Deception is a complex behavior that requires greater cognitive effort than truth-telling, with brain states dynamically adapting to external stimuli and cognitive demands. Investigating these brain states provides valuable insights into the brain's temporal and spatial dynamics. In this study, we designed an experiment paradigm to efficiently simulate lying and constructed a temporal network of brain states. We applied the Louvain community clustering algorithm to identify characteristic brain states associated with lie-telling, inverse-telling, and truth-telling. Our analysis revealed six representative brain states with unique spatial characteristics. Notably, two distinct states-termed truth-preferred and lie-preferred-exhibited significant differences in fractional occupancy and average dwelling time. The truth-preferred state showed higher occupancy and dwelling time during truth-telling, while the lie-preferred state demonstrated these characteristics during lie-telling. Using the average z-score BOLD signals of these two states, we applied generalized linear models with elastic net regularization, achieving a classification accuracy of 88.46%, with a sensitivity of 92.31% and a specificity of 84.62% in distinguishing deception from truth-telling. These findings revealed representative brain states for lie-telling, inverse-telling, and truth-telling, highlighting two states specifically associated with truthful and deceptive behaviors. The spatial characteristics and dynamic attributes of these brain states indicate their potential as biomarkers of cognitive engagement in deception.

The online version contains supplementary material available at 10.1007/s11571-025-10222-4.

Empirical studies have shown immediate detrimental effects of TV viewing on children's executive functions (EFs). Existing theories of TV viewing have proposed that such depletion could occur due to fantastical cartoons triggering an attention bias towards salient features of the stimuli (e.g., stimulus-driven exogenous attention). However, a co-occurrence of salient visual features known to drive attention exogenously in fantastical cartoons means it is unclear which aspect of the content is problematic. In the present study, we matched clips on visual saliency to isolate and test the short-term impact of fantastical content. Specifically, we tested (1) performance on an inhibitory control (IC) task (a gaze-contingent anti-saccade task) as a measure of EF depletion, whilst 36 toddlers (18 months) viewed cartoons with and without fantastical events (7-min viewing duration), and (2) whether differences in IC are associated with increased stimulus-driven exogenous attention. Results confirmed an immediate detrimental effect of fantastical cartoons on toddlers' endogenous control (indexed by anti-saccade behaviours), with toddlers less able to inhibit looks to a distractor to make anticipatory looks to a target. However, fixation durations (FDs) during cartoon viewing and speed of orienting to a distractor on the anti-saccade task did not differ between the two viewing conditions, suggesting no effects on exogenously driven attention. These results point to a detrimental impact of fantastical cartoons on endogenous control mechanisms, which may have arisen from cognitive processing difficulties.

To compare the effect of motivational features on sustained attention in children born very preterm and at term.

EEG was recorded while 34 8-to-11-year-old children born very preterm and 34 term-born peers completed two variants of a cued continuous performance task (CPT-AX); a standard CPT-AX with basic shape stimuli, and structurally similar motivating variant, with a storyline, familiar characters, and feedback.

Higher hit rates, quicker response times and larger event-related potential (ERP) amplitudes were observed during the motivating, compared with the standard, task. Although groups did not differ in task performance, between-task differences in ERPs associated with orienting were larger in term-born than very preterm children.

The findings add to previous evidence of disruption to the brain networks that support salience detection and selective attention in children born preterm. Manipulations that increase intrinsic motivation can promote sustained attention in both term-born and very preterm children.

The propensity to communicate extreme emotional states and arousal through salient, non-referential vocalizations is ubiquitous among mammals and beyond. Screams, whether intended to warn conspecifics or deter aggressors, require a rapid increase of air influx through vocal folds to induce nonlinear distortions of the signal. These distortions contain salient, temporally patterned acoustic features in a restricted range of the audible spectrum. These features may have a biological significance, triggering fast behavioural responses in the receivers. We present converging neurophysiological and behavioural evidence from humans and animals supporting that the properties emerging from nonlinear vocal phenomena are ideally adapted to induce efficient sensory, emotional and behavioural responses. We argue that these fast temporal-rough-modulations are unlikely to be an epiphenomenon of vocal production but rather the result of selective evolutionary pressure on vocal warning signals to promote efficient communication. In this view, rough features may have been selected and conserved as an acoustic trait to recruit ancestral sensory salience pathways and elicit optimal reactions in the receiver. By exploring the impact of rough vocalizations at the receiver's end, we review the perceptual, behavioural and neural factors that may have shaped these signals to evolve as powerful communication tools.This article is part of the theme issue 'Nonlinear phenomena in vertebrate vocalizations: mechanisms and communicative functions'.

Cue validity significantly influences attention guidance, either facilitating or hindering the ability for conflict resolution. Previous studies have demonstrated that the validity effect and conflict resolution are associated with better/worse behavioral performance and specific neural activations; however, the underlying neural mechanism of their interaction remains unclear. We hypothesized that the effect of cue validity might sustain specific sequences of neural activities until target occurrence and throughout the subsequent conflict resolution. In this study, we recorded the scalp electroencephalography during the Attention Network Test paradigm to investigate their interactions in neural dynamics. Specifically, we performed a cluster-level channel-time-frequency analysis to explore significant time-frequency neural activity patterns associated with these interactions, in scalp regions of interest determined by a data-driven strategy. Our results revealed a string of significant neural dynamics in the frontal and parietal regions, including initial broad-band (especially the gamma-band) activations and subsequent complex cognitive processes evoked/effected by the invalid cue, that were firstly elicited. Finally, the resolution of conflict was completed by the frontal behavior-related theta-band power reduction. In summary, our findings advanced the understanding of the temporal and spectral sequences of neural dynamics, with the key regions involved in the resolution of conflict after invalid cueing.

This meta-analysis examined white matter fractional anisotropy (FA) differences across the lifespan to better understand underlying neurobiological mechanisms of major depressive disorder (MDD). Using anisotropic effect size-based-signed differential mapping (AES-SDM), the study meta-analyzed 67 whole-brain FA voxel-based analysis (VBA) and tract-based spatial statistics (TBSS) studies. The sample included 3620 individuals with MDD and 3764 age-matched healthy controls, ranging from adolescence to older adulthood. AES-SDM uses anisotropic kernels combined with random-effects models and permutation tests to perform robust neuroimaging meta-analysis. Between-group analyses uncovered a lateralization effect: Adolescent and adult MDD were associated with left-hemisphere abnormalities, while older adult MDD was associated with right-hemisphere abnormalities. Specifically, MDD was associated with lower left anterior thalamic projection, left pons, left corticospinal projection, and left cingulum FA in adolescents; lower left optic radiation, left striatum, left cingulum, and left inferior longitudinal fasciculus FA in adults; and lower right anterior thalamic projection, right fronto-occipital fasciculus, right striatum, right superior longitudinal fasciculus, and left inferior longitudinal fasciculus FA in older adults. The laterality seen in the current data and previous research could potentially serve as biomarkers to improve diagnostic accuracy. It is recommended that future white matter MDD primary studies include more adolescents and older adults.

Clinically anxious youth are hypervigilant to emotional stimuli and display difficulty shifting attention from emotional to nonemotional stimuli, suggesting impairments in cognitive control over emotion. However, it is unknown whether the neural substrates of such biases vary across the clinical-to-nonclinical range of anxiety or by age.

Youth aged 7 to 17 years with clinical anxiety (n = 119) or without an anxiety diagnosis (n = 41) matched emotional faces or matched shapes flanked by emotional face distractors during magnetic resonance imaging, probing emotion processing and cognitive control over emotion, respectively. Building from the National Institute of Mental Health Research Domain Criteria (RDoC) framework, clinically anxious youth were sampled across diagnostic categories, and non-clinically affected youth were sampled across minimal-to-subclinical severity.

Across both conditions, anxiety severity was associated with hyperactivation in the right inferior parietal lobe, a substrate of hypervigilance. Brain-anxiety associations were also differentiated by attentional state; anxiety severity was associated with greater left ventrolateral prefrontal cortex activation during emotion processing (face matching) and greater activation in the left posterior superior temporal sulcus and temporoparietal junction (and slower responses) during cognitive control over emotion (shape matching). Age also moderated associations between anxiety and cognitive control over emotion, such that anxiety was associated with greater right thalamus and bilateral posterior cingulate cortex activation for children at younger and mean ages, but not for older youth.

Aberrant function in brain regions implicated in stimulus-driven attention to emotional distractors may contribute to anxiety in youth. Results support the potential utility of attention modulation interventions for anxiety that are tailored to developmental stage.

Preferential attention to threat, an adaptive mechanism for detecting danger, is exaggerated in clinically anxious youth. This study included 150 youth aged 7 to 17 years spanning the clinical-nonclinical range of anxiety to examine the effects of anxiety and age on markers of emotion processing and cognitive control over emotion while undergoing MRI scanning. The authors found that more severe anxiety was associated with greater activation in the left ventrolateral prefrontal cortex, a region supporting cognitive control over emotion, which may prevent anxiety-related slowing of response times. Conversely, when participants were prompted to ignore emotional faces, anxiety severity was associated with slower performance and greater activation of the ventral attention network, suggesting greater stimulus-driven attention to emotional distractors. Age moderated associations between anxiety and brain activity during cognitive control over emotion, supporting the potential utility of tailoring interventions for anxiety.

Dimensional Brain Behavior Predictors of CBT Outcomes in Pediatric Anxiety; https://clinicaltrials.gov; NCT02810171.

In complex tasks requiring cognitive control, humans show trial-by-trial alterations in response time (RT), which are evident even when sensory-motor or other contextual aspects of the task remain stable. Exaggerated intra-individual RT variability is associated with brain injuries and frequently seen in aging and neuropsychological disorders. In this opinion, we discuss recent electrophysiology and imaging studies in humans and neurobiological studies in monkeys that indicate RT variability is linked with executive control fluctuation and that prefrontal cortical regions play essential, but dissociable, roles in such fluctuation of control and the resulting behavioral variability. We conclude by discussing emerging models proposing that both extremes of behavioral variability (significantly higher or lower) might reflect aberrant alterations in various aspects of decision-making processes.

Flexibility is a hallmark of cognitive control and can be driven externally and internally, corresponding to reactive and spontaneous flexibility. However, the convergence and divergence between these two types of flexibility and their underlying neural basis during development remain largely unknown. In this study, we aimed to determine the common and unique networks for reactive and spontaneous flexibility as a function of age and sex, leveraging both cross-sectional and longitudinal resting-state functional magnetic resonance imaging datasets with different temporal resolutions (N = 249, 6-35 years old). Functional connectivity strength and nodal flexibility, derived from static and dynamic frameworks respectively, were utilized. We found similar quadratic effects of age on reactive and spontaneous flexibility, which were mediated by the functional connectivity strength and nodal flexibility of the frontoparietal network. Divergence was observed, with the nodal flexibility of the ventral attention network at the baseline visit uniquely predicting the increase in reactive flexibility 24-30 months later, while the nodal flexibility or functional connectivity strength of the dorsal attention network could specifically predict the increase in spontaneous flexibility. Sex differences were found in tasks measuring reactive and spontaneous flexibility simultaneously, which were moderated by the nodal flexibility of the dorsal attention network. This study advances our understanding of distinct types of flexibility in cognition and their underlying mechanisms throughout developmental stages. Our findings also suggest the importance of studying specific types of cognitive flexibility abnormalities in developmental neuropsychiatric disorders.

Research is equivocal on whether attention orienting is atypical in autism. This study investigated two types of attention orienting in autistic people and accounted for the potential confounders of alerting level, co-occurring symptoms of attention-deficit/hyperactivity disorder (ADHD) and anxiety, age, and sex. Twenty-seven autistic participants (14 males; 9-43 years) and 22 age- and sex-matched non-autistic participants (13 males; 9-42 years) completed the exogenous and endogenous Posner tasks. Response time and pupillometric data were recorded. Autistic participants were faster at orienting attention to valid cues in the exogenous task and slower at disengaging from invalid cues in the endogenous task compared to non-autistic participants. With increasing age, autistic participants showed faster exogenous and endogenous orienting, whereas non-autistic participants showed faster exogenous orienting but stable speed of endogenous orienting. Higher ADHD symptoms were associated with slower exogenous orienting in both groups, whereas higher anxiety symptoms were associated with faster exogenous orienting only in autistic participants. No group differences were noted for alerting levels, sex, or pupillary responses. This study provides new evidence of superior exogenous orienting and inefficient endogenous orienting in autistic people and suggests that age and co-occurring symptoms are important to consider when assessing attention orienting in autism.

A deep comparative analysis of brain functional connectome across species in primates has the potential to yield valuable insights for both scientific and clinical applications. However, the interspecies commonality and differences are inherently entangled with each other and with other irrelevant factors. Here we develop a novel contrastive machine learning method, called shared-unique variation autoencoder (SU-VAE), to allow disentanglement of the species-shared and species-specific functional connectome variation between macaque and human brains on large-scale resting-state fMRI datasets. The method was validated by confirming that human-specific features are differentially related to cognitive scores, while features shared with macaque better capture sensorimotor ones. The projection of disentangled connectomes to the cortex revealed a gradient that reflected species divergence. In contrast to macaque, the introduction of human-specific connectomes to the shared ones enhanced network efficiency. We identified genes enriched on 'axon guidance' that could be related to the human-specific connectomes. The code contains the model and analysis can be found in https://github.com/BBBBrain/SU-VAE.

Major depressive disorder (MDD) and generalized anxiety disorder (GAD) are two of the leading causes of impairment to human mental health. These two psychiatric disorders overlap in many symptoms and neurobiological features thus difficult to distinguish in some cases.

We enrolled 102 participants, comprising 40 patients with MDD, 32 patients with GAD and 30 matched healthy controls (HCs), to undergo multimodal magnetic resonance imaging (MRI) scans. We identified 18 major white matter (WM) tracts with automated fiber quantification (AFQ) method, to evaluated microstructure with fractional anisotropy (FA) and function with amplitude of low-frequency fluctuation (ALFF). An analysis of variance (ANOVA) was employed to identify differences among groups. We further explored the correlations of FA and ALFF features with clinical symptoms.

We identified the white matter microstructure and function of 89 participants. ANOVA and post-hoc analysis revealed that GAD group exhibited significantly higher FA of right anterior thalamic radiation (ATR) than in MDD and HC groups. Additionally, MDD group exhibited significantly decreased ALFF in forceps major (FMA), forceps minor (FMI), bilateral corticospinal tracts (CST) and left inferior fronto-occipital fasciculus (IFOF) compared to both GAD and HC group. ALFF of right CST was significantly negatively correlated to HAMA and a moderate effect size and marginal significance was found between FA of the right ATR and HAMA in GAD group.

This study used cross-sectional data and sample size was small.

Tracking microstructure and function of WM with AFQ method has the potential to distinguish different psychiatric diseases.

Statistical learning is the cognitive ability to rapidly identify structure and meaning in unfamiliar streams of sensory experience, even in the absence of feedback. Despite extensive studies, the neurocognitive mechanisms underlying this phenomenon still require further clarification under varying cognitive conditions. Here, we examined neural mechanisms during the first exposure to visually presented sequences in 47 healthy participants. We used two types of visual objects: abstract symbols and pictures of cartoon-like animals. This allowed us to compare informational processing mechanisms with defined distinguishing features. Participants achieved better performance for sequences with easy-to-name than difficult-to-name abstract stimuli. fMRI results revealed greater activation in widespread brain regions in response to random versus statistical sequences for all stimuli types. Behavioral accuracy was associated with increased deactivation of the ventromedial PFC for easy-to-name statistical versus random sequences. For difficult-to-name statistical versus random sequences, performance correlated with dorsomedial prefrontal cortex deactivation. ROI analysis showed a generally positive involvement of the caudate head in sequence processing with significantly stronger activity during the first run of performing the task. Functional connectivity analysis of prefrontal deactivation regions revealed significant connectivity with nodes of the salience network for both object types and inverse connectivity with the caudate head only for easy-to-name objects. The results indicated that distinct subregions of PFC modulate task performance depending on the visual stimulus characteristic. They also showed that among striatal regions, only the head of the caudate was sensitive to initial exposure to visual statistical information.

The role of alpha oscillations (8-13 Hz) in suppressing distractors is extensively debated. One debate concerns whether alpha oscillations suppress anticipated visual distractors through increased power. Whereas some studies suggest that alpha oscillations support distractor suppression, others do not. We identify methodological differences that may explain these discrepancies. A second debate concerns the mechanistic role of alpha oscillations. We and others previously proposed that alpha oscillations implement gain reduction in early visual regions when target load or distractor interference is high. Here, we suggest that parietal alpha oscillations support gating or stabilization of attentional focus and that alpha oscillations in ventral attention network (VAN) support resistance to attention capture. We outline future studies needed to uncover the precise mechanistic role of alpha oscillations.

Previous functional magnetic resonance imaging (fMRI) studies showed an abnormal brainstem-to-cortex functional connectivity (FC) in major depressive disorder. However, only few studies analyzed brainstem substructures in treatment-resistant depression (TRD). In this study, we analyzed resting-state seed-based FC between midbrain, pons, medulla oblongata and cortical/subcortical brain regions in patients with TRD (n = 24) and age- and sex-matched healthy controls (n = 24). FC was analyzed in each group and compared between groups. Correlation analyses assessed the relationship between FC strength and depressive symptom severity in regions showing significant group differences in seed-based connectivity. Our findings reveal an increased FC in the midbrain and pons to the precentral gyrus, postcentral gyrus, and temporal gyrus in patients with TRD compared to healthy controls. Interestingly, in TRD patients, FC between midbrain and cortex was negatively correlated with BDI-II scores, indicating a relationship between altered connectivity and self-reported depression severity. It is essential to note that our naturalistic, cross-sectional approach precludes causal conclusions regarding the relationship between FC and pathophysiology of TRD. The small sample size necessitates confirmation in a larger cohort. Midbrain/pons-to-cortex FC was increased in patients with TRD compared to healthy controls. Future studies should explore the relationship between abnormal brainstem-to-cortex FC and depressive symptomatology in more detail.

There is growing interest in utilizing dynamic methods to investigate psychiatric disorders, particularly the transient dynamic approaches. However, current research predominantly focuses on dynamic abnormalities within a single psychiatric disorder compared to healthy controls, without considering the shared and specific features across different psychiatric conditions. The dynamic abnormality across psychiatric disorders remains unclear. In this study, we employed Co-activation Pattern (CAP) method to investigate the transient configurations of brain activity across different psychiatric conditions, including schizophrenia (SZ, n = 37); bipolar I disorder (BD, n = 40); attention-deficit/hyperactivity disorder (ADHD, n = 37), and healthy controls (HC, n = 110). By conducting k-means clustering analysis, we identified 10 transient activation patterns. Our findings reveal that the specificity of psychiatric disorders is reflected in the transition probabilities between states, with distinct state transition patterns observed across different disorders. Notably, abnormal state transitions are concentrated in the core states (State 1 and State 2), highlighting the common dynamic abnormalities across psychiatric conditions. These core states involve the activation of the attention network and the sensorimotor network and show significant associations with the functional gradient. Furthermore, we found that abnormalities in state transitions are associated with cognitive behavior. Overall, this work provides a dynamic network perspective for understanding the shared and specific characteristic of psychiatric disorders.

Major depressive disorder (MDD) presents a substantial health burden with low treatment response rates. Predicting antidepressant efficacy is challenging due to MDD's complex and varied neuropathology. Identifying biomarkers for antidepressant treatment requires thorough analysis of clinical trial data. Multimodal neuroimaging, combined with advanced data-driven methods, can enhance our understanding of the neurobiological processes influencing treatment outcomes. To address this, we analyzed resting-state fMRI and EEG connectivity data from 130 patients treated with sertraline and 135 patients with placebo from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. A deep learning framework was developed using graph neural networks to integrate data-augmented connectivity and cross-modality correlation, aiming to predict individual symptom changes by revealing multimodal brain network signatures. The results showed that our model demonstrated promising prediction accuracy, with an R2 value of 0.24 for sertraline and 0.20 for placebo. It also exhibited potential in transferring predictions using only EEG. Key brain regions identified for predicting sertraline response included the inferior temporal gyrus (fMRI) and posterior cingulate cortex (EEG), while for placebo response, the precuneus (fMRI) and supplementary motor area (EEG) were critical. Additionally, both modalities identified the superior temporal gyrus and posterior cingulate cortex as significant for sertraline response, while the anterior cingulate cortex and postcentral gyrus were common predictors in the placebo arm. Additionally, variations in the frontoparietal control, ventral attention, dorsal attention, and limbic networks were notably associated with MDD treatment. By integrating fMRI and EEG, our study established novel multimodal brain network signatures to predict individual responses to sertraline and placebo in MDD, providing interpretable neural circuit patterns that may guide future targeted interventions. Trial Registration: Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC) ClinicalTrials.gov Identifier: NCT#01407094.

First-episode schizophrenia spectrum disorders (FESSDs) are associated with significant cognitive impairment that affects daily functioning. While these deficits are well-documented, the neural mechanisms underlying the cognitive impairment in FESSDs remain limited. As the structure robustness and resilience of the brain network in healthy population are reported to be associated with cognitive function, we examined these associations in FESSDs.

This study investigated the relationships of the structure robustness and resilience of the core brain network with cognitive function and psychopathology in patients with FESSDs (n = 340) using data from two independent cohorts in South Korea and China. Age- and sex-matched HC (n = 420) were also recruited. We applied advanced k-shell decomposition techniques to functional brain networks and estimated various measures of structure robustness and resilience.

Patients with FESSDs had brain networks with a less robust structure and resilience than those of HC. Resilience measures were positively correlated with executive function in patients. Core brain network structure and k-core resilience were negatively correlated with the Positive and Negative Syndrome Scale scores. Subgroup analysis showed that structure robustness and resilience at each site matched the main analysis, but correlations with cognitive function and psychopathology were observed only in the South Korea cohort.

Better preservation of brain network structure and resilience is associated with higher cognitive function and milder clinical symptoms in patients with FESSDs. This highlights potential targets for therapeutic interventions aimed at enhancing cognitive function and improving the symptoms of FESSDs.

Visual cortex is thought to show both dorsoventral and hemispheric modularity, but it is not known if the same functional modules emerge spontaneously from an unsupervised network analysis, or how they interact when saccades necessitate increased sharing of spatial information. Here, we address these issues by applying graph theory analysis to fMRI data obtained while human participants decided whether an object's shape or orientation changed, with or without an intervening saccade across the object. BOLD activation from 50 vision-related cortical nodes was used to identify local and global network properties. Modularity analysis revealed three sub-networks during fixation: a bilateral parietofrontal network linking areas implicated in visuospatial processing and two lateralized occipitotemporal networks linking areas implicated in object feature processing. When horizontal saccades required visual comparisons between visual hemifields, functional interconnectivity and information transfer increased, and the two lateralized ventral modules became functionally integrated into a single bilateral sub-network. This network included 'between module' connectivity hubs in lateral intraparietal cortex and dorsomedial occipital areas previously implicated in transsaccadic integration. These results provide support for functional modularity in the visual system and show that the hemispheric sub-networks are modified and functionally integrated during saccades.

The visual environment is complicated, and humans and other animals accordingly prioritize some sources of information over others through the deployment of spatial attention. Cognitive theories propose that one core purpose of this is to gather information that can be used in downstream cognitive processes, including the development of concepts and categories. However, neuroscientific investigation has focused closely on the identification of the systems and algorithms that support attentional control or that instantiate the effect of attention on sensation and perception. Much less is known about how attention impacts the acquisition and activation of concepts. Here, we use machine learning of EEG and concurrently recorded EEG/MRI to temporally and anatomically characterize the neural network that abstracts from attended perceptual information to activate and construct semantic and conceptual representations. We find that variance in the amplitude of N2pc-an event-related potential (ERP) component closely linked to selective attention-predicts the emergence of conceptual information in a network including VMPFC, posterior parietal cortex, and anterior insula. This network appears to play a key role in the attention-mediated translation of perceptual information to concepts, semantics, and action plans.

Despite recent studies suggesting an important association of hoarder disorder (HD) and attention-deficit/hyperactive disorder (ADHD), no neuroimaging study has investigated the differences between patients with HD comorbid with ADHD and those without ADHD. This study investigated the regional spontaneous activity and functional connectivity in HD, focusing on the comorbidity with ADHD. Resting-state functional magnetic resonance imaging (MRI) data were obtained from 24 patients with HD and 31 healthy individuals. We investigated the group differences using the fractional amplitude of low-frequency fluctuation (fALFF). The altered regions in the fALFF were used as seeds in a functional connectivity analysis where we conducted group comparisons among the three groups: healthy controls (HCs), HD with ADHD (HD +ADHD), and HD without ADHD (HD -ADHD). Compared to HCs, patients with HD had a reduced fALFF in the right inferior frontal gyrus (IFG). Functional connectivity analysis revealed that patients with HD + ADHD had reduced functional connectivity between the IFG and dorsolateral prefrontal cortex (DLPFC) compared to HCs, while the HD -ADHD group was intermediate level between HD +ADHD and HCs groups. In conclusion, patients with HD have altered spontaneous activity of the IFG. Additionally, patients with HD + ADHD had significantly reduced functional connectivity between the IFG and the DLPFC. Our findings suggest the potential need to distinguish between subgroups of HD+ADHD to identify novel neurobiological models of HD that could guide future therapeutic strategies.

Portable EEG provides the opportunity to capture neural correlates of attention in a more naturalistic environment. However, the field is still in its infancy, with varied research aims and methodologies. The current scoping review aims to clarify: (1) the research aims of the studies, (2) the portable EEG collection methodologies, and (3) the EEG measures of attention.

The review followed the Preferred Reporting Items for Systematic Review and Meta-Analysis - Scoping Review extension. Two authors extracted data items from 45 eligible studies.

Three research aims were identified in previous studies: examining the effects of learning-related factors on attention captured by portable EEG (n = 23), developing attention classification algorithms (n = 7) and software for monitoring and promoting attention (n = 10), and verifying the signal quality of EEG derived from portable EEG in attentional tasks (n = 5). The testing sites and tasks were predominantly out-of-lab controlled settings and structured learning materials. To quantify attention, 8 studies employed a theory-driven approach, e.g., using EEG measures based on prior research correlating specific spectral power with attention. In contrast, 37 studies used data-driven approaches, e.g., using spectral power as input features for machine learning models to index attention.

Portable EEG has been a promising approach to measuring attention in educational settings. Meanwhile, there are challenges and opportunities related to the better translation of cognitive neuroscience research into practice.

Adolescence is a pivotal phase marked by heightened vulnerability to the onset of psychiatric disorders. However, there are few transdiagnostic studies of dynamic brain networks across major psychiatric disorders during this phase.

We collected resting-state functional MRI data from 189 adolescent patients (61 with bipolar disorder, 73 with major depressive disorder, and 55 with schizophrenia) and 181 healthy adolescents. Functional networks were constructed using a state-of-art edge-centric dynamic functional connectivity (DFC) approach.

Four DFC states were identified for the healthy adolescents that were related to different behavioral and cognitive terms. Disorder-related alterations were observed in two states involving motor and somatosensory processing and one state involving various cognitive functions. Regardless of the state, the three patient groups exhibited lower FC that were mainly involved in edges between different functional subsystems and were predominantly linked to regions in the somatomotor network. The patients with major depressive disorder additionally showed increased FC that were primarily linked to default mode regions. Graph-based network analysis revealed different patterns of disrupted small-world organization and altered nodal degree in the disorders in a state-dependent manner. The nodal degree alterations were correlated with the concentration of various neurotransmitters. Intriguingly, the noradrenaline concentration was engaged in the nodal degree alterations in each patient group. Finally, decreased FC involving regions in the somatomotor network showed significant correlations with clinical variables in the major depressive disorder patients.

These findings may help understand the developmental pathways associated with the heightened vulnerability to major psychiatric disorders during adolescence.

Reorientation of attention to threatening stimuli is a fundamental part of human cognition. Such interaction between cognitive and affective processes is often associated with faster response times. In the present study, the role of the right angular gyrus (AG) in reorienting to threat is examined. An exogenous spatial cueing paradigm was adopted with threatening and nonthreatening targets. Threat was induced by means of differential fear conditioning of the target. Single pulse transcranial magnetic stimulation (TMS) was applied to the right AG at different stimulus onset asynchronies (SOA) after target onset (range 30-300 ms). Transcranial magnetic stimulation was predicted to interfere at an earlier SOA with reorienting (during invalidly cued trials) to threatening targets. Even though an overall decrement in performance to targets contralateral to TMS stimulation was found, TMS to right AG did not specifically affect reorienting, neither to safe nor to threatening targets. We suggest that detection of biologically significant stimuli outside the focus of attention may depend more on the ventral frontoparietal rather than dorsal frontoparietal network of attention.

The review explores the complex interplay between brain structures and their associated functions, presenting a diversity of hierarchical models that enhances our understanding of these relationships. Central to this approach are structure-function flow diagrams, which offer a visual representation of how specific neuroanatomical structures are linked to their functional roles. These diagrams are instrumental in mapping the intricate connections between different brain regions, providing a clearer understanding of how functions emerge from the underlying neural architecture. The study details innovative attempts to develop new functional hierarchies that integrate structural and functional data. These efforts leverage recent advancements in neuroimaging techniques such as fMRI, EEG, MEG, and PET, as well as computational models that simulate neural dynamics. By combining these approaches, the study seeks to create a more refined and dynamic hierarchy that can accommodate the brain's complexity, including its capacity for plasticity and adaptation. A significant focus is placed on the overlap of structures and functions within the brain. The manuscript acknowledges that many brain regions are multifunctional, contributing to different cognitive and behavioral processes depending on the context. This overlap highlights the need for a flexible, non-linear hierarchy that can capture the brain's intricate functional landscape. Moreover, the study examines the interdependence of these functions, emphasizing how the loss or impairment of one function can impact others. Another crucial aspect discussed is the brain's ability to compensate for functional deficits following neurological diseases or injuries. The investigation explores how the brain reorganizes itself, often through the recruitment of alternative neural pathways or the enhancement of existing ones, to maintain functionality despite structural damage. This compensatory mechanism underscores the brain's remarkable plasticity, demonstrating its ability to adapt and reconfigure itself in response to injury, thereby ensuring the continuation of essential functions. In conclusion, the study presents a system of brain functions that integrates structural, functional, and dynamic perspectives. It offers a robust framework for understanding how the brain's complex network of structures supports a wide range of cognitive and behavioral functions, with significant implications for both basic neuroscience and clinical applications.

Neuroplasticity, a phenomenon present throughout the lifespan, is thought to be influenced by physical training. However, the relationship between neuroplastic differences and attentional abilities remains unclear. This study explored the differences in brain function and attentional abilities between professional football athletes and novices, and further investigated the relationship between the two. To address this question, we included 49 football athletes and 63 novices in our study, collecting data on resting-state functional connectivity and Attention Network Test (ANT). Behavioral results from the ANT indicated that football experts had superior orienting attention but weaker alerting functions compared to novices, with no difference in executive control attention. fMRI results revealed that football experts exhibited higher fractional Amplitude of Low-Frequency Fluctuations (fALFF) values in the bilateral anterior cerebellar lobes, bilateral insula, and left superior temporal gyrus. Functional connectivity analysis showed increased connectivity between the left anterior cerebellar lobe and various cortical regions, including the right supramarginal gyrus, left precuneus, left superior frontal gyrus, bilateral posterior cerebellar lobes, and bilateral precentral gyri in experts compared to novices. More importantly, in the expert group but not in novice group, functional connectivity differences significantly predicted attentional orienting scores. Graph theoretical analysis showed that experts exhibited higher betweenness centrality and node efficiency in the right cerebellar lobule III (Cerebelum_3_R) node. Our findings demonstrate that long-term professional football training may significantly affect neuroplasticity and attentional functions. Importantly, our analysis reveals a substantive connection between these two aspects, suggesting that the integration of neuroplastic and attentional changes is likely mediated by cerebellar-cortical connectivity.

Language can be processed with varying levels of attentional involvement; consequently, the interplay between the language and attentional systems in the brain has been extensively studied in spoken languages. However, in sign languages (SLs), this interplay is less well understood. Here, we use Constructed Action (CA) - a meaning-making strategy based on enactment - as a window into the attentional mechanisms recruited in signed language comprehension. We explored the attentional processing of CA by identifying the sequence of processes involved and in which stage CA and its types might be processed differently. Finally, we investigated the associations between the brain mechanisms of CA detection and their behavioral manifestations, as well as with components of attention of the Attention Network Test (ANT). We also measured the electrophysiological correlates of performance on an oddball CA detection task in deaf and hearing L1 signers. We found that processes involved in all signers' active detection of CA involved automatic (indexed by N1 and P2) and attention-based processes (indexed by N2s and P3s). N2 posterior bilateral were also more negative for tokens of overt CA than for PT-only signs, while P3a was more positive for all types of CA than for PT. No significant results were found regarding the ANT. We conclude that specific attentional involvement in CA detection is triggered by the increasing enacting elements and saliency involved in CA. This study yielded new insights into the functional interaction between the neural mechanisms underlying attentional control and those mediating CA processing in SL.

Social attention, guided by cues like gaze direction, is crucial for effective social interactions. However, how dynamic environmental context modulates this process remains unclear. Integrating a hierarchical Bayesian model with fMRI, this study investigated how individuals adjusted attention based on the predictions about cue validity (CV). Thirty-three participants performed a modified Posner location-cueing task with varying CV. Behaviorally, individuals' allocation of social attention was finely tuned to the precision (inverse variance) of CV predictions, with the predictions updated by precision-weighted prediction errors (PEs) about the occurrence of target locations. Neuroimaging results revealed that the interaction between allocation of social attention and CV influenced activity in regions involved in spatial attention and/or social perception. Precision-weighted PEs about target locations specifically modulated activity in the temporoparietal junction (TPJ), superior temporal sulcus (STS), and primary visual cortex (V1), underscoring their roles in refining attentional predictions. Dynamic causal modeling (DCM) further demonstrated that enhanced absolute precision-weighted PEs about target locations strengthened the effective connectivity from V1 and STS to TPJ, emphasizing their roles in conveying residual error signals upwards to high-level critical attention areas. These findings emphasized the pivotal role of precision in attentional modulation, enhancing our understanding of context-dependent social attention.

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterised by motor symptoms. However, approximately half of patients with PD exhibit signs of dementia within a decade of diagnosis. While deficits in working memory and visuospatial abilities are recognised as hallmarks of cognitive decline in PD, these populations are rarely studied using detailed cognitive tools that link cognitive impairments to formal theoretical models, such as the theory of visual attention (TVA).

This cross-sectional study addresses this gap by employing the TVA whole report paradigm to assess visual processing in a cohort of patients with PD, both with and without cognitive impairment. Participants were divided based on their Montreal Cognitive Assessment (MoCA) scores into two PD groups (n=25 each) and a healthy control group (n=25).

Our principal finding is that the visual processing speed (C) and visual short-term memory capacity (K) are significantly diminished in patients with PD with MoCA scores below 26 (Analysis of variance, p=0.016 for C and p<0.001 for K), while no notable differences were observed between controls and patients with PD with MoCA scores of 26 or above. Using a generalised linear model to assess the impact of factors such as age, gender and disease duration, we discovered that the C-parameter was significantly influenced by age, while the K-parameter was notably affected by gender.

TVA parameters demonstrate their suitability for detecting cognitive deficits in PD. Given their independence from motor and non-motor symptoms, TVA parameters may prove to be valuable tools for early diagnosis and longitudinal monitoring of cognitive deficits in individual patients with PD.

Visual attention and object recognition are two critical cognitive functions that shape our perception of the world. While these neural processes converge in the temporal cortex, the nature of their interactions remains largely unclear. Here, we systematically investigated the interplay between visual attention and stimulus feature coding by training macaques to perform a free-gaze visual search task with natural stimuli. Recording from a large number of units across multiple brain areas, we found that units exhibiting visual feature coding showed stronger attentional modulation of responses and spike-local field potential coherence than units without feature coding. Across brain areas, attention directed toward search targets enhanced the neuronal pattern separation of stimuli, with this enhancement more pronounced for units encoding visual features. Together, our results suggest a complex interplay between visual feature and attention coding in the primate brain, likely driven by interactions between brain areas engaged in these processes.

A key question in cognitive neuroscience is how unified identity representations emerge from visual inputs. Here, we recorded intracranial electroencephalography (iEEG) from the human ventral temporal cortex (VTC) and medial temporal lobe (MTL), as well as single-neuron activity in the MTL, to demonstrate how dense feature-based representations in the VTC are translated into sparse identity-based representations in the MTL. First, we characterized the spatiotemporal neural dynamics of face coding in the VTC and MTL. The VTC, particularly the fusiform gyrus, exhibits robust axis-based feature coding. Remarkably, MTL neurons encode a receptive field within the VTC neural feature space, constructed using VTC neural axes, thereby bridging dense feature and sparse identity representations. We further validated our findings using recordings from a macaque. Lastly, inter-areal interactions between the VTC and MTL provide the physiological basis of this computational framework. Together, we reveal the neurophysiological underpinnings of a computational framework that explains how perceptual information is translated into face identities.

Suppressing upsetting thoughts can cause psychological distress but might also enhance mental health when used flexibly to suppress the imagination of future threat during challenging times. To investigate the neural correlates of suppressing and imagining future threat, a cohort of 65 participants underwent a previously examined "Imagine/No-Imagine" paradigm while examining brain activation using magnetic resonance imaging. We observed activity of the inferior frontal gyrus, middle frontal gyrus (MFG), superior parietal lobule, and superior occipital sulcus during thought suppression, whereas imagining future threat elicited activation in the bilateral posterior cingulate cortex (PCC) and ventromedial prefrontal cortex (vmPFC). Subjective levels of anxiety, stress, and depression as covariates did not alter these results. To further examine the group and individual-level network dynamics, we conducted dynamic causal modeling (DCM) and group iterative multiple model estimations (GIMME). The DCM model showed that during suppression, the MFG positively influenced the vmPFC and right PCC. In contrast, the vmPFC and the left and right PCC showed positive connections to the MFG during imagining. This suggests that the neural correlates of self-regulation involve an information flow between the PCC and the PFC. In addition, GIMME identified group-level connections between the right and left PCC and between the left PCC and vmPFC, reflecting the information flow during suppression and imagination of future threat, respectively. Considerable interindividual heterogeneity in the connectivity patterns became apparent, pointing to the existence of different biotypes.

Despite significant research, discovering causal relationships from fMRI remains a challenge. Popular methods such as Granger causality and dynamic causal modeling fall short in handling contemporaneous effects and latent common causes. Methods from causal structure learning literature can address these limitations but often scale poorly with network size and need acyclicity. In this study, we first provide a taxonomy of existing methods and compare their accuracy and efficiency on simulated fMRI from simple topologies. This analysis demonstrates a pressing need for more accurate and scalable methods, motivating the design of Causal discovery for Large-scale Low-resolution Time-series with Feedback (CaLLTiF). CaLLTiF is a constraint-based method that uses conditional independence between contemporaneous and lagged variables to extract causal relationships. On simulated fMRI from the macaque connectome, CaLLTiF achieves significantly higher accuracy and scalability than all tested alternatives. From resting-state human fMRI, CaLLTiF learns causal connectomes that are highly consistent across individuals, show clear top-down flow of causal effect from attention and default mode to sensorimotor networks, exhibit Euclidean distance dependence in causal interactions, and are highly dominated by contemporaneous effects. Overall, this work takes a major step in enhancing causal discovery from whole-brain fMRI and defines a new standard for future investigations.

As infants grow, they develop greater attentional control during interactions with others, shifting from patterns of attention primarily driven by caregivers (exogenous) to those that are also self-directed (endogenous). The ability to endogenously control attention during infancy is thought to reflect ongoing brain development and is influenced by patterns of joint attention between infant and caregiver. However, whether measures of infant attentional control and caregiver behavior during infant-caregiver interactions relate to patterns of infant brain activity is unknown and key for informing developmental models of attentional control. Using data from 43 infant-caregiver dyads, we quantified patterns of visual attention with dyadic, head-mounted eye tracking during infant-caregiver play and associated them with the duration of infant EEG microstate D/4 measured during rest. Importantly, microstate D/4 is a scalp potential topography thought to reflect the organization and function of attention-related brain networks. We found that microstate D/4 associated positively with infant-led joint attention rate but did not associate with caregiver-led joint attention rate, suggesting that infant-led coordination of joint attention during play may be critical for the neurobiological development of attentional control, or vice versa. Further, we found that microstate D/4 associated negatively with infant attention shift rate and positively with infant sustained attention duration, suggesting that increased stability of microstate D/4 may reflect maturation of attentional control and its underlying neural substrates. Together, our findings provide insights into how infant attentional control abilities and infant-caregiver visual behavior during play are associated with the spatial and temporal dynamics of infant brain activity.

The psychological mechanisms that make Conditioned Pain Modulation (CPM) an effective non-pharmacological intervention are still not fully understood. Expectancy is believed to be a critical psychological factor affecting CPM effects, but its specific role has yet to be fully clarified. This study aims to explore the relationship between expectancy and CPM while providing physiological evidence using functional near-infrared spectroscopy (fNIRS).

A standardized CPM induction paradigm was implemented, with verbal guidance used to induce expectancy. The Numeric Rating Scale (NRS) assessed the intensity of the test stimulus (TS), while an 11-point scale evaluated participants' attentional focus on the TS and the effect of expectancy. fNIRS was employed to monitor changes in prefrontal cortex (PFC) activity.

Expectancy significantly amplified the CPM effect (p = 0.036) while markedly reducing attention to the experimental stimulus (p = 0.004). fNIRS findings indicated significant reductions in activity within the left frontal eye field, left dorsolateral prefrontal cortex, and left frontal pole regions. In the post-test, the control group demonstrated significantly higher cortical activity in the right frontal pole region compared to the expectancy group (p < 0.05). Within the expectancy group, bilateral frontal pole cortical activity was significantly lower in the post-test compared to the pre-test (p < 0.05).

Expectancy represents a key psychological mechanism underlying the CPM effect, potentially modulating its magnitude through attention regulation and accompanied by a reduction in oxygenated hemoglobin activity in the frontal pole region and introduced the Expectancy-Attention-CPM Modulation Model (ECAM).

Cooperation is essential for human societies, but not all individuals cooperate to the same degree. This is typically attributed to individual motives - for example, to be prosocial or to avoid risks. Here, we investigate whether cooperative behavior can, in addition, reflect what people pay attention to and whether cooperation may therefore be influenced by manipulations that direct attention. We first analyze the attentional patterns of participants playing one-shot Prisoner's Dilemma games and find that choices indeed relate systematically to attention to specific social outcomes, as well as to individual eye movement patterns reflecting attentional strategies. To test for the causal impact of attention independently of participants' prosocial and risk attitudes, we manipulate the task display and find that cooperation is enhanced when displays facilitate attention to others' outcomes. Machine learning classifiers trained on these attentional patterns confirm that attentional strategies measured using eye-tracking can accurately predict cooperation out-of-sample. Our findings demonstrate that theories of cooperation can benefit from incorporating attention and that attentional interventions can improve cooperative outcomes.

Clinical trials with antidepressants reveal significant improvements in placebo groups, with effects of up to 80% compared to real treatment. While it has been suggested that treatment expectations rely on cognitive control, direct evidence for affective placebo effects is sparse. Here, we investigated how cognitive resources at both the behavioral and neural levels influence the effects of positive expectations on emotional processing. Forty-nine healthy volunteers participated in a cross-over fMRI study where positive expectations were induced through an alleged oxytocin nasal spray and verbal instruction. Participants completed a spatial cueing task that manipulated attention to emotional face distractors while being scanned and were characterized regarding their general attention control ability. Placebo treatment improved mood and reduced distractibility from fearful compared to happy faces, particularly when more attentional resources were available for processing face distractors. This aligned with changes in activation and functional coupling within prefrontal-limbic networks, suggesting that expectations induce top-down regulation of aversive inputs. Additionally, neurobehavioral effects correlated with individual control ability. Our findings highlight the critical role of cognitive resources in verbally instructed placebo effects. This may be particularly relevant in patients with major depressive disorder, who often demonstrate enhanced negativity processing but have limited cognitive control capacity.

The growing popularity of internet gaming among adolescents and young adults has driven an increase in both casual and excessive gaming behavior. Nevertheless, it remains unclear how progressive increases in internet gaming engagement led to changes within and between brain networks. This study aims to investigate these connectivity alterations across varying levels of gaming involvement.

In this cross-sectional study, 231 participants were recruited and classified into three groups according to Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria for Internet Gaming Disorder (IGD): IGD group, highly engaged gaming(HEG) group, and lowly engaged gaming (LEG) group. Resting-state fMRI data from 217 participants (143 males, 74 females) were included in the final analysis. Independent component analysis was used to examine differences in intra- and inter-network functional connectivity (FC)across the three groups.

No significant differences were found in intra-network FC across the three groups. However, significant inter-network differences between the dorsal attention network(dAN)and the visual network (VN) among the three groups were observed. The HEG group exhibited significantly higher dAN-VN functional network connectivity (FNC) compared to the LEG group. Linear correlation analyses showed no significant correlation between the dAN-VN FNC values and IGD-20T scores.

Throughout the development of IGD, increasing levels of engagement are associated with a rise and subsequent decline in FNC of DAN-VN. This pattern may reflect top-down attentional regulation in the early stages of addiction, followed by attentional bias as addiction progresses.

Accurate segmentation of COVID-19 CT images is crucial for reducing the severity and mortality rates associated with COVID-19 infections. In response to blurred boundaries and high variability characteristic of lesion areas in COVID-19 CT images, we introduce CDSE-UNet: a novel UNet-based segmentation model that integrates Canny operator edge detection and a Dual-Path SENet Feature Fusion Block (DSBlock). This model enhances the standard UNet architecture by employing the Canny operator for edge detection in sample images, paralleling this with a similar network structure for semantic feature extraction. A key innovation is the DSBlock, applied across corresponding network layers to effectively combine features from both image paths. Moreover, we have developed a Multiscale Convolution Block (MSCovBlock), replacing the standard convolution in UNet, to adapt to the varied lesion sizes and shapes. This addition not only aids in accurately classifying lesion edge pixels but also significantly improves channel differentiation and expands the capacity of the model. Our evaluations on public datasets demonstrate CDSE-UNet's superior performance over other leading models. Specifically, CDSE-UNet achieved an accuracy of 0.9929, a recall of 0.9604, a DSC of 0.9063, and an IoU of 0.8286, outperforming UNet, Attention-UNet, Trans-Unet, Swin-Unet, and Dense-UNet in these metrics.

Noninvasive brain stimulation (NIBS) has emerged as a promising therapeutic approach for attention-deficit/hyperactivity disorder (ADHD), yet the inaccurate selection of stimulation sites may constrain its efficacy. This study aimed to identify novel NIBS targets for ADHD by integrating meta-analytic findings with cross-dataset validation of functional connectivity patterns. A meta-analysis including 124 functional magnetic resonance imaging (fMRI) studies was first conducted to delineate critical brain regions associated with ADHD, which were defined as regions of interest (ROIs). Subsequently, functional connectivity (FC) analysis was performed using resting-state fMRI data from two independent databases comprising 116 patients with ADHD. Surface brain regions exhibiting consistent FC patterns with the ADHD-related ROIs across both datasets were identified as candidate NIBS targets. These targets were then translated to scalp-level stimulation sites using the 10-20 system and continuous proportional coordinates (CPC). Key regions mapped to the scalp included the bilateral dorsolateral prefrontal cortex, right inferior frontal gyrus, bilateral inferior parietal lobule, supplementary motor area (SMA), and pre-SMA. These findings propose a set of precise stimulation location for NIBS interventions in ADHD, potentially broadening the scope of neuromodulation strategies for this disorder. The study emphasized the utility of cross-dataset functional connectivity analysis in refining NIBS target selection and highlights novel brain targets that warrant further investigation in clinical trials.

Electroencephalography (EEG) microstate analysis is widely used in the study of various neurological and psychiatric disorders. We aim to assess whether EEG microstates are altered in TLE patients with and without cognitive decline.

This study included a total of 47 temporal lobe epilepsy (TLE) patients with or without cognitive decline and 14 healthy controls (HCs). All participants underwent 64-channel EEG monitoring. Two-minute epochs of preprocessed eye closed awake EEG data were extracted for microstate analysis. Participants were divided into groups based on intelligence quotient (IQ) scores assessed by the Wechsler Intelligence Scale: normal cognition group (n = 23, IQ ≥ 90) and abnormal cognition group (n = 24, IQ < 90). We conducted frequency spectral analysis on each frequency sub-bands (delta, 1-4 Hz; theta, 4-8 Hz; alpha, 8-12 Hz; beta, 12-30 Hz) of the three groups. Then the following microstate parameters were extracted for analysis over the full frequency band (1-30 Hz) and frequency sub-bands: duration, coverage, occurrence, and transition probability. Statistical analysis using multivariate analysis of variance (MANOVA) with Bonferroni correction (α = 0.0025).

Microstate analysis in the beta sub-band revealed significant differences among groups. TLE patients with abnormal cognition showed increased occurrence of Map-D; significantly higher transition probabilities from Map-A, Map-B, and Map-C to Map-D; and distinct microstate characteristics compared to patients with normal cognition and HCs. In frequency spectral analysis, the power across all frequency sub-bands showed no significant differences among TLE patients with normal cognition, TLE patients with abnormal cognition, and HCs.

Beta sub-band EEG microstates, particularly Map-D characteristics, may serve as potential neurophysiological markers for cognitive decline in TLE patients.

Spatial attention enables the selection of relevant over irrelevant stimuli through dorsal and ventral fronto-parietal networks. These networks are connected through long white matter tracts, such as the superior longitudinal fasciculus (SLF) and the Inferior Fronto-Occipital fasciculus (IFOF).

The main purpose of this study was to explore, in healthy participants, the causal role of the right Inferior Parietal Lobe (rIPL) in spatial orienting and conscious perception. We also explored how interindividual differences in the microstructural properties of white matter were related to the effects of transcranial magnetic stimulation (TMS) and, secondarily, to attentional orienting effects in the control stimulation condition.

Participants (n=51) performed a behavioural task involving the detection of a visual stimulus at the threshold of consciousness, preceded by either central (endogenous) or peripheral (exogenous) cues. After cue onset, a burst of TMS pulses was applied over the rIPL or a control active region (vertex). White matter properties were explored through diffusion-weighted imaging tractography and whole-brain NODDI analysis.

TMS over the rIPL (compared to the control condition) did not modulate spatial attention nor conscious perception, but it decreased accuracy when attention was endogenously oriented (compared to the exogenous condition) and speeded up reaction times when targets were presented in the attended right hemifield (compared to the left hemifield). Part of the variability in the TMS and attentional orienting effects were explained by the integrity of the SLF and the IFOF.

Individual variability in attentional orienting effects was associated with the anatomical links between attentional networks. Negative correlations between TMS effects and relevant white matter tracts were interpreted as compensatory mechanisms, while positive correlations with tracts innervating the stimulated area could reflect a TMS signal propagation effect. These results will contribute to the understanding of the role of white matter variability in the susceptibility to neuromodulation, with potential implications for research and clinical treatment.

Emerging research suggests the brain operates as a "prediction machine," continuously anticipating sensory, motor, and cognitive outcomes. Central to this capability is the brain's spontaneous activity-ongoing internal processes independent of external stimuli. Neuroimaging and computational studies support that this activity is integral to maintaining and refining mental models of our environment, body, and behaviors, akin to generative models in computation. During rest, spontaneous activity expands the variability of potential representations, enhancing the accuracy and adaptability of these models. When performing tasks, internal models direct brain regions to anticipate sensory and motor states, optimizing performance. This review synthesizes evidence from various species, from C. elegans to humans, highlighting three key aspects of spontaneous brain activity's role in prediction: the similarity between spontaneous and task-related activity, the encoding of behavioral and interoceptive priors, and the high metabolic cost of this activity, underscoring prediction as a fundamental function of brains across species.

Major Depressive Disorder (MDD) and Bipolar Disorder (BD) exhibit overlapping depressive symptoms, complicating their differentiation in clinical practice. Traditional neuroimaging studies have focused on specific regions of interest, but few have employed whole-brain analyses like regional homogeneity (ReHo). This study aims to differentiate MDD from BD by identifying key brain regions with abnormal ReHo and using advanced machine learning techniques to improve diagnostic accuracy.

A total of 63 BD patients, 65 MDD patients, and 70 healthy controls were recruited from the Shanghai Mental Health Center. Resting-state functional MRI (rs-fMRI) was used to analyze ReHo across the brain. We applied Support Vector Machine (SVM) and SVM-Recursive Feature Elimination (SVM-RFE), a robust machine learning model known for its high precision in feature selection and classification, to identify critical brain regions that could serve as biomarkers for distinguishing BD from MDD. SVM-RFE allows for the recursive removal of non-informative features, enhancing the model's ability to accurately classify patients. Correlations between ReHo values and clinical scores were also evaluated.

ReHo analysis revealed significant differences in several brain regions. The study results revealed that, compared to healthy controls, both BD and MDD patients exhibited reduced ReHo in the superior parietal gyrus. Additionally, MDD patients showed decreased ReHo values in the Right Lenticular nucleus, putamen (PUT.R), Right Angular gyrus (ANG.R), and Left Superior occipital gyrus (SOG.L). Compared to the MDD group, BD patients exhibited increased ReHo values in the Left Inferior occipital gyrus (IOG.L). In BD patients only, the reduction in ReHo values in the right superior parietal gyrus and the right angular gyrus was positively correlated with Hamilton Depression Scale (HAMD) scores. SVM-RFE identified the IOG.L, SOG.L, and PUT.R as the most critical features, achieving an area under the curve (AUC) of 0.872, with high sensitivity and specificity in distinguishing BD from MDD.

This study demonstrates that BD and MDD patients exhibit distinct patterns of regional brain activity, particularly in the occipital and parietal regions. The combination of ReHo analysis and SVM-RFE provides a powerful approach for identifying potential biomarkers, with the left inferior occipital gyrus, left superior occipital gyrus, and right putamen emerging as key differentiating regions. These findings offer valuable insights for improving the diagnostic accuracy between BD and MDD, contributing to more targeted treatment strategies.