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Craving is central to methamphetamine use disorder (MUD) and both characterizes the disease and predicts relapse. However, there is currently a lack of robust and reliable biomarkers for monitoring craving and diagnosing MUD. Here, we seek to identify a neurobiological signature of craving based on individual-level functional connectivity pattern differences between healthy control and MUD subjects. We train high-density electroencephalography (EEG)-based models using data recorded during the resting state and then calculate imaginary coherence features between the band-limited time series across different brain regions of interest. Our prediction model demonstrates that eyes-open beta functional connectivity networks have significant predictive value for craving at the individual level and can also identify individuals with MUD. These findings advance the neurobiological understanding of craving through an EEG-tailored computational model of the brain connectome. Dissecting neurophysiological features provides a clinical avenue for personalized treatment of MUD.

Sport-related concussion (SRC) is a complex brain injury. By applying graph-theoretical analysis to networks derived from neuroimaging techniques, studies have shown that despite an overall retention of small-world topology, changes in small-world properties occur after brain injury. Less is known about how exercise during athletes' return to sport (RTS) influences these brain network properties. Therefore, in the present study dense electroencephalography (EEG) datasets were collected pre- and post-moderate aerobic exercise. Small-world properties of whole brain (WB) and the default mode network (DMN) were extracted from the EEG datasets of 21 concussed athletes and 21 healthy matched controls. More specifically, path length (LP), clustering coefficient (CP), and small-world index (SWI) in binary and weighted graphs were calculated in the alpha frequency band (7–13 Hz). Pre-exercise, SRC athletes had higher DMN-CP values compared to controls, while post-exercise SRC athletes had higher WB-LP compared to controls. Weighted WB analysis revealed a significant association between SRC and the absence of small-world topology (SWI ≤ 1) post-exercise. This explorative study provides preliminary evidence that moderate aerobic exercise during athletes' RTS induces an altered network response. Furthermore, this altered response may be related to the clinical characteristics of the SRC athlete. [ABSTRACT FROM AUTHOR]

Hypnotic phenomena exhibit significant inter-individual variability, with some individuals consistently demonstrating efficient responses to hypnotic suggestions, while others show limited susceptibility. Recent neurophysiological studies have added to a growing body of research that shows variability in hypnotic susceptibility is linked to distinct neural characteristics. Building on this foundation, our previous work identified that individuals with high and low hypnotic susceptibility can be differentiated based on the arrhythmic activity observed in resting-state electrophysiology (rs-EEG) outside of hypnosis. However, because previous work has largely focused on mean spectral characteristics, our understanding of the variability over time of these features, and how they relate to hypnotic susceptibility, is still limited. Here we address this gap using a time-resolved assessment of rhythmic alpha peaks and arrhythmic components of the EEG spectrum both prior to and following hypnotic induction. Using multivariate pattern classification, we investigated whether these neural features differ between individuals with high and low susceptibility to hypnosis. Specifically, we used multivariate pattern classification to investigate whether these non-stationary neural features could distinguish between individuals with high and low susceptibility to hypnosis before and after a hypnotic induction. Our analytical approach focused on time-resolved spectral decomposition to capture the intricate dynamics of neural oscillations and their non-oscillatory counterpart, as well as Lempel–Ziv complexity. Our results show that variations in the alpha center frequency are indicative of hypnotic susceptibility, but this discrimination is only evident during hypnosis. Highly hypnotic-susceptible individuals exhibit higher variability in alpha peak center frequency. These findings underscore how dynamic changes in neural states related to alpha peak frequency represent a central neurophysiological feature of hypnosis and hypnotic susceptibility. [ABSTRACT FROM AUTHOR]

Trait narcissism is characterized by significant heterogeneity across individuals. Despite advances in the conceptualization of narcissism, including the increasing recognition that narcissism is a multidimensional construct, the sources of this heterogeneity remain poorly understood. Here, we used a neural trait approach to help better understand "how," and shed light on "why," individuals vary in facets of trait narcissism. Participants (N = 58) first completed personality measures, including the Narcissistic Personality Inventory (NPI), and then in a second session sat passively while resting‐state electroencephalography (rs‐EEG) was recorded. We then regressed source‐localized rs‐EEG activity on the distinct facets of narcissism: Grandiose Exhibitionism (GE), Entitlement/Exploitativeness (EE), and Leadership/Authority (LA). Results revealed that each facet was associated with different (though sometimes overlapping) neural sources. Specifically, GE was associated with reduced activation in the dorsomedial prefrontal cortex (DMPFC). EE was associated with reduced activation in the DMPFC and right lateral PFC. LA was associated with increased activation in the left anterior temporal cortex. These findings support the idea that trait narcissism is a multidimensional construct undergirded by individual differences in neural regions related to social cognition (the DMPFC), self‐regulation (right lateral PFC), and self‐referential processing (left anterior temporal cortex). [ABSTRACT FROM AUTHOR]

Background: Depression presents with altered energy regulation and neural plasticity. Previous electroencephalography (EEG) studies showed that practice in learning tasks increases power in beta range (13–30 Hz) in healthy subjects but not in those with impaired plasticity. Here, we ascertain whether depression presents with alterations of spectral activity and connectivity before and after a learning task. Methods: We used publicly available resting-state EEG recordings (64 electrodes) from 122 subjects. Based on Beck Depression Inventory (BDI) scores, they were assigned to either a high BDI (hBDI, BDI > 13, N = 46) or a control (CTL, BDI < 7, N = 75) group. We analyzed spectral activity, theta–beta, and theta–gamma phase–amplitude coupling (PAC) of EEG recorded at rest before and after a learning task. Results: At baseline, compared to CTL, hBDI exhibited greater power in beta over fronto-parietal regions and in gamma over the right parieto-occipital area. At post task, power increased in all frequency ranges only in CTL. Theta–beta and theta–gamma PAC were greater in hBDI at baseline but not after the task. Conclusions: The lack of substantial post-task growth of beta power in depressed subjects likely represents power saturation due to greater baseline values. We speculate that inhibitory/excitatory imbalance, altered plasticity mechanisms, and energy dysregulation present in depression may contribute to this phenomenon. [ABSTRACT FROM AUTHOR]

Abstract Sport-related concussion (SRC) is a complex brain injury. By applying graph-theoretical analysis to networks derived from neuroimaging techniques, studies have shown that despite an overall retention of small-world topology, changes in small-world properties occur after brain injury. Less is known about how exercise during athletes’ return to sport (RTS) influences these brain network properties. Therefore, in the present study dense electroencephalography (EEG) datasets were collected pre- and post-moderate aerobic exercise. Small-world properties of whole brain (WB) and the default mode network (DMN) were extracted from the EEG datasets of 21 concussed athletes and 21 healthy matched controls. More specifically, path length (LP), clustering coefficient (CP), and small-world index (SWI) in binary and weighted graphs were calculated in the alpha frequency band (7–13 Hz). Pre-exercise, SRC athletes had higher DMN-CP values compared to controls, while post-exercise SRC athletes had higher WB-LP compared to controls. Weighted WB analysis revealed a significant association between SRC and the absence of small-world topology (SWI ≤ 1) post-exercise. This explorative study provides preliminary evidence that moderate aerobic exercise during athletes’ RTS induces an altered network response. Furthermore, this altered response may be related to the clinical characteristics of the SRC athlete.

Recent works point to the importance of emotions in special-numerical associations. There remains a notable gap in understanding the electrophysiological underpinnings of such associations. Exploring resting-state (rs) EEG, particularly in frontal regions, could elucidate emotional aspects, while other EEG measures might offer insights into the cognitive dimensions correlating with behavioral performance. The present work investigated the relationship between rs-EEG measures (emotional and cognitive traits) and performance in the mental number line (MNL). EEG activity in theta (3-- 7 Hz), alpha (8--12 Hz, further subdivided into low-alpha and high-alpha), sensorimotor rhythm (SMR, 13--15 Hz), beta (16--25 Hz), and high-beta/gamma (28--40 Hz) bands was assessed. 76 university students participated in the study, undergoing EEG recordings at rest before engaging in a computerized number-to-position (CNP) task. Analysis revealed significant associations between frontal asymmetry, specific EEG frequencies, and MNL performance metrics (i.e., mean direction bias, mean absolute error, and mean reaction time). Notably, theta and beta asymmetries correlated with direction bias, while alpha peak frequency (APF) and beta activity related to absolute errors in numerical estimation. Moreover, the study identified significant correlations between relative amplitude indices (i.e., theta/beta ratio, theta/SMR ratio) and both absolute errors and reaction times (RTs). Our findings offer novel insights into the emotional and cognitive aspects of EEG patterns and their links to MNL performance. [ABSTRACT FROM AUTHOR]

The present study explored whether, given the association of temporal alpha with fear circuitry (learning and conditioning), exposure to complex childhood trauma (CCT) is reflected in the temporal–posterior alpha power in resting-state electroencephalography (EEG) in complex trauma-exposed adolescents in a sample of 25 adolescents and similar controls aged 12–17 years. Both trauma and psychopathology were screened or assessed, and resting-state EEG was recorded following a preregistered protocol for data collection. Temporal–posterior alpha power, corresponding to the T5 and T6 electrode locations (international 10–20 system), was extracted from resting-state EEG in both eyes-open and eyes-closed conditions. We found that in the eyes-open condition, temporal–posterior alpha was significantly lower in adolescents exposed to CCT relative to healthy controls, suggesting that childhood trauma exposure may have a measurable impact on alpha oscillatory patterns. Our study highlights the importance of considering potential neural markers, such as temporal–posterior alpha power, to understanding the long-term consequences of CCT exposure in developmental samples, with possible important clinical implications in guiding neuroregulation interventions. [ABSTRACT FROM AUTHOR]

Although exercise training has been shown to enhance neurological function, there is a shortage of research on how exercise training affects the temporal-spatial synchronization properties of functional networks, which are crucial to the neurological system. This study recruited 23 professional and 24 amateur dragon boat racers to perform simulated paddling on ergometers while recording EEG. The spatiotemporal dynamics of the brain were analyzed using microstates and omega complexity. Temporal dynamics results showed that microstate D, which is associated with attentional networks, appeared significantly altered, with significantly higher duration, occurrence, and coverage in the professional group than in the amateur group. The transition probabilities of microstate D exhibited a similar pattern. The spatial dynamics results showed the professional group had lower brain complexity than the amateur group, with a significant decrease in omega complexity in the α (8–12 Hz) and β (13–30 Hz) bands. Dragon boat training may strengthen the attentive network and reduce the complexity of the brain. This study provides evidence that dragon boat exercise improves the efficiency of the cerebral functional networks on a spatiotemporal scale. [ABSTRACT FROM AUTHOR]

Background: Mindfulness training (MT) programs have demonstrated utility as cognitive training tools, but there is little consensus on the neurophysiological processes that may underlie its benefits. It has been posited that intrinsic brain activity recorded at rest reflects the functional connectivity of large-scale brain networks and may provide insight into neuroplastic changes that support MT. In the current study, we indexed changes in several resting-state electroencephalography (EEG) parameters to investigate the neurophysiological underpinnings of MT. Methods: Resting-state EEG data were collected from active-duty U.S. military personnel (N = 80) at 2 testing sessions: before (time [T] 1) and after (T2) engaging in an 8-week MT or active comparison intervention (positivity training). We examined longitudinal and/or groupwise differences in several EEG parameters through parameterization of power spectra (individual alpha frequency and 1/f activity) and microstate analysis. Results: While no significant group × time differences were observed in individual alpha frequency, significant group × time effects were observed in several EEG parameters from T1 to T2. Compared with MT, positivity training was associated with a steepening of the 1/f slope and higher 1/f intercepts together with decreased duration and increased global field power of microstates. Conclusions: Taken together, these results suggest that the effects of interventions may be differentiated in resting-state brain activity in a sample of military personnel. Such findings provide insight into the neural underpinnings of MT-related brain changes, but more research is required to elucidate how these may relate to task-related neural and performance changes with MT and whether results generalize to other mindfulness interventions in alternative cohorts and contexts.

Background Subclinical insomnia (sINSO) represents an early stage of insomnia but lacks effective biomarkers for its recognition. The electroencephalogram(EEG) microstates, reflecting brain network dynamics, may provide potential biomarkers by comparing resting-state EEG parameters between sINSO patients and healthy controls.Methods Resting-state EEG data from 20 sINSO subjects and 20 healthy controls, under both open and closed eye conditions, were analyzed using microstate clustering (labeled A, B, C, and D) and machine learning to evaluate their discriminative power.Results The microstate global explained variance of the eyes-closed data was better than that of the eyes-open data. In the sINSO group under closed-eye conditions, the tendencies and transition probabilities for microstate changes are as follows: A to D at 7.7%, B to D at 10.7%, C to A at 7.3%, and D to B at 10.8%. Under open-eye conditions, they are: A to C at 9.1%, B to C at 8.4%, C to D at 9.4%, and D to C at 8.9%. Machine learning classification showed higher accuracy in closed-eye conditions, reaching 77.6%.Conclusion Resting-state EEG microstates exhibit significant differences between sINSO and healthy individuals. These microstates are promising biomarkers for distinguishing sINSO, with closed-eye data providing the most reliable discrimination.

Objective: Electroencephalography (EEG) microstate analysis seeks to cluster the scalp's electric field into semistable topographical EEG activity maps at different time points. Our study aimed to investigate the features of EEG microstates in subjects with focal epilepsy and psychogenic nonepileptic seizures (PNES). Methods: We included 62 adult subjects with focal epilepsy or PNES who received video‐EEG monitoring at the epilepsy monitoring unit. The subjects (mean age = 42.8 ± 21.2 years) were distributed equally between epilepsy and PNES groups. We extracted microstates from a 4.4 ± 1.0‐min, 21‐channel resting‐state EEG. We excluded subjects with interictal epileptiform discharges during resting‐state EEGs. After preprocessing, we derived five main EEG microstates—MS1 to MS5—for the full frequency band (1–30 Hz) and frequency subbands (delta, 1–4 Hz; theta, 4–8 Hz; alpha, 8–12 Hz; beta, 12–30 Hz), using the MATLAB‐based EEGLAB toolkit. Statistical features of microstates (duration, occurrence, contribution, global field power [GFP]) were compared between the groups, using logistic regression corrected for age and sex. Results: We detected no differences in microstate parameters in the full frequency band. We found a longer duration (delta: B = −7.680, p =.046; theta: B = −16.200, p =.043) and a higher contribution (delta: B = −7.414, p =.035; theta: B = −7.509, p =.031) of MS4 in lower frequency bands in the epilepsy group. The PNES group showed a higher occurrence of MS5 in the delta subband (B = 3.283, p =.032). In the theta subband, a higher GFP of MS1 was associated with the PNES group (B = 5.674, p =.025), whereas a higher GFP of MS2 was associated with the epilepsy group (B = −6.579, p =.026). Significance: Microstate features show differences between patients with focal epilepsy and PNES. EEG microstates could be a promising parameter, helping to understand changes in brain dynamics in subjects with epilepsy, and should be explored as a potential biomarker. [ABSTRACT FROM AUTHOR]

Background: Increasing research has examined the factors related to smartphone use disorder. However, limited research has explored its neural basis. Aims: We aimed to examine the relationship between the topology of the resting-state electroencephalography (rs-EEG) brain network and smartphone use disorder using minimum spanning tree analysis. Furthermore, we examined how negative emotions mediate this relationship. Methods: This study included 113 young, healthy adults (mean age 5 20.87 years, 46.9% males). Results: The results showed that the alpha- and delta-band kappas and delta-band leaf fraction were positively correlated with smartphone use disorder. In contrast, the alpha-band diameter was negatively correlated with smartphone use disorder. Negative emotions fully mediated the relationship between alpha-band kappa and alpha-band diameter and smartphone use disorder. Furthermore, negative emotions partially mediated the relationship between delta-band kappa and smartphone use disorder. The findings suggest that excessive scale-free alpha- and delta-band brain networks contribute to the emergence of smartphone use disorder. In addition, the findings also demonstrate that negative emotions and smartphone use disorder share the same neural basis. Negative emotions play a mediating role in the association between topological deviations and smartphone use disorder. Discussion: To the best of our knowledge, this is the first study to examine the neural basis of smartphone use disorder from the perspective of the topology of the rs-EEG brain network. Therefore, neuromodulation may be a potential intervention for smartphone use disorder. [ABSTRACT FROM AUTHOR]

The assessment of consciousness states, especially distinguishing minimally conscious states (MCS) from unresponsive wakefulness states (UWS), constitutes a pivotal role in clinical therapies. Despite that numerous neural signatures of consciousness have been proposed, the effectiveness and reliability of such signatures for clinical consciousness assessment still remains an intense debate. Through a comprehensive review of the literature, inconsistent findings are observed about the effectiveness of diverse neural signatures. Notably, the majority of existing studies have evaluated neural signatures on a limited number of subjects (usually below 30), which may result in uncertain conclusions due to small data bias. This study presents a systematic evaluation of neural signatures with large‐scale clinical resting‐state electroencephalography (EEG) signals containing 99 UWS, 129 MCS, 36 emergence from the minimally conscious state, and 32 healthy subjects (296 total) collected over 3 years. A total of 380 EEG‐based metrics for consciousness detection, including spectrum features, nonlinear measures, functional connectivity, and graph‐based measures, are summarized and evaluated. To further mitigate the effect of data bias, the evaluation is performed with bootstrap sampling so that reliable measures can be obtained. The results of this study suggest that relative power in alpha and delta serve as dependable indicators of consciousness. With the MCS group, there is a notable increase in the phase lag index‐related connectivity measures and enhanced functional connectivity between brain regions in comparison to the UWS group. A combination of features enables the development of an automatic detector of conscious states. [ABSTRACT FROM AUTHOR]

Although exercise training has been shown to enhance neurological function, there is a shortage of research on how exercise training affects the temporal-spatial synchronization properties of functional networks, which are crucial to the neurological system. This study recruited 23 professional and 24 amateur dragon boat racers to perform simulated paddling on ergometers while recording EEG. The spatiotemporal dynamics of the brain were analyzed using microstates and omega complexity. Temporal dynamics results showed that microstate D, which is associated with attentional networks, appeared significantly altered, with significantly higher duration, occurrence, and coverage in the professional group than in the amateur group. The transition probabilities of microstate D exhibited a similar pattern. The spatial dynamics results showed the professional group had lower brain complexity than the amateur group, with a significant decrease in omega complexity in the α (8–12 Hz) and β (13–30 Hz) bands. Dragon boat training may strengthen the attentive network and reduce the complexity of the brain. This study provides evidence that dragon boat exercise improves the efficiency of the cerebral functional networks on a spatiotemporal scale.

Recent works point to the importance of emotions in special-numerical associations. There remains a notable gap in understanding the electrophysiological underpinnings of such associations. Exploring resting-state (rs) EEG, particularly in frontal regions, could elucidate emotional aspects, while other EEG measures might offer insights into the cognitive dimensions correlating with behavioral performance. The present work investigated the relationship between rs-EEG measures (emotional and cognitive traits) and performance in the mental number line (MNL). EEG activity in theta (3–7 Hz), alpha (8–12 Hz, further subdivided into low-alpha and high-alpha), sensorimotor rhythm (SMR, 13–15 Hz), beta (16–25 Hz), and high-beta/gamma (28–40 Hz) bands was assessed. 76 university students participated in the study, undergoing EEG recordings at rest before engaging in a computerized number-to-position (CNP) task. Analysis revealed significant associations between frontal asymmetry, specific EEG frequencies, and MNL performance metrics (i.e., mean direction bias, mean absolute error, and mean reaction time). Notably, theta and beta asymmetries correlated with direction bias, while alpha peak frequency (APF) and beta activity related to absolute errors in numerical estimation. Moreover, the study identified significant correlations between relative amplitude indices (i.e., theta/beta ratio, theta/SMR ratio) and both absolute errors and reaction times (RTs). Our findings offer novel insights into the emotional and cognitive aspects of EEG patterns and their links to MNL performance.

Attentional control theory suggests that test anxiety hinders individuals' attentional control, aiding our understanding of how test anxiety may impair cognitive function. However, various methods used to assess attentional control have yielded inconsistent findings. Moreover, past studies, especially on adolescents, that examine the distinct impacts of worry and the emotional components of test anxiety on individuals' attentional control capacity are scarce. This study, using self-report, behavioral, and resting-state EEG measures, explores how worry and emotionality, impact attentional control in adolescents. It enhances our understanding of the link between test anxiety and cognitive function. Referring to the effect size from prior studies, a total of 42 adolescents took part in the study. We used the Test Anxiety Inventory, due to it can assess worry and emotionality components. We employed three widely-utilized measures of attentional control: the Attentional Control Scale (ACS), the Go/Nogo task, and resting-state electroencephalography measures (alpha oscillation and the theta/beta power ratio). Both worry and emotionality components were significantly and negatively correlated with the ACS scores. Unlike worry, emotionality demonstrated a significant positive correlation with response times for the Go trials and alpha power in the parietal cortex. These results suggest that, emotionality, but not worry, is highly correlated with attentional control deficits in adolescents. This study underscores the significance of distinguishing between the components of test anxiety, which aids in comprehending the negative impacts of test anxiety on adolescents' academic performance.

Continuous Theta Burst Stimulation (cTBS) has been shown to modulate cortical oscillations and induce cortical inhibitory effects. Electroencephalography (EEG) studies have shown some immediate effects of cTBS on brain activity. To investigate both immediate effects and short-term effects of cTBS on dynamic brain changes, cTBS was applied to 22 healthy participants over their left motor cortex. We recorded eyes-open, resting-state EEG and performance in the Nine-Hole Peg Test (NHPT) before cTBS, immediately after cTBS, and 80 minutes after cTBS. We identified nine states using a Hidden Markov Model (HMM)-based approach to describe the process of dynamic brain changes. The spatial activation, temporal profiles of HMM states and behavioral performance of NHPT were assessed and compared. cTBS altered the temporal profiles of S1-S5 immediately after cTBS and the temporal profiles of S5, S6 and S7 80 min after cTBS. Moreover, cTBS improved motor function of the left hand. State 1 was characterized as the activation of right occipito-temporal area, and NHPT behavioral performance of the left hand positively correlated with the occurrence of state 1, and negatively correlated with the interval time of state 1 after cTBS. The transitions between S1 or S7 and other states showed dynamic reconfiguration during after-effect sustained time after cTBS. These results suggest that the dynamic characteristics of state 1 are potential biomarkers for characterizing the aftereffect changes of cTBS.

Background: Depression presents with altered energy regulation and neural plasticity. Previous electroencephalography (EEG) studies showed that practice in learning tasks increases power in beta range (13–30 Hz) in healthy subjects but not in those with impaired plasticity. Here, we ascertain whether depression presents with alterations of spectral activity and connectivity before and after a learning task. Methods: We used publicly available resting-state EEG recordings (64 electrodes) from 122 subjects. Based on Beck Depression Inventory (BDI) scores, they were assigned to either a high BDI (hBDI, BDI > 13, N = 46) or a control (CTL, BDI < 7, N = 75) group. We analyzed spectral activity, theta–beta, and theta–gamma phase–amplitude coupling (PAC) of EEG recorded at rest before and after a learning task. Results: At baseline, compared to CTL, hBDI exhibited greater power in beta over fronto-parietal regions and in gamma over the right parieto-occipital area. At post task, power increased in all frequency ranges only in CTL. Theta–beta and theta–gamma PAC were greater in hBDI at baseline but not after the task. Conclusions: The lack of substantial post-task growth of beta power in depressed subjects likely represents power saturation due to greater baseline values. We speculate that inhibitory/excitatory imbalance, altered plasticity mechanisms, and energy dysregulation present in depression may contribute to this phenomenon.

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 DOCmay arise frommore 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. [ABSTRACT FROM AUTHOR]

Recent electroencephalography (EEG) studies have shown that patterns of brain activity can be used to differentiate amyotrophic lateral sclerosis (ALS) and control groups. These differences can be interrogated by examining EEG microstates, which are distinct, reoccurring topographies of the scalp's electrical potentials. Quantifying the temporal properties of the four canonical microstates can elucidate how the dynamics of functional brain networks are altered in neurological conditions. Here we have analysed the properties of microstates to detect and quantify signal‐based abnormality in ALS. High‐density resting‐state EEG data from 129 people with ALS and 78 HC were recorded longitudinally over a 24‐month period. EEG topographies were extracted at instances of peak global field power to identify four microstate classes (labelled A‐D) using K‐means clustering. Each EEG topography was retrospectively associated with a microstate class based on global map dissimilarity. Changes in microstate properties over the course of the disease were assessed in people with ALS and compared with changes in clinical scores. The topographies of microstate classes remained consistent across participants and conditions. Differences were observed in coverage, occurrence, duration, and transition probabilities between ALS and control groups. The duration of microstate class B and coverage of microstate class C correlated with lower limb functional decline. The transition probabilities A to D, C to B and C to B also correlated with cognitive decline (total ECAS) in those with cognitive and behavioural impairments. Microstate characteristics also significantly changed over the course of the disease. Examining the temporal dependencies in the sequences of microstates revealed that the symmetry and stationarity of transition matrices were increased in people with late‐stage ALS. These alterations in the properties of EEG microstates in ALS may reflect abnormalities within the sensory network and higher‐order networks. Microstate properties could also prospectively predict symptom progression in those with cognitive impairments. [ABSTRACT FROM AUTHOR]

The prolonged disorders of consciousness (PDOC) pose a challenge for an accurate clinical diagnosis, mainly due to patients' scarce or ambiguous behavioral responsiveness. Measurement of brain activity can support better diagnosis, independent of motor restrictions. Methods based on spectral analysis of resting-state EEG appear as a promising path, revealing specific changes within the internal brain dynamics in PDOC patients. In this study we used a robust method of resting-state EEG power spectrum parameter extraction to identify distinct spectral properties for different types of PDOC. Sixty patients and 37 healthy volunteers participated in this study. Patient group consisted of 22 unresponsive wakefulness patients, 25 minimally conscious patients and 13 patients emerging from the minimally conscious state. Ten minutes of resting EEG was acquired during wakefulness and transformed into individual power spectra. For each patient, using the spectral decomposition algorithm, we extracted maximum peak frequency within 1–14 Hz range in the centro-parietal region, and the antero-posterior (AP) gradient of the maximal frequency peak. All patients were behaviorally diagnosed using coma recovery scale-revised (CRS-R). The maximal peak frequency in the 1–14 Hz range successfully predicted both neurobehavioral capacity of patients as indicated by CRS-R total score and PDOC diagnosis. Additionally, in patients in whom only one peak within the 1–14 Hz range was observed, the AP gradient significantly contributed to the accuracy of prediction. We have identified three distinct spectral profiles of patients, likely representing separate neurophysiological modes of thalamocortical functioning. Etiology did not have significant influence on the obtained results. [ABSTRACT FROM AUTHOR]

Background and Aims: In the digital age, Internet addiction (IA) was deemed an epidemic and few treatments had been effectively developed for it. Here, we proposed a solution-focused group counseling (SFGC) as a potentially solution to reduce Internet addiction among college students. The present study examined the short- and long-term effect of a five-week solution-focused group counseling intervention on Internet addiction. Methods: Thirty-two participants were recruited and randomly assigned to either the experimental or control group, and twenty-six participants completed the whole intervention. The experimental group (n = 14) received the intervention, while control group (n = 12) did not. The revised version of the Chinese Internet Addiction Scale (CIAS-R), the Future Time Perspective, and resting-state EEG were administered pre-intervention, post-intervention, and at two follow-up tests (one month and six months after intervention). Results: The results showed that the scores of the CIAS-R in the experimental group were significantly decreased after intervention, and these effects could be sustained for one month and six months follow-ups. Additionally, the intervention conducted an increase in future time perspective. EEG results further suggested that the alpha, beta, and gamma absolute power decreased after the intervention. Conclusion: These results from the pilot-study primarily suggested that solution-focused group counseling could be an effective intervention for Internet addiction. [ABSTRACT FROM AUTHOR]

• We assessed EEG wakefulness regulation with the Vigilance Algorithm Leipzig in transdiagnostic patients after a suicide attempt. • A distinct vigilance pattern was observed in patients, characterized by a steep climb of vigilance during the first 3 minutes. • Significant correlations of suicidal ideation with the vigilance slope and stage A1 could serve as markers of suicidal behavior. Decades of research have not yet produced statistically reliable predictors of preparatory behavior eventually leading to suicide attempts or deaths by suicide. As the nature of suicidal behavior is complex, it is best investigated in a transdiagnostic approach, while assessing objective markers, as proposed by the Research Domain Criteria (Cuthbert, 2013). A 15-min resting-state EEG was recorded in 45 healthy controls, and 49 transdiagnostic in-patients with a recent (<6 months) suicide attempt. Brain arousal regulation in eyes-closed condition was assessed with the Vigilance Algorithm Leipzig (VIGALL) (Sander et al., 2015). A significant incline of median vigilance and vigilance slope was observed in patients within the first 3-min of the EEG recording. Additionally, a significant positive correlation of self-reported suicidal ideation with the vigilance slope over 15-min recording time, as well as a significant negative correlation with EEG vigilance stage A1 during the first 3-min was found. Transdiagnostic patients with a recent suicide attempt show a distinct vigilance regulation pattern. Further studies including a control group consisting of patients without life-time suicide attempts are needed to increase the clinical utility of the findings. These findings might serve as potential objective markers of suicidal behavior. [ABSTRACT FROM AUTHOR]

Over time, pathological, genetic, environmental, and lifestyle factors can age the brain and diminish its functional capabilities. While these factors can lead to disorders that can be diagnosed and treated once they become symptomatic, often treatment is difficult or ineffective by the time significant overt symptoms appear. One approach to this problem is to develop a method for assessing general age-related brain health and function that can be implemented widely and inexpensively. To this end, we trained a machine-learning algorithm on resting-state EEG (RS-EEG) recordings obtained from healthy individuals as the core of a brain-age estimation technique that takes an individual's RS-EEG recorded with the low-cost, user-friendly EMOTIV EPOC X headset and returns that person's estimated brain age. We tested the current version of our machine-learning model against an independent test-set of healthy participants and obtained a correlation coefficient of 0.582 between the chronological and estimated brain ages (r = 0.963 after statistical bias-correction). The test-retest correlation was 0.750 (0.939 after bias-correction) over a period of 1 week. Given these strong results and the ease and low cost of implementation, this technique has the potential for widespread adoption in the clinic, workplace, and home as a method for assessing general brain health and function and for testing the impact of interventions over time.

Background and objective: Quantitative resting-state electroencephalography (rs-EEG) is a convenient method for characterizing the functional impairments and adaptations of the brain that has been shown to be valuable for assessing many neurological and psychiatric disorders, especially in monitoring disease status and assisting neuromodulation treatment. However, it has not yet been explored in patients with neuromyelitis optica spectrum disorder (NMOSD). This study aimed to investigate the rs-EEG features of NMOSD patients and explore the rs-EEG features related to disease characteristics and complications (such as anxiety, depression, and fatigue). Methods: A total of 32 NMOSD patients and 20 healthy controls (HCs) were recruited; their demographic and disease information were collected, and their anxiety, depression, and fatigue symptoms were evaluated. The rs-EEG power spectra of all the participants were obtained. After excluding the participants with low-quality rs-EEG data during processing, statistical analysis was conducted based on the clinical information and rs-EEG data of 29 patients and 19 HCs. The rs-EEG power (the mean spectral energy (MSE) of absolute power and relative power in all frequency bands, as well as the specific power for all electrode sites) of NMOSD patients and HCs was compared. Furthermore, correlation analyses were performed between rs-EEG power and other variables for NMOSD patients (including the disease characteristics and complications). Results: The distribution of the rs-EEG power spectra in NMOSD patients was similar to that in HCs. The dominant alpha-peaks shifted significantly towards a lower frequency for patients when compared to HCs. The delta and theta power was significantly increased in the NMOSD group compared to that in the HC group. The alpha oscillation power was found to be significantly negatively associated with the degree of anxiety (reflected by the anxiety subscore of hospital anxiety and depression scale (HADS)) and the degree of depression (reflected by the depression subscore of HADS). The gamma oscillation power was revealed to be significantly positively correlated with the fatigue severity scale (FSS) score, while further analysis indicated that the electrode sites of almost the whole brain region showing correlations with fatigue. Regarding the disease variables, no statistically significant rs-EEG features were related to the main disease features in NMOSD patients. Conclusion: The results of this study suggest that the rs-EEG power spectra of NMOSD patients show increased slow oscillations and are potential biomarkers of widespread white matter microstructural damage in NMOSD. Moreover, this study revealed the rs-EEG features associated with anxiety, depression, and fatigue in NMOSD patients, which might help in the evaluation of these complications and the development of neuromodulation treatment. Quantitative rs-EEG analysis may play an important role in the management of NMOSD patients, and future studies are warranted to more comprehensively understand its application value.

Hypnotic phenomena exhibit significant inter-individual variability, with some individuals consistently demonstrating efficient responses to hypnotic suggestions, while others show limited susceptibility. Recent neurophysiological studies have added to a growing body of research that shows variability in hypnotic susceptibility is linked to distinct neural characteristics. Building on this foundation, our previous work identified that individuals with high and low hypnotic susceptibility can be differentiated based on the arrhythmic activity observed in resting-state electrophysiology (rs-EEG) outside of hypnosis. However, because previous work has largely focused on mean spectral characteristics, our understanding of the variability over time of these features, and how they relate to hypnotic susceptibility, is still limited. Here we address this gap using a time-resolved assessment of rhythmic alpha peaks and arrhythmic components of the EEG spectrum both prior to and following hypnotic induction. Using multivariate pattern classification, we investigated whether these neural features differ between individuals with high and low susceptibility to hypnosis. Specifically, we used multivariate pattern classification to investigate whether these non-stationary neural features could distinguish between individuals with high and low susceptibility to hypnosis before and after a hypnotic induction. Our analytical approach focused on time-resolved spectral decomposition to capture the intricate dynamics of neural oscillations and their non-oscillatory counterpart, as well as Lempel–Ziv complexity. Our results show that variations in the alpha center frequency are indicative of hypnotic susceptibility, but this discrimination is only evident during hypnosis. Highly hypnotic-susceptible individuals exhibit higher variability in alpha peak center frequency. These findings underscore how dynamic changes in neural states related to alpha peak frequency represent a central neurophysiological feature of hypnosis and hypnotic susceptibility.

The present study explored whether, given the association of temporal alpha with fear circuitry (learning and conditioning), exposure to complex childhood trauma (CCT) is reflected in the temporal–posterior alpha power in resting-state electroencephalography (EEG) in complex trauma-exposed adolescents in a sample of 25 adolescents and similar controls aged 12–17 years. Both trauma and psychopathology were screened or assessed, and resting-state EEG was recorded following a preregistered protocol for data collection. Temporal–posterior alpha power, corresponding to the T5 and T6 electrode locations (international 10–20 system), was extracted from resting-state EEG in both eyes-open and eyes-closed conditions. We found that in the eyes-open condition, temporal–posterior alpha was significantly lower in adolescents exposed to CCT relative to healthy controls, suggesting that childhood trauma exposure may have a measurable impact on alpha oscillatory patterns. Our study highlights the importance of considering potential neural markers, such as temporal–posterior alpha power, to understanding the long-term consequences of CCT exposure in developmental samples, with possible important clinical implications in guiding neuroregulation interventions.

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.

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder and early diagnosis is crucial for effective treatment. Stable and effective biomarkers are essential for understanding the underlying causes of the disorder and improving diagnostic accuracy. Electroencephalography (EEG) signals have proven to be reliable biomarkers for diagnosing ASD. Extracting stable connectivity patterns from EEG signals helps ensure robustness in ASD diagnostic systems. In this study, we propose a hybrid graph convolutional network framework called Rest-HGCN, which utilizes resting-state EEG signals to capture differential patterns of brain connectivity between normal children and ASD patients using graph learning strategies. The Rest-HGCN combines brain network analysis techniques and data-driven strategies to extract discriminative graph features from resting-state EEG signals. By automatically extracting differential graph patterns from these signals, the Rest-HGCN achieves reliable ASD diagnosis. To evaluate the performance of Rest-HGCN, we conducted ASD diagnosis experiments using k-fold cross-validation on the public ABC-CT resting EEG dataset. The proposed Rest-HGCN model achieved accuracies of 87.12 % and 85.32 % in single-subject and cross-experiment analyses, respectively. The results suggest that Rest-HGCN can effectively capture discriminant graph patterns from resting EEG signals and achieve robust ASD diagnosis. This may provide an effective and convenient tool for clinical ASD diagnosis.

Objective: Fragile X Syndrome (FXS) is the leading monogenic cause of intellectual disability and autism spectrum disorder. Currently, there are no established biomarkers for predicting and monitoring drug effects in FXS, and no approved therapies are available. Previous studies have shown electrophysiological changes in the brain using electroencephalography (EEG) in individuals with FXS and animal models. These changes may be influenced by drug therapies. In this study, we aimed to assess the reliability of resting-state EEG measures in individuals with FXS, which could potentially serve as a biomarker for drug discovery. Methods: We collected resting-state EEG data from 35 individuals with FXS participating in placebo-controlled clinical trials (23 males, 12 females; visit age mean+/-std 25.6 +/−8.3). The data were analyzed for various spectral features using intraclass correlation analysis to evaluate test-retest reliability. The intervals between EEG recordings ranged from same-day measurements to up to six weeks apart. Results: Our results showed high reliability for most spectral features, with same-day reliability exceeding 0.8. Features of interest demonstrated ICC values of 0.60 or above at longer intervals. Among the features, alpha band relative power exhibited the highest reliability. Conclusion: These findings indicate that resting-state EEG can provide consistent and reproducible measures of brain activity in individuals with FXS. This supports the potential use of EEG as an objective biomarker for evaluating the effects of new drugs in FXS. Significance: The reliable measurements obtained from power spectrum-based resting-state EEG make it a promising tool for assessing the impact of small molecule drugs in FXS.

Objects: This study aimed to investigate the immediate effects of fronto-parietal θ HD-tACS on a dual task of working memory-postural control. Methods: In this within-subject cross-over pilot study, we assessed the effects of 20 min of 6 Hz-tACS targeting both the left dorsolateral prefrontal cortex (lDLPFC) and posterior parietal cortex (PPC) in 20 healthy adults (age: 21.6 ± 1.3 years). During each session, single- and dual-task behavioral tests (working memory single-task, static tandem standing, and a dual-task of working memory-postural control) and closed-eye resting-state EEG were assessed before and immediately after stimulation. Results: Within the tACS group, we found a 5.3% significant decrease in working memory response time under the dual-task following tACS (t = -3.157, p = 0.005, Cohen's d = 0.742); phase synchronization analysis revealed a significant increase in the phase locking value (PLV) of θ band between F3 and P3 after tACS (p = 0.010, Cohen's d = 0.637). Correlation analyses revealed a significant correlation between increased rs-EEG θ power in the F3 and P3 channels and faster reaction time (r = -0.515, p = 0.02; r = -0.483, p = 0.031, respectively) in the dual-task working memory task after tACS. However, no differences were observed on either upright postural control performance or rs-EEG results (p-values <0.05). Conclusion: Fronto-parietal θ HD-tACS has the potential of being a neuromodulatory tool for improving working memory performance in dual-task situations, but its effect on the modulation of concurrently performed postural control tasks requires further investigation. [ABSTRACT FROM AUTHOR]

A machine-learning-based computer-aided diagnosis (CAD) system can complement the traditional diagnostic error for major depressive disorder (MDD) using trait-like neurophysiological biomarkers. Previous studies have shown that the CAD system has the potential to differentiate between female MDD patients and healthy controls. The aim of this study was to develop a practically useful resting-state electroencephalography (EEG)-based CAD system to assist in the diagnosis of drug-naïve female MDD patients by considering both the drug and gender effects. In addition, the feasibility of the practical use of the resting-state EEG-based CAD system was evaluated using a channel reduction approach. Eyes-closed, resting-state EEG data were recorded from 49 drug-naïve female MDD patients and 49 sex-matched healthy controls. Six different EEG feature sets were extracted: power spectrum densities (PSDs), phase-locking values (PLVs), and network indices for both sensor- and source-level, and four different EEG channel montages (62, 30, 19, and 10-channels) were designed to investigate the channel reduction effects in terms of classification performance. The classification performances for each feature set were evaluated using a support vector machine with leave-one-out cross-validation. The optimum classification performance was achieved when using sensor-level PLVs (accuracy: 83.67 % and area under curve: 0.92). Moreover, the classification performance was maintained until the number of EEG channels was reduced to 19 (over 80 % accuracy). We demonstrated the promising potential of sensor-level PLVs as diagnostic features when developing a resting-state EEG-based CAD system for the diagnosis of drug-naïve female MDD patients and verified the feasibility of the practical use of the developed resting-state EEG-based CAD system using the channel reduction approach. • Gender and drug effects should consider for developing diagnosis tool for depression patients. • Practical EEG-based CAD system was developed using a channel reduction approach. • Best performance was achieved when using sensor-level PLVs (accuracy: 83.67 % and AUC: 0.92). • Performance was maintained until the number of EEG channels was reduced to 19 (over 80 %). [ABSTRACT FROM AUTHOR]

Background: Decision‐making is vital in interpersonal interactions and a country's economic and political conditions. People, especially managers, have to make decisions in different risky situations. There has been a growing interest in identifying managers' personality traits (i.e., risk‐taking or risk‐averse) in recent years. Although there are findings of signal decision‐making and brain activity, the implementation of an intelligent brain‐based technique to predict risk‐averse and risk‐taking managers is still in doubt. Methods: This study proposes an electroencephalogram (EEG)‐based intelligent system to distinguish risk‐taking managers from risk‐averse ones by recording the EEG signals from 30 managers. In particular, wavelet transform, a time‐frequency domain analysis method, was used on resting‐state EEG data to extract statistical features. Then, a two‐step statistical wrapper algorithm was used to select the appropriate features. The support vector machine classifier, a supervised learning method, was used to classify two groups of managers using chosen features. Results: Intersubject predictive performance could classify two groups of managers with 74.42% accuracy, 76.16% sensitivity, 72.32% specificity, and 75% F1‐measure, indicating that machine learning (ML) models can distinguish between risk‐taking and risk‐averse managers using the features extracted from the alpha frequency band in 10 s analysis window size. Conclusions: The findings of this study demonstrate the potential of using intelligent (ML‐based) systems in distinguish between risk‐taking and risk‐averse managers using biological signals. [ABSTRACT FROM AUTHOR]

Individuals exhibit considerable variability in their capacity to learn and retain new information, including novel vocabulary. Prior research has established the importance of vigilance and electroencephalogram (EEG) alpha rhythm in the learning process. However, the interplay between vigilant attention, EEG alpha oscillations, and an individual's word learning ability (WLA) remains elusive. To address this knowledge gap, here we conducted two experiments with a total of 140 young and middle-aged adults who underwent resting EEG recordings prior to completing a paired-associate word learning task and a psychomotor vigilance test (PVT). The results of both experiments consistently revealed significant positive correlations between WLA and resting EEG alpha oscillations in the occipital and frontal regions. Furthermore, the association between resting EEG alpha oscillations and WLA was mediated by vigilant attention, as measured by the PVT. These findings provide compelling evidence supporting the crucial role of vigilant attention in linking EEG alpha oscillations to an individual's learning ability.

Abstract Background Decision‐making is vital in interpersonal interactions and a country's economic and political conditions. People, especially managers, have to make decisions in different risky situations. There has been a growing interest in identifying managers’ personality traits (i.e., risk‐taking or risk‐averse) in recent years. Although there are findings of signal decision‐making and brain activity, the implementation of an intelligent brain‐based technique to predict risk‐averse and risk‐taking managers is still in doubt. Methods This study proposes an electroencephalogram (EEG)‐based intelligent system to distinguish risk‐taking managers from risk‐averse ones by recording the EEG signals from 30 managers. In particular, wavelet transform, a time‐frequency domain analysis method, was used on resting‐state EEG data to extract statistical features. Then, a two‐step statistical wrapper algorithm was used to select the appropriate features. The support vector machine classifier, a supervised learning method, was used to classify two groups of managers using chosen features. Results Intersubject predictive performance could classify two groups of managers with 74.42% accuracy, 76.16% sensitivity, 72.32% specificity, and 75% F1‐measure, indicating that machine learning (ML) models can distinguish between risk‐taking and risk‐averse managers using the features extracted from the alpha frequency band in 10 s analysis window size. Conclusions The findings of this study demonstrate the potential of using intelligent (ML‐based) systems in distinguish between risk‐taking and risk‐averse managers using biological signals.

A single nucleotide polymorphism in the brain-derived neurotrophic factor (BDNF) gene (Val66Met) functions to regulate activity-dependent secretion of (BDNF), which plays an important role in neuroprotection and synaptic plasticity. In several studies, the Met allele was associated with lower electroencephalogram (EEG) α-power values, calculated in the standard frequency range, in young subjects. In addition to α-power, one of the inherited EEG correlates of brain functioning is individual alpha peak frequency (IAPF). Although IAPF has a separate functional role, its association with BDNF Val66Met polymorphism has not been studied. IAPF is also used to determine the boundaries of individual frequency ranges, which, unlike the standard ones, are more consistent with functional rhythm characteristics. Using a sample of 192 subjects aged 18–78 years, the association between parietal-occipital IAPF and BDNF polymorphism, as well as the genotype differences in α-power calculated in standard (8–12 Hz) and individual frequency ranges (from (IAPF –2) to (IAPF +2) Hz) were examined. A decrease of IAPF in Val/Met compared to Val/Val was observed. For power calculated in the individual frequency range, genetic differences in both eyes-closed (Val/Met > homozygous genotypes) and eyes-open (Val-carriers > Met/Met) conditions were revealed. Analysis within the standard frequency range showed differences only in the eyes-open condition, which could be due to a shift of power indicators calculated in the α-rhythm functional range to the low frequency region among Val/Met carriers, which showed a decrease in IAPF. The results suggest that the inclusion of Val/Met in the pooled group of Met carriers in the analysis of genetic differences in brain activity may level out the differences between the Val/Val and Val/Met genotypes and show the advantage of using individual frequency bands in the analysis of BDNF Val66Met-associated EEG features. [ABSTRACT FROM AUTHOR]

Background: Brain oscillations facilitate interaction within the brain network and between the brain and heart activities, and the alpha wave, as a prominent brain oscillation, plays a major role in these coherent activities. We hypothesize that mindfully breathing can make the brain and heart activities more coherent in terms of increased connectivity between the electroencephalogram (EEG) and electrocardiogram (ECG) signals. Methods: Eleven participants (28–52 years) attended 8 weeks of Mindfulness Based Stress Reduction (MBSR) training. EEG and ECG data of two states of mindful breathing and rest, both eye-closed, were recorded before and after the training. EEGLAB was used to analyze the alpha band (8–12 Hz) power, alpha peak frequency (APF), peak power and coherence. FMRIB toolbox was used to extract the ECG data. Heart coherence (HC) and heartbeat evoked potential (HEP) were calculated for further correlation analysis. Results: After 8 weeks of MBSR training, the correlation between APF and HC increased significantly in the middle frontal region and bilateral temporal regions. The correlation between alpha coherence and heart coherence had similar changes, while alpha peak power did not reflect such changes. In contrast, spectrum analysis alone did not show difference before and after MBSR training. Conclusion: The brain works in rhythmic oscillation, and this rhythmic connection becomes more coherent with cardiac activity after 8 weeks of MBSR training. Individual APF is relatively stable and its interplay with cardiac activity may be a more sensitive index than power spectrum by monitoring the brainheart connection. This preliminary study has important implications for the neuroscientific measurement of meditative practice. [ABSTRACT FROM AUTHOR]

Emotional expressive flexibility (EEF) is an important social ability that has prompted scholars to examine its benefits to human mental health. However, the neural underpinnings of individual differences in the EEF remain unclear. In neuroscience, frontal alpha asymmetry (FAA) is regarded as a sensitive indicator of certain emotional modalities and affective styles. To the best of our knowledge, no study has linked FAA with EEF to examine whether FAA could be a potential neural indicator of EEF. In the present study, 47 participants (Mage = 22.38 years, 55.3% women) underwent a resting electroencephalogram and completed the flexible regulation of emotional expression scale (FREE). The results revealed that after controlling for gender, resting FAA scores positively predicted EEF, with relative left frontal activity associated with higher EEF. Additionally, this prediction was reflected in both the enhancement and suppression dimensions of EEF. Furthermore, individuals with relative left frontal activity reported greater enhancement and EEF than individuals with relative right frontal activity. The present study indicated that FAA may be a neural marker of EEF. In the future, more empirical studies are needed to provide causal evidence that the improvement in FAA can enhance EEF. [ABSTRACT FROM AUTHOR]

As indicators of cognitive function, scale‐free neural dynamics are gaining increasing attention in cognitive neuroscience. Although the functional relevance of scale‐free dynamics has been extensively reported, one fundamental question about its association with cognitive ability remains unanswered: is the association universal across a wide spectrum of cognitive abilities or confined to specific domains? Based on dual‐process theory, we designed two categories of tasks to analyze two types of cognitive processes—automatic and controlled—and examined their associations with scale‐free neural dynamics characterized from resting‐state electroencephalography (EEG) recordings obtained from a large sample of human adults (N = 102). Our results showed that resting‐state scale‐free neural dynamics did not predict individuals' behavioral performance in tasks that primarily engaged the automatic process but did so in tasks that primarily engaged the controlled process. In addition, by fitting the scale‐free parameters separately in different frequency bands, we found that the cognitive association of scale‐free dynamics was more strongly manifested in higher‐band EEG spectrum. Our findings indicate that resting‐state scale‐free dynamics are not universal neural indicators for all cognitive abilities but are mainly associated with high‐level cognition that entails controlled processes. This finding is compatible with the widely claimed role of scale‐free dynamics in reflecting properties of complex dynamic systems. The relationship between scale‐free neural dynamics and cognitive functions has been gaining increasing attention. Our findings revealed the cognitive association of scale‐free neural dynamics across different levels of cognitive complexity, fine‐graining the functional role of scale‐free neural dynamics in the cognitive hierarchy. Specifically, the resting‐state scale‐free neural dynamics exhibit exclusive association with complex cognition but not low‐level, automatic cognitive processes—a pattern in line with the nature of complex dynamical system that scale‐free dynamics signify. [ABSTRACT FROM AUTHOR]

Featured Application: This study can be further extended to determine the ability of artificial intelligence to classify professional skills and success in cognitive task solutions according to EEG data. A high level of mathematical education is often associated with high effectiveness in solving cognitive problems and professional success. It is known that cognitive processes are accompanied by specific bioelectric activity in the brain and success in mathematical education as a behavioral phenotype is also reflected in EEG both during mental activity and at rest. This study tested the potential to distinguish volunteers with an advanced level of education in mathematics (AM) from individuals with a basic level of education in mathematics (BM) based on the frequency parameters of the resting-state electroencephalogram (EEG) recorded before the start of cognitive tasks. Further, the volunteers were divided into two groups, highly successful and moderately successful, according to their task-solving performance. The Light Gradient Boosting Machine learning algorithm was used for cross-subject classification based on the power spectral density of seven EEG frequency bands. It most accurately recognized and differentiated EEG of highly successful from highly successful BM subjects. The results indicate that success in solving tasks in combination with a high level of education in mathematics can be reflected in or predicted by the specific rhythmic activity of the brain at rest. [ABSTRACT FROM AUTHOR]

Schizophrenia is a heterogeneous mental disorder with unknown etiology or pathological characteristics. Microstate analysis of the electroencephalogram (EEG) signal has shown significant potential value for clinical research. Importantly, significant changes in microstate-specific parameters have been extensively reported; however, these studies have ignored the information interactions within the microstate network in different stages of schizophrenia. Based on recent findings, since rich information about the functional organization of the brain can be revealed by functional connectivity dynamics, we use the first-order autoregressive model to construct the functional connectivity of intra- and intermicrostate networks to identify information interactions among microstate networks. We demonstrate that, beyond abnormal parameters, disrupted organization of the microstate networks plays a crucial role in different stages of the disease by 128-channel EEG data collected from individuals with first-episode schizophrenia, ultrahigh-risk, familial high-risk, and healthy controls. According to the characteristics of the microstates of patients at different stages, the parameters of microstate class A are reduced, those of class C are increased, and the transitions from intra- to intermicrostate functional connectivity are gradually disrupted. Furthermore, decreased integration of intermicrostate information might lead to cognitive deficits in individuals with schizophrenia and those in high-risk states. Taken together, these findings illustrate that the dynamic functional connectivity of intra- and intermicrostate networks captures more components of disease pathophysiology. Our work sheds new light on the characterization of dynamic functional brain networks based on EEG signals and provides a new interpretation of aberrant brain function in different stages of schizophrenia from the perspective of microstates.

Introduction: As numerous studies have shown, executive dysfunction is the main impairment in attention‐deficit/hyperactivity disorder. According to recent neuroimaging studies, the frontoparietal coherence plays a key role in overall cognitive functions. Therefore, the aim of this study was to compare executive functions during resting‐state EEG by monitoring brain connectivity (coherence) patterns in children with attention deficit hyperactivity disorder (ADHD) with or without reading disability (RD). Methods: The statistical sample of the study consisted of 32 children with ADHD aged between 8 and 12 years old with or without specific RD. Each group consisted of 11 boys and 5 girls that were matched on chronological age and gender. EEG was recorded during eyes‐opened condition and brain connectivity within and between frontal and parietal regions was analyzed within theta, alpha, and beta bands. Results: The results revealed that across the frontal regions, the comorbid group showed a significant reduction in the left intrahemispheric coherence in the alpha and beta bands. The ADHD‐alone group exhibited increased theta and decreased alpha and beta coherence in frontal regions. In the frontoparietal regions, children in the comorbid group showed lower coherence between frontal and parietal networks compared to children without comorbid RD. Conclusion: The findings indicate that brain connectivity (coherence) patterns of children with ADHD with comorbid RD were more abnormal and lend support to more disrupted cortical connectivity in the comorbid group. Thus, these findings can be a useful marker for better recognizing ADHD and comorbid disabilities. [ABSTRACT FROM AUTHOR]

Introduction: Depression is a serious psychiatric disorder characterized by prolonged sadness, loss of interest or pleasure. The dominant alpha peak activity in resting-state EEG is suggested to be an intrinsic neural marker for diagnosis of mental disorders. Methods: To investigate an association between alpha peak activity and depression severity, the present study recorded resting-state EEG (EGI 128 channels, off-line average reference, source reconstruction by a distributed inverse method with the sLORETA normalization, parcellation of 68 Desikan- Killiany regions) from 155 patients with depression (42 males, mean age 35 years) and acquired patients' scores of Self-Rating Depression Scales. We measured both the alpha peak amplitude that is more related to synchronous neural discharging and the alpha peak frequency that is more associated with brain metabolism. Results: The results showed that over widely distributed brain regions, individual patients' alpha peak amplitudes were negatively correlated with their depressive scores, and individual patients' alpha peak frequencies were positively correlated with their depressive scores. Discussion: These results reveal that alpha peak amplitude and frequency are associated with self-rating depressive score in different manners, and the finding suggests the potential of alpha peak activity in resting-state EEG acting as an important neural factor in evaluation of depression severity in supplement to diagnosis. [ABSTRACT FROM AUTHOR]