EEG functional connectivity analysis in the source space

Microstate Analysis of Continuous Infant EEG: Tutorial and Reliability

Microstate analysis of resting-state EEG is a unique data-driven method for identifying patterns of scalp potential topographies, or microstates, that reflect stable but transient periods of synchronized neural activity evolving dynamically over time. During infancy - a critical period of rapid brain development and plasticity - microstate analysis offers a unique opportunity for characterizing the spatial and temporal dynamics of brain activity. However, whether measurements derived from this approach (e.g., temporal properties, transition probabilities, neural sources) show strong psychometric properties (i.e., reliability) during infancy is unknown and key information for advancing our understanding of how microstates are shaped by early life experiences and whether they relate to individual differences in infant abilities. A lack of methodological resources for performing microstate analysis of infant EEG has further hindered adoption of this cutting-edge approach by infant researchers. As a result, in the current study, we systematically addressed these knowledge gaps and report that most microstate-based measurements of brain organization and functioning except for transition probabilities were stable with four minutes of video-watching resting-state data and highly internally consistent with just one minute. In addition to these results, we provide a step-by-step tutorial, accompanying website, and open-access data for performing microstate analysis using a free, user-friendly software called Cartool. Taken together, the current study supports the reliability and feasibility of using EEG microstate analysis to study infant brain development and increases the accessibility of this approach for the field of developmental neuroscience.

Patterns of spontaneous neural activity associated with social communication abilities among infants and toddlers showing signs of autism

Early disruptions to social communication development, including delays in joint attention and language, are among the earliest markers of autism spectrum disorder (autism, henceforth). Although social communication differences are a core feature of autism, there is marked heterogeneity in social communication-related development among infants and toddlers exhibiting autism symptoms. Neural markers of individual differences in joint attention and language abilities may provide important insight into heterogeneity in autism symptom expression during infancy and toddlerhood. This study examined patterns of spontaneous electroencephalography (EEG) activity associated with joint attention and language skills in 70 community-referred 12- to 23-month-olds with autism symptoms and elevated scores on an autism diagnostic instrument. Data-driven cluster-based permutation analyses revealed significant positive associations between relative alpha power (6-9 Hz) and concurrent response to joint attention skills, receptive language, and expressive language abilities. Exploratory analyses also revealed significant negative associations between relative alpha power and measures of core autism features (i.e., social communication difficulties and restricted/repetitive behaviors). These findings shed light on the neural mechanisms underlying typical and atypical social communication development in emerging autism and provide a foundation for future work examining neural predictors of social communication growth and markers of intervention response.

EEG signatures of cognitive decline after mild SARS-CoV-2 infection: an age-dependent study

BACKGROUND

Current research on the neurological impact of SARS-CoV-2 primarily focuses on the elderly or severely ill individuals. This study aims to explore the diverse neurological consequences of SARS-CoV-2 infection, with a particular focus on mildly affected children and adolescents.

METHODS

A cohort study was conducted to collect pre- and post-infection resting-state electroencephalogram (EEG) data from 185 participants and 181 structured questionnaires of long-term symptoms across four distinct age groups. The goal was to comprehensively evaluate the impact of SARS-CoV-2 infection on these different age demographics. The study analyzed EEG changes of SARS-CoV-2 by potential biomarkers across age groups using both spatial and temporal approaches.

RESULTS

Spatial analysis indicated that children and adolescents exhibit smaller changes in brain network and microstate patterns post-infection, implying a milder cognitive impact. Sequential linear analyses showed that SARS-CoV-2 infection is associated with a marked rise in low-complexity, synchronized neural activity within low-frequency EEG bands. This is evidenced by a significant increase in Hjorth activity within the theta band and Hjorth mobility in the delta band. Sequential nonlinear analysis indicated a significant reduction in the Hurst exponent across all age groups, pointing to increased chaos and complexity within the cognitive system following infection. Furthermore, linear regression analysis based on questionnaires established a significant positive relationship between the magnitude of changes in these neural indicators and the persistence of long-term symptoms post-infection.

CONCLUSIONS

The findings underscore the enduring neurological impacts of SARS-CoV-2 infection, marked by cognitive decline and increased EEG disarray. Although children and adolescents experienced milder effects, cognitive decline and heightened low-frequency electrical activity were evident. These observations might contribute to understanding potential anxiety, insomnia, and neurodevelopmental implications.

Neurodevelopment of children exposed to prolonged anesthesia in infancy: GABA study interim analysis of resting-state brain networks at 2, 4, and 10-months old

BACKGROUND

Previous studies have raised concerns regarding neurodevelopmental impacts of early exposures to general anesthesia and surgery. Electroencephalography (EEG) can be used to study ontogeny of brain networks during infancy. As a substudy of an ongoing study, we examined measures of functional connectivity in awake infants with prior early and prolonged anesthetic exposures and in control infants.

METHODS

EEG functional connectivity was assessed using debiased weighted phase lag index at source and sensor levels and graph theoretical measures for resting state activity in awake infants in the early anesthesia (n = 26 at 10 month visit, median duration of anesthesia = 4 [2, 7 h]) and control (n = 38 at 10 month visit) groups at ages approximately 2, 4 and 10 months. Theta and low alpha frequency bands were of primary interest. Linear mixed models incorporated impact of age and cumulative hours of general anesthesia exposure.

RESULTS

Models showed no significant impact of cumulative hours of general anesthesia exposure on debiased weighted phase lag index, characteristic path length, clustering coefficient or small-worldness (conditional R2 0.05-0.34). An effect of age was apparent in many of these measures.

CONCLUSIONS

We could not demonstrate significant impact of general anesthesia in the first months of life on early development of resting state brain networks over the first postnatal year. Future studies will explore these networks as these infants grow older.

Favoritism or bias? Cooperation and competition under different intergroup relationships: evidence from EEG hyperscanning

Cooperation and competition are the most common forms of social interaction in various social relationships. Intergroup relationships have been posited to influence individuals' interpersonal interactions significantly. Using electroencephalography hyperscanning, this study aimed to establish whether intergroup relationships influence interpersonal cooperation and competition and the underlying neural mechanisms. According to the results, the in-group Coop-index is better than the out-group, whereas the out-group Comp-index is stronger than the in-group. The in-group functional connectivity between the frontal-central region and the right temporoparietal junction in the β band was stronger in competition than cooperation. The out-group functional connectivity between the frontal-central region and the left temporoparietal junction in the α band was stronger in cooperation than competition. In both cooperation and competition, the in-group exhibited higher interbrain synchronization between the prefrontal cortex and parietal region in the θ band, as well as between the frontal-central region and frontal-central region in the α band, compared to the out-group. The intrabrain phase-locking value in both the α and β bands can effectively predict performance in competition tasks. Interbrain phase-locking value in both the α and θ bands can be effectively predicted in a performance cooperation task. This study offers neuroscientific evidence for in-group favoritism and out-group bias at an interpersonal level.

Electroencephalography connectome changes in chronic insomnia disorder are correlated with neurochemical signatures

STUDY OBJECTIVES

This study aimed to investigate the alterations in resting-state electroencephalography (EEG) global brain connectivity (GBC) in patients with chronic insomnia disorder (CID) and to explore the correlation between macroscale connectomic variances and microscale neurotransmitter distributions.

METHODS

We acquired 64-channel EEG from 35 female CID patients and 34 healthy females. EEG signals were source-localized using individual brain anatomy and orthogonalized to mitigate volume conduction. Correlation coefficients between band-limited source-space power envelopes of the DK 68 atlas were computed and averaged across regions to determine specific GBC values. A support vector machine (SVM) classifier utilizing GBC features was employed to differentiate CID patients from controls. We further used Neurosynth and a 3D atlas of neurotransmitter receptors/transporters to assess the cognitive functions and neurotransmitter landscape associated with CID cortical abnormality maps, respectively.

RESULTS

CID patients exhibited elevated GBC within the medial prefrontal cortex and limbic cortex, particularly at the gamma carrier frequency, compared to controls (pFDR<0.05). GBC patterns were found to effectively distinguish CID patients from controls with a precision of 90.8% in the SVM model. The cortical abnormality maps were significantly correlated with meta-analytic terms like "cognitive control" and "emotion regulation." Notably, GBC patterns were associated with neurotransmitter profiles (pspin<0.05), with neurotransmitter systems such as norepinephrine, dopamine, and serotonin making significant contributions.

CONCLUSIONS

This work characterizes the EEG connectomic profile of CID, facilitating the cost-effective clinical translation of EEG-derived markers. Additionally, the linkage between GBC patterns and neurotransmitter distribution offers promising avenues for developing targeted treatment strategies for CID.

Hybrid computational model depicts the contribution of non-significant lobes of human brain during the perception of emotional stimuli

Emotions are synchronizing responses of human brain while executing cognitive tasks. Earlier studies had revealed strong correlation between specific lobes of the brain to different types of emotional valence. In the current study, a comprehensive three-dimensional mapping of human brain for executing emotion specific tasks had been formulated. A hybrid computational machine learning model customized from Custom Weight Allocation Model (CWAM) and defined as Custom Rank Allocation Model (CRAM). This regression-based hybrid computational model computes the allocated tasks to different lobes of the brain during their respective executive stage. Event Related Potentials (ERP) were obtained with significant effect at P1, P2, P3, N170, N2, and N4. These ERPs were configured at Pz, Cz, F3, and T8 regions of the brain with maximal responses; while regions like Cz, C4 and F4 were also found to make effective contributions to elevate the responses of the brain, and thus these regions were configured as augmented source regions of the brain. In another circumstance of frequent -deviant - equal (FDE) presentation of the emotional stimuli, it was observed that the brain channels C3, C4, P3, P4, O1, O2, and Oz were contributing their emotional quotient to the overall response of the brain regions; whereas, the interaction effect was found presentable at O2, Oz, P3, P4, T8 and C3 regions of brain. The proposed computational model had identified the potential neural pathways during the execution of emotional task.

Complexity of STG signals and linguistic rhythm: a methodological study for EEG data

The superior temporal and the Heschl's gyri of the human brain play a fundamental role in speech processing. Neurons synchronize their activity to the amplitude envelope of the speech signal to extract acoustic and linguistic features, a process known as neural tracking/entrainment. Electroencephalography has been extensively used in language-related research due to its high temporal resolution and reduced cost, but it does not allow for a precise source localization. Motivated by the lack of a unified methodology for the interpretation of source reconstructed signals, we propose a method based on modularity and signal complexity. The procedure was tested on data from an experiment in which we investigated the impact of native language on tracking to linguistic rhythms in two groups: English natives and Spanish natives. In the experiment, we found no effect of native language but an effect of language rhythm. Here, we compare source projected signals in the auditory areas of both hemispheres for the different conditions using nonparametric permutation tests, modularity, and a dynamical complexity measure. We found increasing values of complexity for decreased regularity in the stimuli, giving us the possibility to conclude that languages with less complex rhythms are easier to track by the auditory cortex.

Dementia classification using a graph neural network on imaging of effective brain connectivity

Alzheimer's disease (AD) and Parkinson's disease (PD) are two of the most common forms of neurodegenerative diseases. The literature suggests that effective brain connectivity (EBC) has the potential to track differences between AD, PD and healthy controls (HC). However, how to effectively use EBC estimations for the research of disease diagnosis remains an open problem. To deal with complex brain networks, graph neural network (GNN) has been increasingly popular in very recent years and the effectiveness of combining EBC and GNN techniques has been unexplored in the field of dementia diagnosis. In this study, a novel directed structure learning GNN (DSL-GNN) was developed and performed on the imaging of EBC estimations and power spectrum density (PSD) features. In comparison to the previous studies on GNN, our proposed approach enhanced the functionality for processing directional information, which builds the basis for more efficiently performing GNN on EBC. Another contribution of this study is the creation of a new framework for applying univariate and multivariate features simultaneously in a classification task. The proposed framework and DSL-GNN are validated in four discrimination tasks and our approach exhibited the best performance, against the existing methods, with the highest accuracy of 94.0% (AD vs. HC), 94.2% (PD vs. HC), 97.4% (AD vs. PD) and 93.0% (AD vs. PD vs. HC). In a word, this research provides a robust analytical framework to deal with complex brain networks containing causal directional information and implies promising potential in the diagnosis of two of the most common neurodegenerative conditions.

Technical and clinical considerations for electroencephalography-based biomarkers for major depressive disorder

Major depressive disorder (MDD) is a prevalent and debilitating psychiatric disease that leads to substantial loss of quality of life. There has been little progress in developing new MDD therapeutics due to a poor understanding of disease heterogeneity and individuals' responses to treatments. Electroencephalography (EEG) is poised to improve this, owing to the ease of large-scale data collection and the advancement of computational methods to address artifacts. This review summarizes the viability of EEG for developing brain-based biomarkers in MDD. We examine the properties of well-established EEG preprocessing pipelines and consider factors leading to the discovery of sensitive and reliable biomarkers.

Role of Entorhinal and Parahippocampal in Diagnosis of Alzheimer's Disease from Resting State EEG

Alzheimer's disease (AD) poses a significant public health problem. An early diagnosis during the initial development stage of this neurodegenerative brain disorder is a requisite. Initial symptoms of AD involve dysfunctioning in brain memory which later progresses toward language, comprehension, reasoning, and social behavior. The degree of dysfunctionality in AD is accompanied by neuronal damage thereby leading to alteration in functional connectivity of brain regions with AD progression. In literature, substantial studies have focused on topological brain function characteristics, scalp EEG temporal features, and functional MRI or PET scan to diagnose AD. However, source domain based study using EEG is limited. This work establishes the significance of EEG based source domain connectivity in AD diagnosis. In particular, the cortical sources, Parahippocampal and Entorhinal, are particularly studied for cognitive processes and memory in AD and healthy control (HC). A publicly available AD and HC resting state EGG dataset is utilized for this purpose. The dipole imaging method converts surface EEG information into source space. Functional connectivity (FC), supervised classification, frequency analysis, and clustering are then utilized to establish the importance of the entorhinal and parahippocampal in AD diagnosis. The entorhinal source involved in memory is found to be a potential biomarker for AD diagnosis. This memory-associated source dynamics-based approach can further lead to early diagnosis of AD.

Breakdown of long-range spatial correlations of infraslow amplitude fluctuations of EEG oscillations in patients with current and past major depressive disorder

Introduction

Identifying biomarkers for depression from brain activity is important for the diagnosis and treatment of depression disorders. We investigated spatial correlations of the amplitude fluctuations of electroencephalography (EEG) oscillations as a potential biomarker of depression. The amplitude fluctuations of EEG oscillations intrinsically reveal both temporal and spatial correlations, indicating rapid and functional organization of the brain networks. Amid these correlations, long-range temporal correlations are reportedly impaired in patients with depression, exhibiting amplitude fluctuations closer to a random process. Based on this occurrence, we hypothesized that the spatial correlations of amplitude fluctuations would also be altered by depression.

Methods

In the present study, we extracted the amplitude fluctuations of EEG oscillations by filtering them through infraslow frequency band (0.05-0.1 Hz).

Results

We found that the amplitude fluctuations of theta oscillations during eye-closed rest depicted lower levels of spatial correlation in patients with major depressive disorder (MDD) compared to control individuals. This breakdown of spatial correlations was most prominent in the left fronto - temporal network, specifically in patients with current MDD rather than in those with past MDD. We also found that the amplitude fluctuations of alpha oscillations during eye-open rest exhibited lower levels of spatial correlation in patients with past MDD compared to control individuals or patients with current MDD.

Discussion

Our results suggest that breakdown of long-range spatial correlations may offer a biomarker for the diagnosis of depression (current MDD), as well as the tracking of the recovery from depression (past MDD).

Decreased Functional Connectivity of Brain Networks in the Alpha Band after Sleep Deprivation Is Associated with Decreased Inhibitory Control in Young Male Adults

Sleep deprivation leads to reduced inhibitory control in individuals. However, the underlying neural mechanisms are poorly understood. Accordingly, this study aimed to investigate the effects of total sleep deprivation (TSD) on inhibitory control and their neuroelectrophysiological mechanisms from the perspective of the time course of cognitive processing and brain network connectivity, using event-related potential (ERP) and resting-state functional connectivity techniques. Twenty-five healthy male participants underwent 36 h of TSD (36-h TSD), completing Go/NoGo tasks and resting-state data acquisition before and after TSD; their behavioral and electroencephalogram data were recorded. Compared to baseline, participants' false alarms for NoGo stimuli increased significantly (t = -4.187, p < 0.001) after 36-h TSD. ERP results indicated that NoGo-N2 negative amplitude increased and latency was prolonged (t = 4.850, p < 0.001; t = -3.178, p < 0.01), and NoGo-P3 amplitude significantly decreased and latency was prolonged (t = 5.104, p < 0.001; t = -2.382, p < 0.05) after 36-h TSD. Functional connectivity analysis showed that the connectivity of the default mode and visual networks in the high alpha band was significantly reduced after TSD (t = 2.500, p = 0.030). Overall, the results suggest that the negative amplitude increase in N2 after 36-h TSD may reveal that more attention and cognitive resources are invested after TSD; the significant decrease in P3 amplitude may indicate the impairment of advanced cognitive processing. Further functional connectivity analysis indicated impairment of the brain's default mode network and visual information processing after TSD.

Source-Space Brain Functional Connectivity Features in Electroencephalogram-Based Driver Fatigue Classification

This study examined the brain source space functional connectivity from the electroencephalogram (EEG) activity of 48 participants during a driving simulation experiment where they drove until fatigue developed. Source-space functional connectivity (FC) analysis is a state-of-the-art method for understanding connections between brain regions that may indicate psychological differences. Multi-band FC in the brain source space was constructed using the phased lag index (PLI) method and used as features to train an SVM classification model to classify driver fatigue and alert conditions. With a subset of critical connections in the beta band, a classification accuracy of 93% was achieved. Additionally, the source-space FC feature extractor demonstrated superiority over other methods, such as PSD and sensor-space FC, in classifying fatigue. The results suggested that source-space FC is a discriminative biomarker for detecting driving fatigue.

Neural oscillations during acupuncture imagery partially parallel that of real needling

Introduction

Tasks involving mental practice, relying on the cognitive rehearsal of physical motors or other activities, have been reported to have similar patterns of brain activity to overt execution. In this study, we introduced a novel imagination task called, acupuncture imagery and aimed to investigate the neural oscillations during acupuncture imagery.

Methods

Healthy volunteers were guided to watch a video of real needling in the left and right KI3 (Taixi point). The subjects were then asked to perform tasks to keep their thoughts in three 1-min states alternately: resting state, needling imagery left KI3, and needling imagery right KI3. Another group experienced real needling in the right KI3. A 31-channel-electroencephalography was synchronously recorded for each subject. Microstate analyses were performed to depict the brain dynamics during these tasks.

Results

Compared to the resting state, both acupuncture needling imagination and real needling in KI3 could introduce significant changes in neural dynamic oscillations. Moreover, the parameters involving microstate A of needling imagery in the right KI3 showed similar changes as real needling in the right KI3.

Discussion

These results confirm that needling imagination and real needling have similar brain activation patterns. Needling imagery may change brain network activity and play a role in neural regulation. Further studies are needed to explore the effects of acupuncture imagery and the potential application of acupuncture imagery in disease recovery.

Facilitating early parent-infant emotional connection improves cortical networks in preterm infants

Exposure to environmental adversities during early brain development, such as preterm birth, can affect early brain organization. Here, we studied whether development of cortical activity networks in preterm infants may be improved by a multimodal environmental enrichment via bedside facilitation of mother-infant emotional connection. We examined functional cortico-cortical connectivity at term age using high-density electroencephalography recordings in infants participating in a randomized controlled trial of Family Nurture Intervention (FNI). Our results identify several large-scale, frequency-specific network effects of FNI, most extensively in the alpha frequency in fronto-central cortical regions. The connectivity strength in this network was correlated to later neurocognitive performance, and it was comparable to healthy term-born infants rather than the infants receiving standard care. These findings suggest that preterm neurodevelopmental care can be improved by a biologically driven environmental enrichment, such as early facilitation of direct human connection.

Influencing factors and mechanism of high-speed railway passenger overall comfort: Insights from source functional brain network and subjective report

Overall comfort is the priority for the high-speed railway (HSR) passengers, while its influencing factors and mechanism are not yet apparent. According to the source functional brain network and subjective report, this study revealed the potential influencing factors and mechanisms of passengers overall comfort in high-speed railway environments. Here, an ergonomics field test with 20 subjects was conducted where subjective reports and electroencephalography (EEG) were collected. The electric-source imaging and functional connectivity were used to build the source functional brain network from EEG and network indices were extracted. Statistics analysis results showed that static comfort played the most critical role in the overall comfort, followed by emotional valence, emotional arousal, aural pressure comfort, vibration comfort, and noise comfort. Thermal and visual comfort were insignificant due to the well-designed heating, ventilation, and air conditioning (HVAC) and lighting system of HSR. In addition, the source functional brain network of passengers who felt uncomfortable had the higher clustering coefficient, assortativity coefficient and global efficiency, which meant greater activation of brain compared with passengers who were in a state of comfort. According to the local attributes indices analysis, most key brain regions were located in the frontal and hippocampus, which revealed emotion and spatial perception contribute to the whole comfort degradation process. This work proposed novel insights into HSR passengers overall comfort according to subjective and objective methods. Our findings demonstrate emotional regulation and seat improvements are key factors for future improvement of HSR passengers overall comfort.