Resting-state Electroencephalogram Research Articles

Critical dynamics in spontaneous EEG predict anesthetic-induced loss of consciousness and perturbational complexity

Consciousness has been proposed to be supported by electrophysiological patterns poised at criticality, a dynamical regime which exhibits adaptive computational properties, maximally complex patterns and divergent sensitivity to perturbation. Here, we investigate dynamical properties of the resting-state electroencephalogram (EEG) of healthy subjects undergoing general anesthesia with propofol, xenon or ketamine. Importantly, all participants were unresponsive under anesthesia, while consciousness was retained only during ketamine anesthesia (in the form of vivid dreams), enabling an experimental dissociation between unresponsiveness and unconsciousness. For each condition, we measure (i) avalanche criticality, (ii) chaoticity, and (iii) criticality-related metrics, revealing that states of unconsciousness are characterized by a distancing from both avalanche criticality and the edge of chaos. We then ask whether these same dynamical properties are predictive of the perturbational complexity index (PCI), a TMS-based measure that has shown remarkably high sensitivity in detecting consciousness independently of behavior. We successfully predict individual subjects’ PCI values with considerably high accuracy from resting-state EEG dynamical properties alone. Our results establish a firm link between perturbational complexity and criticality, and provide further evidence that criticality is a necessary condition for the emergence of consciousness.

Loneliness and brain rhythmic activity in resting state: An exploratory report.

Recent studies using resting-state functional magnetic resonance imaging have shown that loneliness is associated with altered blood oxygenation in several brain regions. However, the relationship between loneliness and changes in neuronal rhythm activity in the brain remains unclear. To evaluate brain rhythm, we conducted an exploratory resting-state electroencephalogram (EEG) study of loneliness. We recorded resting-state EEG signals from 139 participants (94 women; mean age = 19.96 years) and analyzed power spectrum density (PSD) and functional connectivity (FC) in both the electrode and source spaces. The PSD analysis revealed significant correlations between loneliness scores and decreased beta-band powers, which may indicate negative emotion, attention, reward, and/or sensorimotor processing. The FC analysis revealed a trend of alpha-band FC associated with individuals' loneliness scores. These findings provide new insights into the neural basis of loneliness, which will facilitate the development of neurobiologically informed interventions for loneliness.

Modulation of brain activity in brain-injured patients with a disorder of consciousness in intensive care with repeated 10-Hz transcranial alternating current stimulation (tACS): a randomised controlled trial protocol

IntroductionTherapeutic interventions for disorders of consciousness lack consistency; evidence supports non-invasive brain stimulation, but few studies assess neuromodulation in acute-to-subacute brain-injured patients. This study aims to validate the feasibility and...

Alterations in electroencephalographic functional connectivity in individuals with major depressive disorder: a resting-state electroencephalogram study.

Major depressive disorder (MDD) is the leading cause of disability among all mental illnesses with increasing prevalence. The diagnosis of MDD is susceptible to interference by several factors, which has led to a trend of exploring objective biomarkers. Electroencephalography (EEG) is a non-invasive procedure that is being gradually applied to detect and diagnose MDD through some features such as functional connectivity (FC). In this research, we analyzed the resting-state EEG of patients with MDD and healthy controls (HCs) in both eyes-open (EO) and eyes-closed (EC) conditions. The phase locking value (PLV) method was utilized to explore the connection and synchronization of neuronal activities spatiotemporally between different brain regions. We compared the PLV between participants with MDD and HCs in five frequency bands (theta, 4-8 Hz; alpha, 8-12 Hz; beta1, 12-16 Hz; beta2, 16-24 Hz; and beta3, 24-40 Hz) and further analyzed the correlation between the PLV of connections with significant differences and the severity of depression (via the scores of 17-item Hamilton Depression Rating Scale, HDRS-17). During the EO period, lower PLVs were found in the right temporal-left midline occipital cortex (RT-LMOC; theta, alpha, beta1, and beta2) and posterior parietal-right temporal cortex (PP-RT; beta1 and beta2) in the MDD group compared with the HC group, while PLVs were higher in the MDD group in LT-LMOC (beta2). During the EC period, for the MDD group, lower theta and beta (beta1, beta2, and beta3) PLVs were found in PP-RT, as well as lower theta, alpha, and beta (beta1, beta2, and beta3) PLVs in RT-LMOC. Additionally, in the left midline frontal cortex-right temporal cortex (LMFC-RT) and posterior parietal cortex-right temporal cortex (PP-RMOC), higher PLVs were observed in beta2. There were no significant correlations between PLVs and HDRS-17 scores when connections with significantly different PLVs (all p > 0.05) were checked. Our study confirmed the presence of differences in FC between patients with MDD and healthy individuals. Lower PLVs in the connection of the right temporal-left occipital cortex were mostly observed, whereas an increase in PLVs was observed in patients with MDD in the connections of the left temporal with occipital lobe (EO), the circuits of the frontal-temporal lobe, and the parietal-occipital lobe. The trends in FC involved in this study were not correlated with the level of depression. The study was limited due to the lack of further analysis of confounding factors and follow-up data. Future studies with large-sampled and long-term designs are needed to further explore the distinguishable features of EEG FC in individuals with MDD.

Individual posterior alpha rhythms and cognitive reserve as possible early prognostic markers in people with subjective memory complaints

Subjective memory complaints (SMCs) are a memory disorder that often precedes mild cognitive impairment (MCI) or Alzheimer’s disease (AD). Both individual alpha rhythms and cognitive reserve (CR) represent key features of SMCs and provide useful tools to characterize and predict the course of the disorder. We studied whether older people with SMCs may also present some abnormal resting state electroencephalogram (rsEEG) alpha rhythms, and whether alpha rhythms are associated with CR. To do this, eyes-closed rsEEG were recorded in 68 older people with and without SMCs. The individual alpha indexes alpha/theta transition frequency (TF) and individual alpha frequency peak (IAFp) were computed. TF and IAFp were also used to determine the alpha1, alpha2, and alpha3 power frequency. Results indicated no differences in TF or IAFp between older people with SMCs and controls. The SMCs group showed a reduction in alpha3 power in comparison with controls. Specifically, women with SMCs were characterized by a significant decrease in alpha3 power compared to control women. Furthermore, only in SMCs group, greater CR was associated with slow IAFp. In sum, these results suggest that TF and IAFp are two stable indexes that are not influenced by the presence of SMCs. However, the reduction in alpha3, as observed in women with SMCs, shows an abnormal posterior rsEEG at alpha power. Finally, the compensatory mechanisms of CR appear to interact with the neurophysiological mechanisms that underlie the regulation of alpha rhythms.

A Hybrid Deep Spatiotemporal Attention‐Based Model for Parkinson's Disease Diagnosis Using Resting State EEG Signals

ABSTRACTParkinson's disease (PD), a severe and progressive neurological illness, affects millions of individuals worldwide. For effective treatment and management of PD, an accurate and early diagnosis is crucial. This study presents a deep learning‐based model for the diagnosis of PD using a resting state electroencephalogram (EEG) signal. The objective of the study is to develop an automated model that can extract complex hidden nonlinear features from EEG and demonstrate its generalizability on unseen data. The model is designed using a hybrid model, consisting of a convolutional neural network (CNN), bidirectional gated recurrent unit (Bi‐GRU), and attention mechanism. The proposed method is evaluated on three public datasets (UC San Diego, PRED‐CT, and University of Iowa [UI] dataset), with one dataset used for training and the other two for evaluation. The proposed model demonstrated remarkable performance, attaining high accuracy scores of 99.4%, 84%, and 73.2% using UC San Diego, PRED‐CT, and UI datasets, respectively. These results justify the effectiveness and robustness of the proposed model across diverse datasets, highlighting its potential for versatile applications in data analysis and prediction tasks. Our proposed hybrid spatiotemporal attention‐based model has been developed with 10‐fold cross‐validation (CV) for UC San Diego dataset and 10‐fold CV and leave‐one‐out cross‐validation (LOOCV) strategies for PRED‐CT and UI datasets. Our results indicate that the proposed PD detection system is accurate and robust. The developed prototype can be used for other neurodegenerative diseases such as Alzheimer's disease, Huntington's disease, and so forth.

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

BackgroundCurrent 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.MethodsA 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.ResultsSpatial 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.ConclusionsThe 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.

Verbal Support From a Stranger Reduces the Development of Mechanical Hypersensitivity: Behavioral and Neurophysiological Evidence

Hand-holding reduces experimentally induced acute pain and buffers against the development of mechanical secondary hypersensitivity, an indirect proxy of central sensitization. Here, we tested if verbal support from a stranger, a common occurrence in clinical contexts, exerts the same effects. In this preregistered study, 44 healthy female participants were assigned to an alone or support group whereby a supportive female stranger encouraged them through the painful procedure leading to secondary mechanical hypersensitivity. Mechanical hypersensitivity was measured via self-reports and by the size of the anteroposterior and mediolateral spread of mechanical hypersensitivity. We investigated the moderating role of attachment style on self-reports and the effects of support on skin conductance level, salivary cortisol, and pinprick-evoked potentials. We also tested whether theta/beta ratio in the resting-state electroencephalogram predicted mechanical hypersensitivity. Self-reported ratings and the late part of the pinprick-evoked potentials were reduced in the support group, but the spread of mechanical hypersensitivity was not. Attachment anxiety and avoidance moderated the self-reported intensity such that individuals with higher attachment anxiety and avoidance scores reported lower intensity ratings in the support group. No significant effect of the verbal support was observed on skin conductance level and salivary cortisol. The theta/beta ratio did not predict the extent of hypersensitivity. Our data indicate that, in women, verbal support during intense pain leading to hypersensitivity is effective on some behavioral outcomes, but altogether the lack of group differences in cortisol, self-reported stress, and skin conductance does not provide strong support for the stress-buffering hypothesis. PerspectiveVerbal support by a stranger during a painful procedure leading to secondary mechanical hypersensitivity attenuated the development of some measures of mechanical hypersensitivity and associated neural responses in healthy female participants. No evidence was found for the role of stress. Data AvailabilityThe authors will make all data available upon request.

Assessment of the excitation-inhibition ratio in the Fmr1 KO2 mouse using neuronal oscillation dynamics.

In vitro and ex vivo studies have shown consistent indications of hyperexcitability in the Fragile X Messenger Ribonucleoprotein 1 (Fmr1) knockout mouse model of autism spectrum disorder. We recently introduced a method to quantify network-level functional excitation-inhibition ratio from the neuronal oscillations. Here, we used this measure to study whether the implicated synaptic excitation-inhibition disturbances translate to disturbances in network physiology in the Fragile X Messenger Ribonucleoprotein 1 (Fmr1) gene knockout model. Vigilance-state scoring was used to extract segments of inactive wakefulness as an equivalent behavioral condition to the human resting-state and, subsequently, we performed high-frequency resolution analysis of the functional excitation-inhibition biomarker, long-range temporal correlations, and spectral power. We corroborated earlier studies showing increased high-frequency power in Fragile X Messenger Ribonucleoprotein 1 (Fmr1) knockout mice. Long-range temporal correlations were higher in the gamma frequency ranges. Contrary to expectations, functional excitation-inhibition was lower in the knockout mice in high frequency ranges, suggesting more inhibition-dominated networks. Exposure to the Gamma-aminobutyric acid (GABA)-agonist clonazepam decreased the functional excitation-inhibition in both genotypes, confirming that increasing inhibitory tone results in a reduction of functional excitation-inhibition. In addition, clonazepam decreased electroencephalogram power and increased long-range temporal correlations in both genotypes. These findings show applicability of these new resting-state electroencephalogram biomarkers to animal for translational studies and allow investigation of the effects of lower-level disturbances in excitation-inhibition balance.

88. Atypical Resting-State Electroencephalogram Theta-Beta Ratio in Autistic Adults: Preliminary Results

88. Atypical Resting-State Electroencephalogram Theta-Beta Ratio in Autistic Adults: Preliminary Results

Prolonged intermittent theta burst stimulation for post-stroke aphasia: protocol of a randomized, double-blinded, sham-controlled trial.

Post-stroke aphasia (PSA) is one of the most devastating symptoms after stroke, yet limited treatment options are available. Prolonged intermittent theta burst stimulation (piTBS) is a promising therapy for PSA. However, its efficacy remains unclear. Therefore, we aim to investigate the efficacy of piTBS over the left supplementary motor area (SMA) in improving language function for PSA patients and further explore the mechanism of language recovery. This is a randomized, double-blinded, sham-controlled trial. A total of 30 PSA patients will be randomly allocated to receive either piTBS stimulation or sham stimulation for 15 sessions over a period of 3 weeks. The primary outcome is the Western Aphasia Battery Revised (WAB-R) changes after treatment. The secondary outcomes include The Stroke and Aphasia Quality of Life Scale (SAQOL-39 g), resting-state electroencephalogram (resting-state EEG), Event-related potentials (ERP), brain derived neurotrophic factor (BDNF). These outcome measures are assessed before treatment, after treatment, and at 4-weeks follow up. This study was registered in Chinese Clinical Trial Registry (No. ChiCTR23000203238). This study protocol is promising for improving language in PSA patients. Resting-state EEG, ERP, and blood examination can be used to explore the neural mechanisms of PSA treatment with piTBS. https://www.chictr.org.cn/index.html, ChiCTR2300074533.

A resting-state EEG dataset for sleep deprivation

To investigate the impact of sleep deprivation (SD) on mood, alertness, and resting-state electroencephalogram (EEG), we present an eyes-open resting-state EEG dataset. The dataset comprises EEG recordings and cognitive data from 71 participants undergoing two testing sessions: one involving SD and the other normal sleep. In each session, participants engaged in eyes-open resting-state EEG. The Psychomotor Vigilance Task (PVT) was employed for alertness measurement. Emotional and sleepiness were measured using Positive and Negative Affect Scale (PANAS) and Stanford Sleepiness Scale (SSS). Additionally, to examine the influence of individual sleep quality and traits on SD, Pittsburgh Sleep Quality Index (PSQI) and Buss-Perry Aggression Questionnaire (BPAQ) were utilized. This dataset’s sharing may contribute to open EEG measurements in the field of SD.

A prospective study on EEG default mode network associated with subsequent posttraumatic stress disorder following sexual assault

This study aimed to explore the predictors of posttraumatic stress disorder (PTSD) in women who have recently experienced sexual assault, by examining psychological and neurophysiological factors using a prospective design with resting-state electroencephalogram (EEG) functional connectivity. The study enrolled 33 women who had been recently traumatized by sexual assault and conducted assessments within a month of the trauma. These survivors were evaluated for PTSD three months later and were classified into two groups: PTSD positive (n = 12) and PTSD negative (n = 21). They were compared to two control groups comprising women who had not experienced any extremely traumatic events: 25 with depression and 25 healthy controls. The evaluation focused on resting-state EEG functional connectivity within default mode network (DMN) using small-worldness (SW), based on graph theory. We also assessed self-reported levels of depression, anxiety, anger, and executive functions. The findings indicated that survivors who developed PTSD three months post-trauma exhibited higher anxiety levels and reduced DMN SW in the beta 3 frequency, compared to those who did not develop PTSD. Contrary to expectations, survivors without PTSD showed decreased executive functioning and lower prefrontal centrality compared to those with PTSD. This study underscores the importance of early assessment and intervention for sexual assault survivors at risk of developing PTSD.

Characterizing Major Depressive Disorder (MDD) using alpha-band activity in resting-state electroencephalogram (EEG) combined with MATRICS Consensus Cognitive Battery (MCCB)

BackgroundThe diagnosis of major depressive disorder (MDD) is commonly based on the subjective evaluation by experienced psychiatrists using clinical scales. Hence, it is particularly important to find more objective biomarkers to aid in diagnosis and further treatment. Alpha-band activity (7-13 Hz) is the most prominent component in resting electroencephalogram (EEG), which is also thought to be a potential biomarker. Recent studies have shown the existence of multiple sub-oscillations within the alpha band, with distinct neural underpinnings. However, the specific contribution of these alpha sub-oscillations to the diagnosis and treatment of MDD remains unclear. MethodsIn this study, we recorded the resting-state EEG from MDD and HC populations in both open and closed-eye state conditions. We also assessed cognitive processing using the MATRICS Consensus Cognitive Battery (MCCB). ResultsWe found that the MDD group showed significantly higher power in the high alpha range (10.5–11.5 Hz) and lower power in the low alpha range (7–8.5 Hz) compared to the HC group. Notably, high alpha power in the MDD group is negatively correlated with working memory performance in MCCB, whereas no such correlation was found in the HC group. Furthermore, using five established classification algorithms, we discovered that combining alpha oscillations with MCCB scores as features yielded the highest classification accuracy compared to using EEG or MCCB scores alone. ConclusionsOur results demonstrate the potential of sub-oscillations within the alpha frequency band as a potential distinct biomarker. When combined with psychological scales, they may provide guidance relevant for the diagnosis and treatment of MDD.

Using scalp EEG to predict seizure recurrence and electrical status epilepticus in children with idiopathic focal epilepsy

PurposeSome individuals with idiopathic focal epilepsy (IFE) experience recurring seizures accompanied by the evolution of electrical status epilepticus during sleep (ESES). Here, we aimed to develop a predictor for the early detection of seizure recurrence with ESES in children with IFE using resting state electroencephalogram (EEG) data. MethodsThe study group included 15 IFE patients who developed seizure recurrence with ESES. There were 17 children in the control group who did not experience seizure recurrence with ESES during at least 2-year follow-up. We used the degree value of the partial directed coherence (PDC) from the EEG data to predict seizure recurrence with ESES via 6 machine learning (ML) algorithms. ResultsAmong the models, the Xgboost Classifier (XGBC) model achieved the highest specificity of 0.90, and a remarkable sensitivity and accuracy of 0.80 and 0.85, respectively. The CATC showed balanced performance with a specificity of 0.85, sensitivity of 0.73, and an accuracy of 0.80, with an AUC equal to 0.78. For both of these models, F4, Fz and T4 were the overlaps of the top 4 features. ConclusionsConsidering its high classification accuracy, the XGBC model is an effective and quantitative tool for predicting seizure recurrence with ESES evolution in IFE patients. We developed an ML-based tool for predicting the development of IFE using resting state EEG data. This could facilitate the diagnosis and treatment of patients with IFE.

The Dynamics of Resting-State EEG and Salivary Trace Elements in Patients with Obsessive-Compulsive Disorder.

The study of salivary microelements and their neurophysiological and behavioral correlates in patients with obsessive-compulsive disorder (OCD) is a pressing issue in modern psychiatry, which, however, lacks adequate research at this time. In this study, we tested the dynamics of behavioral parameters, resting-state electroencephalogram (EEG), and salivary iron, copper, manganese, magnesium, and zinc in 30 healthy volunteers and 30 individuals with OCD before and after an emotional antisaccade task. The eye-movement data served as a measure of behavioral performance. Our research revealed consistently higher manganese concentrations in the OCD group compared to healthy volunteers associated with a higher EEG ratio of amplitude transformation and symptom severity. The dynamics of salivary microelements and resting-state EEG, possibly influenced by cognitive and emotional load during the anticsaccade task, differed between groups. In healthy volunteers, there was a decrease in salivary iron level with an increase in high-frequency power spectral density of EEG. The OCD group showed a decrease in salivary copper with an increased Hjorth mobility of EEG.

Characteristics of Resting-State Electroencephalogram Network in α-Band of Table Tennis Athletes.

Table tennis athletes have been extensively studied for their cognitive processing advantages and brain plasticity. However, limited research has focused on the resting-state function of their brains. This study aims to investigate the network characteristics of the resting-state electroencephalogram in table tennis athletes and identify specific brain network biomarkers. A total of 48 healthy right-handed college students participated in this study, including 24 table tennis athletes and 24 controls with no exercise experience. Electroencephalogram data were collected using a 64-conductive active electrode system during eyes-closed resting conditions. The analysis involved examining the average power spectral density and constructing brain functional networks using the weighted phase-lag index. Network topological characteristics were then calculated. The results revealed that table tennis athletes exhibited significantly higher average power spectral density in the α band compared to the control group. Moreover, athletes not only demonstrated stronger functional connections, but they also exhibited enhanced transmission efficiency in the brain network, particularly at the local level. Additionally, a lateralization effect was observed, with more potent interconnected hubs identified in the left hemisphere of the athletes' brain. Our findings imply that the α band may be uniquely associated with table tennis athletes and their motor skills. The brain network characteristics of athletes during the resting state are worth further attention to gain a better understanding of adaptability of and changes in their brains during training and competition.

Variational mode decomposition-based EEG analysis for the classification of disorders of consciousness.

Aberrant alterations in any of the two dimensions of consciousness, namely awareness and arousal, can lead to the emergence of disorders of consciousness (DOC). The development of DOC may arise from more severe or targeted lesions in the brain, resulting in widespread functional abnormalities. However, when it comes to classifying patients with disorders of consciousness, particularly utilizing resting-state electroencephalogram (EEG) signals through machine learning methods, several challenges surface. The non-stationarity and intricacy of EEG data present obstacles in understanding neuronal activities and achieving precise classification. To address these challenges, this study proposes variational mode decomposition (VMD) of EEG before feature extraction along with machine learning models. By decomposing preprocessed EEG signals into specified modes using VMD, features such as sample entropy, spectral entropy, kurtosis, and skewness are extracted across these modes. The study compares the performance of the features extracted from VMD-based approach with the frequency band-based approach and also the approach with features extracted from raw-EEG. The classification process involves binary classification between unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS), as well as multi-class classification (coma vs. UWS vs. MCS). Kruskal-Wallis test was applied to determine the statistical significance of the features and features with a significance of p < 0.05 were chosen for a second round of classification experiments. Results indicate that the VMD-based features outperform the features of other two approaches, with the ensemble bagged tree (EBT) achieving the highest accuracy of 80.5% for multi-class classification (the best in the literature) and 86.7% for binary classification. This approach underscores the potential of integrating advanced signal processing techniques and machine learning in improving the classification of patients with disorders of consciousness, thereby enhancing patient care and facilitating informed treatment decision-making.

Relationship Between Decision Making and Resting-State EEG in Adolescents With Different Emotional Stabilities

Despite the varied decision responses are revealed between adolescents with emotional stability and instability (ES and EI), the possible association underlying decision-making and resting-state activity remains unknown. The study explored the potential relationship between the resting-state electroencepha-logram network and decision responses when adolescents with different emotional stabilities participated in the ultimatum game (UG). A significant relationship between the resting-state activa-tion of the two frequency bands (i.e., delta and alpha) and the UG acceptance rate. Further, the ES adolescents mainly activated the frontal cortex in the delta band compared to the EI adolescents, whereas the EI adolescents exhibited stronger frontal-occipital linkages in the alpha band than the ES group. During deci-sion-making, the delta oscillation played a positive role in the cognitive process, while the alpha activity decreased. Finally, emotional stability could be predicted by a multivariable linear prediction model based on the resting-state metrics while adoles-cents made decisions in the UG task. The distinct resting-state electrophysiological evidence may deepen our knowledge of deci-sion-making in individuals with different emotional traits, which may further facilitate the prediction of individual decision be-haviors.

Evaluating the depression state during perinatal period by non-invasive scalp EEG.

Perinatal depression, with a prevalence of 10 to 20% in United States, is usually missed as multiple symptoms of perinatal depression are common in pregnant women. Worse, the diagnosis of perinatal depression still largely relies on questionnaires, leaving the objective biomarker being unveiled yet. This study suggested a safe and non-invasive technique to diagnose perinatal depression and further explore its underlying mechanism. Considering the non-invasiveness and clinical convenience of electroencephalogram for mothers-to-be and fetuses, we collected the resting-state electroencephalogram of pregnant women at the 38th week of gestation. Subsequently, the difference in network topology between perinatal depression patients and healthy mothers-to-be was explored, with related spatial patterns being adopted to achieve the classification of pregnant women with perinatal depression from those healthy ones. We found that the perinatal depression patients had decreased brain network connectivity, which indexed impaired efficiency of information processing. By adopting the spatial patterns, the perinatal depression could be accurately recognized with an accuracy of 87.88%; meanwhile, the depression severity at the individual level was effectively predicted, as well. These findings consistently illustrated that the resting-state electroencephalogram network could be a reliable tool for investigating the depression state across pregnant women, and will further facilitate the clinical diagnosis of perinatal depression.