams revealed with magnetoencephalography

The hierarchical organization between 25 ventral stream visual cortical regions and 180 cortical regions was measured with magnetoencephalography using the Human Connectome Project Multimodal Parcellation atlas in 83 Human Connectome Project participants performing a visual memory task. The aim was to reveal the hierarchical organization using a whole-brain model based on generative effective connectivity with this fast neuroimaging method. V1-V4 formed a first group of interconnected regions. Especially V4 had connectivity to a ventrolateral visual stream: V8, the fusiform face cortex, and posterior inferior temporal cortex PIT. These regions in turn had effectivity connectivity to inferior temporal cortex visual regions TE2p and TE1p. TE2p and TE1p then have connectivity to anterior temporal lobe regions TE1a, TE1m, TE2a, and TGv, which are multimodal. In a ventromedial visual stream, V1-V4 connect to ventromedial regions VMV1-3 and VVC. VMV1-3 and VVC connect to the medial parahippocampal gyrus PHA1-3, which, with the VMV regions, include the parahippocampal scene area. The medial parahippocampal PHA1-3 regions have connectivity to the hippocampal system regions the perirhinal cortex, entorhinal cortex, and hippocampus. These effective connectivities of two ventral visual cortical streams measured with magnetoencephalography provide support to the hierarchical organization of brain systems measured with fMRI, and new evidence on directionality.

Neural fingerprinting on MEG time series using MiniRocket

Neural fingerprinting is the identification of individuals in a cohort based on neuroimaging recordings of brain activity. In magneto- and electroencephalography (M/EEG), it is common practice to use second-order statistical measures, such as correlation or connectivity matrices, when neural fingerprinting is performed. These measures or features typically require coupling between signal channels and often ignore the individual temporal dynamics. In this study, we show that, following recent advances in multivariate time series classification, such as the development of the RandOm Convolutional KErnel Transformation (ROCKET) classifier, it is possible to perform classification directly on short time segments from MEG resting-state recordings with remarkably high classification accuracies. In a cohort of 124 subjects, it was possible to assign windows of time series of 1 s in duration to the correct subject with above 99% accuracy. The achieved accuracies are vastly superior to those of previous methods while simultaneously requiring considerably shorter time segments.

Non-reversibility outperforms functional connectivity in characterisation of brain states in MEG data

Characterising brain states during tasks is common practice for many neuroscientific experiments using electrophysiological modalities such as electroencephalography (EEG) and magnetoencephalography (MEG). Brain states are often described in terms of oscillatory power and correlated brain activity, i.e. functional connectivity. It is, however, not unusual to observe weak task induced functional connectivity alterations in the presence of strong task induced power modulations using classical time-frequency representation of the data. Here, we propose that non-reversibility, or the temporal asymmetry in functional interactions, may be more sensitive to characterise task induced brain states than functional connectivity. As a second step, we explore causal mechanisms of non-reversibility in MEG data using whole brain computational models. We include working memory, motor, language tasks and resting-state data from participants of the Human Connectome Project (HCP). Non-reversibility is derived from the lagged amplitude envelope correlation (LAEC), and is based on asymmetry of the forward and reversed cross-correlations of the amplitude envelopes. Using random forests, we find that non-reversibility outperforms functional connectivity in the identification of task induced brain states. Non-reversibility shows especially better sensitivity to capture bottom-up gamma induced brain states across all tasks, but also alpha band associated brain states. Using whole brain computational models we find that asymmetry in the effective connectivity and axonal conduction delays play a major role in shaping non-reversibility across the brain. Our work paves the way for better sensitivity in characterising brain states during both bottom-up as well as top-down modulation in future neuroscientific experiments.

Topological data analysis for revealing dynamic brain reconfiguration in MEG data

In recent years, the focus of the functional connectivity community has shifted from stationary approaches to the ones that include temporal dynamics. Especially, non-invasive electrophysiological data (magnetoencephalography/electroencephalography (MEG/EEG)) with high temporal resolution and good spatial coverage have made it possible to measure the fast alterations in the neural activity in the brain during ongoing cognition. In this article, we analyze dynamic brain reconfiguration using MEG images collected from subjects during the rest and the cognitive tasks. Our proposed topological data analysis method, called Mapper, produces biomarkers that differentiate cognitive tasks without prior spatial and temporal collapse of the data. The suggested method provides an interactive visualization of the rapid fluctuations in electrophysiological data during motor and cognitive tasks; hence, it has the potential to extract clinically relevant information at an individual level without temporal and spatial collapse.

Identifying oscillatory brain networks with hidden Gaussian graphical spectral models of MEEG

Identifying the functional networks underpinning indirectly observed processes poses an inverse problem for neurosciences or other fields. A solution of such inverse problems estimates as a first step the activity emerging within functional networks from EEG or MEG data. These EEG or MEG estimates are a direct reflection of functional brain network activity with a temporal resolution that no other in vivo neuroimage may provide. A second step estimating functional connectivity from such activity pseudodata unveil the oscillatory brain networks that strongly correlate with all cognition and behavior. Simulations of such MEG or EEG inverse problem also reveal estimation errors of the functional connectivity determined by any of the state-of-the-art inverse solutions. We disclose a significant cause of estimation errors originating from misspecification of the functional network model incorporated into either inverse solution steps. We introduce the Bayesian identification of a Hidden Gaussian Graphical Spectral (HIGGS) model specifying such oscillatory brain networks model. In human EEG alpha rhythm simulations, the estimation errors measured as ROC performance do not surpass 2% in our HIGGS inverse solution and reach 20% in state-of-the-art methods. Macaque simultaneous EEG/ECoG recordings provide experimental confirmation for our results with 1/3 times larger congruence according to Riemannian distances than state-of-the-art methods.

Unsupervised representation learning of spontaneous MEG data with Nonlinear ICA

Resting-state magnetoencephalography (MEG) data show complex but structured spatiotemporal patterns. However, the neurophysiological basis of these signal patterns is not fully known and the underlying signal sources are mixed in MEG measurements. Here, we developed a method based on the nonlinear independent component analysis (ICA), a generative model trainable with unsupervised learning, to learn representations from resting-state MEG data. After being trained with a large dataset from the Cam-CAN repository, the model has learned to represent and generate patterns of spontaneous cortical activity using latent nonlinear components, which reflects principal cortical patterns with specific spectral modes. When applied to the downstream classification task of audio-visual MEG, the nonlinear ICA model achieves competitive performance with deep neural networks despite limited access to labels. We further validate the generalizability of the model across different datasets by applying it to an independent neurofeedback dataset for decoding the subject's attentional states, providing a real-time feature extraction and decoding mindfulness and thought-inducing tasks with an accuracy of around 70% at the individual level, which is much higher than obtained by linear ICA or other baseline methods. Our results demonstrate that nonlinear ICA is a valuable addition to existing tools, particularly suited for unsupervised representation learning of spontaneous MEG activity which can then be applied to specific goals or tasks when labelled data are scarce.

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.

Minimizing the distortions in electrophysiological source imaging of cortical oscillatory activity via Spectral Structured Sparse Bayesian Learning

Oscillatory processes at all spatial scales and on all frequencies underpin brain function. Electrophysiological Source Imaging (ESI) is the data-driven brain imaging modality that provides the inverse solutions to the source processes of the EEG, MEG, or ECoG data. This study aimed to carry out an ESI of the source cross-spectrum while controlling common distortions of the estimates. As with all ESI-related problems under realistic settings, the main obstacle we faced is a severely ill-conditioned and high-dimensional inverse problem. Therefore, we opted for Bayesian inverse solutions that posited a priori probabilities on the source process. Indeed, rigorously specifying both the likelihoods and a priori probabilities of the problem leads to the proper Bayesian inverse problem of cross-spectral matrices. These inverse solutions are our formal definition for cross-spectral ESI (cESI), which requires a priori of the source cross-spectrum to counter the severe ill-condition and high-dimensionality of matrices. However, inverse solutions for this problem were NP-hard to tackle or approximated within iterations with bad-conditioned matrices in the standard ESI setup. We introduce cESI with a joint a priori probability upon the source cross-spectrum to avoid these problems. cESI inverse solutions are low-dimensional ones for the set of random vector instances and not random matrices. We achieved cESI inverse solutions through the variational approximations via our Spectral Structured Sparse Bayesian Learning (ssSBL) algorithm https://github.com/CCC-members/Spectral-Structured-Sparse-Bayesian-Learning. We compared low-density EEG (10-20 system) ssSBL inverse solutions with reference cESIs for two experiments: (a) high-density MEG that were used to simulate EEG and (b) high-density macaque ECoG that were recorded simultaneously with EEG. The ssSBL resulted in two orders of magnitude with less distortion than the state-of-the-art ESI methods. Our cESI toolbox, including the ssSBL method, is available at https://github.com/CCC-members/BC-VARETA_Toolbox.

Functional connectivity fingerprints of the frontal eye field and inferior frontal junction suggest spatial versus nonspatial processing in the prefrontal cortex

Neuroimaging evidence suggests that the frontal eye field (FEF) and inferior frontal junction (IFJ) govern the encoding of spatial and nonspatial (such as feature- or object-based) representations, respectively, both during visual attention and working memory tasks. However, it is still unclear whether such contrasting functional segregation is also reflected in their underlying functional connectivity patterns. Here, we hypothesized that FEF has predominant functional coupling with spatiotopically organized regions in the dorsal ('where') visual stream whereas IFJ has predominant functional connectivity with the ventral ('what') visual stream. We applied seed-based functional connectivity analyses to temporally high-resolving resting-state magnetoencephalography (MEG) recordings. We parcellated the brain according to the multimodal Glasser atlas and tested, for various frequency bands, whether the spontaneous activity of each parcel in the ventral and dorsal visual pathway has predominant functional connectivity with FEF or IFJ. The results show that FEF has a robust power correlation with the dorsal visual pathway in beta and gamma bands. In contrast, anterior IFJ (IFJa) has a strong power coupling with the ventral visual stream in delta, beta and gamma oscillations. Moreover, while FEF is phase-coupled with the superior parietal lobe in the beta band, IFJa is phase-coupled with the middle and inferior temporal cortex in delta and gamma oscillations. We argue that these intrinsic connectivity fingerprints are congruent with each brain region's function. Therefore, we conclude that FEF and IFJ have dissociable connectivity patterns that fit their respective functional roles in spatial versus nonspatial top-down attention and working memory control.

Individual theta-band cortical entrainment to speech in quiet predicts word-in-noise comprehension

Speech elicits brain activity time-locked to its amplitude envelope. The resulting speech-brain synchrony (SBS) is thought to be crucial to speech parsing and comprehension. It has been shown that higher speech-brain coherence is associated with increased speech intelligibility. However, studies depending on the experimental manipulation of speech stimuli do not allow conclusion about the causality of the observed tracking. Here, we investigate whether individual differences in the intrinsic propensity to track the speech envelope when listening to speech-in-quiet is predictive of individual differences in speech-recognition-in-noise, in an independent task. We evaluated the cerebral tracking of speech in source-localized magnetoencephalography, at timescales corresponding to the phrases, words, syllables and phonemes. We found that individual differences in syllabic tracking in right superior temporal gyrus and in left middle temporal gyrus (MTG) were positively associated with recognition accuracy in an independent words-in-noise task. Furthermore, directed connectivity analysis showed that this relationship is partially mediated by top-down connectivity from premotor cortex-associated with speech processing and active sensing in the auditory domain-to left MTG. Thus, the extent of SBS-even during clear speech-reflects an active mechanism of the speech processing system that may confer resilience to noise.

Association between cognitive impairments and aberrant dynamism of overlapping brain sub-networks in unmedicated major depressive disorder: A resting-state MEG study

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Late dominance of the right hemisphere during narrative comprehension

PET and fMRI studies suggest that auditory narrative comprehension is supported by a bilateral multilobar cortical network. The superior temporal resolution of magnetoencephalography (MEG) makes it an attractive tool to investigate the dynamics of how different neuroanatomic substrates engage during narrative comprehension. Using beta-band power changes as a marker of cortical engagement, we studied MEG responses during an auditory story comprehension task in 31 healthy adults. The protocol consisted of two runs, each interleaving 7 blocks of the story comprehension task with 15 blocks of an auditorily presented math task as a control for phonological processing, working memory, and attention processes. Sources at the cortical surface were estimated with a frequency-resolved beamformer. Beta-band power was estimated in the frequency range of 16-24 Hz over 1-sec epochs starting from 400 msec after stimulus onset until the end of a story or math problem presentation. These power estimates were compared to 1-second epochs of data before the stimulus block onset. The task-related cortical engagement was inferred from beta-band power decrements. Group-level source activations were statistically compared using non-parametric permutation testing. A story-math contrast of beta-band power changes showed greater bilateral cortical engagement within the fusiform gyrus, inferior and middle temporal gyri, parahippocampal gyrus, and left inferior frontal gyrus (IFG) during story comprehension. A math-story contrast of beta power decrements showed greater bilateral but left-lateralized engagement of the middle frontal gyrus and superior parietal lobule. The evolution of cortical engagement during five temporal windows across the presentation of stories showed significant involvement during the first interval of the narrative of bilateral opercular and insular regions as well as the ventral and lateral temporal cortex, extending more posteriorly on the left and medially on the right. Over time, there continued to be sustained right anterior ventral temporal engagement, with increasing involvement of the right anterior parahippocampal gyrus, STG, MTG, posterior superior temporal sulcus, inferior parietal lobule, frontal operculum, and insula, while left hemisphere engagement decreased. Our findings are consistent with prior imaging studies of narrative comprehension, but in addition, they demonstrate increasing right-lateralized engagement over the course of narratives, suggesting an important role for these right-hemispheric regions in semantic integration as well as social and pragmatic inference processing.

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.

Control-theoretic integration of stimulation and electrophysiology for cognitive enhancement

Transcranial electrical stimulation (tES) technology and neuroimaging are increasingly coupled in basic and applied science. This synergy has enabled individualized tES therapy and facilitated causal inferences in functional neuroimaging. However, traditional tES paradigms have been stymied by relatively small changes in neural activity and high inter-subject variability in cognitive effects. In this perspective, we propose a tES framework to treat these issues which is grounded in dynamical systems and control theory. The proposed paradigm involves a tight coupling of tES and neuroimaging in which M/EEG is used to parameterize generative brain models as well as control tES delivery in a hybrid closed-loop fashion. We also present a novel quantitative framework for cognitive enhancement driven by a new computational objective: shaping how the brain reacts to potential "inputs" (e.g., task contexts) rather than enforcing a fixed pattern of brain activity.

Robust estimation of 1/f activity improves oscillatory burst detection

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.

Revealing Continuous Brain Dynamical Organization with Multimodal Graph Transformer

Brain large-scale dynamics is constrained by the heterogeneity of intrinsic anatomical substrate. Little is known how the spatio-temporal dynamics adapt for the heterogeneous structural connectivity (SC). Modern neuroimaging modalities make it possible to study the intrinsic brain activity at the scale of seconds to minutes. Diffusion magnetic resonance imaging (dMRI) and functional MRI reveals the large-scale SC across different brain regions. Electrophysiological methods (i.e. MEG/EEG) provide direct measures of neural activity and exhibits complex neurobiological temporal dynamics which could not be solved by fMRI. However, most of existing multimodal analytical methods collapse the brain measurements either in space or time domain and fail to capture the spatio-temporal circuit dynamics. In this paper, we propose a novel spatio-temporal graph Transformer model to integrate the structural and functional connectivity in both spatial and temporal domain. The proposed method learns the heterogeneous node and graph representation via contrastive learning and multi-head attention based graph Transformer using multimodal brain data (i.e. fMRI, MRI, MEG and behavior performance). The proposed contrastive graph Transformer representation model incorporates the heterogeneity map constrained by T1-to-T2-weighted (T1w/T2w) to improve the model fit to structure-function interactions. The experimental results with multimodal resting state brain measurements demonstrate the proposed method could highlight the local properties of large-scale brain spatio-temporal dynamics and capture the dependence strength between functional connectivity and behaviors. In summary, the proposed method enables the complex brain dynamics explanation for different modal variants.

The NIMH intramural healthy volunteer dataset: A comprehensive MEG, MRI, and behavioral resource

The NIMH Healthy Research Volunteer Dataset is a collection of phenotypic data characterizing healthy research volunteers using clinical assessments such as assays of blood and urine, mental health assessments, diagnostic and dimensional measures of mental health, cognitive and neuropsychological functioning, structural and functional magnetic resonance imaging (MRI), along with diffusion tensor imaging (DTI), and a comprehensive magnetoencephalography battery (MEG). In addition, blood samples of healthy volunteers are banked for future analyses. All data collected in this protocol are broadly shared in the OpenNeuro repository, in the Brain Imaging Data Structure (BIDS) format. In addition, task paradigms and basic pre-processing scripts are shared on GitHub. There are currently few open access MEG datasets, and multimodal neuroimaging datasets are even more rare. Due to its depth of characterization of a healthy population in terms of brain health, this dataset may contribute to a wide array of secondary investigations of non-clinical and clinical research questions.

Good scientific practice in EEG and MEG research: Progress and perspectives

Good scientific practice (GSP) refers to both explicit and implicit rules, recommendations, and guidelines that help scientists to produce work that is of the highest quality at any given time, and to efficiently share that work with the community for further scrutiny or utilization. For experimental research using magneto- and electroencephalography (MEEG), GSP includes specific standards and guidelines for technical competence, which are periodically updated and adapted to new findings. However, GSP also needs to be regularly revisited in a broader light. At the LiveMEEG 2020 conference, a reflection on GSP was fostered that included explicitly documented guidelines and technical advances, but also emphasized intangible GSP: a general awareness of personal, organizational, and societal realities and how they can influence MEEG research. This article provides an extensive report on most of the LiveMEEG contributions and new literature, with the additional aim to synthesize ongoing cultural changes in GSP. It first covers GSP with respect to cognitive biases and logical fallacies, pre-registration as a tool to avoid those and other early pitfalls, and a number of resources to enable collaborative and reproducible research as a general approach to minimize misconceptions. Second, it covers GSP with respect to data acquisition, analysis, reporting, and sharing, including new tools and frameworks to support collaborative work. Finally, GSP is considered in light of ethical implications of MEEG research and the resulting responsibility that scientists have to engage with societal challenges. Considering among other things the benefits of peer review and open access at all stages, the need to coordinate larger international projects, the complexity of MEEG subject matter, and today's prioritization of fairness, privacy, and the environment, we find that current GSP tends to favor collective and cooperative work, for both scientific and for societal reasons.

Reliability and subject specificity of personalized whole-brain dynamical models

Dynamical whole-brain models were developed to link structural (SC) and functional connectivity (FC) together into one framework. Nowadays, they are used to investigate the dynamical regimes of the brain and how these relate to behavioral, clinical and demographic traits. However, there is no comprehensive investigation on how reliable and subject specific the modeling results are given the variability of the empirical FC. In this study, we show that the parameters of these models can be fitted with a "poor" to "good" reliability depending on the exact implementation of the modeling paradigm. We find, as a general rule of thumb, that enhanced model personalization leads to increasingly reliable model parameters. In addition, we observe no clear effect of the model complexity evaluated by separately sampling results for linear, phase oscillator and neural mass network models. In fact, the most complex neural mass model often yields modeling results with "poor" reliability comparable to the simple linear model, but demonstrates an enhanced subject specificity of the model similarity maps. Subsequently, we show that the FC simulated by these models can outperform the empirical FC in terms of both reliability and subject specificity. For the structure-function relationship, simulated FC of individual subjects may be identified from the correlations with the empirical SC with an accuracy up to 70%, but not vice versa for non-linear models. We sample all our findings for 8 distinct brain parcellations and 6 modeling conditions and show that the parcellation-induced effect is much more pronounced for the modeling results than for the empirical data. In sum, this study provides an exploratory account on the reliability and subject specificity of dynamical whole-brain models and may be relevant for their further development and application. In particular, our findings suggest that the application of the dynamical whole-brain modeling should be tightly connected with an estimate of the reliability of the results.

A reusable benchmark of brain-age prediction from M/EEG resting-state signals

Population-level modeling can define quantitative measures of individual aging by applying machine learning to large volumes of brain images. These measures of brain age, obtained from the general population, helped characterize disease severity in neurological populations, improving estimates of diagnosis or prognosis. Magnetoencephalography (MEG) and Electroencephalography (EEG) have the potential to further generalize this approach towards prevention and public health by enabling assessments of brain health at large scales in socioeconomically diverse environments. However, more research is needed to define methods that can handle the complexity and diversity of M/EEG signals across diverse real-world contexts. To catalyse this effort, here we propose reusable benchmarks of competing machine learning approaches for brain age modeling. We benchmarked popular classical machine learning pipelines and deep learning architectures previously used for pathology decoding or brain age estimation in 4 international M/EEG cohorts from diverse countries and cultural contexts, including recordings from more than 2500 participants. Our benchmarks were built on top of the M/EEG adaptations of the BIDS standard, providing tools that can be applied with minimal modification on any M/EEG dataset provided in the BIDS format. Our results suggest that, regardless of whether classical machine learning or deep learning was used, the highest performance was reached by pipelines and architectures involving spatially aware representations of the M/EEG signals, leading to R^2 scores between 0.60-0.71. Hand-crafted features paired with random forest regression provided robust benchmarks even in situations in which other approaches failed. Taken together, this set of benchmarks, accompanied by open-source software and high-level Python scripts, can serve as a starting point and quantitative reference for future efforts at developing M/EEG-based measures of brain aging. The generality of the approach renders this benchmark reusable for other related objectives such as modeling specific cognitive variables or clinical endpoints.

Predicting time-resolved electrophysiological brain networks from structural eigenmodes

How temporal modulations in functional interactions are shaped by the underlying anatomical connections remains an open question. Here, we analyse the role of structural eigenmodes, in the formation and dissolution of temporally evolving functional brain networks using resting-state magnetoencephalography and diffusion magnetic resonance imaging data at the individual subject level. Our results show that even at short timescales, phase and amplitude connectivity can partly be expressed by structural eigenmodes, but hardly by direct structural connections. Albeit a stronger relationship was found between structural eigenmodes and time-resolved amplitude connectivity. Time-resolved connectivity for both phase and amplitude was mostly characterised by a stationary process, superimposed with very brief periods that showed deviations from this stationary process. For these brief periods, dynamic network states were extracted that showed different expressions of eigenmodes. Furthermore, the eigenmode expression was related to overall cognitive performance and co-occurred with fluctuations in community structure of functional networks. These results implicate that ongoing time-resolved resting-state networks, even at short timescales, can to some extent be understood in terms of activation and deactivation of structural eigenmodes and that these eigenmodes play a role in the dynamic integration and segregation of information across the cortex, subserving cognitive functions.

Alpha rhythm modulations in the intraparietal sulcus reflect decision signals during item recognition

Theoretical work and empirical observations suggest a contribution of regions along the intraparietal sulcus to the process of evidence accumulation during episodic memory retrieval. In the present study, we recorded magnetoencephalographic signals in a group of healthy human participants to test whether the pattern of oscillatory modulations in the lateral parietal lobe is consistent with the mnemonic accumulator hypothesis. To this aim, the dynamic properties and the spatial distribution of MEG oscillatory power modulations were investigated during an item recognition task in which the amount of evidence for old vs. new memory decisions was manipulated across three levels. A data-driven approach was employed to identify brain nodes where oscillatory activity was sensitive to both retrieval success and the amount of evidence for old decisions. The analysis identified three nodes in the left lateral parietal lobe where the event-related desynchronization (ERD) in the alpha frequency band showed both effects. Further analyses revealed that the alpha ERD in the intraparietal sulcus, but not in other parietal nodes: i. showed modulation of duration in response to the amount of evidence for both old and new decisions, ii. was behaviorally significant, and iii. more accurately tracked the subjective memory judgment rather than the objective memory status. The present findings provide support for a recent anatomical-functional model of the parietal involvement in episodic memory retrieval and suggest that the alpha ERD in the intraparietal sulcus might represent a neural signature of the evidence accumulation process during simple memory-based decisions.

Variability and task-responsiveness of electrophysiological dynamics: scale-free stability and oscillatory flexibility

Cortical oscillations and scale-free neural activity are thought to influence a variety of cognitive functions, but their differential relationships to neural stability and flexibility has never been investigated. Based on the existing literature, we hypothesize that scale-free and oscillatory processes in the brain exhibit different trade-offs between stability and flexibility; specifically, cortical oscillations may reflect variable, task-responsive aspects of brain activity, while scale-free activity is proposed to reflect a more stable and task-unresponsive aspect. We test this hypothesis using data from two large-scale MEG studies (HCP: n = 89; CamCAN: n = 195), operationalizing stability and flexibility by task-responsiveness and spontaneous intra-subject variability in resting state. We demonstrate that the power-law exponent of scale-free activity is a highly stable parameter, which responds little to external cognitive demands and shows minimal spontaneous fluctuations over time. In contrast, oscillatory power, particularly in the alpha range (8-13 Hz), responds strongly to tasks and exhibits comparatively large spontaneous fluctuations over time. In sum, our data support differential roles for oscillatory and scale-free activity in the brain with respect to neural stability and flexibility. This result carries implications for criticality-based theories of scale-free activity, state-trait models of variability, and homeostatic views of the brain with regulated variables vs. effectors.

Sharing individualised template MRI data for MEG source reconstruction: A solution for open data while keeping subject confidentiality

The increasing requirements for adoption of FAIR data management and sharing original research data from neuroimaging studies can be at odds with protecting the anonymity of the research participants due to the person-identifiable anatomical features in the data. We propose a solution to this dilemma for anatomical MRIs used in MEG source analysis. In MEG analysis, the channel-level data is reconstructed to the source-level using models derived from anatomical MRIs. Sharing data, therefore, requires sharing the anatomical MRI to replicate the analysis. The suggested solution is to replace the individual anatomical MRIs with individualised warped templates that can be used to carry out the MEG source analysis and that provide sufficient geometrical similarity to the original participants' MRIs. First, we demonstrate how the individualised template warping can be implemented with one of the leading open-source neuroimaging analysis toolboxes. Second, we compare results from four different MEG source reconstruction methods performed with an individualised warped template to those using the participant's original MRI. While the source reconstruction results are not numerically identical, there is a high similarity between the results for single dipole fits, dynamic imaging of coherent sources beamforming, and atlas-based virtual channel beamforming. There is a moderate similarity between minimum-norm estimates, as anticipated due to this method being anatomically constrained and dependent on the exact morphological features of the cortical sheet. We also compared the morphological features of the warped template to those of the original MRI. These showed a high similarity in grey matter volume and surface area, but a low similarity in the average cortical thickness and the mean folding index within cortical parcels. Taken together, this demonstrates that the results obtained by MEG source reconstruction can be preserved with the warped templates, whereas the anatomical and morphological fingerprint is sufficiently altered to protect the anonymity of research participants. In cases where participants consent to sharing anatomical MRI data, it remains preferable to share the original defaced data with an appropriate data use agreement. In cases where participants did not consent to share their MRIs, the individualised warped MRI template offers a good compromise in sharing data for reuse while retaining anonymity for research participants.

Evaluation of classification approaches for distinguishing brain states predictive of episodic memory performance from electroencephalography: Abbreviated Title: Evaluating methods of classifying memory states from EEG

Previous studies have attempted to separate single trial neural responses for events a person is likely to remember from those they are likely to forget using machine learning classification methods. Successful single trial classification holds potential for translation into the clinical realm for real-time detection of memory and other cognitive states to provide real-time interventions (i.e., brain-computer interfaces). However, most of these studies-and classification analyses in general- do not make clear if the chosen methodology is optimally suited for the classification of memory-related brain states. To address this problem, we systematically compared different methods for every step of classification (i.e., feature extraction, feature selection, classifier selection) to investigate which methods work best for decoding episodic memory brain states-the first analysis of its kind. Using an adult lifespan sample EEG dataset collected during performance of an episodic context encoding and retrieval task, we found that no specific feature type (including Common Spatial Pattern (CSP)-based features, mean, variance, correlation, features based on AR model, entropy, phase, and phase synchronization) outperformed others consistently in distinguishing different memory classes. However, extracting all of these feature types consistently outperformed extracting only one type of feature. Additionally, the combination of filtering and sequential forward selection was the optimal method to select the effective features compared to filtering alone or performing no feature selection at all. Moreover, although all classifiers performed at a fairly similar level, LASSO was consistently the highest performing classifier compared to other commonly used options (i.e., naïve Bayes, SVM, and logistic regression) while naïve Bayes was the fastest classifier. Lastly, for multiclass classification (i.e., levels of context memory confidence and context feature perception), generalizing the binary classification using the binary decision tree performed better than the voting or one versus rest method. These methods were shown to outperform alternative approaches for three orthogonal datasets (i.e., EEG working memory, EEG motor imagery, and MEG working memory), supporting their generalizability. Our results provide an optimized methodological process for classifying single-trial neural data and provide important insight and recommendations for a cognitive neuroscientist's ability to make informed choices at all stages of the classification process for predicting memory and other cognitive states.

Dementia ConnEEGtome: Towards multicentric harmonization of EEG connectivity in neurodegeneration

The proposal to use brain connectivity as a biomarker for dementia phenotyping can be potentiated by conducting large-scale multicentric studies using high-density electroencephalography (hd- EEG). Nevertheless, several barriers preclude the development of a systematic "ConnEEGtome" in dementia research. Here we review critical sources of variability in EEG connectivity studies, and provide general guidelines for multicentric protocol harmonization. We describe how results can be impacted by the choice for data acquisition, and signal processing workflows. The implementation of a particular processing pipeline is conditional upon assumptions made by researchers about the nature of EEG. Due to these assumptions, EEG connectivity metrics are typically applicable to restricted scenarios, e.g., to a particular neurocognitive disorder. "Ground truths" for the choice of processing workflow and connectivity analysis are impractical. Consequently, efforts should be directed to harmonizing experimental procedures, data acquisition, and the first steps of the preprocessing pipeline. Conducting multiple analyses of the same data and a proper integration of the results need to be considered in additional processing steps. Furthermore, instead of using a single connectivity measure, using a composite metric combining different connectivity measures brings a powerful strategy to scale up the replicability of multicentric EEG connectivity studies. These composite metrics can boost the predictive strength of diagnostic tools for dementia. Moreover, the implementation of multi-feature machine learning classification systems that include EEG-based connectivity analyses may help to exploit the potential of multicentric studies combining clinical-cognitive, molecular, genetics, and neuroimaging data towards a multi-dimensional characterization of the dementia.

Reproducibility of graph measures derived from resting-state MEG functional connectivity metrics in sensor and source spaces

Prior studies have used graph analysis of resting‐state magnetoencephalography (MEG) to characterize abnormal brain networks in neurological disorders. However, a present challenge for researchers is the lack of guidance on which network construction strategies to employ. The reproducibility of graph measures is important for their use as clinical biomarkers. Furthermore, global graph measures should ideally not depend on whether the analysis was performed in the sensor or source space. Therefore, MEG data of the 89 healthy subjects of the Human Connectome Project were used to investigate test–retest reliability and sensor versus source association of global graph measures. Atlas‐based beamforming was used for source reconstruction, and functional connectivity (FC) was estimated for both sensor and source signals in six frequency bands using the debiased weighted phase lag index (dwPLI), amplitude envelope correlation (AEC), and leakage‐corrected AEC. Reliability was examined over multiple network density levels achieved with proportional weight and orthogonal minimum spanning tree thresholding. At a 100% density, graph measures for most FC metrics and frequency bands had fair to excellent reliability and significant sensor versus source association. The greatest reliability and sensor versus source association was obtained when using amplitude metrics. Reliability was similar between sensor and source spaces when using amplitude metrics but greater for the source than the sensor space in higher frequency bands when using the dwPLI. These results suggest that graph measures are useful biomarkers, particularly for investigating functional networks based on amplitude synchrony.

Cortical oscillatory dysrhythmias in visual snow syndrome: a magnetoencephalography study

Visual snow refers to the persistent visual experience of static in the whole visual field of both eyes. It is often reported by patients with migraine and co-occurs with conditions such as tinnitus and tremor. The underlying pathophysiology of the condition is poorly understood. Previously, we hypothesized that visual snow syndrome may be characterized by disruptions to rhythmical activity within the visual system. To test this, data from 18 patients diagnosed with visual snow syndrome, and 16 matched controls, were acquired using magnetoencephalography. Participants were presented with visual grating stimuli, known to elicit decreases in alpha-band (8–13 Hz) power and increases in gamma-band power (40–70 Hz). Data were mapped to source-space using a beamformer. Across both groups, decreased alpha power and increased gamma power localized to early visual cortex. Data from the primary visual cortex were compared between groups. No differences were found in either alpha or gamma peak frequency or the magnitude of alpha power, p > 0.05. However, compared with controls, our visual snow syndrome cohort displayed significantly increased primary visual cortex gamma power, p = 0.035. This new electromagnetic finding concurs with previous functional MRI and PET findings, suggesting that in visual snow syndrome, the visual cortex is hyperexcitable. The coupling of alpha-phase to gamma amplitude within the primary visual cortex was also quantified. Compared with controls, the visual snow syndrome group had significantly reduced alpha–gamma phase–amplitude coupling, p < 0.05, indicating a potential excitation–inhibition imbalance in visual snow syndrome, as well as a potential disruption to top-down ‘noise-cancellation’ mechanisms. Overall, these results suggest that rhythmical brain activity in the primary visual cortex is both hyperexcitable and disorganized in visual snow syndrome, consistent with this being a condition of thalamocortical dysrhythmia.

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.

Spectral signature of attentional reorienting in the human brain

As we move in the environment, attention shifts to novel objects of interest based on either their sensory salience or behavioral value (reorienting). This study measures with magnetoencephalography (MEG) different properties (amplitude, onset-to-peak duration) of event-related desynchronization/synchronization (ERD/ERS) of oscillatory activity during a visuospatial attention task designed to separate activity related to reorienting vs. maintaining attention to the same location, controlling for target detection and response processes. The oscillatory activity was measured both in fMRI-defined regions of interest (ROIs) of the dorsal attention (DAN) and visual (VIS) networks, previously defined as task-relevant in the same subjects, or whole-brain in a pre-defined set of cortical ROIs encompassing the main brain networks. Reorienting attention (shift cues) as compared to maintaining attention (stay cues) produced a temporal sequence of ERD/ERS modulations at multiple frequencies in specific anatomical regions/networks. An early (∼330 ms), stronger, transient theta ERS occurred in task-relevant (DAN, VIS) and control networks (VAN, CON, FPN), possibly reflecting an alert/reset signal in response to the cue. A more sustained, behaviorally relevant, low-beta band ERD peaking ∼450 ms following shift cues (∼410 for stay cues) localized in frontal and parietal regions of the DAN. This modulation is consistent with a control signal re-routing information across visual hemifields. Contralateral vs. ipsilateral shift cues produced in occipital visual regions a stronger, sustained alpha ERD (peak ∼470 ms) and a longer, transient high beta/gamma ERS (peak ∼490 ms) related to preparatory visual modulations in advance of target occurrence. This is the first description of a cascade of oscillatory processes during attentional reorienting in specific anatomical regions and networks. Among these processes, a behaviorally relevant beta desynchronization in the FEF is likely associated with the control of attention shifts.

The Human Connectome Project: A retrospective

The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the "WU-Minn-Ox" HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The "HCP-style" neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium.