Heritability of "small-world" networks in the brain: a graph theoretical analysis of resting-state EEG functional connectivity

A hierarchical trait and state model for decoding dyadic social interactions

Traits are patterns of brain signals and behaviors that are stable over time but differ across individuals, whereas states are phasic patterns that vary over time, are influenced by the environment, yet oscillate around the traits. The quality of a social interaction depends on the traits and states of the interacting agents. However, it remains unclear how to decipher both traits and states from the same set of brain signals. To explore the hidden neural traits and states in relation to the behavioral ones during social interactions, we developed a pipeline to extract latent dimensions of the brain from electroencephalogram (EEG) data collected during a team flow task. Our pipeline involved two stages of dimensionality reduction: non-negative matrix factorization (NMF), followed by linear discriminant analysis (LDA). This pipeline resulted in an interpretable, seven-dimensional EEG latent space that revealed a trait to state (trait-state) hierarchical structure, with macro-segregation capturing neural traits and micro-segregation capturing neural states. Out of the seven latent dimensions, we found three that significantly contributed to variations across individuals and task states. Using representational similarity analysis, we mapped the EEG latent space to a skill-cognition space, establishing a connection between hidden neural signatures and social interaction behaviors. Our method demonstrates the feasibility of representing both traits and states within a single model that correlates with changes in social behavior.

Distinct Brain Connectivity Patterns in Sickle Cell Disease: A Biomarker for Chronic Pain Severity

Background: Central nervous system complications are common in sickle cell disease (SCD), and the defining associated biomarkers are becoming increasingly relevant for physicians in diagnostic and prognostic contexts. Recent studies have reported altered brain connectivity in pain processing, highlighting a new avenue for developing sensitive measures of SCD severity. Method: This cross-sectional study used graph theory concepts to analyze effective connectivity in individuals with SCD and healthy controls during rest and motor imagery tasks. The SCD group was further divided into two subgroups based on pain intensity (less pain or more pain) during the evaluation. Results: Individuals with SCD and chronic pain exhibited a distinct brain connectivity signature compared to healthy individuals and within pain sublevels. Conclusion: Chronic pain in SCD shows a unique brain connectivity pattern when compared to healthy subjects and across different pain levels. The results support the hypothesis that chronic pain condition is associated with decreased interhub connections and increased intrahub connections for specific brain rhythms. Furthermore, the small-world parameter can distinguish SCD individuals from controls and differentiate pain levels within SCD individuals, offering a promising biomarker for clinical assessment.

Small-world networks propensity in spontaneous speech signals of Alzheimer's disease: visibility graph analysis

Exploiting complex network methods to describe dynamical behavior based on speech time series can provide fundamental insights into the function of underlying dynamical processes in Alzheimer's disease (AD). This study scrutinizes the dynamic alterations in Alzheimer's speech through abstract concepts of small-world networks. The visibility graph (VG) of the time series of spontaneous speech is introduced as a quantitative method to differentiate between healthy individuals and those with Alzheimer's. The dynamic speech patterns across three AD and healthy subjects stages are analyzed by examining the small-world feature structure, characterized by a high clustering coefficient (C) and short average path length (L) in the VG. These characteristics are calculated based on degree K. The results demonstrate the practical utility of C and L in identifying the underlying pathological mechanisms of AD. Furthermore, all speech series exhibit small-world topology based on VG, with changes reflecting the brain system's pathology that impacts individuals' language skills.

Rapid dynamics of electrophysiological connectome states are heritable

Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, are a prominent feature of brain activity with broad functional implications. While infraslow (<0.1 Hz) connectome dynamics have been extensively studied with fMRI, rapid dynamics highly relevant for cognition are poorly understood. Here, we asked whether rapid electrophysiological connectome dynamics constitute subject-specific brain traits and to what extent they are under genetic influence. Using source-localized EEG connectomes during resting state (N = 928, 473 females), we quantified the heritability of multivariate (multistate) features describing temporal or spatial characteristics of connectome dynamics. States switched rapidly every ∼60-500 ms. Temporal features were heritable, particularly Fractional Occupancy (in theta, alpha, beta, and gamma bands) and Transition Probability (in theta, alpha, and gamma bands), representing the duration spent in each state and the frequency of state switches, respectively. Genetic effects explained a substantial proportion of the phenotypic variance of these features: Fractional Occupancy in beta (44.3%) and gamma (39.8%) bands and Transition Probability in theta (38.4%), alpha (63.3%), beta (22.6%), and gamma (40%) bands. However, we found no evidence for the heritability of dynamic spatial features, specifically states' Modularity and connectivity pattern. We conclude that genetic effects shape individuals' connectome dynamics at rapid timescales, specifically states' overall occurrence and sequencing.

Hierarchical Trait-State Model for Decoding Dyadic Social Interactions

Traits are patterns of brain signals and behaviors that are stable over time but differ across individuals, whereas states are phasic patterns that vary over time, are influenced by the environment, yet oscillate around the traits. The quality of a social interaction depends on the traits and states of the interacting agents. However, it remains unclear how to decipher both traits and states from the same set of brain signals. To explore the hidden neural traits and states in relation to the behavioral ones during social interactions, we developed a pipeline to extract latent dimensions of the brain from electroencephalogram (EEG) data collected during a team flow task. Our pipeline involved two stages of dimensionality reduction: first, non-negative matrix factorization (NMF), followed by linear discriminant analysis (LDA). This pipeline resulted in an interpretable, seven-dimensional EEG latent space that revealed a trait-state hierarchical structure, with macro-segregation capturing neural traits and micro-segregation capturing neural states. Out of the seven latent dimensions, we found that three that significantly contributed to variations across individuals and task states. Using representational similarity analysis, we mapped the EEG latent space to a skill-cognition space, establishing a connection between hidden neural signatures and social interaction behaviors. Our method demonstrates the feasibility of representing both traits and states within a single model that correlates with changes in social behavior.

Revisiting the functional monitoring of brain development in premature neonates. A new direction in clinical care and research

The first 1000 days of life are of paramount importance for neonatal development. Premature newborns are exposed early to the external environment, modifying the fetal exposome and leading to overexposure in some sensory domains and deprivation in others. The resulting neurodevelopmental effects may persist throughout the individual's lifetime. Several neonatal neuromonitoring techniques can be used to investigate neural mechanisms in early postnatal development. EEG is the most widely used, as it is easy to perform, even at the patient's bedside. It is not expensive and provides information with a high temporal resolution and relatively good spatial resolution when performed in high-density mode. Functional near-infrared spectroscopy (fNIRS), a technique for monitoring vascular network dynamics, can also be used at the patient's bedside. It is not expensive and has a good spatial resolution at the cortical surface. These two techniques can be combined for simultaneous monitoring of the neuronal and vascular networks in premature newborns, providing insight into neurodevelopment before term. However, the extent to which more general conclusions about fetal development can be drawn from findings for premature neonates remains unclear due to considerable differences in environmental and medical situations. Fetal MEG (fMEG, as an alternative to EEG for preterm infants) and fMRI (as an alternative to fNIRS for preterm infants) can also be used to investigate fetal neurodevelopment on a trimester-specific basis. These techniques should be used for validation purposes as they are the only tools available for evaluating neuronal dysfunction in the fetus at the time of the gene-environment interactions influencing transient neuronal progenitor populations in brain structures. But what do these techniques tell us about early neurodevelopment? We address this question here, from two points of view. We first discuss spontaneous neural activity and its electromagnetic and hemodynamic correlates. We then explore the effects of stimulating the immature developing brain with information from exogenous sources, reviewing the available evidence concerning the characteristics of electromagnetic and hemodynamic responses. Once the characteristics of the correlates of neural dynamics have been determined, it will be essential to evaluate their possible modulation in the context of disease and in at-risk populations. Evidence can be collected with various neuroimaging techniques targeting both spontaneous and exogenously driven neural activity. A multimodal approach combining the neuromonitoring of different functional compartments (neuronal and vascular) is required to improve our understanding of the normal functioning and dysfunction of the brain and to identify neurobiomarkers for predicting the neurodevelopmental outcome of premature neonate and fetus. Such an approach would provide a framework for exploring early neurodevelopment, paving the way for the development of tools for earlier diagnosis in these vulnerable populations, thereby facilitating preventive, rescue and reparative neurotherapeutic interventions.

Similarity of brain activity patterns during learning and subsequent resting state predicts memory consolidation

Spontaneous reactivation of brain activity from learning to a subsequent off-line period has been implicated as a neural mechanism underlying memory consolidation. However, similarities in brain activity may also emerge as a result of individual, trait-like characteristics. Here, we introduced a novel approach for analyzing continuous electroencephalography (EEG) data to investigate learning-induced changes as well as trait-like characteristics in brain activity underlying memory consolidation. Thirty-one healthy young adults performed a learning task, and their performance was retested after a short (∼1 h) delay. Consolidation of two distinct types of information (serial-order and probability) embedded in the task were tested to reveal similarities in functional networks that uniquely predict the changes in the respective memory performance. EEG was recorded during learning and pre- and post-learning rest periods. To investigate brain activity associated with consolidation, we quantified similarities in EEG functional connectivity between learning and pre-learning rest (baseline similarity) and learning and post-learning rest (post-learning similarity). While comparable patterns of these two could indicate trait-like similarities, changes from baseline to post-learning similarity could indicate learning-induced changes, possibly spontaneous reactivation. Higher learning-induced changes in alpha frequency connectivity (8.5-9.5 Hz) were associated with better consolidation of serial-order information, particularly for long-range connections across central and parietal sites. The consolidation of probability information was associated with learning-induced changes in delta frequency connectivity (2.5-3 Hz) specifically for more local, short-range connections. Furthermore, there was a substantial overlap between the baseline and post-learning similarities and their associations with consolidation performance, suggesting robust (trait-like) differences in functional connectivity networks underlying memory processes.

Genetic fingerprinting with heritable phenotypes of the resting-state brain network topology

Cognitive, behavioral, and disease traits are influenced by both genetic and environmental factors. Individual differences in these traits have been associated with graph theoretical properties of resting-state networks, indicating that variations in connectome topology may be driven by genetics. In this study, we establish the heritability of global and local graph properties of resting-state networks derived from functional MRI (fMRI) and magnetoencephalography (MEG) using a large sample of twins and non-twin siblings from the Human Connectome Project. We examine the heritability of MEG in the source space, providing a more accurate estimate of genetic influences on electrophysiological networks. Our findings show that most graph measures are more heritable for MEG compared to fMRI and the heritability for MEG is greater for amplitude compared to phase synchrony in the delta, high beta, and gamma frequency bands. This suggests that the fast neuronal dynamics in MEG offer unique insights into the genetic basis of brain network organization. Furthermore, we demonstrate that brain network features can serve as genetic fingerprints to accurately identify pairs of identical twins within a cohort. These results highlight novel opportunities to relate individual connectome signatures to genetic mechanisms underlying brain function.

Functional brain network properties correlate with individual risk tolerance in young adults

Background

Individuals differ substantially in their degree of acceptance of risks, referred to as risk tolerance, and these differences are associated with real-life outcomes such as risky health-related behaviors. While previous studies have identified brain regions that are functionally associated with individual risk tolerance, little is known about the relationship between individual risk tolerance and whole-brain functional organization.

Methods

This study investigated whether the topological properties of individual functional brain networks in healthy young adults (n = 67) are associated with individual risk tolerance using resting-state fMRI data in conjunction with a graph theoretical analysis approach.

Results

The analysis revealed that individual risk tolerance was positively associated with global topological properties, including the normalized clustering coefficient and small-worldness, which represent the degree of information segregation and the balance between information segregation and integration in a network, respectively. Additionally, individuals with higher risk tolerance exhibited greater centrality in the ventromedial prefrontal cortex (vmPFC), which is associated with the subjective value of the available options.

Conclusion

These results extend our understanding of how individual differences in risk tolerance, especially in young adults, are associated with functional brain organization, particularly regarding the balance between segregation and integration in functional networks, and highlight the important role of the connections between the vmPFC and the rest of the brain in the functional networks in relation to risk tolerance.

Investigating neuromodulatory effect of transauricular vagus nerve stimulation on resting-state electroencephalography

Purpose: The purpose of this study was to investigate the neuromodulatory effects of transauricular vagus nerve stimulation (taVNS) and determine optimal taVNS duration to induce the meaningful neuromodulatroty effects using resting-state electroencephalography (EEG). Method: Fifteen participants participated in this study and taVNS was applied to the cymba conchae for a duration of 40 min. Resting-state EEG was measured before and during taVNS application. EEG power spectral density (PSD) and brain network indices (clustering coefficient and path length) were calculated across five frequency bands (delta, theta, alpha, beta and gamma), respectively, to assess the neuromodulatory effect of taVNS. Moreover, we divided the whole brain region into the five regions of interest (frontal, central, left temporal, right temporal, and occipital) to confirm the neuromodulation effect on each specific brain region. Result: Our results demonstrated a significant increase in EEG frequency powers across all five frequency bands during taVNS. Furthermore, significant changes in network indices were observed in the theta and gamma bands compared to the pre-taVNS measurements. These effects were particularly pronounced after approximately 10 min of stimulation, with a more dominant impact observed after approximately 20-30 min of taVNS application. Conclusion: The findings of this study indicate that taVNS can effectively modulate the brain activity, thereby exerting significant effects on brain characteristics. Moreover, taVNS duration of approximately 20-30 min was considered appropriate for inducing a stable and efficient neuromodulatory effects. Consequently, these findings have the potential to contribute to research aimed at enhancing cognitive and motor functions through the modulation of EEG using taVNS.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13534-024-00361-8.

Topological state-space estimation of functional human brain networks

We introduce an innovative, data-driven topological data analysis (TDA) technique for estimating the state spaces of dynamically changing functional human brain networks at rest. Our method utilizes the Wasserstein distance to measure topological differences, enabling the clustering of brain networks into distinct topological states. This technique outperforms the commonly used k-means clustering in identifying brain network state spaces by effectively incorporating the temporal dynamics of the data without the need for explicit model specification. We further investigate the genetic underpinnings of these topological features using a twin study design, examining the heritability of such state changes. Our findings suggest that the topology of brain networks, particularly in their dynamic state changes, may hold significant hidden genetic information.

Neurogenetics of Brain Connectivity: Current Approaches to the Study (Review)

Owing to the advances of neuroimaging techniques, a number of functional brain networks associated both with specific functions and the state of relative inactivity has been distinguished. A sufficient bulk of information has been accumulated on changes in connectivity (links between brain regions) in psychopathologies, for example, depression, schizophrenia, autism. Their genetic markers are being actively investigated using a candidate-gene approach or a genome-wide association study. At the same time, there is not much data considering connectivity as an intermediate link in the genotype-pathology chain, although it seems to be a reliable endophenotype, since it demonstrates a high stability and high heritability. In the present review, we consider the results of investigations devoted to the search for biomarkers, molecular and genetic associations of functional, partially anatomical, and effective connectivity. The main approaches to the evaluation of connectivity neurogenetics have been described, as well as specific genetic variants, for which the association with brain connectivity in psychiatric pathologies has been detected.

Rapid dynamics of electrophysiological connectome states are heritable

Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, are a prominent feature of brain activity with broad functional implications. While infra-slow (<0.1Hz) connectome dynamics have been extensively studied with fMRI, rapid dynamics highly relevant for cognition are poorly understood. Here, we asked whether rapid electrophysiological connectome dynamics constitute subject-specific brain traits and to what extent they are under genetic influence. Using source-localized EEG connectomes during resting-state (N=928, 473 females), we quantified heritability of multivariate (multi-state) features describing temporal or spatial characteristics of connectome dynamics. States switched rapidly every ~60-500ms. Temporal features were heritable, particularly, Fractional Occupancy (in theta, alpha, beta, and gamma bands) and Transition Probability (in theta, alpha, and gamma bands), representing the duration spent in each state and the frequency of state switches, respectively. Genetic effects explained a substantial proportion of phenotypic variance of these features: Fractional Occupancy in beta (44.3%) and gamma (39.8%) bands and Transition Probability in theta (38.4%), alpha (63.3%), beta (22.6%), and gamma (40%) bands. However, we found no evidence for heritability of spatial features, specifically states' Modularity and connectivity pattern. We conclude that genetic effects strongly shape individuals' connectome dynamics at rapid timescales, specifically states' overall occurrence and sequencing.

Disentanglement of Resting State Brain Networks for Localizing Epileptogenic Zone in Focal Epilepsy

The objective of this study is to extract pathological brain networks from interictal period of E/MEG recordings to localize epileptic foci for presurgical evaluation. We proposed here a resting state E/MEG analysis framework, to disentangle brain functional networks represented by neural oscillations. By using an Embedded Hidden Markov Model, we constructed a state space for resting state recordings consisting of brain states with different spatiotemporal patterns. Functional connectivity analysis along with graph theory was applied on the extracted brain states to quantify the network features of the extracted brain states, based on which the source location of pathological states is determined. The method is evaluated by computer simulations and our simulation results revealed the proposed framework can extract brain states with high accuracy regarding both spatial and temporal profiles. We further evaluated the framework as compared with intracranial EEG defined seizure onset zone in 10 patients with drug-resistant focal epilepsy who underwent MEG recordings and were seizure free after surgical resection. The real patient data analysis showed very good localization results using the extracted pathological brain states in 6/10 patients, with localization error of about 15 mm as compared to the seizure onset zone. We show that the pathological brain networks can be disentangled from the resting-state electromagnetic recording and could be identified based on the connectivity features. The framework can serve as a useful tool in extracting brain functional networks from noninvasive resting state electromagnetic recordings, and promises to offer an alternative to aid presurgical evaluation guiding intracranial EEG electrodes implantation.

Effect of network size on comparing different stock networks

We analyzed complex networks generated by the threshold method in the Korean and Indian stock markets during the non-crisis period of 2004 and the crisis period of 2008, while varying the size of the system. To create the stock network, we randomly selected N stock indices from the market and constructed the network based on cross-correlation among the time series of stock prices. We computed the average shortest path length L and average clustering coefficient C for several ensembles of generated stock networks and found that both metrics are influenced by network size. Since L and C are affected by network size N, a direct comparison of graph measures between stock networks with different numbers of nodes could lead to erroneous conclusions. However, we observed that the dependency of network measures on N is significantly reduced when comparing larger networks with normalized shortest path lengths. Additionally, we discovered that the effect of network size on network measures during the crisis period is almost negligible compared to the non-crisis periods.

White matter-based brain network topological properties associated with individual impulsivity

Delay discounting (DD), a parameter derived from the intertemporal choice task, is a representative behavioral indicator of choice impulsivity. Previous research reported not only an association between DD and impulsive control disorders and negative health outcomes but also the neural correlates of DD. However, to date, there are few studies investigating the structural brain network topologies associated with individual differences in DD and whether self-reported measures (BIS-11) of impulsivity associated with DD share the same or distinct neural mechanisms is still unclear. To address these issues, here, we combined graph theoretical analysis with diffusion tensor imaging to investigate the associations between DD and the topological properties of the structural connectivity network and BIS-11 scores. Results revealed that people with a steep DD (greater impatience) had decreased small-worldness (a shift toward weaker small-worldnization) and increased degree centrality in the medial superior prefrontal cortex, associated with subjective value in the task. Though DD was associated with the BIS-11 motor impulsiveness subscale, this subscale was linked to topological properties different from DD; that is, high motor impulsiveness was associated with decreased local efficiency (less segregation) and decreased degree centrality in the precentral gyrus, involved in motor control. These findings provide insights into the systemic brain characteristics underlying individual differences in impulsivity and potential neural markers which could predict susceptibility to impulsive behaviors.

Adult lifespan trajectories of neuromagnetic signals and interrelations with cortical thickness

Oscillatory power and phase synchronization map neuronal dynamics and are commonly studied to differentiate the healthy and diseased brain. Yet, little is known about the course and spatial variability of these features from early adulthood into old age. Leveraging magnetoencephalography (MEG) resting-state data in a cross-sectional adult sample (n = 350), we probed lifespan differences (18-88 years) in connectivity and power and interaction effects with sex. Building upon recent attempts to link brain structure and function, we tested the spatial correspondence between age effects on cortical thickness and those on functional networks. We further probed a direct structure-function relationship at the level of the study sample. We found MEG frequency-specific patterns with age and divergence between sexes in low frequencies. Connectivity and power exhibited distinct linear trajectories or turning points at midlife that might reflect different physiological processes. In the delta and beta bands, these age effects corresponded to those on cortical thickness, pointing to co-variation between the modalities across the lifespan. Structure-function coupling was frequency-dependent and observed in unimodal or multimodal regions. Altogether, we provide a comprehensive overview of the topographic functional profile of adulthood that can form a basis for neurocognitive and clinical investigations. This study further sheds new light on how the brain's structural architecture relates to fast oscillatory activity.

Mnemonic-trained brain tuning to a regular odd-even pattern subserves digit memory in children

It is said that our species use mnemonics - that "magic of memorization" - to engrave an enormous amount of information in the brain. Yet, it is unclear how mnemonics affect memory and what the neural underpinnings are. In this electroencephalography study, we examined the hypotheses whether mnemonic training improved processing-efficiency and/or altered encoding-pattern to support memory enhancement. By 22-day training of a digit-image mnemonic (a custom memory technique used by world-class mnemonists), a group of children showed increased short-term memory after training, but with limited gain generalization. This training resulted in regular odd-even neural patterns (i.e., enhanced P200 and theta power during the encoding of digits at even- versus odd- positions in a sequence). Critically, the P200 and theta power effects predicted the training-induced memory improvement. These findings provide evidence of how mnemonics alter encoding pattern, as reflected in functional brain organization, to support memory enhancement.

Neuromodulatory feasibility of a current limiter-based tDCS device: a resting-state electroencephalography study

Recently, we introduced a current limiter-based novel transcranial direct-current stimulation (tDCS) device that does not generate significant tDCS-induced electrical artifacts, thereby facilitating simultaneous electroencephalography (EEG) measurement during tDCS application. In this study, we investigated the neuromodulatory effect of the tDCS device using resting-state EEG data measured during tDCS application in terms of EEG power spectral densities (PSD) and brain network indices (clustering coefficient and path length). Resting-state EEG data were recorded from 10 healthy subjects during both eyes-open (EO) and eyes-closed (EC) states for each of five different conditions (baseline, sham, post-sham, tDCS, and post-tDCS). In the tDCS condition, tDCS was applied for 12 min with a current intensity of 1.5 mA, whereas tDCS was applied only for the first 30 s in the sham condition. EEG PSD and brain network indices were computed for the alpha frequency band most closely associated with resting-state EEG. Both alpha PSD and network indices were found to significantly increase during and after tDCS application compared to those of the baseline condition in the EO state, but not in the EC state owing to the ceiling effect. Our results demonstrate the neuromodulatory effect of the tDCS device that does not generate significant tDCS-induced electrical artifacts, thereby allowing simultaneous measurement of electrical brain activity. We expect our novel tDCS device to be practically useful in exploring the impact of tDCS on neuromodulation more precisely using ongoing EEG data simultaneously measured during tDCS application.

Functional connectivity analysis on electroencephalography signals reveals potential biomarkers for treatment response in major depression

BACKGROUND

The treatment efficacy varies across individual patients with major depressive disorder (MDD). It lacks robust electroencephalography (EEG) markers for an antidepressant-responsive phenotype.

METHOD

This is an observational study enrolling 28 patients with MDD and 33 healthy controls (mean age of 40.7 years, and 71.4% were women). Patients underwent EEG exams at baseline (week0) and week1, while controls' EEG recordings were acquired only at week0. A resting eye-closing EEG segment was analyzed for functional connectivity (FC). Four parameters were used in FC analysis: (1) node strength (NS), (2) global efficiency (GE), (3) clustering coefficient (CC), and (4) betweenness centrality (BC).

RESULTS

We found that controls had higher values in delta wave in the indices of NS, GE, BC, and CC than MDD patients at baseline. After treatment with antidepressants, patients' FC indices improved significantly, including GE, mean CC, and mean NS in the delta wave. The FC in the alpha and beta bands of the responders were higher than those of the non-responders.

CONCLUSIONS

The FC of the MDD patients at baseline without treatment was worse than that of controls. After treatment, the FC improved and was close to the values of controls. Responders showed better FC in the high-frequency bands than non-responders, and this feature exists in both pre-treatment and post-treatment EEG.

Altered brain functional networks after Quchi (LI 11) acupuncture: An EEG analysis

BACKGROUND

As a unique traditional Chinese medicine therapy, the central effect of acupuncture has received increasing attention. Functional brain networks can provide connectivity information among brain regions.

OBJECTIVE

The study goal is to explore the regulatory effect of acupuncture on the brain functional network.

METHODS

This paper analyzes the electroencephalography (EEG)-based power spectrum and brain functional network elicited by acupuncture at Quchi (LI 11).

RESULTS

The power spectrum results showed that acupuncture at LI 11 decreased the energy in the alpha frequency, mainly in the central region, left parietal lobe, left temporal lobe and left frontal lobe. Moreover, functional brain networks converted from the magnitude-squared coherence matrix in the alpha band are reconstructed. The results show that acupuncture did not alter the basic properties of the brain functional connection network. During acupuncture, the average node degree, average clustering coefficient, and small-world property of the brain functional connection network decreased after acupuncture compared with that before it. However, the average characteristic path length increased after acupuncture compared with before.

CONCLUSION

Acupuncture at LI 11 altered the brain's electrical activity. In the meantime, this acupuncture reduced the network's internal connectivity and information transfer efficiency.

TOPOLOGICAL LEARNING FOR BRAIN NETWORKS

This paper proposes a novel topological learning framework that integrates networks of different sizes and topology through persistent homology. Such challenging task is made possible through the introduction of a computationally efficient topological loss. The use of the proposed loss bypasses the intrinsic computational bottleneck associated with matching networks. We validate the method in extensive statistical simulations to assess its effectiveness when discriminating networks with different topology. The method is further demonstrated in a twin brain imaging study where we determine if brain networks are genetically heritable. The challenge here is due to the difficulty of overlaying the topologically different functional brain networks obtained from resting-state functional MRI onto the template structural brain network obtained through diffusion MRI.

Toward Best Practices for Imaging Transcriptomics of the Human Brain

Modern brainwide transcriptional atlases provide unprecedented opportunities for investigating the molecular correlates of brain organization, as quantified using noninvasive neuroimaging. However, integrating neuroimaging data with transcriptomic measures is not straightforward, and careful consideration is required to make valid inferences. In this article, we review recent work exploring how various methodological choices affect 3 main phases of imaging transcriptomic analyses, including 1) processing of transcriptional atlas data; 2) relating transcriptional measures to independently derived neuroimaging phenotypes; and 3) evaluating the functional implications of identified associations through gene enrichment analyses. Our aim is to facilitate the development of standardized and reproducible approaches for this rapidly growing field. We identify sources of methodological variability, key choices that can affect findings, and considerations for mitigating false positive and/or spurious results. Finally, we provide an overview of freely available open-source toolboxes implementing current best-practice procedures across all 3 analysis phases.

Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification Results

Most studies on EEG-based biometry recognition report results based on signal databases, with a limited number of recorded EEG sessions using the same single EEG recording for both training and testing a proposed model. However, the EEG signal is highly vulnerable to interferences, electrode placement, and temporary conditions, which can lead to overestimated assessments of the considered methods. Our study examined how different numbers of distinct recording sessions used as training sessions would affect EEG-based verification. We analyzed the original data from 29 participants with 20 distinct recorded sessions each, as well as 23 additional impostors with only one session each. We applied raw coefficients of power spectral density estimate, and the coefficients of power spectral density estimate converted to the decibel scale, as the input to a shallow neural network. Our study showed that the variance introduced by multiple recording sessions affects sensitivity. We also showed that increasing the number of sessions above eight did not improve the results under our conditions. For 15 training sessions, the achieved accuracy was 96.7 ± 4.2%, and for eight training sessions and 12 test sessions, it was 94.9 ± 4.6%. For 15 training sessions, the rate of successful impostor attacks over all attack attempts was 3.1 ± 2.2%, but this number was not significantly different from using six recording sessions for training. Our findings indicate the need to include data from multiple recording sessions in EEG-based recognition for training, and that increasing the number of test sessions did not significantly affect the obtained results. Although the presented results are for the resting-state, they may serve as a baseline for other paradigms.

Aberrant degree centrality of functional brain networks in subclinical depression and major depressive disorder

BACKGROUND

As one of the most common diseases, major depressive disorder (MDD) has a significant adverse impact on the li of patients. As a mild form of depression, subclinical depression (SD) serves as an indicator of progression to MDD. This study analyzed the degree centrality (DC) for MDD, SD, and healthy control (HC) groups and identified the brain regions with DC alterations.

METHODS

The experimental data were composed of resting-state functional magnetic resonance imaging (rs-fMRI) from 40 HCs, 40 MDD subjects, and 34 SD subjects. After conducting a one-way analysis of variance, two-sample t-tests were used for further analysis to explore the brain regions with changed DC. Receiver operating characteristic (ROC) curve analysis of single index and composite index features was performed to analyze the distinguishable ability of important brain regions.

RESULTS

For the comparison of MDD vs. HC, increased DC was found in the right superior temporal gyrus (STG) and right inferior parietal lobule (IPL) in the MDD group. For SD vs. HC, the SD group showed a higher DC in the right STG and the right middle temporal gyrus (MTG), and a smaller DC in the left IPL. For MDD vs. SD, increased DC in the right middle frontal gyrus (MFG), right IPL, and left IPL, and decreased DC in the right STG and right MTG was found in the MDD group. With an area under the ROC (AUC) of 0.779, the right STG could differentiate MDD patients from HCs and, with an AUC of 0.704, the right MTG could differentiate MDD patients from SD patients. The three composite indexes had good discriminative ability in each pairwise comparison, with AUCs of 0.803, 0.751, and 0.814 for MDD vs. HC, SD vs. HC, and MDD vs. SD, respectively.

CONCLUSION

Altered DC in the STG, MTG, IPL, and MFG were identified in depression groups. The DC values of these altered regions and their combinations presented good discriminative ability between HC, SD, and MDD. These findings could help to find effective biomarkers and reveal the potential mechanisms of depression.

An Explainable Statistical Method for Seizure Prediction Using Brain Functional Connectivity from EEG

Background. Epilepsy is a group of chronic neurological disorders characterized by recurrent and abrupt seizures. The accurate prediction of seizures can reduce the burdens of this disorder. Now, existing studies use brain network features to classify patients’ preictal or interictal states, enabling seizure prediction. However, most predicting methods are based on deep learning techniques, which have weak interpretability and high computational complexity. To address these issues, in this study, we proposed a novel two-stage statistical method that is interpretable and easy to compute. Methods. We used two datasets to evaluate the performance of the proposed method, including the well-known public dataset CHB-MIT. In the first stage, we estimated the dynamic brain functional connectivity network for each epoch. Then, in the second stage, we used the derived network predictor for seizure prediction. Results. We illustrated the results of our method in seizure prediction in two datasets separately. For the FH-PKU dataset, our approach achieved an AUC value of 0.963, a prediction sensitivity of 93.1%, and a false discovery rate of 7.7%. For the CHB-MIT dataset, our approach achieved an AUC value of 0.940, a prediction sensitivity of 93.0%, and a false discovery rate of 11.1%, outperforming existing state-of-the-art methods. Significance. This study proposed an explainable statistical method, which can estimate the brain network using the scalp EEG method and use the net-work predictor to predict epileptic seizures. Availability and Implementation. R Source code is available at https://github.com/HaoChen1994/Seizure-Prediction.

Who's driving? The default mode network in healthy elderly individuals at risk of cognitive decline

Introduction

Age is the main risk factor for the development of neurocognitive disorders, with Alzheimer's disease being the most common. Its physiopathological features may develop decades before the onset of clinical symptoms. Quantitative electroencephalography (qEEG) is a promising and cost-effective tool for the prediction of cognitive decline in healthy older individuals that exhibit an excess of theta activity. The aim of the present study was to evaluate the feasibility of brain connectivity variable resolution electromagnetic tomography (BC-VARETA), a novel source localization algorithm, as a potential tool to assess brain connectivity with 19-channel recordings, which are common in clinical practice.

Methods

We explored differences in terms of functional connectivity among the nodes of the default mode network between two groups of healthy older participants, one of which exhibited an EEG marker of risk for cognitive decline.

Results

The risk group exhibited increased levels of delta, theta, and beta functional connectivity among nodes of the default mode network, as well as reversed directionality patterns of connectivity among nodes in every frequency band when compared to the control group.

Discussion

We propose that an ongoing pathological process may be underway in healthy elderly individuals with excess theta activity in their EEGs, which is further evidenced by changes in their connectivity patterns. BC-VARETA implemented on 19-channels EEG recordings appears to be a promising tool to detect dysfunctions at the connectivity level in clinical settings.

Impact of EEG Frequency Bands and Data Separation on the Performance of Person Verification Employing Neural Networks

The paper is devoted to the study of EEG-based people verification. Analyzed solutions employed shallow artificial neural networks using spectral EEG features as input representation. We investigated the impact of the features derived from different frequency bands and their combination on verification results. Moreover, we studied the influence of a number of hidden neurons in a neural network. The datasets used in the analysis consisted of signals recorded during resting state from 29 healthy adult participants performed on different days, 20 EEG sessions for each of the participants. We presented two different scenarios of training and testing processes. In the first scenario, we used different parts of each recording session to create the training and testing datasets, and in the second one, training and testing datasets originated from different recording sessions. Among single frequency bands, the best outcomes were obtained for the beta frequency band (mean accuracy of 91 and 89% for the first and second scenarios, respectively). Adding the spectral features from more frequency bands to the beta band features improved results (95.7 and 93.1%). The findings showed that there is not enough evidence that the results are different between networks using different numbers of hidden neurons. Additionally, we included results for the attack of 23 external impostors whose recordings were not used earlier in training or testing the neural network in both scenarios. Another significant finding of our study shows worse sensitivity results in the second scenario. This outcome indicates that most of the studies presenting verification or identification results based on the first scenario (dominating in the current literature) are overestimated when it comes to practical applications.

Abnormal Brain Topological Structure of Mild Depression During Visual Search Processing Based on EEG Signals

Studies have shown that attention bias can affect behavioral indicators in patients with depression, but it is still unclear how this bias affects the brain network topology of patients with mild depression (MD). Therefore, a novel functional brain network analysis and hierarchical clustering methods were used to explore the abnormal brain topology of MD patients based on EEG signals during the visual search paradigm. The behavior results showed that the reaction time of MD group was significantly higher than that of normal group. The results of functional brain network indicated significant differences in functional connections between the two groups, the amount of inter-hemispheric long-distance connections are much larger than intra-hemispheric short-distance connections. Patients with MD showed significantly lower local efficiency and clustering coefficient, destroyed community structure of frontal lobe and parietal-occipital lobe, frontal asymmetry, especially in beta band. In addition, the average value of long-distance connections between left frontal and right parietal-occipital lobes presented significant correlation with depressive symptoms. Our results suggested that MD patients achieved long-distance connections between the frontal and parietal-occipital regions by sacrificing the connections within the regions, which might provide new insights into the abnormal cognitive processing mechanism of depression.

Intermittent brain network reconfigurations and the resistance to social media influence

Since its development, social media has grown as a source of information and has a significant impact on opinion formation. Individuals interact with others and content via social media platforms in a variety of ways, but it remains unclear how decision-making and associated neural processes are impacted by the online sharing of informational content, from factual to fabricated. Here, we use EEG to estimate dynamic reconfigurations of brain networks and probe the neural changes underlying opinion change (or formation) within individuals interacting with a simulated social media platform. Our findings indicate that the individuals who changed their opinions are characterized by less frequent network reconfigurations while those who did not change their opinions tend to have more flexible brain networks with frequent reconfigurations. The nature of these frequent network configurations suggests a fundamentally different thought process between intervals in which individuals are easily influenced by social media and those in which they are not. We also show that these reconfigurations are distinct to the brain dynamics during an in-person discussion with strangers on the same content. Together, these findings suggest that brain network reconfigurations may not only be diagnostic to the informational context but also the underlying opinion formation.

Resting-state networks of believers and non-believers: An EEG microstate study

Atheism and agnosticism are becoming increasingly popular, yet the neural processes underpinning individual differences in religious belief and non-belief remain poorly understood. In the current study, we examined differences between Believers and Non-Believers with regard to fundamental neural resting networks using EEG microstate analysis. Results demonstrated that Non-Believers show increased contribution from a resting-state network associated with deliberative or analytic processing (Microstate D), and Believers show increased contribution from a network associated with intuitive or automatic processing (Microstate C). Further, analysis of resting-state network communication suggested that Non-Believers may process visual information in a more deliberative or top-down manner, and Believers may process visual information in a more intuitive or bottom-up manner. These results support dual process explanations of individual differences in religious belief and add to the representation of non-belief as more than merely a lack of belief.

Where the genome meets the connectome: Understanding how genes shape human brain connectivity

The integration of modern neuroimaging methods with genetically informative designs and data can shed light on the molecular mechanisms underlying the structural and functional organization of the human connectome. Here, we review studies that have investigated the genetic basis of human brain network structure and function through three complementary frameworks: (1) the quantification of phenotypic heritability through classical twin designs; (2) the identification of specific DNA variants linked to phenotypic variation through association and related studies; and (3) the analysis of correlations between spatial variations in imaging phenotypes and gene expression profiles through the integration of neuroimaging and transcriptional atlas data. We consider the basic foundations, strengths, limitations, and discoveries associated with each approach. We present converging evidence to indicate that anatomical connectivity is under stronger genetic influence than functional connectivity and that genetic influences are not uniformly distributed throughout the brain, with phenotypic variation in certain regions and connections being under stronger genetic control than others. We also consider how the combination of imaging and genetics can be used to understand the ways in which genes may drive brain dysfunction in different clinical disorders.

Topological Learning and Its Application to Multimodal Brain Network Integration

A long-standing challenge in multimodal brain network analyses is to integrate topologically different brain networks obtained from diffusion and functional MRI in a coherent statistical framework. Existing multimodal frameworks will inevitably destroy the topological difference of the networks. In this paper, we propose a novel topological learning framework that integrates networks of different topology through persistent homology. Such challenging task is made possible through the introduction of a new topological loss that bypasses intrinsic computational bottlenecks and thus enables us to perform various topological computations and optimizations with ease. We validate the topological loss in extensive statistical simulations with ground truth to assess its effectiveness of discriminating networks. Among many possible applications, we demonstrate the versatility of topological loss in the twin imaging study where we determine the extend to which brain networks are genetically heritable.

Resting-state EEG for the diagnosis of idiopathic epilepsy and psychogenic nonepileptic seizures: A systematic review

Quantitative markers extracted from resting-state electroencephalogram (EEG) reveal subtle neurophysiological dynamics which may provide useful information to support the diagnosis of seizure disorders. We performed a systematic review to summarize evidence on markers extracted from interictal, visually normal resting-state EEG in adults with idiopathic epilepsy or psychogenic nonepileptic seizures (PNES). Studies were selected from 5 databases and evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2. 26 studies were identified, 19 focusing on people with epilepsy, 6 on people with PNES, and one comparing epilepsy and PNES directly. Results suggest that oscillations along the theta frequency (4-8 Hz) may have a relevant role in idiopathic epilepsy, whereas in PNES there was no evident trend. However, studies were subject to a number of methodological limitations potentially introducing bias. There was often a lack of appropriate reporting and high heterogeneity. Results were not appropriate for quantitative synthesis. We identify and discuss the challenges that must be addressed for valid resting-state EEG markers of epilepsy and PNES to be developed.

Uncovering Statistical Links Between Gene Expression and Structural Connectivity Patterns in the Mouse Brain

Finding links between genes and structural connectivity is of the utmost importance for unravelling the underlying mechanism of the brain connectome. In this study we identify links between the gene expression and the axonal projection density in the mouse brain, by applying a modified version of the Linked ICA method to volumetric data from the Allen Institute for Brain Science for identifying independent sources of information that link both modalities at the voxel level. We performed separate analyses on sets of projections from the visual cortex, the caudoputamen and the midbrain reticular nucleus, and we determined those brain areas, injections and genes that were most involved in independent components that link both gene expression and projection density data, while we validated their biological context through enrichment analysis. We identified representative and literature-validated cortico-midbrain and cortico-striatal projections, whose gene subsets were enriched with annotations for neuronal and synaptic function and related developmental and metabolic processes. The results were highly reproducible when including all available projections, as well as consistent with factorisations obtained using the Dictionary Learning and Sparse Coding technique. Hence, Linked ICA yielded reproducible independent components that were preserved under increasing data variance. Taken together, we have developed and validated a novel paradigm for linking gene expression and structural projection patterns in the mouse mesoconnectome, which can power future studies aiming to relate genes to brain function.

Neural and Genetic Bases for Human Ability Traits

The judgement of human ability is ubiquitous, from school admissions to job performance reviews. The exact make-up of ability traits, however, is often narrowly defined and lacks a comprehensive basis. We attempt to simplify the spectrum of human ability, similar to how five personality traits are widely believed to describe most personalities. Finding such a basis for human ability would be invaluable since neuropsychiatric disease diagnoses and symptom severity are commonly related to such differences in performance. Here, we identified four underlying ability traits within the National Institutes of Health Toolbox normative data (n = 1, 369): (1) Motor-endurance, (2) Emotional processing, (3) Executive and cognitive function, and (4) Social interaction. We used the Human Connectome Project young adult dataset (n = 778) to show that Motor-endurance and Executive and cognitive function were reliably associated with specific brain functional networks (r ² = 0.305 ± 0.021), and the biological nature of these ability traits was also shown by calculating their heritability (31 and 49%, respectively) from twin data.

Resting-state EEG Dynamics Reveals Differences in Network Organization and its Fluctuation between Frequency Bands

Functional connectivity in EEG resting-state is not stable but fluctuates considerably. The aim of this study was to investigate how efficient information flows through a network, i.e. how resting-state EEG networks are organized and whether this organization it also subject to fluctuations. Differences of the network organization (small-worldness), degree of clustered connectivity, and path length as an indicator of how information is integrated into the network across time was compared between theta, alpha and beta bands. We show robust differences in network organization (small-worldness) between frequency bands. Fluctuations in network organization were larger in the theta, compared to the alpha and beta frequency. Variation in network organization and not the frequency of fluctuations differs between frequency bands. Furthermore, the degree of clustered connectivity and its modulation across time is the same across frequency bands, but the path length revealed the same modulatory pattern as the small-world metric. It is therefore the interplay of local processing efficiency and global information processing efficiency in the brain that fluctuates in a frequency-specific way. Properties of how information can be integrated is subject to fluctuations in a frequency-specific way in the resting-state. The possible relevance of these resting-state EEG properties is discussed including its clinical relevance.

Back to basics: the neuronal substrates and mechanisms that underlie the electroencephalogram in premature neonates

Electroencephalography is the only clinically available technique that can address the premature neonate normal and pathological functional development week after week. The changes in the electroencephalogram (EEG) result from gradual structural and functional modifications that arise during the last trimester of pregnancy. Here, we review the structural changes over time that underlie the establishment of functional immature neural networks, the impact of certain anatomical specificities (fontanelles, connectivity, etc.) on the EEG, limitations in EEG interpretation, and the utility of high-resolution EEG (HR-EEG) in premature newborns (a promising technique with a high degree of spatiotemporal resolution). In particular, we classify EEG features according to whether they are manifestations of endogenous generators (i.e. theta activities that coalesce with a slow wave or delta brushes) or come from a broader network. Furthermore, we review publications on EEG in premature animals because the data provide a better understanding of what is happening in premature newborns. We then discuss the results and limitations of functional connectivity analyses in premature newborns. Lastly, we report on the magnetoelectroencephalographic studies of brain activity in the fetus. A better understanding of complex interactions at various structural and functional levels during normal neurodevelopment (as assessed using electroencephalography as a benchmark method) might lead to better clinical care and monitoring for premature neonates.

Tau pathology in early Alzheimer's disease is linked to selective disruptions in neurophysiological network dynamics

Understanding the role of Tau protein aggregation in the pathogenesis of Alzheimer's disease is critical for the development of new Tau-based therapeutic strategies to slow or prevent dementia. We tested the hypothesis that Tau pathology is associated with functional organization of widespread neurophysiological networks. We used electro-magnetoencephalography with [¹⁸F]AV-1451 PET scanning to quantify Tau-dependent network changes. Using a graph theoretical approach to brain connectivity, we quantified nodal measures of functional segregation, centrality, and the efficiency of information transfer and tested them against levels of [¹⁸F]AV-1451. Higher Tau burden in early Alzheimer's disease was associated with a shift away from the optimal small-world organization and a more fragmented network in the beta and gamma bands, whereby parieto-occipital areas were disconnected from the anterior parts of the network. Similarly, higher Tau burden was associated with decreases in both local and global efficiency, especially in the gamma band. The results support the translational development of neurophysiological "signatures" of Alzheimer's disease, to understand disease mechanisms in humans and facilitate experimental medicine studies.

Characterization of Young and Old Adult Brains: An EEG Functional Connectivity Analysis

Brain connectivity studies have reported that functional networks change with older age. We aim to (1) investigate whether electroencephalography (EEG) data can be used to distinguish between individual functional networks of young and old adults; and (2) identify the functional connections that contribute to this classification. Two eyes-open resting-state EEG recording sessions with 64 electrodes for each of 22 younger adults (19-37 years) and 22 older adults (63-85 years) were conducted. For each session, imaginary coherence matrices in delta, theta, alpha, beta and gamma bands were computed. A range of machine learning classification methods were utilized to distinguish younger and older adult brains. A support vector machine (SVM) classifier was 93% accurate in classifying the brains by age group. We report decreased functional connectivity with older age in delta, theta, alpha and gamma bands, and increased connectivity with older age in beta band. Most connections involving frontal, temporal, and parietal electrodes, and more than half of connections involving occipital electrodes, showed decreased connectivity with older age. Slightly less than half of the connections involving central electrodes showed increased connectivity with older age. Functional connections showing decreased strength with older age were not significantly different in electrode-to-electrode distance than those that increased with older age. Most of the connections used by the classifier to distinguish participants by age group belonged to the alpha band. Findings suggest a decrease in connectivity in key networks and frequency bands associated with attention and awareness, and an increase in connectivity of the sensorimotor functional networks with aging during a resting state.

Partial directed coherence based graph convolutional neural networks for driving fatigue detection

The mental state of a driver can be accurately and reliably evaluated by detecting the driver's electroencephalogram (EEG) signals. However, traditional machine learning and deep learning methods focus on the single electrode feature analysis and ignore the functional connection of the brain. In addition, the recent brain function connection network method needs to manually extract substantial brain network features, which results in cumbersome operation. For this reason, this paper introduces graph convolution combined with brain function connection theory into the study of mental fatigue and proposes a method for driving fatigue detection based on the partial directed coherence graph convolutional neural network (PDC-GCNN), which can analyze the characteristics of single electrodes while automatically extracting the topological features of the brain network. We designed a fatigue driving simulation experiment and collected the EEG signals. In the present work, the PDC method constructs the adjacency matrix to describe the relationship between EEG channels, and the GCNN combines single-electrode local brain area information and brain area connection information to further improve the performance of detecting fatigue states. Based on the features of differential entropy (DE) and power spectral density (PSD), the average recognition accuracy of ten-fold cross validation is 84.32% and 83.84%, respectively. For further experiments on each subject, the average recognition results are 95.24%/5.10% (PSD) and 96.01%/3.81% (DE). This research can be embedded in the vehicle driving fatigue detection system, which has practical application value.

Analysis of KCNH2 and CACNA1C schizophrenia risk genes on EEG functional network modulation during an auditory odd-ball task

A deficit in task-related functional connectivity modulation from electroencephalogram (EEG) has been described in schizophrenia. The use of measures of neuronal connectivity as an intermediate phenotype may allow identifying genetic factors involved in these deficits, and therefore, establishing underlying pathophysiological mechanisms. Genes involved in neuronal excitability and previously associated with the risk for schizophrenia may be adequate candidates in relation to functional connectivity alterations in schizophrenia. The objective was to study the association of two genes of voltage-gated ion channels (CACNA1C and KCNH2) with the functional modulation of the cortical networks measured with EEG and graph-theory parameter during a cognitive task, both in individuals with schizophrenia and healthy controls. Both CACNA1C (rs1006737) and KCNH2 (rs3800779) were genotyped in 101 controls and 50 schizophrenia patients. Small-world index (SW) was calculated from EEG recorded during an odd-ball task in two different temporal windows (pre-stimulus and response). Modulation was defined as the difference in SW between both windows. Genetic, group and their interaction effects on SW in the pre-stimulus window and in modulation were evaluated using ANOVA. The CACNA1C genotype was not associated with SW properties. KCNH2 was significantly associated with SW modulation. Healthy subjects showed a positive SW modulation irrespective of the KCNH2 genotype, whereas within patients allele-related differences were observed. Patients carrying the KCNH2 risk allele (A) presented a negative SW modulation and non-carriers showed SW modulation similar to the healthy subjects. Our data suggest that KCNH2 genotype contributes to the efficient modulation of brain electrophysiological activity during a cognitive task in schizophrenia patients.

Cortical electrophysiological evidence for individual-specific temporal organization of brain functional networks

The human brain has been demonstrated to rapidly and continuously form and dissolve networks on a subsecond timescale, offering effective and essential substrates for cognitive processes. Understanding how the dynamic organization of brain functional networks on a subsecond level varies across individuals is, therefore, of great interest for personalized neuroscience. However, it remains unclear whether features of such rapid network organization are reliably unique and stable in single subjects and, therefore, can be used in characterizing individual networks. Here, we used two sets of 5‐min magnetoencephalography (MEG) resting data from 39 healthy subjects over two consecutive days and modeled the spontaneous brain activity as recurring networks fast shifting between each other in a coordinated manner. MEG cortical maps were obtained through source reconstruction using the beamformer method and subjects' temporal structure of recurring networks was obtained via the Hidden Markov Model. Individual organization of dynamic brain activity was quantified with the features of the network‐switching pattern (i.e., transition probability and mean interval time) and the time‐allocation mode (i.e., fractional occupancy and mean lifetime). Using these features, we were able to identify subjects from the group with significant accuracies (~40% on average in 0.5–48 Hz). Notably, the default mode network displayed a distinguishable pattern, being the least frequently visited network with the longest duration for each visit. Together, we provide initial evidence suggesting that the rapid dynamic temporal organization of brain networks achieved in electrophysiology is intrinsic and subject specific.

Toward Leveraging Human Connectomic Data in Large Consortia: Generalizability of fMRI-Based Brain Graphs Across Sites, Sessions, and Paradigms

While graph theoretical modeling has dramatically advanced our understanding of complex brain systems, the feasibility of aggregating connectomic data in large imaging consortia remains unclear. Here, using a battery of cognitive, emotional and resting fMRI paradigms, we investigated the generalizability of functional connectomic measures across sites and sessions. Our results revealed overall fair to excellent reliability for a majority of measures during both rest and tasks, in particular for those quantifying connectivity strength, network segregation and network integration. Processing schemes such as node definition and global signal regression (GSR) significantly affected resulting reliability, with higher reliability detected for the Power atlas (vs. AAL atlas) and data without GSR. While network diagnostics for default-mode and sensori-motor systems were consistently reliable independently of paradigm, those for higher-order cognitive systems were reliable predominantly when challenged by task. In addition, based on our present sample and after accounting for observed reliability, satisfactory statistical power can be achieved in multisite research with sample size of approximately 250 when the effect size is moderate or larger. Our findings provide empirical evidence for the generalizability of brain functional graphs in large consortia, and encourage the aggregation of connectomic measures using multisite and multisession data.

An Improved Classification Model for Depression Detection Using EEG and Eye Tracking Data

At present, depression has become a main health burden in the world. However, there are many problems with the diagnosis of depression, such as low patient cooperation, subjective bias and low accuracy. Therefore, reliable and objective evaluation method is needed to achieve effective depression detection. Electroencephalogram (EEG) and eye movements (EMs) data have been widely used for depression detection due to their advantages of easy recording and non-invasion. This research proposes a content based ensemble method (CBEM) to promote the depression detection accuracy, both static and dynamic CBEM were discussed. In the proposed model, EEG or EMs dataset was divided into subsets by the context of the experiments, and then a majority vote strategy was used to determine the subjects' label. The validation of the method is testified on two datasets which included free viewing eye tracking and resting-state EEG, and these two datasets have 36,34 subjects respectively. For these two datasets, CBEM achieves accuracies of 82.5% and 92.65% respectively. The results show that CBEM outperforms traditional classification methods. Our findings provide an effective solution for promoting the accuracy of depression identification, and provide an effective method for identificationof depression, which in the future could be used for the auxiliary diagnosis of depression.

Resting-state EEG activity predicts frontoparietal network reconfiguration and improved attentional performance

Mounting evidence indicates that resting-state EEG activity is related to various cognitive functions. To trace physiological underpinnings of this relationship, we investigated EEG and behavioral performance of 36 healthy adults recorded at rest and during visual attention tasks: visual search and gun shooting. All measures were repeated two months later to determine stability of the results. Correlation analyses revealed that within the range of 2–45 Hz, at rest, beta-2 band power correlated with the strength of frontoparietal connectivity and behavioral performance in both sessions. Participants with lower global beta-2 resting-state power (gB2rest) showed weaker frontoparietal connectivity and greater capacity for its modifications, as indicated by changes in phase correlations of the EEG signals. At the same time shorter reaction times and improved shooting accuracy were found, in both test and retest, in participants with low gB2rest compared to higher gB2rest values. We posit that weak frontoparietal connectivity permits flexible network reconfigurations required for improved performance in everyday tasks.

Human brain networks: a graph theoretical analysis of cortical connectivity normative database from EEG data in healthy elderly subjects

Moving from the hypothesis that aging processes modulate brain connectivity networks, 170 healthy elderly volunteers were submitted to EEG recordings in order to define age-related normative limits. Graph theory functions were applied to exact low-resolution electromagnetic tomography on cortical sources in order to evaluate the small-world parameter as a representative model of network architecture. The analyses were carried out in the whole brain-as well as for the left and the right hemispheres separately-and in three specific resting state subnetworks defined as follows: attentional network (AN), frontal network (FN), and default mode network (DMN) in the EEG frequency bands (delta, theta, alpha 1, alpha 2, beta 1, beta 2, gamma). To evaluate the stability of the investigated parameters, a subgroup of 32 subjects underwent three separate EEG recording sessions in identical environmental conditions after a few days interval. Results showed that the whole right/left hemispheric evaluation did not present side differences, but when individual subnetworks were considered, AN and DMN presented in general higher SW in low (delta and/or theta) and high (gamma) frequency bands in the left hemisphere, while for FN, the alpha 1 band was lower in the left with respect to the right hemisphere. It was also evident the test-retest reliability and reproducibility of the present methodology when carried out in clinically stable subjects.Evidences from the present study suggest that graph theory represents a reliable method to address brain connectivity patterns from EEG data and is particularly suitable to study the physiological impact of aging on brain functional connectivity networks.

A New Mutual Information Measure to Estimate Functional Connectivity: Preliminary Study

Functional Connectivity (FC) is a powerful tool to investigate brain networks both in rest and while performing tasks. Functional magnetic resonance imaging (fMRI) gave good spatial estimation of FC but lacked the temporal resolution. Electroencephalography (EEG) allows estimating FC with good temporal resolution. In this study we introduce a new method based on Mutual Information and Multivariate Improved Weighted Multi-scale Permutation Entropy to estimate FC of brain using EEG. We applied this method on resting-state EEG signals from healthy children. Using network measures of nodes and Wilcoxon signed-rank test, we identified the most important nodes in the estimated networks. Comparing the localization of those outstanding nodes with the regions involved in resting-state networks (RSNs) estimated from fMRI showed that our proposal is efficient in the identification of nodes belonging to RSNs and could be used as a general estimator for FC without having to band-pass the signals into frequency bands.