Associative theories of creativity posit that high-creativity individuals possess flexible semantic memory structures that allow broad access to varied information. However, the semantic memory structure characteristics and neural substrates of creative writing are unclear. Here, we explored the semantic network features and the predictive whole-brain functional connectivity associated with creative writing and generated mediation models. Participants completed two creative story continuation tasks. We found that keywords from written texts with superior creative writing performance encompassed more semantic categories and were highly interconnected and transferred efficiently. Connectome predictive modeling (CPM) was conducted with resting-state functional magnetic resonance imaging (fMRI) data to identify whole-brain functional connectivity patterns related to creative writing, dominated by default mode network (DMN). Semantic network features were found to mediate the relationship between brain functional connectivity and creative writing performance. These results highlight how semantic memory structure and the DMN-driven brain functional connectivity patterns support creative writing performance. Our findings extend prior research on the role of semantic memory structure and the DMN in creativity, expand upon previous research on semantic creativity, and provide insight into the cognitive and neural foundations of creative writing.

The default mode network (DMN) is intricately linked with processes such as self-referential thinking, episodic memory recall, goal-directed cognition, self-projection, and theory of mind. In recent years, there has been a surge in the number of studies examining its functional connectivity, particularly its relationship with frontoparietal networks involved in top-down attention, executive function, and cognitive control. The fluidity in switching between these internal and external modes of processing, which is highlighted by anticorrelated functional connectivity, has been proposed as an indicator of cognitive health. Due to the ease of estimation of functional connectivity-based measures through resting-state functional magnetic resonance imaging paradigms, there is now a wealth of large-scale datasets, paving the way for standardized connectivity benchmarks. In this review, we explore the promising role of DMN connectivity metrics as potential biomarkers of cognitive state across attention, internal mentation, mind wandering, and meditation states and investigate deviations in trait-level measures across aging and in clinical conditions such as Alzheimer's disease, Parkinson's disease, depression, attention-deficit/hyperactivity disorder, and others. We also tackle the issue of reliability of network estimation and functional connectivity and share recommendations for using functional connectivity measures as a biomarker of cognitive health.

The prefrontal cortex, well-developed in humans, plays a crucial role in various behavioural outcomes. Neuronal resources are limited, however, and must be appropriately allocated. A specific type of theta activity, known as frontal midline theta (Fmθ), is intermittently observed in the frontal midline region during intense concentration to tasks. Meanwhile, the power of Fmθ activity is stronger in individuals with excessive concentration than in those who are not engaged in such state. Fmθ activity in healthy young adults is therefore speculated to be reduced when shifting attention from one target to another. However, the neuronal mechanisms of Fmθ reduction are largely unknown. In this study, 20 healthy young male adults performed 2-back cognitive tasks including two pieces of information (colour and position). Participants initially pay attention to one of them at the start of the task, and then to shift attention to the other when the screen's background colour was changed. Transient reduction of theta activity was observed with the attentional shift, and high current source density was observed in the medial prefrontal and the anterior cingulate regions. Reduction of alpha activity in the left inferior parietal lobe was simultaneously observed during the attentional shift. Moreover, there was a significant positive correlation between the reduction of Fmθ and alpha activity in the left parietal area. The reduction of Fmθ activity and the change in activity of the inferior parietal lobe may play an essential role in disengaging from intense concentration and the appropriate attentional shift. Trial Registration: UMIN000051023.

How meaning is conveyed from creator to observer is debated in the psychology of art. The Mirror Model of Art represents a theoretical framework for bridging the psychological processes that underpin creative production and aesthetic appreciation of art. Specifically, it postulates that creating art and having an aesthetic experience are "mirrored" processes such that the early stage of aesthetic appreciation corresponds to the late stage of creative production, and conversely, that the late stage of aesthetic appreciation corresponds to the early stage of creative production. We conducted a meta-analysis of fMRI studies in the visual domain to test this hypothesis. Our results reveal that creative production engages the prefrontal cortex, which we attribute to its role in idea generation, whereas aesthetic appreciation engages the visual cortex, anterior insula, parahippocampal gyrus, the fusiform gyrus, and the frontal lobes, regions involved primarily in sensory, perceptual, reward and mnemonic processing. Their direct comparison revealed that creative production was associated with greater activation in the prefrontal cortex, whereas aesthetic appreciation was associated with greater activation in the visual cortex. This meta-analysis largely supports predictions derived from the Mirror Model of Art, by providing a snapshot of neural activity in the relatively early stages in art creators' and observers' minds. Future studies that capture brain function across longer spans of time are needed to understand the expression of creativity and aesthetic appreciation in different stages of information processing in relation to the Mirror Model of Art.

The study investigated the neural correlates of mind wandering using eyeblink rate (EBR) and variability (EBV) proxies. Dopamine, a neurotransmitter integral to the brain's reward system, has been implicated in regulating both task-unrelated and task-focused thinking. This study sought to clarify the relationships between dopaminergic function and cognitive control during a task by utilizing EBR and EBV as proxy measures.

Vertical electrooculogram and brain event-related potential (ERP) data were gathered from 24 adult participants while they performed a computerized cognitive task. During the task (3-stimulus visual oddball procedure), participants discriminated between an infrequently seen target stimulus, an infrequent novel stimulus (for evaluating task engagement and distraction), and a commonly occurring nontarget stimulus. A retrospective questionnaire (Dundee Stress State Questionnaire, DSSQ) assessed task-unrelated (TUT) and task-related (TRT) thinking directly after task completion. The P3a ERP brain indexes at the Cz and Fz scalp electrode sites were also considered as a secondary proxy measure of dopamine function.

The main finding revealed that higher EBR was associated with higher TUT, suggesting a link between elevated dopaminergic activity and mind wandering. There was also a marginal negative correlation with P3a latency at the Fz scalp location and TUT, indicative of heightened responsiveness to distraction in general. For TRT, there was a positive correlation with P3a amplitudes at Fz, suggesting a role in task-related engagement and focus on all stimuli during the task. Regarding behavior, EBR and EBV were negatively correlated with Sigma ex-Gaussian task reaction time (RT), suggesting that more stable cognitive states are associated with higher blink rates and variability. Tau RT positively correlated with blink variability and P3a amplitudes at Fz and Cz, indicative of attentional lapse. Regression analyses showed that EBR and Mu RT predicted TUT, while TRT was predicted by P3a amplitude at Fz. More blinks and slower responses were related to TUT, whereas greater focus on the task stimuli (P3a amplitude) was related to TRT.

These data underscore the importance of dopamine during mind wandering and task focus. In addition, this study argues for using ex-Gaussian analysis to understand the complex dynamics of attentional control during mind wandering.

We must reconcile the needs of the internal world and the demands of the external world to make decisions relevant to homeostasis, well-being, and flexible behavior. Engagement with the internal (eg interoceptive) world is linked to medial brain systems, whereas the extrapersonal space (eg exteroceptive) is associated with lateral brain systems. Using Human Connectome Project data, we found three association tracts connecting the action-related frontal lobe with perception-related posterior lobes. A lateral dorsal tract and a medial dorsal tract interact independently with a ventral tract at frontal and posterior hubs. The two frontal and the two posterior hubs are interconnected, forming a meta-loop that integrates lateral and medial brain systems. The four anatomical hubs correspond to the common nodes of the intrinsic cognitive brain networks such as the default mode network. These functional networks depend on the integration of both realms. Thus, the positioning of functional cognitive networks can be understood as the intersection of long anatomical association tracts. The strength of structural connectivity within lateral and medial brain systems correlates with performance on behavioral tests assessing theory of mind. The meta-loop provides an anatomical framework to associate neurological and psychiatric symptoms with functional and structural changes.

The default mode network (DMN) is implicated in many aspects of complex thought and behavior. Here, we leverage postmortem histology and in vivo neuroimaging to characterize the anatomy of the DMN to better understand its role in information processing and cortical communication. Our results show that the DMN is cytoarchitecturally heterogenous, containing cytoarchitectural types that are variably specialized for unimodal, heteromodal and memory-related processing. Studying diffusion-based structural connectivity in combination with cytoarchitecture, we found the DMN contains regions receptive to input from sensory cortex and a core that is relatively insulated from environmental input. Finally, analysis of signal flow with effective connectivity models showed that the DMN is unique amongst cortical networks in balancing its output across the levels of sensory hierarchies. Together, our study establishes an anatomical foundation from which accounts of the broad role the DMN plays in human brain function and cognition can be developed.

Meaningful variation in internal states that impacts cognition and behavior remains challenging to discover and characterize. Here we leverage trial-to-trial fluctuations in the brain-wide signal recorded using functional MRI to test if distinct sets of brain regions are activated on different trials when accomplishing the same task. Across three different perceptual decision-making experiments, we estimate the brain activations for each trial. We then cluster the trials based on their similarity using modularity-maximization, a data-driven classification method. In each experiment, we find multiple distinct but stable subtypes of trials, suggesting that the same task can be accomplished in the presence of widely varying brain activation patterns. Surprisingly, in all experiments, one of the subtypes exhibits strong activation in the default mode network, which is typically thought to decrease in activity during tasks that require externally focused attention. The remaining subtypes are characterized by activations in different task-positive areas. The default mode network subtype is characterized by behavioral signatures that are similar to the other subtypes exhibiting activation with task-positive regions. These findings demonstrate that the same perceptual decision-making task is accomplished through multiple brain activation patterns.

Evidence suggests that (almost) everyone dreams during their sleep and may actually do so for a large part of the night. Yet, dream recall shows large interindividual variability. Understanding the factors that influence dream recall is crucial for advancing our knowledge regarding dreams' origin, significance, and functions. Here, we tackled this issue by prospectively collecting dream reports along with demographic information and psychometric, cognitive, actigraphic, and electroencephalographic measures in 217 healthy adults (18-70 y, 116 female participants, 101 male participants). We found that attitude towards dreaming, proneness to mind wandering, and sleep patterns are associated with the probability of reporting a dream upon morning awakening. The likelihood of recalling dream content was predicted by age and vulnerability to interference. Moreover, dream recall appeared to be influenced by night-by-night changes in sleep patterns and showed seasonal fluctuations. Our results provide an account for previous observations regarding inter- and intra-individual variability in morning dream recall.

Early efforts to understand the human cerebral cortex focused on localization of function, assigning functional roles to specific brain regions. More recent evidence depicts the cortex as a dynamic system, organized into flexible networks with patterns of spatiotemporal activity corresponding to attentional demands. In functional MRI (fMRI), dynamic analysis of such spatiotemporal patterns is highly promising for providing non-invasive biomarkers of neurodegenerative diseases and neural disorders. However, there is no established neurotypical spectrum to interpret the burgeoning literature of dynamic functional connectivity from fMRI across attentional states. In the present study, we apply dynamic analysis of network-scale spatiotemporal patterns in a range of fMRI datasets across numerous tasks including a left-right moving dot task, visual working memory tasks, congruence tasks, multiple resting state datasets, mindfulness meditators, and subjects watching TV. We find that cortical networks show shifts in dynamic functional connectivity across a spectrum that tracks the level of external to internal attention demanded by these tasks. Dynamics of networks often grouped into a single task positive network show divergent responses along this axis of attention, consistent with evidence that definitions of a single task positive network are misleading. Additionally, somatosensory and visual networks exhibit strong phase shifting along this spectrum of attention. Results were robust on a group and individual level, further establishing network dynamics as a potential individual biomarker. To our knowledge, this represents the first study of its kind to generate a spectrum of dynamic network relationships across such an axis of attention.

Sustained attention fluctuates between periods of good and poor attentional performance. Two major methodologies exist to study these fluctuations: an objective approach that identifies "in-the-zone" states of consistent response times (RTs) and "out-of-the-zone" states of erratic RTs and a subjective approach that asks participants whether they are on-task or mind wandering. Although both approaches effectively predict attentional lapses, it remains unclear whether they capture the same or distinct attentional fluctuations. We combined both approaches within a single sustained attention task requiring frequent responses and response inhibition to rare targets to explore their consistency (N = 40). Behaviorally, both objective out-of-the-zone and subjective mind-wandering states were associated with more attentional lapses. However, the percentage of time spent out-of-the-zone did not differ between on-task and mind-wandering periods and both objective and subjective states independently predicted error-proneness, suggesting that the two methods do not capture the same type of attention fluctuations. Whereas attentional preparation before correct inhibitions was greater during out-of-the-zone compared with in-the-zone periods, preparation did not differ by subjective state. In contrast, posterror slowing differed by both objective and subjective states, but in opposite directions: slowing was observed when participants were objectively out-of-the-zone or subjectively on-task. Overall, our results provide evidence that objective and subjective approaches capture distinct attention fluctuations during sustained attention tasks. Integrating both objective and subjective measures is crucial for fully understanding the mechanisms underlying our ability to remain focused.

EEG microstates offer a unique window into the dynamics of emotional experiences. This study delved into the emotional responses of happiness and sadness triggered by music videos, employing microstate analysis and eLoreta source-level investigation in the alpha band. The results of the microstate analysis showed that regardless of gender, participants during happy music video significantly upregulated class D microstate and downregulated class C microstate, leading to a significantly enhanced global explained variance (GEV), coverage, occurrence, duration, and global field power (GFP) for class D. Conversely, sad music video had the opposite effect. The eLoreta study revealed that during the happy state, there was enhanced CSD in the central parietal regions across both genders and diminished functional connectivity in the precuneus for female participants compared to the sad state. Class D and class C microstates are linked to attention and mind-wandering, respectively. The findings suggest that (1) increased class D and CSD activity could explain heightened attentiveness observed during happy music, and (2) increased class C activity and functional connectivity could explain enhanced mind wandering observed during sad music. Additionally, female participants exhibited significantly higher mean occurrence than males, and the sad state showed significantly higher mean occurrence than the happy state.

Recent studies have revealed disrupted dynamic functional network connectivity (dFNC) in stroke or dementia brains. However, it remains unclear how the dFNC was altered in post-stroke dementia (PSD).

This study aimed to explore PSD-specific alterations in the dFNC and their association with clinical assessments.

We included 19 normal controls (NC), 16 PSD, and 20 post-stroke non-dementia (PSND) patients who underwent resting-state functional magnetic resonance imaging scan. Independent component analysis, combined with a sliding-window approach, was employed to calculate dFNC. The cognitive performance was assessed by both Mini-Mental State Examination and MiniCog assessments, which were subsequently used for multiple regression analysis to investigate the relationships with dFNC.

We identified 13 meaningful resting-state networks, and the dFNC among them derived four states exhibiting different connection patterns. In state III with strong connections within high-order networks, PSD and PSND both showed increased connectivity between visual network (VN) and high-order networks relative to NC; in state I with weak connections among all networks, PSD showed weaker connectivity between default mode network (DMN) and executive control network and between VN and DMN compared to PSND. Moreover, the dFNC measures showed significant correlations with cognitive assessments of patients.

Our findings suggest that PSD-specific functional network connectivity (FNC) alterations are state-dependent, and dFNC reveals a coexistence mechanism of functional impairment and compensation in large-scale brain networks, which is not observed in static FNC. This offers a new perspective to understand the neural mechanisms of PSD.

Mild-to-moderate traumatic brain injury (mmTBI) that lead to deficits in balance and gait are difficult to resolve through standard therapy protocols, and these deficits can severely impact a patient's quality of life. Recently, translingual neural stimulation (TLNS) has emerged as a potential therapy for mmTBI-related balance and gait deficits by inducing neuroplastic changes in the brain gray matter structure. However, it is still unclear how interactions within and between functional networks in brain are affected by TLNS. The current study aimed to extend our previous resting-state functional connectivity (RSFC) study investigating the effects of TLNS intervention on outcome measures related to gait and balance.

An experimental PoNS device was utilized to deliver the TLNS. The 2-week TLNS intervention program, specifically stimulation during focused physical therapy focused on recovery of gait and balance, included twice-daily treatment in the laboratory and the same program at home during the intervening weekend. The resting-state fMRI datasets at pre- and post-interventions were collected by 3T MRI scanner with nine mmTBI patients. All participants also received both Sensory Organization Test (SOT) and Dynamic Gait Index (DGI) testing pre- and post-intervention as part of the behavioral assessment.

Compared to baseline, TLNS intervention led to statistically significant improvements in both the SOT [t (8) = 2.742, p = 0.028] and the DGI [t (8) = 2.855, p = 0.024] scores. Moreover, significant increases in intra- and inter-network RSFC were observed, particularly within the visual, default mode, dorsal attention, frontoparietal (FPN), and somatosensory (SMN) networks. Additionally, there were significant correlations between the SOT and inter-network FC [between FPN and SMN, r (9) = -0.784, p = 0.012] and between the DGI and intra-network FC [within SMN, r (9) = 0.728, p = 0.026].

These findings suggest that TLNS intervention is an effective in increasing somatosensory processing, vestibular-visual interaction, executive control and flexible shifting, and TLNS may be an effective approach to inducing brain network plasticity and may serve as a potential therapy for mmTBI-related gait and balance deficits.

Mind wandering (MW) encompasses both a deliberate and a spontaneous disengagement of attention from the immediate external environment to unrelated internal thoughts. Importantly, MW has been suggested to have an inverse relationship with mindfulness, a state of nonjudgmental awareness of present-moment experience. Although they are, respectively, associated with increased and decreased activity in the default mode network (DMN), the specific contributions of deliberate and spontaneous MW, and their relationships with mindfulness abilities and resting-state macro networks remain to be elucidated. Therefore, resting-state MRI scans from 76 participants were analyzed with group independent component analysis to decompose brain networks into independent macro-networks and to see which of them predicted specific aspects of spontaneous and deliberate MW or mindfulness traits. Our results show that temporal variability of the resting-state DMN predicts spontaneous MW, which in turn is negatively associated with the acting with awareness facet of mindfulness. This finding shows that the DMN is not directly associated with overall mindfulness, but rather demonstrates that there exists a close relationship between DMN and MW, and furthermore, that the involvement of mindfulness abilities in this dynamic may be secondary. In sum, our study contributes to a better understanding of the neural bases of spontaneous MW and its relationship with mindfulness. These results open up the possibility of intervening on specific aspects of our cognitive abilities: for example, our data suggest that training the mindfulness facet acting with awareness would allow lessening our tendency for MW at inopportune times.

Acute experimental models of antidepressant placebo effects suggest that expectancies, encoded within the salience network (SN), are reinforced by sensory evidence and mood fluctuations. However, whether these dynamics extend to longer timescales remains unknown. To answer this question, we investigated how SN and default mode network (DMN) functional connectivity during the processing of antidepressant expectancies facilitates the shift from salience attribution to contextual cues in the SN to belief-induced mood responses in the DMN, both acutely and long term.

Sixty psychotropic-free patients with major depressive disorder completed an acute antidepressant placebo functional magnetic resonance imaging experiment manipulating placebo-associated expectancies and their reinforcement while assessing trial-by-trial mood improvement before entering an 8-week double-blind, randomized, placebo-controlled trial of a selective serotonin reuptake inhibitor or placebo.

Learned antidepressant expectancies predicted by a reinforcement learning model modulated SN-DMN connectivity. Acutely, greater modulation predicted higher effects of expectancy and reinforcement manipulations on reported expectancies and mood. Over 8 weeks, no significant drug effects on mood improvement were observed. However, participants who believed that they were receiving an antidepressant exhibited significantly greater mood improvement irrespective of the actual treatment received. Moreover, increased SN-DMN connectivity predicted mood improvement, especially in placebo-treated participants who believed that they received a selective serotonin reuptake inhibitor.

SN-DMN interactions may play a critical role in the evolution of antidepressant response expectancies, drug-assignment beliefs, and their effects on mood.

Metaverse integrates people into the virtual world, and challenges depend on advances in human, technological, and procedural dimensions. Until now, solutions to these challenges have not involved extensive neurosociological research. The study explores the pioneering neurosociological paradigm in metaverse, emphasizing its potential to revolutionize our understanding of social interactions through advanced methodologies such as hyperscanning and interbrain synchrony. This convergence presents unprecedented opportunities for neurotypical and neurodivergent individuals due to technology personalization. Traditional face-to-face, interbrain coupling, and metaverse interactions are empirically substantiated. Biomarkers of social interaction as feedback between social brain networks and metaverse is presented. The innovative contribution of findings to the broader literature on metaverse and neurosociology is substantiated. This article also discusses the ethical aspects of integrating the neurosociological paradigm into the metaverse.

Coma and disorders of consciousness are frequently considered in terms of two linked anatomic-functional systems: the arousal system and the awareness system. The mesopontine tegmentum (namely the cuneiform/subcuneiform nuclei of the caudal midbrain and the pontis oralis nucleus of the rostral pons) and the monoamine nuclei generate signals of arousal. These signals are augmented in lateral hypothalamus and basal forebrain, which then project to the thalamus and diffusely across the cortex. The medial dorsal tegmental tract is the main conduit for the ascending arousal system to directly activate the thalamic intralaminar nuclei and modulate thalamocortical networks, while the lateral dorsal tegmental tract connects to the thalamic reticular nucleus for regulation of intrathalamic inhibitory networks. The central thalamus (particularly the intralaminar nuclei) and the mesocircuit regulate the arousal system. Lesions to any part of this system, particularly paramedian and bilateral lesions, result in a depressed level of arousal. Distinct from the arousal pathways, the awareness system runs continuously as a stream of consciousness. It consists of large-scale distributed cortical networks that are necessary for representations of the external (executive control network with the dorsal/ventral attention networks) and the internal world (executive control network in conjunction with the default network). A feature of the awareness system is that it does not capture external and internal worlds at once and instead, holds singular representations, serially moment-by-moment. The medial dorsal nucleus of the thalamus serves as the associative nuclei of the default network, and the thalamic reticular nucleus regulates the awareness system. Lesions that disrupt large-scale networks, particularly nodes of cortical hubs, result in lack of awareness. Integrative paradigms such as the integrated information theory and the global neuronal workspace models are attempts to bind awareness and arousal into a unified experience of consciousness.

As a subset of active learning, gamification involves the application of gaming principles as a means of improving student outcomes in the classroom. Recent work has shown that such active learning strategies may be particularly effective at reducing the rate of failure in STEM courses. In this retrospective case study, I examined the effects on student exam performance, rate of failure, and perception of instruction following a semester-long course improvement project that involved implementing a novel tabletop style roleplaying game (Build-a-Zombie) during lab sessions in an undergraduate neuroanatomy course. The game I developed tasked students with using their knowledge from lecture to design their own pathological zombie nervous system. When compared to a previous cohort, students in the gamified version of the course showed significantly increased exam scores, a trend toward decreased rates of failure, and a more positive perception of instruction, even though lecture and exam content remained the same.

Mind-wandering is an essential cognitive process in which people engage for 30-50% of their waking day and is highly associated with neuroticism. The current study identified the factor structure of retrospective self-report items related to mind-wandering and perseverative cognition content and explored these associations with neuroticism. In an adult community sample (N = 309), items from the NYC Cognition Questionnaire, the Penn State Worry Questionnaire Short Form, and the Rumination Responses Brooding Subscale were entered into factor analyses to test the optimal factor structure of these items. We employed a structural model to investigate associations of mind-wandering facets with neuroticism. A correlated three factor solution best fit the data (CFI = .94, TLI = .93, SRMR = .07, RMSEA = .07). Bifactor models failed to provide evidence for a general mind-wandering construct above and beyond variance explained by mind-wandering and perseverative cognition facets. The structural model revealed differential associations of each facet with neuroticism. A wandering mind is not always an unhappy mind. Whereas worry and rumination are associated with higher levels of neuroticism, mind-wandering has other components that relate to positively valenced cognition and lower neuroticism. Adaptive and maladaptive mind-wandering should be tested together in future studies of personality and psychopathology.

Spontaneous thought is a universal, complex, and heterogeneous cognitive activity that significantly impacts mental activity and strongly correlates with mental disorders.

Utilizing the think-aloud method, we captured spontaneous thoughts during rest from 38 diagnosed with depression, alongside 36 healthy controls and 137 healthy individuals. Through a comprehensive assessment of various dimensions of thought content, we compared thought content between individuals with depression and healthy controls, and between healthy women and men. Finally, we employed natural language processing (NLP) to develop regression models for multidimensional content assessment and a classification model to differentiate between individuals with and without depression.

Compared to healthy controls, individuals with depression had more internally oriented and less externally oriented spontaneous thoughts. They focused more on themselves and negative things, and less on positive things, experiencing higher levels of negative emotions and lower levels of positive emotions. Besides, we found that compared to healthy men, healthy women's spontaneous thoughts focus more on interoception, the self, past events, and negative events, and they experience higher levels of negative emotions. Meanwhile, we identified the potential application of the think-aloud method to collect spontaneous thoughts and integrate NLP in the field of depression.

This study offers direct insights into the stream of thought during individuals' resting state, revealing differences between individuals with depression and healthy controls, as well as sex differences in the content of spontaneous thoughts. It enhances our understanding of spontaneous thought and offers a new perspective for preventing, diagnosing, and treating depression.

Unlike classic audio guides, intelligent audio guides can detect users' level of attention and help them regain focus. In this paper, we investigate the detection of mind wandering (MW) from eye movements in a use case with a long focus distance. We present a novel MW annotation method for combined audio-visual stimuli and collect annotated MW data for the use case of audio-guided city panorama viewing. In two studies, MW classifiers are trained and validated, which are able to successfully detect MW in a 1-s time window. In study 1 (n = 27), MW classifiers from gaze features with and without eye vergence are trained (area under the curve of at least 0.80). We then re-validate the classifier with unseen data (study 2, n = 31) that are annotated using a memory task and find a positive correlation (repeated measure correlation = 0.49, p < 0.001) between incorrect quiz answering and the percentage of time users spent mind wandering. Overall, this paper contributes significant new knowledge on the detection of MW from gaze for use cases with audio-visual stimuli.

Mind-wandering is a highly dynamic phenomenon involving frequent fluctuations in cognition. However, the dynamics of functional connectivity between brain regions during mind-wandering have not been extensively studied.

We employed an analytical approach aimed at extracting recurring network states of multilayer networks built using amplitude envelope correlation and imaginary phase-locking value of delta, theta, alpha, beta, or gamma frequency band. These networks were constructed based on electroencephalograph (EEG) data collected while participants engaged in a video-learning task with mind-wandering and focused learning conditions. Recurring multilayer network states were defined via clustering based on overlapping node closeness centrality.

We observed similar multilayer network states across the five frequency bands. Furthermore, the transition patterns of network states were not entirely random. We also found significant differences in metrics that characterize the dynamics of multilayer network states between mind-wandering and focused learning. Finally, we designed a classification algorithm, based on a hidden Markov model using state sequences as input, that achieved a 0.888 mean area under the receiver operating characteristic curve for within-participant detection of mind-wandering.

Our approach offers a novel perspective on analyzing the dynamics of EEG data and shows potential application to mind-wandering detection.

Background: Previous studies have demonstrated that mind wandering during incubation phases enhances post-incubation creative performance. Recent empirical evidence, however, has highlighted a specific form of mind wandering closely related to creativity, termed freely moving mind wandering (FMMW). In this study, we examined the behavioral and neural associations between FMMW and creativity. Methods: We initially validated a questionnaire measuring FMMW by comparing its results with those from the Sustained Attention to Response Task (SART). Data were collected from 1316 participants who completed resting-state fMRI scans, the FMMW questionnaire, and creative tasks. Correlation analysis and Bayes factors indicated that FMMW was associated with creative thinking (AUT). To elucidate the neural mechanisms underlying the relationship between FMMW and creativity, Hidden Markov Models (HMM) were employed to analyze the temporal dynamics of the resting-state fMRI data. Results: Our findings indicated that brain dynamics associated with FMMW involve integration within multiple networks and between networks (r = -0.11, pFDR < 0.05). The links between brain dynamics associated with FMMW and creativity were mediated by FMMW (c' = 0.01, [-0.0181, -0.0029]). Conclusions: These findings demonstrate the relationship between FMMW and creativity, offering insights into the neural mechanisms underpinning this relationship.

Although anxiety is a common psychological condition, its symptoms are related to a cardiopulmonary strain which can cause palpitation, dyspnea, dizziness, and syncope. Severe anxiety can be disabling and lead to cardiac events such as those seen in Takotsubo cardiomyopathy. Since torso stiffness is a stress response to unpredictable situations or unexpected outcomes, studying the biomechanics behind it may provide a better understanding of the pathophysiology of anxiety on circulation, especially on venous impedance. Any degree of torso stiffness related to anxiety would limit venous return, which in turn drops cardiac output because the heart can pump only what it receives. Various methods and habits used to relieve stress seem to reduce torso stiffness. Humans are large obligatory bipedal upright primates and thus need to use the torso carefully for smooth upright activities with an accurate prediction. The upright nature of human activity itself seems to contribute to anxiety due to the needed torso stiffness using the very unstable spine. Proper planning of actions with an accurate prediction of outcomes of self and non-self would be critical to achieving motor control and ventilation in bipedal activities. Many conditions linked to prediction errors are likely to cause various degrees of torso stiffness due to incomplete learning and unsatisfactory execution of actions, which will ultimately contribute to anxiety. Modifying environmental factors to improve predictability seems to be an important step in treating anxiety. The benefit of playful aerobic activity and proper breathing on anxiety may be from the modulation of torso stiffness and enhancement of central circulation resulting in prevention of the negative effect on the cardiopulmonary system.

A growing body of literature demonstrates that ethical leadership has positive effects on employees' work outcomes. Ethical leadership upholds the importance of "normatively appropriate conduct through personal actions and interpersonal relationships" (Brown et al., 2005, p. 120; doi:10.1016/j.obhdp.2005.03.002). However, extant empirical research does not answer the question- of how ethical leaders balance their relation maintenance (i.e., relationship-oriented role) and performance maintenance (i.e., task-oriented role) behaviors with their employees to be perceived as ethical leaders. In the present paper, drawing upon the propositions informed by opposing domains theory and related research, I theorize that leaders' relationship-oriented and task-oriented roles create synergistic effects that predict their employees' perceptions of ethical leadership. Results across two studies (an experiment and a correlational study involving samples from two different cultures) convergently confirmed the hypothesized relationships. I conclude by discussing several key theoretical and practical implications of these findings.

Interoceptive attention to internal sensory signals, such as the breath, is fundamental to mindfulness. However, interoceptive attention can be difficult to study, with many studies relying on subjective and retrospective measures. Response consistency is an established method for evaluating variability of attention on exteroceptive attention tasks, but it has rarely been applied to interoceptive attention tasks.

In this study, we measured consistency of response times on a breath-monitoring task with simultaneous electroencephalography in individuals across the life span (15-91 years of age, N = 324).

We found that consistency on the breath-monitoring task was positively correlated with attentive performance on an exteroceptive inhibitory control task. Electroencephalography source reconstruction showed that on-task alpha band (8-12 Hz) activity was greater than that measured at rest. Low-consistency/longer breath responses were associated with elevated brain activity compared with high-consistency responses, particularly in posterior default mode network (pDMN) brain regions. pDMN activity was inversely linked with functional connectivity to the frontoparietal network and the cingulo-opercular network on task but not at rest, suggesting a role for these frontal networks in on-task regulation of pDMN activity. pDMN activity within the precuneus region was greater in participants who reported low subjective mindfulness and was adaptively modulated by task difficulty in an independent experiment.

Elevated pDMN alpha activity serves as an objective neural marker for low-consistency responding during interoceptive breath attention, scales with task difficulty, and is associated with low subjective mindfulness.

The neuroscience of meditation is providing insight into meditation's beneficial effects on well-being and informing understanding of consciousness. However, further research is needed to explicate mechanisms linking brain activity and meditation. Non-invasive brain stimulation (NIBS) presents a promising approach for causally investigating neural mechanisms of meditation. Prior NIBS-meditation research has predominantly targeted frontal and parietal cortices suggesting that it might be possible to boost the behavioral and neural effects of meditation with NIBS. Moreover, NIBS has revealed distinct neural signatures in long-term meditators. Nonetheless, methodological variations in NIBS-meditation research contributes to challenges for definitive interpretation of previous results. Future NIBS studies should further investigate core substrates of meditation, including specific brain networks and oscillations, and causal neural mechanisms of advanced meditation. Overall, NIBS-meditation research holds promise for enhancing meditation-based interventions in support of well-being and resilience in both non-clinical and clinical populations, and for uncovering the brain-mind mechanisms of meditation and consciousness.

Background: Naturalistic stimuli have become increasingly popular in modern cognitive neuroscience. These stimuli have high ecological validity due to their rich and multilayered features. However, their complexity also presents methodological challenges for uncovering neural network reconfiguration. Dynamic functional connectivity using the sliding-window technique is commonly used but has several limitations. In this study, we introduce a new method called intersubject dynamic conditional correlation (ISDCC). Method: ISDCC uses intersubject analysis to remove intrinsic and non-neuronal signals, retaining only intersubject-consistent stimuli-induced signals. It then applies dynamic conditional correlation (DCC) based on the generalized autoregressive conditional heteroskedasticity to calculate the framewise functional connectivity. To validate ISDCC, we analyzed simulation data with known network reconfiguration patterns and two publicly available narrative functional Magnetic Resonance Imaging (fMRI) datasets. Results: (1) ISDCC accurately unveiled the underlying network reconfiguration patterns in simulation data, demonstrating greater sensitivity than DCC; (2) ISDCC identified synchronized network reconfiguration patterns across listeners; (3) ISDCC effectively differentiated between stimulus types with varying temporal coherence; and (4) network reconfigurations unveiled by ISDCC were significantly correlated with listener engagement during narrative comprehension. Conclusion: ISDCC is a precise and dynamic method for tracking network implications in response to naturalistic stimuli.

Previous emotion regulation studies revealed that emotional distraction decreases unpleasant emotions. This study examined whether distraction tasks decrease unpleasant task-unrelated thoughts (TUTs) and unpleasant emotions when recalling stressful daily interpersonal events. Amygdala activity was examined to assess implicit emotional changes using functional magnetic resonance imaging (fMRI). The behavioral data of 20 university students (mean age: 20.35 ± 1.42 years; range: 18-24 years) and fMRI data of 18 students were examined. As an emotion induction procedure, participants initially freely recalled memories of daily stressful interpersonal events and then responded to a series of questions about their recalled memories presented on a monitor. In the distraction experiment, the questions were re-represented as an emotional stimulation; a distraction task (nonconstant or constant finger tapping) or rest condition was then performed, and ratings were given for attentional state, thought types conceived during the tasks, and emotional state. Decreases in unpleasant emotions and TUTs were defined as distraction effects. We found that unpleasant TUTs decreased in the nonconstant relative to rest condition (p < .05). Furthermore, increased right amygdala activation positively correlated with unpleasant emotions, and bilateral amygdala activation correlated with unpleasant TUTs only in the rest condition, indicating the existence of amygdala activation associated with unpleasant emotions and thoughts. However, such associations were not under nonconstant or constant conditions, indicating distraction effects. Notably, this study showed that emotional distraction can decrease the degree of unpleasant emotions and the occurrence of unpleasant thoughts regarding common daily emotions.

Meaningful variation in internal states that impacts cognition and behavior remains challenging to discover and characterize. Here we leveraged trial-to-trial fluctuations in the brain-wide signal recorded using functional MRI to test if distinct sets of brain regions are activated on different trials when accomplishing the same task. Across three different perceptual decision-making experiments, we estimated the brain activations for each trial. We then clustered the trials based on their similarity using modularity-maximization, a data-driven classification method. In each experiment, we found multiple distinct but stable subtypes of trials, suggesting that the same task can be accomplished in the presence of widely varying brain activation patterns. Surprisingly, in all experiments, one of the subtypes exhibited strong activation in the default mode network, which is typically thought to decrease in activity during tasks that require externally focused attention. The remaining subtypes were characterized by activations in different task-positive areas. The default mode network subtype was characterized by behavioral signatures that were similar to the other subtypes exhibiting activation with task-positive regions. These findings demonstrate that the same perceptual decision-making task is accomplished through multiple brain activation patterns.

Mind-wandering is a frequent, daily mental activity, experienced in unique ways in each person. Yet neuroimaging evidence relating mind-wandering to brain activity, for example in the default mode network (DMN), has relied on population- rather than individual-based inferences owing to limited within-person sampling. Here, three densely sampled individuals each reported hundreds of mind-wandering episodes while undergoing multi-session functional magnetic resonance imaging. We found reliable associations between mind-wandering and DMN activation when estimating brain networks within individuals using precision functional mapping. However, the timing of spontaneous DMN activity relative to subjective reports, and the networks beyond DMN that were activated and deactivated during mind-wandering, were distinct across individuals. Connectome-based predictive modeling further revealed idiosyncratic, whole-brain functional connectivity patterns that consistently predicted mind-wandering within individuals but did not fully generalize across individuals. Predictive models of mind-wandering and attention that were derived from larger-scale neuroimaging datasets largely failed when applied to densely sampled individuals, further highlighting the need for personalized models. Our work offers novel evidence for both conserved and variable neural representations of self-reported mind-wandering in different individuals. The previously unrecognized interindividual variations reported here underscore the broader scientific value and potential clinical utility of idiographic approaches to brain-experience associations.

Understanding how spontaneous brain activity influences the response to neurostimulation is crucial for the development of neurotherapeutics and brain-computer interfaces. Localized brain activity is suggested to influence the response to neurostimulation, but whether fast-fluctuating (i.e., tens of milliseconds) large-scale brain dynamics also have any such influence is unknown. By stimulating the prefrontal cortex using combined transcranial magnetic stimulation (TMS) and electroencephalography, we examined how dynamic global brain state patterns, as defined by microstates, influence the magnitude of the evoked brain response. TMS applied during what resembled the canonical Microstate C was found to induce a greater evoked response for up to 80 ms compared with other microstates. This effect was found in a repeated experimental session, was absent during sham stimulation, and was replicated in an independent dataset. Ultimately, ongoing and fast-fluctuating global brain states, as probed by microstates, may be associated with intrinsic fluctuations in connectivity and excitation-inhibition balance and influence the neurostimulation outcome. We suggest that the fast-fluctuating global brain states be considered when developing any related paradigms.

Neuro-imaging data can often be represented as statistical networks, especially for functional magnetic resonance imaging (fMRI) data, where brain regions are defined as nodes and the functional interactions between those regions are taken as edges. Such networks are commonly divided into classes depending on the type of edges, namely binary or weighted. A binary network means edges can either be present or absent. Whereas the edges of a weighted network are associated with weight values, and fMRI networks belong to weighted networks. Statistical methods are often adopted to analyse such networks, among which, the exponential random graph model (ERGM) is an important network analysis approach. Typically ERGMs are applied to binary networks, and weighted networks often need to be binarised by arbitrarily selecting a threshold value to define the presence of the edges, which can lead to non-robustness and loss of valuable edge weight information representing the strength of fMRI interaction in fMRI networks. While it is therefore important to gain deeper insight in adopting ERGM on weighted networks, there only exists a few different ERGM frameworks for weighted networks; some of these are not directly implementable on fMRI networks based on their original proposal. We systematically review, implement, analyse and compare five such frameworks via a simulation study and provide guidelines on each modelling framework as well as conclude the suitability of them on fMRI networks based on a range of criteria. We concluded that Multi-Layered ERGM is currently the most suitable framework.

Antagonistic activity of brain networks likely plays a fundamental role in how the brain optimizes its performance by efficient allocation of computational resources. A prominent example involves externally/internally oriented attention tasks, implicating two anticorrelated, intrinsic brain networks: the default mode network (DMN) and the dorsal attention network (DAN). To elucidate electrophysiological underpinnings and causal interplay during attention switching, we recorded intracranial EEG (iEEG) from 25 epilepsy patients with electrode contacts localized in the DMN and DAN. We show antagonistic network dynamics of activation-related changes in high-frequency (> 50 Hz) and low-frequency (< 30 Hz) power. The temporal profile of information flow between the networks estimated by functional connectivity suggests that the activated network inhibits the other one, gating its activity by increasing the amplitude of the low-frequency oscillations. Insights about inter-network communication may have profound implications for various brain disorders in which these dynamics are compromised.

Cognitive functioning is a crucial aspect in schizophrenia (SZ), and when altered it has devastating effects on patients' quality of life and treatment outcomes. Several studies suggested that they could result from altered communication between the cortex and cerebellum. However, the neural correlates underlying these impairments have not been identified. In this study, we investigated resting state functional connectivity (rsFC) in SZ patients, by considering the interactions between cortical networks supporting cognition and cerebellum. In addition, we investigated the relationship between SZ patients' rsFC and their symptoms. We used fMRI data from 74 SZ patients and 74 matched healthy controls (HC) downloaded from the publicly available database SchizConnect. We implemented a seed-based connectivity approach to identify altered functional connections between specific cortical networks and cerebellum. We considered ten commonly studied resting state networks, whose functioning encompasses specific cognitive functions, and the cerebellum, whose involvement in supporting cognition has been recently identified. We then explored the relationship between altered rsFC values and Positive and Negative Syndrome Scale (PANSS) scores. The SZ group showed increased connectivity values compared with HC group for cortical networks involved in attentive processes, which were also linked to PANSS items describing attention and language-related processing. We also showed decreased connectivity between cerebellar regions, and increased connectivity between them and attentive networks, suggesting the contribution of cerebellum to attentive and affective deficits. In conclusion, our findings highlighted the link between negative symptoms in SZ and altered connectivity within the cerebellum and between the same and cortical networks supporting cognition.

Mind wandering is a common phenomenon in our daily lives and can have both an adaptive and detrimental impact. Recently, a dynamic framework has been proposed to characterise the heterogeneity of internal thoughts, suggesting there are three distinct thought types which can change over time - freely moving, deliberately constrained, and automatically constrained (thoughts). There is currently very little evidence on how different types of dynamic thought are represented in the brain. Previous research has applied non-invasive transcranial direct current stimulation (tDCS) to causally implicate the prefrontal cortex and inferior parietal lobule in mind wandering. However, a more recently developed and nuanced technique, high-definition tDCS (HD-tDCS), delivers more focal stimulation able to target specific brain regions. Therefore, the current study investigated the effect of anodal HD-tDCS applied to the left prefrontal and right inferior parietal cortices (with the occipital cortex included as an active control) on mind wandering, and specifically, the causal neural substrates of the three internal dynamic thought types. This was a single session study using a novel task which allows investigation into how dynamic thoughts are associated with behavioural variability and the recruitment of executive control operations across the three brain regions. There was no evidence to support our hypothesised effect of stimulation reducing task unrelated thought. Furthermore, the hypothesis driven analyses found no evidence of stimulation affecting the dynamic thought types, nor any evidence for our hypothesised effects of stimulation reducing behavioural variability and increasing randomness. There was only evidence for a relationship between these two measures of performance when participants thoughts were freely moving. However, there was evidence from our exploratory analyses that anodal stimulation to the prefrontal cortex decreased freely moving thought and anodal stimulation to the parietal lobule decreased deliberately constrained thought, relative to the sham conditions. The exploratory analyses also suggested stimulation may increase freely moving thought in the occipital cortex. Overall, these findings suggest stimulation does not affect the dynamic thought types, however there is preliminary evidence to support the heterogenous nature of mind wandering, whereby different brain regions may be causally implicated in distinct dynamic thought types.

To explore the utility of high frequency oscillations (HFO) and long-range temporal correlations (LRTCs) in preoperative assessment of epilepsy.

MEG ripples were detected in 59 drug-resistant epilepsy patients, comprising 5 with parietal lobe epilepsy (PLE), 21 with frontal lobe epilepsy (FLE), 14 with lateral temporal lobe epilepsy (LTLE), and 19 with mesial temporal lobe epilepsy (MTLE) to identify the epileptogenic zone (EZ). The results were compared with clinical MEG reports and resection area. Subsequently, LRTCs were quantified at the source-level by detrended fluctuation analysis (DFA) and life/waiting -time at 5 bands for 90 cerebral cortex regions. The brain regions with larger DFA exponents and standardized life-waiting biomarkers were compared with the resection results.

Compared to MEG sensor-level data, ripple sources were more frequently localized within the resection area. Moreover, source-level analysis revealed a higher proportion of DFA exponents and life-waiting biomarkers with relatively higher rankings, primarily distributed within the resection area (p<0.01). Moreover, these two LRCT indices across five distinct frequency bands correlated with EZ.

HFO and source-level LRTCs are correlated with EZ. Integrating HFO and LRTCs may be an effective approach for presurgical evaluation of epilepsy.

The organization of semantic memory, including memory for word meanings, has long been a central question in cognitive science. Although there is general agreement that word meaning representations must make contact with sensory-motor and affective experiences in a non-arbitrary fashion, the nature of this relationship remains controversial. One prominent view proposes that word meanings are represented directly in terms of their experiential content (i.e., sensory-motor and affective representations). Opponents of this view argue that the representation of word meanings reflects primarily taxonomic structure, that is, their relationships to natural categories. In addition, the recent success of language models based on word co-occurrence (i.e., distributional) information in emulating human linguistic behavior has led to proposals that this kind of information may play an important role in the representation of lexical concepts. We used a semantic priming paradigm designed for representational similarity analysis (RSA) to quantitatively assess how well each of these theories explains the representational similarity pattern for a large set of words. Crucially, we used partial correlation RSA to account for intercorrelations between model predictions, which allowed us to assess, for the first time, the unique effect of each model. Semantic priming was driven primarily by experiential similarity between prime and target, with no evidence of an independent effect of distributional or taxonomic similarity. Furthermore, only the experiential models accounted for unique variance in priming after partialling out explicit similarity ratings. These results support experiential accounts of semantic representation and indicate that, despite their good performance at some linguistic tasks, the distributional models evaluated here do not encode the same kind of information used by the human semantic system.

A relationship between migraine without aura (MO) and patent foramen ovale (PFO) has been observed, but the neural basis underlying this relationship remains elusive. Utilizing independent component analysis via functional magnetic resonance imaging, we examined functional connectivity (FC) within and across networks in 146 patients with MO (75 patients with and 71 patients without PFO) and 70 healthy controls (35 patients each with and without PFO) to elucidate the individual effects of MO and PFO, as well as their interaction, on brain functional networks. The main effect of PFO manifested exclusively in the FC among the visual, auditory, default mode, dorsal attention and salience networks. Furthermore, the interaction effect between MO and PFO was discerned in brain clusters of the left frontoparietal network and lingual gyrus network, as well as the internetwork FC between the left frontoparietal network and the default mode network (DMN), the occipital pole and medial visual networks, and the dorsal attention and salience networks. Our findings suggest that the presence of a PFO shunt in patients with MO is accompanied by various FC changes within and across networks. These changes elucidate the intricate mechanisms linked to PFO-associated migraines and provide a basis for identifying novel noninvasive biomarkers.

Youth with a family history of bipolar disorder (BD) may be at increased risk for mood disorders and for developing side effects after antidepressant exposure. The neurobiological basis of these risks remains poorly understood. We aimed to identify biomarkers underlying risk by characterizing abnormalities in the brain connectome of symptomatic youth at familial risk for BD.

Depressed and/or anxious youth (n = 119, age = 14.9 ± 1.6 years) with a family history of BD but no prior antidepressant exposure and typically developing controls (n = 57, age = 14.8 ± 1.7 years) received functional magnetic resonance imaging (fMRI) during an emotional continuous performance task. A generalized psychophysiological interaction (gPPI) analysis was performed to compare their brain connectome patterns, followed by machine learning of topological metrics.

High-risk youth showed weaker connectivity patterns that were mainly located in the default mode network (DMN) (network weight = 50.1%) relative to controls, and connectivity patterns derived from the visual network (VN) constituted the largest proportion of aberrant stronger pairs (network weight = 54.9%). Global local efficiency (Elocal, p = .022) and clustering coefficient (Cp, p = .029) and nodal metrics of the right superior frontal gyrus (SFG) (Elocal: p < .001; Cp: p = .001) in the high-risk group were significantly higher than those in healthy subjects, and similar patterns were also found in the left insula (degree: p = .004; betweenness: p = .005; age-by-group interaction, p = .038) and right hippocampus (degree: p = .003; betweenness: p = .003). The case-control classifier achieved a cross-validation accuracy of 78.4%.

Our findings of abnormal connectome organization in the DMN and VN may advance mechanistic understanding of risk for BD. Neuroimaging biomarkers of increased network segregation in the SFG and altered topological centrality in the insula and hippocampus in broader limbic systems may be used to target interventions tailored to mitigate the underlying risk of brain abnormalities in these at-risk youth.

Juvenile myoclonic epilepsy (JME) is associated with brain dysconnectivity in the default mode network (DMN). Most previous studies of patients with JME have assessed static functional connectivity in terms of the temporal correlation of signal intensity among different brain regions. However, more recent studies have shown that the directionality of brain information flow has a more significant regional impact on patients' brains than previously assumed in the present study. Here, we introduced an empirical approach incorporating independent component analysis (ICA) and spectral dynamic causal modeling (spDCM) analysis to study the variation in effective connectivity in DMN in JME patients. We began by collecting resting-state functional magnetic resonance imaging (rs-fMRI) data from 37 patients and 37 matched controls. Then, we selected 8 key nodes within the DMN using ICA; finally, the key nodes were analyzed for effective connectivity using spDCM to explore the information flow and detect patient abnormalities. This study found that compared with normal subjects, patients with JME showed significant changes in the effective connectivity among the precuneus, hippocampus, and lingual gyrus (p < 0.05 with false discovery rate (FDR) correction) with most of the effective connections being strengthened. In addition, previous studies have found that the self-connection of normal subjects' nodes showed strong inhibition, but the self-connection inhibition of the anterior cingulate cortex and lingual gyrus of the patient was decreased in this experiment (p < 0.05 with FDR correction); as the activity in these areas decreased, the nodes connected to them all appeared abnormal. We believe that the changes in the effective connectivity of nodes within the DMN are accompanied by changes in information transmission that lead to changes in brain function and impaired cognitive and executive function in patients with JME. Overall, our findings extended the dysconnectivity hypothesis in JME from static to dynamic causal and demonstrated that aberrant effective connectivity may underlie abnormal brain function in JME patients at early phase of illness, contributing to the understanding of the pathogenesis of JME.

The online version contains supplementary material available at 10.1007/s11571-023-09994-4.

People with autism spectrum conditions (ASC) have difficulties imagining events, which might result from difficulty mentally generating and maintaining a coherent spatial scene. This study compared this scene construction ability between autistic (N = 55) and neurotypical (N = 63) adults. Results showed that scene construction was diminished in autistic compared to neurotypical participants, and participants with fewer autistic traits had better scene construction ability. ASC diagnosis did not influence the frequency of mentions of the self or of sensory experiences. Exploratory analysis suggests that scene construction ability is associated with the ability to understand our own and other people's mental states, and that these individual-level preferences/cognitive styles can overrule typical group-level characteristics.

The functional activities of the brain during any task like imaginary, motor, or cognitive are different in pattern as well as their area of activation in the brain is also different. This variation in pattern is also found in the brain's electrical variations that can be measured from the scalp of the brain using an electroencephalogram (EEG). This work exclusively studied a group of subjects' EEG data (available at: https://archive.physionet.org/physiobank/database/eegmat/) to unravel the activation pattern of the human brain during a mental arithmetic task. Since any cognitive task creates variations in EEG signal pattern, the relative changes in the signal power also occur which is also known as event-related desynchronization/synchronization (ERD/ERS). In this work, ERD/ERS have calculated the band-wise power spectral density (PSD) using Welch's method from the EEG signals. Besides, the coherence analysis was also performed to verify the results of ERD/ERS analysis from several randomly chosen subjects' EEG data. Here, subjects performing mental arithmetic tasks were grouped based on their performances: good (subtraction solved > 10 on average) and bad (subtraction solved ≤ 10 on average) to conduct group-specific ERD/ERS analysis regarding their performance in cognitive tasks. It was found that when the brain is on count condition, the variations found in the band power of theta and beta. The amounts of ERS in the left hemisphere are increased. When the task complexity increases, it contributes to an increase in relative ERD/ERS amounts and durations. The left and right hemispheres were asymmetrically distributed by both the pre-stimulus stages of the corresponding band power and relative ERD/ERS.