Graph Theory Analysis of Functional Connectivity in Major Depression Disorder With High-Density Resting State EEG Data

BrainDAS: Structure-aware domain adaptation network for multi-site brain network analysis

In the medical field, datasets are mostly integrated across sites due to difficult data acquisition and insufficient data at a single site. The domain shift problem caused by the heterogeneous distribution among multi-site data makes autism spectrum disorder (ASD) hard to identify. Recently, domain adaptation has received considerable attention as a promising solution. However, domain adaptation on graph data like brain networks has not been fully studied. It faces two major challenges: (1) complex graph structure; and (2) multiple source domains. To overcome the issues, we propose an end-to-end structure-aware domain adaptation framework for brain network analysis (BrainDAS) using resting-state functional magnetic resonance imaging (rs-fMRI). The proposed approach contains two stages: supervision-guided multi-site graph domain adaptation with dynamic kernel generation and graph classification with attention-based graph pooling. We evaluate our BrainDAS on a public dataset provided by Autism Brain Imaging Data Exchange (ABIDE) which includes 871 subjects from 17 different sites, surpassing state-of-the-art algorithms in several different evaluation settings. Furthermore, our promising results demonstrate the interpretability and generalization of the proposed method. Our code is available at https://github.com/songruoxian/BrainDAS.

GCTNet: a graph convolutional transformer network for major depressive disorder detection based on EEG signals

Objective.Identifying major depressive disorder (MDD) using objective physiological signals has become a pressing challenge.Approach.Hence, this paper proposes a graph convolutional transformer network (GCTNet) for accurate and reliable MDD detection using electroencephalogram (EEG) signals. The developed framework integrates a residual graph convolutional network block to capture spatial information and a Transformer block to extract global temporal dynamics. Additionally, we introduce the contrastive cross-entropy (CCE) loss that combines contrastive learning to enhance the stability and discriminability of the extracted features, thereby improving classification performance.Main results. The effectiveness of the GCTNet model and CCE loss was assessed using EEG data from 41 MDD patients and 44 normal controls, in addition to a publicly available dataset. Utilizing a subject-independent data partitioning method and 10-fold cross-validation, the proposed method demonstrated significant performance, achieving an average Area Under the Curve of 0.7693 and 0.9755 across both datasets, respectively. Comparative analyses demonstrated the superiority of the GCTNet framework with CCE loss over state-of-the-art algorithms in MDD detection tasks.Significance. The proposed method offers an objective and effective approach to MDD detection, providing valuable support for clinical-assisted diagnosis.

EPIC: Emotion Perception by Spatio-Temporal Interaction Context of Gait

Recently, psychophysiological computing has received considerable attention. Due to easy acquisition at a distance and less conscious initiation, gait-based emotion recognition is considered as a valuable research branch in the field of psychophysiological computing. However, most existing methods rarely explore the spatio-temporal context of gait, which limits the ability to capture the higher-order relationship between emotion and gait. In this paper, we utilize a range of research, including psychophysiological computing and artificial intelligence, to propose an integrated emotion perception framework called EPIC, which can find novel joint topology and generate thousands of synthetic gaits by spatio-temporal interaction context. First, we analyze the joint coupling among non-adjacent joints by calculating Phase Lag Index (PLI), which can discover the latent connection among body joints. Second, to synthesize more sophisticated and accurate gait sequences, we explore the effect of spatio-temporal constraints, and propose a new loss function that utilizes the Dynamic Time Warping (DTW) algorithm and pseudo-velocity curve to constrain the output of Gated Recurrent Units (GRU). Finally, Spatial Temporal Graph Convolution Networks (ST-GCN) is used to classify emotions using the generation and the real data. Experimental results demonstrate our approach achieves the accuracy of 89.66%, and outperforms the state-of-the-art methods on Emotion-Gait dataset.

An epilepsy detection method based on multi-dimensional feature extraction and dual-branch hypergraph convolutional network

Epilepsy is a disease caused by abnormal neural discharge, which severely harms the health of patients. Its pathogenesis is complex and variable with various forms of seizures, leading to significant differences in epilepsy manifestations among different patients. The changes of brain network are strongly correlated with related pathologies. Therefore, it is crucial to effectively and deeply explore the intrinsic features of epilepsy signals to reveal the rules of epilepsy occurrence and achieve accurate detection. Existing methods have faced the following issues: 1) single approach for feature extraction, resulting in insufficient classification information due to the lack of rich dimensions in captured features; 2) inability to deeply analyze the essential commonality of epilepsy signal after feature extraction, making the model susceptible to data distribution and noise interference. Thus, we proposed a high-precision and robust model for epileptic seizure detection, which, for the first time, applies hypergraph convolution to the field of epilepsy detection. Through a hypergraph network structure constructed based on relationships between channels in electroencephalogram (EEG) signals, the model explores higher-order characteristics of epilepsy EEG data. Specifically, we use the Conv-LSTM module and Power spectral density (PSD), a two-branch parallel method, to extract channel features from space-time and frequency domains to solve the problem of insufficient feature extraction, and can adequately describe the data structure and distribution from multiple perspectives through double-branch parallel feature extraction. In addition, we construct a hypergraph on the captured features to explore the intrinsic features in the high-dimensional space in an attempt to reveal the essential commonality of epileptic signal feature extraction. Finally, using the ensemble learning concept, we accomplished epilepsy detection on the dual-branch hypergraph convolution. The model underwent leave-one-out cross-validation on the TUH dataset, achieving an average accuracy of 96.9%, F1 score of 97.3%, Pre of 98.2% and Re of 96.7%. In addition, the model was generalized performance tested on CHB-MIT scalp EEG dataset with leave-one-out cross-validation, and the average ACC, F1 score, Pre and Re were 94.4%, 95.1%, 95.8%, and 93.9% respectively. Experimental results indicate that the model outperforms related literature, providing valuable reference for the clinical application of epilepsy detection.

Analysis of reading-task-based brain connectivity in dyslexic children using EEG signals

Developmental dyslexia, a neurodevelopment reading disorder, can impact even children with average intelligence. The present study examined the brain connectivity in dyslexic and control children during the reading task using graph theory. 19-channel electroencephalogram (EEG) signals were recorded from 15 dyslexic children and 15 control children. Functional connectivity was estimated by measuring the EEG coherence at 19 electrode locations, and graph measures were calculated using the graph theory method. Reading task results identified deprived task performance in dyslexic children against controls. Graph measures revealed longer path length, reduced clustering coefficient and reduced network efficiencies (in theta and alpha bands) of dyslexic group. At the nodal level, we found a significant increase in delta strength (T4 and T5 electrode locations) and reduced strength in theta (T6, P4, Fp1, F8 and F3) and alpha bands (T4, T3, P4 and F3) during the reading task in dyslexic group. In conclusion, the present study identified distinct graph measures between groups when performing a reading task and showed possible evidence for compromised brain networks in dyslexic group.

Decoding emotion with phase-amplitude fusion features of EEG functional connectivity network

Decoding emotional neural representations from the electroencephalographic (EEG)-based functional connectivity network (FCN) is of great scientific importance for uncovering emotional cognition mechanisms and developing harmonious human-computer interactions. However, existing methods mainly rely on phase-based FCN measures (e.g., phase locking value [PLV]) to capture dynamic interactions between brain oscillations in emotional states, which fail to reflect the energy fluctuation of cortical oscillations over time. In this study, we initially examined the efficacy of amplitude-based functional networks (e.g., amplitude envelope correlation [AEC]) in representing emotional states. Subsequently, we proposed an efficient phase-amplitude fusion framework (PAF) to fuse PLV and AEC and used common spatial pattern (CSP) to extract fused spatial topological features from PAF for multi-class emotion recognition. We conducted extensive experiments on the DEAP and MAHNOB-HCI datasets. The results showed that: (1) AEC-derived discriminative spatial network topological features possess the ability to characterize emotional states, and the differential network patterns of AEC reflect dynamic interactions in brain regions associated with emotional cognition. (2) The proposed fusion features outperformed other state-of-the-art methods in terms of classification accuracy for both datasets. Moreover, the spatial filter learned from PAF is separable and interpretable, enabling a description of affective activation patterns from both phase and amplitude perspectives.

The effects of electroencephalography functional connectivity during emotional recognition among patients with major depressive disorder and healthy controls

BACKGROUND

The brain of major depressive disorder (MDD) is associated with altered functional connectivity (FC) compared to that of healthy individuals when processing positive and negative visual stimuli. Building upon alterations in brain connectivity, some researchers have employed electroencephalography (EEG) to study FC in MDD, aiming to enhance both diagnosis and treatment; however, the results have been inconsistent and the studies involving FC during emotional recognition are limited. This study aims to 1) investigate the effects of MDD on EEG patterns during visual emotional processing, 2) explore the therapeutic effects of antidepressant treatment on brain FC within the first week, and assess whether these effects can be predictive of treatment outcomes four weeks later, and 3) study baseline FC parameter biomarkers that can be used to predict treatment responsiveness in MDD patients.

METHODS

This clinical observational study recruited 38 healthy controls (HC) and 48 MDD patients. Patients underwent an EEG exam while looking at validated images of happy and sad faces at week 0 and 1. MDD patients were categorized into treatment responders and non-responders after 4 weeks of treatment. We conducted the FC analysis (node strength (NS), global efficiency (GE), and cluster coefficient (CC)) on HC and MDD patients using graph theoretical analysis. Multivariable linear regression was used to evaluate the influence of MDD on FC compared to HC, while controlling for confounding variables including age, gender, and academic degrees.

RESULTS

At week 0 and week 1, MDD patients revealed to have significant reductions in FC parameters (NS, GE and CC) compared to HC. When comparing MDD patients at week 1 post-antidepressant treatment and pre-treatment, no significant differences in FC changes were observed. Multivariable regression revealed a significant negative effect on FC of MDD. Compared to the treatment non-responsive group, the responsive group revealed a significantly higher FC in delta band frequency at baseline.

CONCLUSIONS

MDD patient group showed impaired FC during visual emotion-processing and we observed baseline FC parameters to be associated with treatment response at week 4. While signs of FC changes were observed in the brain after a week of treatment, it is possible that one week may still be insufficient to demonstrate significant alterations in the brain. Our results suggest the potential utilization of EEG-based FC as an indicative measure for predicting treatment response and monitoring treatment progress in MDD patients.

Revealing brain's cognitive process deeply: a study of the consistent EEG patterns of audio-visual perceptual holistic

Introduction

To investigate the brain's cognitive process and perceptual holistic, we have developed a novel method that focuses on the informational attributes of stimuli.

Methods

We recorded EEG signals during visual and auditory perceptual cognition experiments and conducted ERP analyses to observe specific positive and negative components occurring after 400ms during both visual and auditory perceptual processes. These ERP components represent the brain's perceptual holistic processing activities, which we have named Information-Related Potentials (IRPs). We combined IRPs with machine learning methods to decode cognitive processes in the brain.

Results

Our experimental results indicate that IRPs can better characterize information processing, particularly perceptual holism. Additionally, we conducted a brain network analysis and found that visual and auditory perceptual holistic processing share consistent neural pathways.

Discussion

Our efforts not only demonstrate the specificity, significance, and reliability of IRPs but also reveal their great potential for future brain mechanism research and BCI applications.

Graph theoretical brain connectivity measures to investigate neural correlates of music rhythms associated with fear and anger

The present study tests the hypothesis that emotions of fear and anger are associated with distinct psychophysiological and neural circuitry according to discrete emotion model due to contrasting neurotransmitter activities, despite being included in the same affective group in many studies due to similar arousal-valance scores of them in emotion models. EEG data is downloaded from OpenNeuro platform with access number of ds002721. Brain connectivity estimations are obtained by using both functional and effective connectivity estimators in analysis of short (2 sec) and long (6 sec) EEG segments across the cortex. In tests, discrete emotions and resting-states are identified by frequency band specific brain network measures and then contrasting emotional states are deep classified with 5-fold cross-validated Long Short Term Memory Networks. Logistic regression modeling has also been examined to provide robust performance criteria. Commonly, the best results are obtained by using Partial Directed Coherence in Gamma (31.5 - 60.5 H z ) sub-bands of short EEG segments. In particular, Fear and Anger have been classified with accuracy of 91.79%. Thus, our hypothesis is supported by overall results. In conclusion, Anger is found to be characterized by increased transitivity and decreased local efficiency in addition to lower modularity in Gamma-band in comparison to fear. Local efficiency refers functional brain segregation originated from the ability of the brain to exchange information locally. Transitivity refer the overall probability for the brain having adjacent neural populations interconnected, thus revealing the existence of tightly connected cortical regions. Modularity quantifies how well the brain can be partitioned into functional cortical regions. In conclusion, PDC is proposed to graph theoretical analysis of short EEG epochs in presenting robust emotional indicators sensitive to perception of affective sounds.

Divergent electroencephalogram resting-state functional network alterations in subgroups of autism spectrum disorder: a symptom-based clustering analysis

Autism spectrum disorder (ASD) is characterized by etiological and phenotypic heterogeneity. Despite efforts to categorize ASD into subtypes, research on specific functional connectivity changes within ASD subgroups based on clinical presentations is limited. This study proposed a symptom-based clustering approach to identify subgroups of ASD based on multiple clinical rating scales and investigate their distinct Electroencephalogram (EEG) functional connectivity patterns. Eyes-opened resting-state EEG data were collected from 72 children with ASD and 63 typically developing (TD) children. A data-driven clustering approach based on Social Responsiveness Scales-Second Edition and Vinland-3 scores was used to identify subgroups. EEG functional connectivity and topological characteristics in four frequency bands were assessed. Two subgroups were identified: mild ASD (mASD, n = 37) and severe ASD (sASD, n = 35). Compared to TD, mASD showed increased functional connectivity in the beta band, while sASD exhibited decreased connectivity in the alpha band. Significant between-group differences in global and regional topological abnormalities were found in both alpha and beta bands. The proposed symptom-based clustering approach revealed the divergent functional connectivity patterns in the ASD subgroups that was not observed in typical ASD studies. Our study thus provides a new perspective to address the heterogeneity in ASD research.

Spatial-Temporal EEG Fusion Based on Neural Network for Major Depressive Disorder Detection

In view of the major depressive disorder characteristics such as high mortality as well as high recurrence, it is important to explore an objective and effective detection method for major depressive disorder. Considering the advantages complementary of different machine learning algorithms in information mining process, as well as the fusion complementary of different information, in this study, the spatial-temporal electroencephalography fusion framework using neural network is proposed for major depressive disorder detection. Since electroencephalography is a typical time series signal, we introduce recurrent neural network embedded in long short-term memory unit for extract temporal domain features to solve the problem of long-distance information dependence. To reduce the volume conductor effect, the temporal electroencephalography data are mapping into a spatial brain functional network using phase lag index, then the spatial domain features were extracted from brain functional network using 2D convolutional neural networks. Considering the complementarity between different types of features, the spatial-temporal electroencephalography features are fused to achieve data diversity. The experimental results show that spatial-temporal features fusion can improve the detection accuracy of major depressive disorder with a highest of 96.33%. In addition, our research also found that theta, alpha, and full frequency band in brain regions of left frontal, left central, right temporal are closely related to MDD detection, especially theta frequency band in left frontal region. Only using single-dimension EEG data as decision basis, it is difficult to fully explore the valuable information hidden in the data, which affects the overall detection performance of MDD. Meanwhile, different algorithms have their own advantages for different application scenarios. Ideally, different algorithms should use their respective advantages to jointly address complex problems in engineering fields. To this end, we propose a computer-aided MDD detection framework based on spatial-temporal EEG fusion using neural network, as shown in Fig. 1. The simplified process is as follows: (1) Raw EEG data acquisition and preprocessing. (2) The time series EEG data of each channel are input as recurrent neural network (RNN), and RNN is used to process and extract temporal domain (TD) features. (3) The BFN among different EEG channels is constructed, and CNN is used to process and extract the spatial domain (SD) features of the BFN. (4) Based on the theory of information complementarity, the spatial-temporal information is fused to realize efficient MDD detection. Fig. 1 MDD detection framework based on spatial-temporal EEG fusion.

EEG Based Functional Brain Network Analysis and Classification of Dyslexic Children During Sustained Attention Task

Reading is a complex cognitive skill that involves visual, attention, and linguistic skills. Because attention is one of the most important cognitive skills for reading and learning, the current study intends to examine the functional brain network connectivity implicated during sustained attention in dyslexic children. 15 dyslexic children (mean age 9.83±1.85 years) and 15 non-dyslexic children (mean age 9.91±1.97 years) were selected for this study. The children were asked to perform a visual continuous performance task (VCPT) while their electroencephalogram (EEG) signals were recorded. In dyslexic children, significant variations in task measurements revealed considerable omission and commission errors. During task performance, the dyslexic group with the absence of a small-world network had a lower clustering coefficient, a longer characteristic pathlength, and lower global and local efficiency than the non-dyslexic group (mainly in theta and alpha bands). When classifying data from the dyslexic and non-dyslexic groups, the current study achieved the maximum classification accuracy of 96.7% using a k-nearest neighbor (KNN) classifier. To summarize, our findings revealed indications of poor functional segregation and disturbed information transfer in dyslexic brain networks during a sustained attention task.

Using shallow neural networks with functional connectivity from EEG signals for early diagnosis of Alzheimer's and frontotemporal dementia

Introduction

Dementia is a neurological disorder associated with aging that can cause a loss of cognitive functions, impacting daily life. Alzheimer's disease (AD) is the most common cause of dementia, accounting for 50-70% of cases, while frontotemporal dementia (FTD) affects social skills and personality. Electroencephalography (EEG) provides an effective tool to study the effects of AD on the brain.

Methods

In this study, we propose to use shallow neural networks applied to two sets of features: spectral-temporal and functional connectivity using four methods. We compare three supervised machine learning techniques to the CNN models to classify EEG signals of AD / FTD and control cases. We also evaluate different measures of functional connectivity from common EEG frequency bands considering multiple thresholds.

Results and discussion

Results showed that the shallow CNN-based models achieved the highest accuracy of 94.54% with AEC in test dataset when considering all connections, outperforming conventional methods and providing potentially an additional early dementia diagnosis tool.

Functional brain network features specify DBS outcome for patients with treatment resistant depression

Deep brain stimulation (DBS) has shown therapeutic benefits for treatment resistant depression (TRD). Stimulation of the subcallosal cingulate gyrus (SCG) aims to alter dysregulation between subcortical and cortex. However, the 50% response rates for SCG-DBS indicates that selection of appropriate patients is challenging. Since stimulation influences large-scale network function, we hypothesized that network features can be used as biomarkers to inform outcome. In this pilot project, we used resting-state EEG recorded longitudinally from 10 TRD patients with SCG-DBS (11 at baseline). EEGs were recorded before DBS-surgery, 1-3 months, and 6 months post surgery. We used graph theoretical analysis to calculate clustering coefficient, global efficiency, eigenvector centrality, energy, and entropy of source-localized EEG networks to determine their topological/dynamical features. Patients were classified as responders based on achieving a 50% or greater reduction in Hamilton Depression (HAM-D) scores from baseline to 12 months post surgery. In the delta band, false discovery rate analysis revealed that global brain network features (segregation, integration, synchronization, and complexity) were significantly lower and centrality of subgenual anterior cingulate cortex (ACC) was higher in responders than in non-responders. Accordingly, longitudinal analysis showed SCG-DBS increased global network features and decreased centrality of subgenual ACC. Similarly, a clustering method separated two groups by network features and significant correlations were identified longitudinally between network changes and depression symptoms. Despite recent speculation that certain subtypes of TRD are more likely to respond to DBS, in the SCG it seems that underlying brain network features are associated with ability to respond to DBS. SCG-DBS increased segregation, integration, and synchronizability of brain networks, suggesting that information processing became faster and more efficient, in those patients in whom it was lower at baseline. Centrality results suggest these changes may occur via altered connectivity in specific brain regions especially ACC. We highlight potential mechanisms of therapeutic effect for SCG-DBS.

Investigating Emotion EEG Patterns for Depression Detection with Attentive Simple Graph Convolutional Network

Depression severely limits daily functioning, diminishes quality of life and possibly leads to self-harm and suicide. Noninvasive electroencephalography (EEG) has been shown effective as biomarkers for objective depression diagnose and treatment response prediction, and dry EEG electrodes further extend its availability for clinical use. Even though many efforts have been made to identify depression biomarkers, searching reliable EEG biomarkers for depression detection remains challenging. This work presents a systematic investigation of capabilities of emotion EEG patterns for depression detection using a dry EEG electrode system. We design an emotion elicitation paradigm with happy, neutral and sad emotions and collect EEG signals during film watching from 33 depressed patients and 40 healthy controls. The mean activation levels at frontal and temporal sites in the alpha, beta and gamma bands of the depressed group are different to those of the healthy group, indicating the impacts of depressive symptoms on the emotion experiences. To leverage the topology information among EEG channels for emotion recognition and depression detection, an Attentive Simple Graph Convolutional network is built. The deep depression-health classifier achieves a sensitivity of 81.93% and a specificity of 91.69% on the happy emotions, suggesting the promising use of the emotion neural patterns for distinguishing the depressed patients from the healthy controls.

Deep Depression Detection with Resting-State and Cognitive-Task EEG

Depression is a common mental disorder that negatively affects physical health and personal, social and occupational functioning. Currently, accurate and objective diagnosis of depression remains challenging, and electroencephalography (EEG) provides promising clinical practice or home use due to considerable performance and low cost. This work investigates the capabilities of deep neural networks with EEG-based neural patterns from both resting states and cognitive tasks for depression detection. We collect EEG signals from 33 depressed patients and 40 healthy controls using wearable dry electrodes and build Attentive Simple Graph Convolutional network and Transformer neural network for objective depression detection. Four experiment stages, including two resting states and two cognitive tasks, are designed to characterize the alteration of relevant neural patterns in the depressed patients, in terms of decreased energy and impaired performance in sustained attention and response inhibition. The Transformer model achieves an AUC of 0.94 on the Continuous Performance Test-Identical Pairs version (sensitivity: 0.87, specificity: 0.91) and the Stroop Color Word Test (sensitivity: 0.93, specificity: 0.88), and an AUC of 0.89 on the two resting states (sensitivity: 0.85 and 0.87, specificity: 0.88 and 0.90, respectively), indicating the potential of EEG-based neural patterns in identifying depression. These findings provide new insights into the research of depression mechanisms and EEG-based depression biomarkers.

How does Music Affect Your Brain? A Pilot Study on EEG and Music Features for Automatic Analysis

Music can effectively induce specific emotion and usually be used in clinical treatment or intervention. The electroencephalogram can help reflect the impact of music. Previous studies showed that the existing methods achieved relatively good performance in predicting emotion response to music. However, these methods tend to be time consuming and expensive due to their complexity. To this end, this study proposes a grey wolf optimiser-based method to predict the induced emotion through fusing electroencephalogram features and music features. Experimental results show that, the proposed method can reach a promising performance for predicting emotional response to music and outperform the alternative method. In addition, we analyse the relationship between the music features and electroencephalogram features and the results demonstrate that, musical timbre features are significantly related to the electroencephalogram features.Clinical relevance- This study targets the automatic prediction of the human response to music. It further explores the correlation between EEG features and music features aiming to provide the basis for the extension to the application of music. The grey wolf optimiser-based method proposed in this study could supply a promising avenue for the emotion prediction as induced by music.

Cross-Frequency Multilayer Network Analysis with Bispectrum-based Functional Connectivity: A Study of Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder known to affect functional connectivity (FC) across many brain regions. Linear FC measures have been applied to study the differences in AD by splitting neurophysiological signals, such as electroencephalography (EEG) recordings, into discrete frequency bands and analysing them in isolation from each other. We address this limitation by quantifying cross-frequency FC in addition to the traditional within-band approach. Cross-bispectrum, a higher-order spectral analysis approach, is used to measure the nonlinear FC and is compared with the cross-spectrum, which only measures the linear FC within bands. This work reports the reconstruction of a cross-frequency FC network where each frequency band is treated as a layer in a multilayer network with both inter- and intra-layer edges. Cross-bispectrum detects cross-frequency differences, mainly increased FC in AD cases in δ-θ coupling. Overall, increased strength of low-frequency coupling and decreased level of high-frequency coupling is observed in AD cases in comparison to healthy controls (HC). We demonstrate that a graph-theoretic analysis of cross-frequency brain networks is crucial to obtain a more detailed insight into their structure and function. Vulnerability analysis reveals that the integration and segregation properties of networks are enabled by different frequency couplings in AD networks compared to HCs. Finally, we use the reconstructed networks for classification. The extra cross-frequency coupling information can improve the classification performance significantly, suggesting an important role of cross-frequency FC. The results highlight the importance of studying nonlinearity and including cross-frequency FC in characterising AD.

Alterations in EEG functional connectivity in individuals with depression: A systematic review

The brain works as an organised, network-like structure of functionally interconnected regions. Disruptions to interconnectivity in certain networks have been linked to symptoms of depression and impairments in cognition. Electroencephalography (EEG) is a low-burden tool by which differences in functional connectivity (FC) can be assessed. This systematic review aims to provide a synthesis of evidence relating to EEG FC in depression. A comprehensive electronic literature search for terms relating to depression, EEG, and FC was conducted on studies published before the end of November 2021, according to PRISMA guidelines. Studies comparing EEG measures of FC of individuals with depression to that of healthy control groups were included. Data was extracted by two independent reviewers, and the quality of EEG FC methods was assessed. Fifty-two studies assessing EEG FC in depression were identified: 36 assessed resting-state FC, and 16 assessed task-related or other (i.e., sleep) FC. Somewhat consistent findings in resting-state studies suggest for no differences between depression and control groups in EEG FC in the delta and gamma frequencies. However, while most resting-state studies noted a difference in alpha, theta, and beta, no clear conclusions could be drawn about the direction of the difference, due to considerable inconsistencies between study design and methodology. This was also true for task-related and other EEG FC. More robust research is needed to understand the true differences in EEG FC in depression. Given that the FC between brain regions drives behaviour, cognition, and emotion, characterising how FC differs in depression is essential for understanding the aetiology of depression.

Correlation between electroencephalographic markers in the healthy brain

Mental disorders have an increasing tendency and represent the main burden of disease to society today. A wide variety of electroencephalographic (EEG) markers have been successfully used to assess different symptoms of mental disorders. Different EEG markers have demonstrated similar classification accuracy, raising a question of their independence. The current study is aimed to investigate the hypotheses that different EEG markers reveal partly the same EEG features reflecting brain functioning and therefore provide overlapping information. The assessment of the correlations between EEG signal frequency band power, dynamics, and functional connectivity markers demonstrates that a statistically significant correlation is evident in 37 of 66 (56%) comparisons performed between 12 markers of different natures. A significant correlation between the majority of the markers supports the similarity of information in the markers. The results of the performed study confirm the hypotheses that different EEG markers reflect partly the same features in brain functioning. Higuchi's fractal dimension has demonstrated a significant correlation with the 82% of other markers and is suggested to reveal a wide spectrum of various brain disorders. This marker is preferable in the early detection of symptoms of mental disorders.

Functional connectivity learning via Siamese-based SPD matrix representation of brain imaging data

Measurement of brain functional connectivity has become a dominant approach to explore the interaction dynamics between brain regions of subjects under examination. Conventional functional connectivity measures largely originate from deterministic models on empirical analysis, usually demanding application-specific settings (e.g., Pearson's Correlation and Mutual Information). To bridge the technical gap, this study proposes a Siamese-based Symmetric Positive Definite (SPD) Matrix Representation framework (SiameseSPD-MR) to derive the functional connectivity of brain imaging data (BID) such as Electroencephalography (EEG), thus the alternative application-independent measure (in the form of SPD matrix) can be automatically learnt: (1) SiameseSPD-MR first exploits graph convolution to extract the representative features of BID with the adjacency matrix computed considering the anatomical structure; (2) Adaptive Gaussian kernel function then applies to obtain the functional connectivity representations from the deep features followed by SPD matrix transformation to address the intrinsic functional characteristics; and (3) Two-branch (Siamese) networks are combined via an element-wise product followed by a dense layer to derive the similarity between the pairwise inputs. Experimental results on two EEG datasets (autism spectrum disorder, emotion) indicate that (1) SiameseSPD-MR can capture more significant differences in functional connectivity between neural states than the state-of-the-art counterparts do, and these findings properly highlight the typical EEG characteristics of ASD subjects, and (2) the obtained functional connectivity representations conforming to the proposed measure can act as meaningful markers for brain network analysis and ASD discrimination.

Brain function changes reveal rapid antidepressant effects of nitrous oxide for treatment-resistant depression:Evidence from task-state EEG

Nitrous oxide has rapid antidepressant effects in patients with treatment-resistant depression (TRD), but its underlying mechanisms of therapeutic actions are not well understood. Moreover, most of the current studies lack objective biological indicators to evaluate the changes of nitrous oxide-induced brain function for TRD. Therefore, this study assessed the effect of nitrous oxide on brain function for TRD based on event-related potential (ERP) components and functional connectivity networks (FCNs) methods. In this randomized, longitudinal, placebo-controlled trial, all TRD participants were divided into two groups to receive either a 1-hour inhalation of nitrous oxide or a placebo treatment, and they took part in the same task-state electroencephalogram (EEG) experiment before and after treatment. The experimental results showed that nitrous oxide improved depressive symptoms better than placebo in terms of 17-Hamilton Depression Rating Scale score (HAMD-17). Statistical analysis based on ERP components showed that nitrous oxide-induced significant differences in amplitude and latency of N1, P1, N2, P2. In addition, increased brain functional connectivity was found after nitrous oxide treatment. And the change of network metrics has a significant correlation with decreased depressive symptoms. These findings may suggest that nitrous oxide improves depression symptoms for TRD by modifying brain function.

Functional near infrared spectroscopy for brain functional connectivity analysis: A graph theoretic approach

Background

Functional Near-Infrared Spectroscopy is an optical brain monitoring technique which uses NIRS to perform functional neuroimaging. It uses near-infrared light for measuring brain activity and to estimate the cortical hemodynamic activity in the brain due to motor activity. Functional NIRS measures the changes in oxygen levels in oxygenated and deoxygenated hemoglobin by optical absorption. One of the main challenges in the analysis of fNIRS signals is the signal degradation due to the interference from noise and artifacts from multiple sources.

Methods

In this context, this research aims to analyze the connectivity between different regions of the brain using graph theory and hence the geometrical association of brain networks in terms of functional parameters. In this study, the impact of two noise removal processes (CBSI and TDDR), along with two types of correlation fNIRS such as Pearson's Correlation (PC), and Cross Correlation (CC) and various whole-brain network architectures on the reproducibility of graph measurements for individual participants has been carefully examined for different densities ranging from 5% to 50%.The graph measures' repeatability at the individual level was studied using the test-retest variability (TRT).

Results

The test-retest variability for global measurements in binary networks was substantially large at low densities, regardless of the noise removal method or the kind of correlation. Very low test -reset values are observed for weighted networks and great reproducibility for measures of the entire graph. When comparing the test-retest values for various methods, the kind of correlation, the absolute value of the correlation, and the weight calculation method on the raw correlation value all had significant major effects.

Conclusion

Based on a weighted network with the absolute cross correlation functioning as the weight, this study revealed that normalized global graph measurements were reliable. The node definition techniques that were utilized to remove noise were not essential for the normalized graph measures to be reproducible.

Effect of Heartfulness Meditation Among Long-Term, Short-Term and Non-meditators on Prefrontal Cortex Activity of Brain Using Machine Learning Classification: A Cross-Sectional Study

Background Meditation is a mental practice with health benefits and may increase activity in the prefrontal cortex of the brain. Heartfulness meditation (HM) is a modified form of rajyoga meditation supported by a unique feature called "yogic transmission." This feasibility study aimed to explore the effect of HM on electroencephalogram (EEG) connectivity parameters of long-term meditators (LTM), short-term meditators (STM), and non-meditators (NM) with an application of machine learning models and determining classifier methods that can effectively discriminate between the groups. Materials and methods EEG data were collected from 34 participants. The functional connectivity parameters, correlation coefficient, clustering coefficient, shortest path, and phase locking value were utilized as a feature vector for classification. To evaluate the various states of HM practice, the categorization was done between (LTM, NM) and (STM, NM) using a multitude of machine learning classifiers. Results The classifier's performances were evaluated based on accuracy using 10-fold cross-validation. The results showed that the accuracy of machine learning models ranges from 84% to 100% while classifying LTM and NM, and accuracy from 80% to 93% while classifying STM and NM. It was found that decision trees, support vector machines, k-nearest neighbors, and ensemble classifiers performed better than linear discriminant analysis and logistic regression. Conclusion This is the first study to our knowledge employing machine learning for the classification among HM meditators and NM The results indicated that machine learning classifiers with EEG functional connectivity as a feature vector could be a viable marker for accessing meditation ability.

Fusion Graph Representation of EEG for Emotion Recognition

Various relations existing in Electroencephalogram (EEG) data are significant for EEG feature representation. Thus, studies on the graph-based method focus on extracting relevancy between EEG channels. The shortcoming of existing graph studies is that they only consider a single relationship of EEG electrodes, which results an incomprehensive representation of EEG data and relatively low accuracy of emotion recognition. In this paper, we propose a fusion graph convolutional network (FGCN) to extract various relations existing in EEG data and fuse these extracted relations to represent EEG data more comprehensively for emotion recognition. First, the FGCN mines brain connection features on topology, causality, and function. Then, we propose a local fusion strategy to fuse these three graphs to fully utilize the valuable channels with strong topological, causal, and functional relations. Finally, the graph convolutional neural network is adopted to represent EEG data for emotion recognition better. Experiments on SEED and SEED-IV demonstrate that fusing different relation graphs are effective for improving the ability in emotion recognition. Furthermore, the emotion recognition accuracy of 3-class and 4-class is higher than that of other state-of-the-art methods.

Analysis of functional connectivity in depression based on a weighted hyper-network method

Objective. Brain connectivity network is a vital tool to reveal the interaction between different brain regions. Currently, most functional connectivity methods can only capture pairs of information to construct brain networks which ignored the high-order correlations between brain regions.Approach. Therefore, this study proposed a weighted connectivity hyper-network based on resting-state EEG data, and then applied to depression identification and analysis. The hyper-network model was build based on least absolute shrinkage and selection operator sparse regression method to effectively represent the higher-order relationships of brain regions. On this basis, by integrating the correlation-based weighted hyper-edge information, the weighted hyper-network is constructed, and the topological features of the network are extracted for classification.Main results. The experimental results obtained an optimal accuracy compared to the traditional coupling methods. The statistical results on network metrics proved that there were significant differences between depressive patients and normal controls. In addition, some brain regions and electrodes were found and discussed to highly correlate with depression by analyzing of the critical nodes and hyper-edges.Significance. These may help discover disease-related biomarkers important for depression diagnosis.

Altered Functional Connectivity and Complexity in Major Depressive Disorder after Musical Stimulation

Major depressive disorder (MDD) is a common mental illness. This study used electroencephalography (EEG) to explore the effects of music therapy on brain networks in MDD patients and to elucidate changes in functional brain connectivity in subjects before and after musical stimulation. EEG signals were collected from eight MDD patients and eight healthy controls. The phase locking value was adopted to calculate the EEG correlation of different channels in different frequency bands. Correlation matrices and network topologies were studied to analyze changes in functional connectivity between brain regions. The results of the experimental analysis found that the connectivity of the delta and beta bands decreased, while the connectivity of the alpha band increased. Regarding the characteristics of the EEG functional network, the average clustering coefficient, characteristic path length and degree of each node in the delta band decreased significantly after musical stimulation, while the characteristic path length in the beta band increased significantly. Characterized by the average clustering coefficient and characteristic path length, the classification of depression and healthy controls reached 93.75% using a support vector machine.

Abnormal Properties of Cortical Functional Brain Network in Major Depressive Disorder: Graph Theory Analysis Based on Electroencephalography-Source Estimates

Studies of scalp electroencephalography (EEG) had shown altered topological organization of functional brain networks in patients with major depressive disorder (MDD). However, most previous EEG-based network analyses were performed at sensor level, while the interpretation of obtained results was not straightforward due to volume conduction effect. To reduce the impact of this defect, the whole cortical functional brain networks of MDD patients were studied during resting state based on EEG-source estimates in this paper. First, scalp EEG signals were recorded from 19 patients with MDD and 20 normal controls under resting eyes-closed state, and cortical neural signals were estimated by using sLORETA method. Then, the correntropy coefficient of wavelet packet coefficients was performed to calculate functional connectivity (FC) matrices in four different frequency bands: δ, θ, α, β, respectively. Afterwards, topological properties of brain networks were analyzed by graph theory approaches. The results showed that the global FC strength of MDD patients was significantly higher than that of healthy subjects in α band. Also, it was found that MDD patients have abnormally increased clustering coefficient and local efficiency in both α and β bands compared to normal people. Furthermore, patients with MDD exhibited increased nodal clustering coefficients in the left lingual gryus and left precuneus in α band. In addition, β band global clustering coefficient was positively correlated with the scores of depression severity. Therefore, the findings indicated the cortical functional brain networks in MDD patients were disruptions, which suggested it would be one of potential causes of depression.

Feature-level fusion based on spatial-temporal of pervasive EEG for depression recognition

BACKGROUND AND OBJECTIVE

In view of the depression characteristics such as high prevalence, high disability rate, high fatality rate, and high recurrence rate, early identification and early intervention are the most effective methods to prevent irreversible damage of brain function over time. The traditional method of depression recognition based on questionnaires and interviews is time-consuming and labor-intensive, and heavily depends on the doctor's subjective experience. Therefore, accurate, convenient and effective recognition of depression has important social value and scientific significance.

METHODS

This paper proposes a depression recognition framework based on feature-level fusion of spatial-temporal pervasive electroencephalography (EEG). Time series EEG data were collected by portable three-electrode EEG acquisition instrument, and mapped to a spatial complex network called visibility graph (VG). Then temporal EEG features and spatial VG metric features were extracted and selected. Based on the correlation between features and categories, the differences in contribution of individual feature are explored, and different contribution coefficients are assigned to different features as the data basis of feature-level fusion to ensure the diversity of data. A cascade forest model based on three different decision forests is designed to realize the efficient depression recognition using spatial-temporal feature-level fusion data.

RESULTS

Experimental data were obtained from 26 depressed patients and 29 healthy controls (HC). The results of multiple control experiments show that compared with single type feature, feature-level fusion without contribution coefficient, and independent classifiers, the feature-level method with contribution coefficient of spatial-temporal has a stronger recognition ability of depression, and the highest accuracy is 92.48%.

CONCLUSION

Feature-level fusion method provides an effective computer-aided tool for rapid clinical diagnosis of depression.

Analysis of EEG brain connectivity of children with ADHD using graph theory and directional information transfer

Research shows that Attention Deficit Hyperactivity Disorder (ADHD) is related to a disorder in brain networks. The purpose of this study is to use an effective connectivity measure and graph theory to examine the impairments of brain connectivity in ADHD. Weighted directed graphs based on electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children were constructed. The edges between two nodes (electrodes) were calculated by Phase Transfer Entropy (PTE). PTE is calculated for five frequency bands: delta, theta, alpha, beta, and gamma. The graph theory measures were divided into two categories: global and local. Statistical analysis with global measures indicates that in children with ADHD, the segregation of brain connectivity increases while the integration of the brain connectivity decreases compared to healthy children. These brain network differences were identified in the delta and theta frequency bands. The classification accuracy of 89.4% is obtained for both in-degree and strength measures in the theta band. Our result indicated local graph measures classified ADHD and healthy subjects with accuracy of 91.2 and 90% in theta and delta bands, respectively. Our analysis may provide a new understanding of the differences in the EEG brain network of children with ADHD and healthy children.

EEG based functional brain networks analysis in dyslexic children during arithmetic task

Developmental Dyslexia is a neuro-developmental disorder that often refers to a phonological processing deficit regardless of average IQ. The present study investigated the distinct functional changes in brain networks of dyslexic children during arithmetic task performance using an electroencephalogram. Fifteen dyslexic children and fifteen normally developing children (NDC) were recruited and performed an arithmetic task. Brain functional network measures such as node strength, clustering coefficient, characteristic pathlength and small-world were calculated using graph theory methods for both groups. Task performance showed significantly less performance accuracy in dyslexics against NDC. The neural findings showed increased connectivity in the delta band and reduced connectivity in theta, alpha, and beta band at temporoparietal, and prefrontal regions in dyslexic group while performing the task. The node strengths were found to be significantly high in delta band (T3, O1, F8 regions) and low in theta (T5, P3, Pz regions), beta (Pz) and gamma band (T4 and prefrontal regions) during the task in dyslexics compared to the NDC. The clustering coefficient was found to be significantly low in the dyslexic group (theta and alpha band) and characteristic pathlength was found to be significantly high in the dyslexic group (theta and alpha band) compared to the NDC group while performing task. In conclusion, the present study shows evidence for poor fact-retrieval mechanism and altered network topology in dyslexic brain networks during arithmetic task performance.

Brain functional connectivity analysis in patients with relapsing-remitting multiple sclerosis: A graph theory approach of EEG resting state

Multiple sclerosis (MS) is an autoimmune disease related to the central nervous system (CNS). This study aims to investigate the effects of MS on the brain's functional connectivity network using the electroencephalogram (EEG) resting-state signals and graph theory approach. Resting-state eyes-closed EEG signals were recorded from 20 patients with relapsing-remitting MS (RRMS) and 18 healthy cases. In this study, the prime objective is to calculate the connectivity between EEG channels to assess the differences in brain functional network global features. The results demonstrated lower cortical activity in the alpha frequency bands and higher activity for the gamma frequency bands in patients with RRMS compared to the healthy group. In this study, graph metric calculations revealed a significant difference in the diameter of the functional brain network based on the directed transfer function (DTF) measure between the two groups, indicating a higher diameter in RRMS cases for the alpha frequency band. A higher diameter for the functional brain network in MS cases can result from anatomical damage. In addition, considerable differences between the networks' global efficiency and transitivity based on the imaginary part of the coherence (iCoh) measure were observed, indicating higher global efficiency and transitivity in the delta, theta, and beta frequency bands for RRMS cases, which can be related to the compensatory functional reaction from the brain. This study indicated that in RRMS cases, some of the global characteristics of the brain's functional network, such as diameter and global efficiency, change and can be illustrated even in the resting-state condition when the brain is not under cognitive load.

High-order brain functional network for electroencephalography-based diagnosis of major depressive disorder

Brain functional network (BFN) based on electroencephalography (EEG) has been widely used to diagnose brain diseases, such as major depressive disorder (MDD). However, most existing BFNs only consider the correlation between two channels, ignoring the high-level interaction among multiple channels that contain more rich information for diagnosing brain diseases. In such a sense, the BFN is called low-order BFN (LO-BFN). In order to fully explore the high-level interactive information among multiple channels of the EEG signals, a scheme for constructing a high-order BFN (HO-BFN) based on the “correlation’s correlation” strategy is proposed in this paper. Specifically, the entire EEG time series is firstly divided into multiple epochs by sliding window. For each epoch, the short-term correlation between channels is calculated to construct a LO-BFN. The correlation time series of all channel pairs are formulated by these LO-BFNs obtained from all epochs to describe the dynamic change of short-term correlation along the time. To construct HO-BFN, we cluster all correlation time series to avoid the problems caused by high dimensionality, and the correlation of the average correlation time series from different clusters is calculated to reflect the high-order correlation among multiple channels. Experimental results demonstrate the efficiency of the proposed HO-BFN in MDD identification, and its integration with the LO-BFN can further improve the recognition rate.

Classifying oscillatory brain activity associated with Indian Rasas using network metrics

Neural signatures for the western classification of emotions have been widely discussed in the literature. The ancient Indian treatise on performing arts known as Natyashastra categorizes emotions into nine classes, known as Rasas. Rasa-as opposed to a pure emotion-is defined as a superposition of certain transitory, dominant, and temperamental emotional states. Although Rasas have been widely discussed in the text, dedicated brain imaging studies have not been conducted in their research. Our study examines the neural oscillations, recorded through electroencephalography (EEG) imaging, that are elicited while experiencing emotional states corresponding to Rasas. We identify differences among them using network-based functional connectivity metrics in five different frequency bands. Further, Random Forest models are trained on the extracted network features, and we present our findings based on classifier predictions. We observe slow (delta) and fast brain waves (beta and gamma) exhibited the maximum discriminating features between Rasas, whereas alpha and theta bands showed fewer distinguishable pairs. Out of nine Rasas, Sringaram (love), Bibhatsam (odious), and Bhayanakam (terror) were distinguishable from other Rasas the most across frequency bands. On the scale of most network metrics, Raudram (rage) and Sringaram are on the extremes, which also resulted in their good classification accuracy of 95%. This is reminiscent of the circumplex model where anger and contentment/happiness are on extremes on the pleasant scale. Interestingly, our results are consistent with the previous studies which highlight the significant role of higher frequency oscillations in the classification of emotions, in contrast to the alpha band that has shows non-significant differences across emotions. This research contributes to one of the first attempts to investigate the neural correlates of Rasas. Therefore, the results of this study can potentially guide the explorations into the entrainment of brain oscillations between performers and viewers, which can further lead to better performances and viewer experience.

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.

Graph Theory Analysis of the Cortical Functional Network During Sleep in Patients With Depression

Depression, a common mental illness that seriously affects the psychological health of patients, is also thought to be associated with abnormal brain functional connectivity. This study aimed to explore the differences in the sleep-state functional network topology in depressed patients. A total of 25 healthy participants and 26 depressed patients underwent overnight 16-channel electroencephalography (EEG) examination. The cortical networks were constructed by using functional connectivity metrics of participants based on the weighted phase lag index (WPLI) between the EEG signals. The results indicated that depressed patients exhibited higher global efficiency and node strength than healthy participants. Furthermore, the depressed group indicated right-lateralization in the δ band. The top 30% of connectivity in both groups were shown in undirected connectivity graphs, revealing the distinct link patterns between the depressed and control groups. Links between the hemispheres were noted in the patient group, while the links in the control group were only observed within each hemisphere, and there were many long-range links inside the hemisphere. The altered sleep-state functional network topology in depressed patients may provide clues for a better understanding of the depression pathology. Overall, functional network topology may become a powerful tool for the diagnosis of depression.

Electroconvulsive Therapy-Induced Changes in Functional Brain Network of Major Depressive Disorder Patients: A Longitudinal Resting-State Electroencephalography Study

Objectives

Several studies have shown abnormal network topology in patients with major depressive disorder (MDD). However, changes in functional brain networks associated with electroconvulsive therapy (ECT) remission based on electroencephalography (EEG) signals have yet to be investigated.

Methods

Nineteen-channel resting-state eyes-closed EEG signals were collected from 24 MDD patients pre- and post-ECT treatment. Functional brain networks were constructed by using various coupling methods and binarization techniques. Changes in functional connectivity and network metrics after ECT treatment and relationships between network metrics and clinical symptoms were explored.

Results

ECT significantly increased global efficiency, edge betweenness centrality, local efficiency, and mean degree of alpha band after ECT treatment, and an increase in these network metrics had significant correlations with decreased depressive symptoms in repeated measures correlation. In addition, ECT regulated the distribution of hubs in frontal and occipital lobes.

Conclusion

ECT modulated the brain’s global and local information-processing patterns. In addition, an ECT-induced increase in network metrics was associated with clinical remission.

Significance

These findings might present the evidence for us to understand how ECT regulated the topology organization in functional brain networks of clinically remitted depressive patients.

Dopaminergic Modulation of Local Non-oscillatory Activity and Global-Network Properties in Parkinson’s Disease: An EEG Study

Dopaminergic medication for Parkinson’s disease (PD) modulates neuronal oscillations and functional connectivity (FC) across the basal ganglia-thalamic-cortical circuit. However, the non-oscillatory component of the neuronal activity, potentially indicating a state of excitation/inhibition balance, has not yet been investigated and previous studies have shown inconsistent changes of cortico-cortical connectivity as a response to dopaminergic medication. To further elucidate changes of regional non-oscillatory component of the neuronal power spectra, FC, and to determine which aspects of network organization obtained with graph theory respond to dopaminergic medication, we analyzed a resting-state electroencephalography (EEG) dataset including 15 PD patients during OFF and ON medication conditions. We found that the spectral slope, typically used to quantify the broadband non-oscillatory component of power spectra, steepened particularly in the left central region in the ON compared to OFF condition. In addition, using lagged coherence as a FC measure, we found that the FC in the beta frequency range between centro-parietal and frontal regions was enhanced in the ON compared to the OFF condition. After applying graph theory analysis, we observed that at the lower level of topology the node degree was increased, particularly in the centro-parietal area. Yet, results showed no significant difference in global topological organization between the two conditions: either in global efficiency or clustering coefficient for measuring global and local integration, respectively. Interestingly, we found a close association between local/global spectral slope and functional network global efficiency in the OFF condition, suggesting a crucial role of local non-oscillatory dynamics in forming the functional global integration which characterizes PD. These results provide further evidence and a more complete picture for the engagement of multiple cortical regions at various levels in response to dopaminergic medication in PD.

A multi-modal open dataset for mental-disorder analysis

According to the WHO, the number of mental disorder patients, especially depression patients, has overgrown and become a leading contributor to the global burden of disease. With the rising of tools such as artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applications for mental disorder diagnosis has become a new research hot topic. We present a multi-modal open dataset for mental-disorder analysis. The dataset includes EEG and recordings of spoken language data from clinically depressed patients and matching normal controls, who were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes data collected using a traditional 128-electrodes mounted elastic cap and a wearable 3-electrode EEG collector for pervasive computing applications. The 128-electrodes EEG signals of 53 participants were recorded as both in resting state and while doing the Dot probe tasks; the 3-electrode EEG signals of 55 participants were recorded in resting-state; the audio data of 52 participants were recorded during interviewing, reading, and picture description.

Measurement(s)	Human Brainwave • spoken language
Technology Type(s)	EEG collector • audio recorder
Sample Characteristic - Organism	Homo Sapiens
Sample Characteristic - Location	China

Computer-aided Recognition Based on Decision-level Multimodal Fusion for Depression

Aiming at the problem of depression recognition, this paper proposes a computer-aided recognition framework based on decision-level multimodal fusion. In Song Dynasty of China, the idea of multimodal fusion was contained in "one gets different impressions of a mountain when viewing it from the front or sideways, at a close range or from afar" poetry. Objective and comprehensive analysis of depression can more accurately restore its essence, and multimodal can represent more information about depression compared to single modal. Linear electroencephalography (EEG) features based on adaptive auto regression (AR) model and typical nonlinear EEG features are extracted. EEG features related to depression and graph metric features in depression related brain regions are selected as the data basis of multimodal fusion to ensure data diversity. Based on the theory of multi-agent cooperation, the computer-aided depression recognition model of decision-level is realized. The experimental data comes from 24 depressed patients and 29 healthy controls (HC). The results of multi-group controlled trials show that compared with single modal or independent classifiers, the decision-level multimodal fusion method has a stronger ability to recognize depression, and the highest accuracy rate 92.13% was obtained. In addition, our results suggest that improving the brain region associated with information processing can help alleviate and treat depression. In the field of classification and recognition, our results clarify that there is no universal classifier suitable for any condition.

Hypofunction of directed brain network within alpha frequency band in depressive patients: a graph-theoretic analysis

Directed brain networks may provide new insights into exploring physiological mechanism and neuromarkers for depression. This study aims to investigate the abnormalities of directed brain networks in depressive patients. We constructed the directed brain network based on resting electroencephalogram for 19 depressive patients and 20 healthy controls with eyes closed and eyes open. The weighted directed brain connectivity was measured by partial directed coherence for α, β, γ frequency band. Furthermore, topological parameters (clustering coefficient, characteristic path length, and et al.) were computed based on graph theory. The correlation between network metrics and clinical symptom was also examined. Depressive patients had a significantly weaker value of partial directed coherence at alpha frequency band in eyes-closed state. Clustering coefficient and characteristic path length were significantly lower in depressive patients (both p < .01). More importantly, in depressive patients, disruption of directed connectivity was noted in left-to-left (p < .05), right-to-left (p < .01) hemispheres and frontal-to-central (p < .01), parietal-to-central (p < .05), occipital-to-central (p < .05) regions. Furthermore, connectivity in LL and RL hemispheres was negatively correlated with depression scale scores (both p < .05). Depressive patients showed a more randomized network structure, disturbed directed interaction of left-to-left, right-to-left hemispheric information and between different cerebral regions. Specifically, left-to-left, right-to-left hemispheric connectivity was negatively correlated with the severity of depression. Our analysis may serve as a potential neuromarker of depression.

Theta oscillations: A rhythm difference comparison between major depressive disorder and anxiety disorder

BACKGROUND

Due to substantial comorbidities of major depressive disorder (MDD) and anxiety disorder (AN), these two disorders must be distinguished. Accurate identification and diagnosis facilitate effective and prompt treatment. EEG biomarkers are a potential research hotspot for neuropsychiatric diseases. The purpose of this study was to investigate the differences in EEG power spectrum at theta oscillations between patients with MDD and patients with AN.

METHODS

Spectral analysis was used to study 66 patients with MDD and 43 patients with AN. Participants wore 16-lead EEG caps to measure resting EEG signals. The EEG power spectrum was measured using the fast Fourier transform. Independent samples t-test was used to analyze the EEG power values of the two groups, and p < 0.05 was statistically significant.

RESULTS

EEG power spectrum of the MDD group significantly differed from the AN group in the theta oscillation on 4-7 Hz at eight electrode points at F3, O2, T3, P3, P4, FP1, FP2, and F8.

CONCLUSION

Participants with anxiety demonstrated reduced power in the prefrontal cortex, left temporal lobe, and right occipital regions. Confirmed by further studies, theta oscillations could be another biomarker that distinguishes MDD from AN.

Machine learning approaches for diagnosing depression using EEG: A review

Depression has become one of the most crucial public health issues, threatening the quality of life of over 300 million people throughout the world. Nevertheless, the clinical diagnosis of depression is now still hampered by behavioral diagnostic methods. Due to the lack of objective laboratory diagnostic criteria, accurate identification and diagnosis of depression remained elusive. With the rise of computational psychiatry, a growing number of studies have combined resting-state electroencephalography with machine learning (ML) to alleviate diagnosis of depression in recent years. Despite the exciting results, these were worrisome of these studies. As a result, ML prediction models should be continuously improved to better screen and diagnose depression. Finally, this technique would be used for the diagnosis of other psychiatric disorders in the future.

Abnormal core functional connectivity on the pathology of MDD and antidepressant treatment: A systematic review

RATIONALE/IMPORTANCE

Researches have highlighted communication deficits between resting-state brain networks in major depressive disorder (MDD), as reflected in abnormal functional connectivity (FC). However, it is unclear whether impaired FC is associated with MDD pathology or is simply incidental to MDD symptoms. Moreover, there is no generalized theory to analyze the impact of treatment modalities on MDD.

OBJECTIVES

To address the issues, we conducted a systematic review of 49 eligible papers to provide insight into the pathological mechanisms of MDD patients by summarizing resting-state FC alterations involving mood and cognitive abnormalities and the effects of medications on them.

RESULTS

Mood disorders in MDD were characterized by abnormal FC between the amygdala, insula, anterior cingulate cortex (ACC), and prefrontal cortex (PFC). Cognitive impairment manifests as deficits in executive function, attention, memory, and rumination, primarily modulated by dysfunction between the fronto-parietal network and default mode network. Especially, we proposed the set of core abnormal FC (CA-FC) contributing to mood and cognitive impairment in MDD, currently including ACC-left precuneus/amygdala, rostral ACC-left dorsolateral PFC, left subgenual ACC-left cerebellar, left PFC- anterior subcallosal, and left precuneus-left pulvinar. After treatment, patients with normalized CA-FC showed remission of depressive symptoms.

CONCLUSIONS

We propose a CA-FC set for possible causative principle of MDD, which unifies the FC results from specific, difficult-to-analyze conditions into one outcome set for screening. Furthermore, CA-FC varies from person to person, and the low success rate of a single treatment may be due to the inability to cover too many CA-FC.

Shared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor Factorization

Ongoing electroencephalography (EEG) signals are recorded as a mixture of stimulus-elicited EEG, spontaneous EEG and noises, which poses a huge challenge to current data analyzing techniques, especially when different groups of participants are expected to have common or highly correlated brain activities and some individual dynamics. In this study, we proposed a data-driven shared and unshared feature extraction framework based on nonnegative and coupled tensor factorization, which aims to conduct group-level analysis for the EEG signals from major depression disorder (MDD) patients and healthy controls (HC) when freely listening to music. Constrained tensor factorization not only preserves the multilinear structure of the data, but also considers the common and individual components between the data. The proposed framework, combined with music information retrieval, correlation analysis, and hierarchical clustering, facilitated the simultaneous extraction of shared and unshared spatio-temporal-spectral feature patterns between/in MDD and HC groups. Finally, we obtained two shared feature patterns between MDD and HC groups, and obtained totally three individual feature patterns from HC and MDD groups. The results showed that the MDD and HC groups triggered similar brain dynamics when listening to music, but at the same time, MDD patients also brought some changes in brain oscillatory network characteristics along with music perception. These changes may provide some basis for the clinical diagnosis and the treatment of MDD patients.

EEG-connectivity: A fundamental guide and checklist for optimal study design and evaluation

Brain connectivity can be estimated through many analyses applied to electroencephalographic (EEG) data. However, substantial heterogeneity in the implementation of connectivity methods exist. Heterogeneity in conceptualization of connectivity measures, data collection, or data pre-processing may be associated with variability in robustness of measurement. While it is difficult to compare the results of studies using different EEG connectivity measures, standardization of processing and reporting may facilitate the task. We discuss how factors such as referencing, epoch length and number, controls for volume conduction, artefact removal, and statistical control of multiple comparisons influence the EEG connectivity estimate for connectivity measures, and what can be done to control for potential confounds associated with these factors. Based on the results reported in previous literature, this article presents recommendations and a novel checklist developed for quality assessment of EEG connectivity studies. This checklist and its recommendations are made in an effort to draw attention to factors that may influence connectivity estimates and factors that need to be improved in future research. Standardization of procedures and reporting in EEG connectivity may lead to EEG connectivity studies to be made more synthesisable and comparable despite variations in the methodology underlying connectivity estimates.

Brain controllability distinctiveness between depression and cognitive impairment

Alzheimer's disease (AD) is a progressive form of dementia marked by cognitive and memory deficits, estimated to affect ∼5.7 million Americans and account for ∼$277 billion in medical costs in 2018. Depression is one of the most common neuropsychiatric disorders that accompanies AD, appearing in up to 50% of patients. AD and Depression commonly occur together with overlapped symptoms (depressed mood, anxiety, apathy, and cognitive deficits.) and pose diagnostic challenges early in the clinical presentation. Understanding their relationship is critical for advancing treatment strategies, but the interaction remains poorly studied and thus often leads to a rapid decline in functioning. Modern systems and control theory offer a wealth of novel methods and concepts to assess the important property of a complex control system, such as the brain. In particular, the brain controllability analysis captures the ability to guide the brain behavior from an initial state (healthy or diseased) to a desired state in finite time, with suitable choice of inputs such as external or internal stimuli. The controllability property of the brain's dynamic processes will advance our understanding of the emergence and progression of brain diseases and thus helpful in the early diagnosis and novel treatment approaches. This study aims to assess the brain controllability differences between mild cognitive impairment (MCI), as prodromal AD, and Depression. This study used diffusion tensor imaging (DTI) data from 60 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI): 15 cognitively normal subjects and 45 patients with MCI, including 15 early MCI (EMCI) patients without depression, 15 EMCI patients with mild depression (EMCID), and 15 late MCI (LMCI) patients without depression. The structural brain network was firstly constructed and the brain controllability was characterized for each participant. The controllability of default mode network (DMN) and its sub-regions were then compared across groups in a structural basis. Results indicated that the brain average controllability of DMN in EMCI, LMCI, and EMCID were significantly decreased compared to healthy subjects (P < 0.05). The EMCI and LMCI groups also showed significantly greater average controllability of DMN versus the EMCID group. Furthermore, compared to healthy subjects, the regional controllability of the left/right superior prefrontal cortex and the left/right cingulate gyrus in the EMCID group showed a significant decrease (P < 0.01). Among these regions, the left superior prefrontal region's controllability was significantly decreased (P < 0.05) in the EMCID group compared with EMCI and LMCI groups. Our results provide a new perspective in understanding depressive symptoms in MCI patients and provide potential biomarkers for diagnosing depression from MCI and AD.

EEG-derived brain graphs are reliable measures for exploring exercise-induced changes in brain networks

The interaction of acute exercise and the central nervous system evokes increasing interest in interdisciplinary research fields of neuroscience. Novel approaches allow to monitor large-scale brain networks from mobile electroencephalography (EEG) applying graph theory, but it is yet uncertain whether brain graphs extracted after exercise are reliable. We therefore aimed to investigate brain graph reliability extracted from resting state EEG data before and after submaximal exercise twice within one week in male participants. To obtain graph measures, we extracted global small-world-index (SWI), clustering coefficient (CC) and characteristic path length (PL) based on weighted phase leg index (wPLI) and spectral coherence (Coh) calculation. For reliability analysis, Intraclass-Correlation-Coefficient (ICC) and Coefficient of Variation (CoV) were computed for graph measures before (REST) and after POST) exercise. Overall results revealed poor to excellent measures at PRE and good to excellent ICCs at POST in the theta, alpha-1 and alpha-2, beta-1 and beta-2 frequency band. Based on bootstrap-analysis, a positive effect of exercise on reliability of wPLI based measures was observed, while exercise induced a negative effect on reliability of Coh-based graph measures. Findings indicate that brain graphs are a reliable tool to analyze brain networks in exercise contexts, which might be related to the neuroregulating effect of exercise inducing functional connections within the connectome. Relative and absolute reliability demonstrated good to excellent reliability after exercise. Chosen graph measures may not only allow analysis of acute, but also longitudinal studies in exercise-scientific contexts.

Exploring intensity-dependent modulations in EEG resting-state network efficiency induced by exercise

PURPOSE

Exhaustive cardiovascular load can affect neural processing and is associated with decreases in sensorimotor performance. The purpose of this study was to explore intensity-dependent modulations in brain network efficiency in response to treadmill running assessed from resting-state electroencephalography (EEG) measures.

METHODS

Sixteen trained participants were tested for individual peak oxygen uptake (VO2 peak) and performed an incremental treadmill exercise at 50% (10 min), 70% (10 min) and 90% speed VO2 peak (all-out) followed by cool-down running and active recovery. Before the experiment and after each stage, borg scale (BS), blood lactate concentration (BLa), resting heartrate (HRrest) and 64-channel EEG resting state were assessed. To analyze network efficiency, graph theory was applied to derive small world index (SWI) from EEG data in theta, alpha-1 and alpha-2 frequency bands.

RESULTS

Analysis of variance for repeated measures revealed significant main effects for intensity on BS, BLa, HRrest and SWI. While BS, BLa and HRrest indicated maxima after all-out, SWI showed a reduction in the theta network after all-out.

CONCLUSION

Our explorative approach suggests intensity-dependent modulations of resting-state brain networks, since exhaustive exercise temporarily reduces brain network efficiency. Resting-state network assessment may prospectively play a role in training monitoring by displaying the readiness and efficiency of the central nervous system in different training situations.