Resting-state Electroencephalogram Research Articles

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.

Effects of 90 dB pure tone exposure on auditory and cardio-cerebral system functions in macaque monkeys

Excessive noise exposure presents significant health risks to humans, affecting not just the auditory system but also the cardiovascular and central nervous systems. This study focused on three male macaque monkeys as subjects. 90 dB sound pressure level (SPL) pure tone exposure (frequency: 500Hz, repetition rate: 40Hz, 1 min per day, continuously exposed for 5 days) was administered. Assessments were performed before exposure, during exposure, immediately after exposure, and at 7-, 14-, and 28-days post-exposure, employing auditory brainstem response (ABR) tests, electrocardiograms (ECG), and electroencephalograms (EEG). The study found that the average threshold for the Ⅴ wave in the right ear increased by around 30 dB SPL right after exposure (P < 0.01) compared to pre-exposure. This elevation returned to normal within 7 days. The ECG results indicated that one of the macaque monkeys exhibited an RS-type QRS wave, and inverted T waves from immediately after exposure to 14 days, which normalized at 28 days. The other two monkeys showed no significant changes in their ECG parameters. Changes in EEG parameters demonstrated that main brain regions exhibited significant activation at 40Hz during noise exposure. After noise exposure, the power spectral density (PSD) in main brain regions, particularly those represented by the temporal lobe, exhibited a decreasing trend across all frequency bands, with no clear recovery over time. In summary, exposure to 90 dB SPL noise results in impaired auditory systems, aberrant brain functionality, and abnormal electrocardiographic indicators, albeit with individual variations. It has implications for establishing noise protection standards, although the precise mechanisms require further exploration by integrating pathological and behavioral indicators.

Spectral decomposition of resting state electroencephalogram reveals unique theta/alpha activity in schizophrenia.

Resting state electroencephalographic (EEG) activity in schizophrenia (SZ) is frequently characterised by increased power at slow frequencies and/or a reduction of peak alpha frequency. Here we investigated the nature of these effects. As most studies to date have been limited by reliance on a priori frequency bands which impose an assumed structure on the data, we performed a data-driven analysis of resting EEG recorded in SZ patients and healthy controls (HC). The sample consisted of 39 chronic SZ and 36 matched HC. The EEG was recorded with a dense electrode array. Power spectral densities were decomposed via Varimax-rotated principal component analysis (PCA) over all participants and for each group separately. Spectral PCA was repeated at the cortical level on cortical current source density computed from standardised low resolution brain electromagnetic tomography. There was a trend for power in the theta/alpha range to be increased in SZ compared to HC, and peak alpha frequency was significantly reduced in SZ. PCA revealed that this frequency shift was because of the presence of a spectral component in the theta/alpha range (6-9 Hz) that was unique to SZ. The source distribution of the SZ > HC theta/alpha effect involved mainly prefrontal and parahippocampal areas. Abnormal low frequency resting EEG activity in SZ was accounted for by a unique theta/alpha oscillation. Other reports have described a similar phenomenon suggesting that the neural circuits oscillating in this range are relevant to SZ pathophysiology.

Temporal hyper-connectivity and frontal hypo-connectivity within gamma band in schizophrenia: A resting state EEG study

ObjectiveThe brain network serves as the physiological foundation for information processing of the brain. Many studies have reported abnormalities of gamma oscillations in Schizophrenia. The aim of this study was to investigate the gamma-band connectivity in Schizophrenia patients. MethodsWe recorded the resting state electroencephalogram (EEG) for 15 schizophrenia patients with refractory auditory hallucinations and 14 healthy controls, with eyes open and closed. The brain network was constructed based on weighted phase lag index for gamma band. Whole scalp metrics (clustering coefficient, global efficiency and local efficiency) and local region metrics (degree and betweenness centrality) in the frontal and temporal lobes were computed. Correlation analyses between network metrics and symptom scales were examined to find associations with symptom severity. ResultsSchizophrenia patients had larger global efficiency and local efficiency (p < 0.05) with eyes closed, probably representing greater brain activity and information exchange. For degree and betweenness centrality, schizophrenia patients showed an increase (p < 0.05) in the temporal lobe but a decrease (p < 0.05) in the frontal lobe with eyes closed and open, potentially account for the patients' symptoms such as hallucinations and thought disorders. Local efficiency and frontal lobe degree were positively and negatively correlated with the scales, respectively (both p < 0.05). ConclusionsAltered connectivity of the resting state brain network has been revealed and may be associated with the core symptoms of schizophrenia. Our study provides promising evidence for the investigation of the pathological basis of Schizophrenia and could aid in objective diagnosis.

Prediction of brain sex from EEG: using large-scale heterogeneous dataset for developing a highly accurate and interpretable ML model

This study presents a comprehensive examination of sex-related differences in resting-state electroencephalogram (EEG) data, leveraging two different types of machine learning models to predict an individual's sex. We utilized data from the Two Decades-Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) EEG study, affirming that gender prediction can be attained with noteworthy accuracy. The best performing model achieved an accuracy of 85% and an ROC AUC of 89%, surpassing all prior benchmarks set using EEG data and rivaling the top-tier results derived from fMRI studies. A comparative analysis of LightGBM and Deep Convolutional Neural Network (DCNN) models revealed DCNN's superior performance, attributed to its ability to learn complex spatial-temporal patterns in the EEG data and handle large volumes of data effectively. Despite this, interpretability remained a challenge for the DCNN model. The LightGBM interpretability analysis revealed that the most important EEG features for accurate sex prediction were related to left fronto-central and parietal EEG connectivity. We also showed the role of both low (delta and theta) and high (beta and gamma) activity in the accurate sex prediction. These results, however, have to be approached with caution, because it was obtained from a dataset comprised largely of participants with various mental health conditions, which limits the generalizability of the results and necessitates further validation in future studies. . Overall, the study illuminates the potential of interpretable machine learning for sex prediction, alongside highlighting the importance of considering individual differences in prediction sex from brain activity.

Associations between abnormal electroencephalogram microstates and childhood emotional abuse in adolescent depression.

Childhood traumatic experiences greatly influenced the brain network activities of patients with depression, and there is an urgent need to explore the temporal dynamics for these changes. This study aims to investigate the abnormalities of resting-state electroencephalogram (EEG) microstates in eye-open state of depressed adolescents and to explore the correlations between their EEG microstates and the childhood traumatic experience. Using resting-state EEG microstate analysis, we explored the temporal dynamics of brain activity in patients with adolescent depression. This study selected 66 adolescents with depression as a patient group, and 27 healthy adolescents as a healthy control group. A modified k-means clustering algorithm was used to classify the 64-channel resting-state EEG data into different microstates. Independent sample t-tests were used to compare the microstate parameters between the 2 groups and further assciations between these parameters and childhood traumatic experience in patients were explored via using Spearman correlation. In this study, significant differences were observed in the occurrence and transition probabilities of EEG microstates between the healthy control and the patient group. Notably, there was a statistically significant difference (P<0.05) in the occurrence of microstate A across 2 groups, exhibiting a negative correlation with the emotional abuse component within the childhood trauma scores (Spearman's rho=-0.31, P=0.013). Furthermore, patient-specific, non-random transitions from microstate B to A (Spearman's rho=-0.30, P=0.015) and C to A (Spearman's rho=-0.31, P=0.013) were inversely associated with the scores of emotional abuse factors from childhood trauma in the patient group, showing statistically significant differences when comparing to the healthy controls (P<0.05). Upon stratification into quartiles based on the emotional abuse factor scores, the occurrence of microstate A, as well as the transition rates from microstates B to A and C to A, retained statistical significance following adjustment for multiple comparisons (all P<0.05). The abnormal temporal dynamics in brain networks of adolescents with depression are linked to childhood emotional abuse. Those who have suffered severe emotional abuse may show greater impairments in the brain's visual and central executive networks. EEG microstate analysis could be a potential tool for detecting adolescent depression with severe childhood trauma.

An examination of frontal asymmetry in relation to eating in the absence of hunger and loss-of-control eating

Loss-of-control (LOC) eating involves a subjective feeling that one cannot stop eating or control one's eating. Individuals with LOC eating may exhibit strong appetitive drives and weak inhibitory control, and these two opposing motivations have been related to EEG measurements of frontal asymmetry or lateralized frontal activation. The present study investigated whether frontal asymmetry is related to hedonic hunger, LOC eating severity and frequency, and eating in the absence of hunger (EAH) in the laboratory. Fifty-nine individuals participated in an ostensible taste study after resting-state electroencephalogram (EEG) recordings. After the EEGs, they were provided a meal to eat until fullness, followed by an array of snacks and instructions to eat as much as they would like. The results indicated that several measures of right-frontal asymmetry were related to greater EAH and greater self-reported LOC eating severity. Although right-frontal asymmetry has been theorized to reflect avoidance motivation, recent evidence suggests it may indicate effortful control during approach-avoidance conflicts. Because individuals with LOC eating presumably experience heightened conflict between drives to eat beyond energy needs and to minimize such eating, those experiencing greater LOC may exert greater effort to manage these conflicting motivations. An integration of these neurobiological correlates of LOC eating may help provide a more comprehensive understanding of LOC eating and inform treatments.

Risk of data leakage in estimating the diagnostic performance of a deep-learning-based computer-aided system for psychiatric disorders

Deep-learning approaches with data augmentation have been widely used when developing neuroimaging-based computer-aided diagnosis (CAD) systems. To prevent the inflated diagnostic performance caused by data leakage, a correct cross-validation (CV) method should be employed, but this has been still overlooked in recent deep-learning-based CAD studies. The goal of this study was to investigate the impact of correct and incorrect CV methods on the diagnostic performance of deep-learning-based CAD systems after data augmentation. To this end, resting-state electroencephalogram (EEG) data recorded from post-traumatic stress disorder patients and healthy controls were augmented using a cropping method with different window sizes, respectively. Four different CV approaches were used to estimate the diagnostic performance of the CAD system, i.e., subject-wise CV (sCV), overlapped sCV (oSCV), trial-wise CV (tCV), and overlapped tCV (otCV). Diagnostic performances were evaluated using two deep-learning models based on convolutional neural network. Data augmentation can increase the performance with all CVs, but inflated diagnostic performances were observed when using incorrect CVs (tCV and otCV) due to data leakage. Therefore, the correct CV (sCV and osCV) should be used to develop a deep-learning-based CAD system. We expect that our investigation can provide deep-insight for researchers who plan to develop neuroimaging-based CAD systems for psychiatric disorders using deep-learning algorithms with data augmentation.

There is a party in my head and no one is invited: Resting-state electrocortical activity and solitude.

What are the motivational underpinnings of solitude? We know from self-report studies that increases in solitude are associated with drops in approach motivation and rises in avoidance motivation, but only when solitude is experienced as non-self-determined (i.e., non-autonomous). However, the extent to which individual differences in solitude relate to neurophysiological markers of approach-avoidance motivation derived from resting-state electroencephalogram (EEG) is unknown. These markers are Frontal Alpha Asymmetry, beta suppression, and midline Posterior versus Frontal EEG Theta Activity. We assessed the relation among individual differences in the reasons for solitude (i.e., preference for solitude, motivation for solitude), approach-avoidance motivation, and resting-state EEG markers of approach-avoidance motivation (N = 115). General preference for solitude was negatively related to approach motivation, observed in both self-reported measures and EEG markers of approach motivation. Self-determined solitude was positively related to both self-reported approach motivation and avoidance motivation in the social domain (i.e., friendship). Non-self-determined solitude was negatively associated with self-reported avoidance motivation. This research was a preliminary attempt to address the neurophysiological underpinnings of solitude in the context of motivation.