Common and distinct patterns of aberrant amygdala functional connectivity in major depressive disorder and bipolar disorder: A voxel-wise comparative meta-analysis.

Bilingualism is associated with distinct patterns of resting-state functional brain connectivity – a consequence of ongoing language control demands that do not apply to monolinguals. However, it is not well understood how these patterns affect, and are affected by, brain activation for domain-general cognitively demanding tasks. Here, we employ a novel task-driven resting-state electroencephalography design including an implicit Lindenmayer grammar learning task, which tracks aperiodic and hierarchical dependencies, to determine task-related functional connectivity changes in bilinguals. Quantified bilingual experience was used as a predictor of directional effects, using Generalised Additive Models to account for non-linear patterns. Our results revealed post-task alterations to connectivity involving increased involvement of occipital regions, reduced involvement of frontal and central regions, and faster reaction times to stimuli at higher levels of bilingual experience. Crucially, the regions implicated post-task appear to reflect task-relevant regions which are involved in the language and executive control networks, reflecting greater short-term task-driven flexibility. These findings have important implications for our understanding of how domain-general processing and connectivity are shaped by linguistic experience.

Panic disorder (PD) and generalized anxiety disorder (GAD) are among the most prevalent anxiety disorders (ADs), yet their neural mechanisms remain unclear. This study aimed to characterize EEG microstate patterns and their functional connectivity (FC) in patients with GAD and PD to explore the neural mechanisms underlying anxiety symptoms. Resting-state EEG was collected from 35 patients with PD, 31 patients with GAD, and 39 healthy controls (HCs). Four microstate classes (A-D) were selected to calculate the parameters, including the mean duration, time coverage, occurrence, mean global field power (GFP), and transitions. Furthermore, the FC patterns underlying each microstate class were analyzed. Correlation analyses were performed between anxiety symptoms and microstate metrics. Compared with HCs, ADs presented increased duration of microstate D and decreased time coverage of microstate A, suggesting altered neural dynamics in ADs, characterized by impaired sensory processing and executive functioning.The correlation analysis revealed that the features of microstate C (associated with self-referential processing) were positively correlated with anxiety symptoms. In contrast, the features of microstates A and B (involved in sensory network functioning) showed consistent negative correlations with anxiety symptoms. Furthermore, PD and GAD groups exhibited distinct FC patterns within microstate A. These FC differences in microstate A demonstrated potential value in distinguishing between GAD and PD.

Current research in predicting traumatic brain injury risk focuses on the relationship between head impacts and the likelihood of white matter damage, often overlooking the role of neurovascular coupling in the injury response. To fill this gap, we combined biomechanical and neurodynamic principles to simulate alterations to large-scale resting-state brain activity following head impacts. We simulated cortical neural activity with a network of Kuramoto phase oscillators, using structural connectivity to define coupling and a vascular-informed local resource term to capture neurovascular coupling. By combining the neurodynamic model with a brain mechanics model, we investigated two mechanistic pathways of network dysfunction: (1) white matter damage, reflected in degrading network edges, and (2) local tissue oxygenation decline, reflected in adjusting the resource term at each network node. We simulated 53 real-world head impacts using a vasculature template to evaluate the changes in simulated functional connectivity (FC) and neural dynamics relative to injury outcomes (concussion vs. no concussion). To assess vascular variability, simulations were repeated across 41 individual vasculature maps. Our results show simulated FC changes (measured by Pearson's correlation) consistently correlated well with injury outcomes, regardless of injury mechanism (AUC = 0.89 and 0.90), However, the two injury models yielded distinct FC patterns as indicated by graph metrics. Vascular variability substantially influenced how injury affected FC, with some brains exhibiting resilience to synchrony disruption depending on their vascular structure. These findings offer testable insight into the neurovascular mechanism of brain dysfunction after TBI and have important implications for individualized protection and treatment.

Neurophysiological differentiation of Boder's dyslexia subtypes using harmonised quantitative EEG cross-spectra.

The mechanistic role of psychological expectations in human-humanoid robot cooperation.

Neurobehavioral correlates of dopamine agonist-induced eye-blinking in the marmoset monkey.

The thalamus is a key hub in seizure propagation, and its nuclei are emerging targets for neuromodulation. However, the contributions of individual nuclei to epileptic networks remain unclear, particularly in children, who are less studied than adults. We investigated structural and functional thalamic alterations across different pediatric focal epilepsies and their associations with clinical features and postsurgical outcomes. We retrospectively studied children with temporal lobe epilepsy (TLE), frontal lobe epilepsy (FLE), and posterior quadrant epilepsy (PQE) and healthy controls. The thalamus was segmented into four nuclei groups (anterior, lateral, medial, pulvinar) using the THOMAS pipeline on T1-weighted magnetic resonance imaging (MRI) to estimate volumes. Functional connectivity was assessed with functional MRI using node strength, capturing total thalamic connectivity across the brain. We compared patients with controls and evaluated associations with hippocampal sclerosis, history of focal to bilateral tonic-clonic seizures, and postsurgical seizure freedom. Among 136 children with focal epilepsy (81 TLE, 36 FLE, 19 PQE; mean age = 13.0 years) and 70 controls (mean age = 13.4 years), ipsilateral thalamic volume reductions were observed in the following: anterior and lateral nuclei and pulvinar in TLE, anterior and lateral nuclei in FLE, and pulvinar in PQE (Cohen d = .52-.70, all Bonferroni-corrected p < .05). In contrast, medial nuclei volume increase was associated with history of seizure generalization (partial η2 = .06). Functional connectivity was bilaterally reduced across epilepsy groups (partial η2 = .03), most consistently in the pulvinar (Cohen d = .25-.68). Within TLE, hippocampal sclerosis was associated with increased anterior nucleus connectivity (partial η2 = .17), distinguishing it from other pathologies. We demonstrate both shared and syndrome-specific thalamic abnormalities in pediatric focal epilepsy. Common patterns included ipsilateral thalamic volume loss, indicating cumulative disease burden, and reduced bilateral functional connectivity, particularly in the pulvinar, likely reflecting thalamocortical decoupling. These findings advance understanding of seizure networks beyond the epileptogenic zone and provide a foundation for personalized thalamic-targeted neuromodulation strategies.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and long COVID are chronic debilitating illnesses featuring fatigue, post-exertional malaise (PEM) and neurocognitive deficits. Temporal correlation of neural activity between distinct brain regions, also referred to as functional connectivity (FC), can provide insights into how brain networks coordinate, at rest or during task. Therefore, we explored intrinsic FC correlates of cognitive fatigue in ME/CFS and long COVID patients during two Stroop-colour-word paradigms on 7 Tesla fMRI. 450 sagittal volumes were acquired from seventy-eight participants: 32 patients with MECFS (pwME/CFS); 19 long COVID (pwLC) and 27 healthy controls (HC) during performance of baseline or Pre (before/during fatigue build-up) and repeat Post (fatigue set-in) Stroop tasks. Structural and functional data were analysed using the CONN toolbox. Regions of interest (ROI-to-ROI) analysis revealed significantly increased FC in subcortical regions in HC for Pre vs Post. Relative to HC, pwLC showed significantly reduced FC between nucleus accumbens and vermis 3 (p = 0.02) in Pre and increased FC in the prefrontal cortex and hippocampus (p = 0.02) in Post. pwME/CFS showed a significantly increased FC between the left cuneiform nucleus and right medulla (p = 0.03). Compared to HC, reduced FC was significant in pwLC during Pre, and between medulla and hippocampus (p = 0.04) and between nucleus accumbens and vermis (p = 0.001) during Post. Aberrant FC was significant for pwME/CFS in core networks during Pre. Core network FC to the cerebellum, amygdala, caudate and red nucleus correlated with symptom scores for cognition in both pwME/CFS and pwLC. Hippocampus and cerebellar FC correlated with duration of illness in pwME/CFS. Our findings of reduced dopaminergic hippocampal-nucleus-accumbens connectivity imply blunted motivation and cognition. Extensive FC differences in subcortical and core networks in patient cohorts were detected relative to an increased FC in HC. High regional communication indicative of greater task engagement by HC was distinctive while FC differences in ME/CFS and long COVID patients indicated reduced and dysregulated regional coordination that may serve as candidate biomarkers of symptomatology in long COVID and ME/CFS.

Children with High-Functioning Autism (HFA) often show marked deficits in executive functioning, particularly during verbal fluency tasks (VFTs). These behavioral impairments are believed to stem from neurophysiological abnormalities in the prefrontal cortex (PFC) functioning, characterized by atypical activation patterns and disrupted functional connectivity. This study utilized functional near-infrared spectroscopy (fNIRS) to investigate hemodynamic responses and connectivity metrics during VFT performance. By comparing children with HFA to age-matched typically developing (TD) controls, this study aimed to clarify the neural mechanisms underlying the executive control of language production in HFA. The sample included 29 children who met diagnostic criteria for HFA and 26 TD controls. All participants had a Full-Scale Intelligence Quotient of 70 or higher and were matched for age and cognitive ability. During a standardized phonemic VFT, cortical hemodynamics were continuously monitored using a 19-channel fNIRS system, with analyses focusing on changes in oxygenated hemoglobin concentration within PFC regions. Compared with TD controls, children with HFA exhibited reduced cortical activation across multiple prefrontal regions, including channels 1 (t = -2.975, p = 0.017), 2 (t = -4.320, p = 0.001), 3 (t = -3.381, p = 0.012), 9 (t = -3.127, p = 0.014), and 19 (t = -3.279, p = 0.012). These regions correspond anatomically to the inferior prefrontal gyrus, frontopolar cortex, and dorsolateral PFC. Functional connectivity analyses demonstrated significantly reduced interregional coupling in the HFA group (p < 0.001), with mean connectivity values of 0.512 (SD = 0.076) compared with 0.566 (SD = 0.069) in TD participants. Furthermore, Oxy-Hb changes in prefrontal channels 1 (r = -0.424, p = 0.022), 2 (r = -0.432, p = 0.019), and 3 (r = -0.394, p = 0.034) were negatively correlated with Social Responsiveness Scale total scores, indicating that weaker prefrontal activation was associated with greater social impairment. The results reveal distinct cortical activation and functional connectivity alterations in children with HFA during VFTs. These findings support the hypothesis that disrupted interregional brain coordination underlies executive difficulties in language production in HFA children, who exhibit reduced PFC activation and weaker interregional functional connectivity during the VFT.

ABSTRACTAlzheimer's disease (AD) is a heterogeneous disorder characterized by brain accumulation of amyloid‐beta (Aß, simplified as A for the AD biological model) and tau (T) proteins, with Aß emerging first. However, a significant proportion of individuals exhibit discordant biomarkers' profiles, such as elevated phosphorylated tau181 (p‐tau181) with normal Aß42 from cerebrospinal fluid (CSF), posing diagnostic and mechanistic challenges. This study investigated whether functional and structural brain connectivity can distinguish individuals with discordant CSF profiles (A−T+) from those with concordant patterns (A+T+), hypothesizing that distinct connectivity patterns may reflect early divergent pathophysiological processes. Data from cognitively unimpaired or mildly impaired individuals in the ADNI3 repository were analyzed, selecting those with resting‐state functional MRI (rsfMRI) and/or diffusion MRI (dMRI) within 18 months of CSF testing for Aß and p‐tau181. Participants were grouped into A−T+ or A+T+ groups. Structural and functional connectivity gradients were generated for each participant and summarized using a Euclidean distance measure from reference gradient templates derived from cognitively unimpaired individuals without pathology (A−T−). We applied linear mixed models and analysis of variance to assess connectivity‐based gradient differences between A−T+ and A+T+ groups, adjusting for relevant variables. Classification analyzes using logistic regression and support vector machine, along with feature importance via the Boruta algorithm, evaluated the discriminative power of gradient connectivity profiles. Multimodal integration was performed using partial least square canonical analysis (PLSC), and relationships between gradients and cognition were assessed via UMAP‐based dimensionality reduction and bootstrapped linear regressions. Results were compared with a classical network analysis examining within‐ and between‐network connectivity differences. Among 424 participants, n = 67 were classified as A−T+, n = 106 as A+T+, and n = 56 as cognitively healthy A−T−. The remaining 195 participants (n = 86 A+T+ and n = 109 cognitively impaired A−T−) were not included. A−T+ individuals (age = 75 ± 8.2) exhibited less cognitive impairment but greater functional connectivity gradients' distance to the reference templates (false discovery rate‐corrected p < 0.05) in the temporo‐occipital axis compared to A+T+ (age = 76.1 ± 7.7). Structural connectivity differences were not significant. FC‐based models classified A−T+ and A+T+ with good accuracy (AUC = 0.77), loading on the same temporo‐occipital regions, unlike SC (AUC = 0.52). The posterior brain involvement in A−T+ was confirmed by PLSC analyzes. A+T+ individuals showed a significant relation between cognitive scores and functional connectivity, primarily mapping the default mode network (DMN). A shift was observed in relation to executive functions and functional connectivity in A−T+. Discordant CSF profiles (A−T+) exhibit distinct functional connectivity patterns, particularly in posterior brain regions, compared to concordant CSF patterns (A+T+), which are characterized by a significant cognitive‐DMN connectivity association. These results suggest that CSF p‐tau181 accumulation in the absence of Aß42 may be associated with specific functional trajectories, suggesting specific pathophysiological patterns.

Functional near-infrared spectroscopy (fNIRS) is increasingly used to examine cognitive abilities and support the diagnosis of bipolar disorder (BD). Despite its growing application, research on functional connectivity in BD remains limited, and task-induced functional connectivity remains under-investigated. This study aims to differentiate patients with BD from healthy controls (HCs) based on fNIRS-derived functional connectivity. We analyzed fNIRS data from 50 patients with BD and 50 HCs during resting state and two cognitive tasks, including the verbal fluency test (VFT) and Stroop color-word test (Stroop). Functional connectivity was quantified using coherence and correlation evaluations, and four network characteristics, such as local efficiency, global efficiency, global clustering coefficient, and average closeness centrality, were extracted. A thresholding method was applied by filtering the top 10% to 50% to highlight significant network connections. Group differences were evaluated using t-tests with false discovery rate. Patients with BD showed significantly altered coherence values, especially between channels 4 and 10 (p<0.001, t=-5.534, η2=0.562) and lower global clustering coefficients (p<0.001, t=-5.666, η2=0.578) compared to HCs. The k-nearest neighbor (KNN) classifier using coherence-based network metrics during the VFT achieved the highest classification performance with an accuracy of 0.818, precision of 0.819, recall of 0.824, and an F1-score of 0.817. Our findings suggest that task-induced functional connectivity, particularly coherence-based metrics derived during the VFT, reflects distinct functional connectivity patterns that differentiate patients with BD from HCs. These findings support the utility of network-based approaches under cognitive task conditions in characterizing functional connectivity alterations associated with BD.

Understanding how structural and functional connectivity shape hippocampal interactions with the rest of the brain is critical for elucidating its role in cognition. Here, we combine high-resolution diffusion MRI, a novel fibre-tracking pipeline designed to specifically probe anatomical connectivity of the in vivo human hippocampus, and track-weighted dynamic functional connectivity (TW-dFC) to investigate how direct anatomical connections between the hippocampus and the rest of the brain relate to time-varying functional interactions. In Study 1, TW-dFC maps were computed for 10 participants from the Human Connectome Project and subjected to ICA and k-means clustering to derive a data-driven parcellation of the hippocampus based on its structure-function relationships. This revealed circumscribed clusters distributed along anterior-posterior and medial-lateral axes, which broadly aligned with hippocampal subfields. In Study 2, we examined the resting-state functional connectivity profiles of each TW-dFC derived cluster in an independent sample of 100 participants. Each hippocampal cluster displayed distinct patterns of functional connectivity with specific substructures within medial temporal, parietal, frontal and occipital cortices as well as subcortical and cerebellar regions. Our findings demonstrate that TW-dFC provides a powerful framework for anatomically informed functional parcellation of the hippocampus and offers new insights into the structural-functional organisation underlying hippocampal-(sub)cortical interactions. Our approach opens new avenues for probing memory systems in health and their disruption in aging and disease.

Alterations in dynamic brain functional connectivity (dFC) have been observed in epilepsy, few studies have directly compared the dynamic functional network connectivity (dFNC) patterns between patients with self-limited epilepsy with centrotemporal spikes (SeLECTS) and those with childhood absence epilepsy (CAE). This study aimed to explore differences in dFNC between these two epilepsy types and investigate how these patterns relate to clinical features. Resting-state functional MRI data were collected from 34 SeLECTS patients, 22 CAE patients, and 32 healthy controls. Independent component analysis (ICA) was combined with a sliding-window technique to examine characteristics of dynamic FNC, including state transitions, connectivity strength, and temporal properties. Three recurring dFNC states were identified. SeLECTS patients spent significantly more time in a highly flexible state characterized by strong network integration, whereas CAE patients more frequently occupied a state marked by weak inter-network connectivity. Furthermore, SeLECTS patients showed greater variability in dFNC states over time. Certain clinical factors-particularly seizure frequency-were found to correlate with specific dFNC states, most notably in the SeLECTS group. The study highlights distinct dynamic connectivity patterns between SeLECTS and CAE patients, suggesting that these two epilepsy types involve different network-level mechanisms. These findings contribute to a deeper understanding of epilepsy subtypes and may inform future diagnostic and treatment strategies. This study identifies distinct dFNC patterns in two common childhood epilepsies: SeLECTS and CAE. It demonstrates the value of dynamic resting-state brain network analysis in pediatric epilepsy. These findings provide new neuroimaging biomarkers for early classification of epilepsy subtypes. Results may contribute to the development of personalized diagnosis and treatment strategies in children with epilepsy.

Boredom is a common yet understudied emotional state that can adversely impact cognitive performance, motivation, and mental well-being. Gaining insight into its neural basis is crucial for developing strategies to manage or reduce its impact across various settings, including education contexts. The present study investigated brain functional connectivity during boredom in an educational context using electroencephalography (EEG). It was hypothesized that the brain exhibits distinct connections during the experience of boredom. Eighty-four healthy adults (mean age = 26.90 ± 5.29 years) were asked to watch two educational videos designed to induce boredom or neutral states while their EEG signals were captured. Functional connectivity matrices were constructed using coherence in traditional EEG frequency bands. Clustering coefficient :left({C}_{eff}right), characteristic path length :left({C}_{pl}right), global efficiency :left({E}_{glo}right), local efficiency :left({E}_{loc};right), and node strength :left({N}_{str}right) were calculated to compare scalp-level network characteristics between boredom and neutral states. The results showed significant differences in functional connectivity in the alpha, beta, and gamma bands. Boredom was characterized as having significantly higher :{E}_{glo} and lower :{C}_{pl}::in the alpha, beta, and gamma bands, relative to the neutral condition. Gamma band :{C}_{eff}:and :{E}_{loc} were also found to be higher during boredom. The study findings suggest that boredom is associated with distinct patterns of brain functional connectivity, potentially reflecting increased internal processing. The study findings contribute to the broader field of affective neuroscience by highlighting how boredom can influence functional brain network activity in educational contexts.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-19245-7.

Functional near-infrared spectroscopy (fNIRS) has shown potential in attention deficit/hyperactivity disorder (ADHD) research, though it is not yet widely used as a primary diagnostic tool. While most previous studies have focused on children and resting-state conditions, research on adult ADHD, particularly under task-state conditions, is increasing but still limited compared to studies on children. Since ADHD is associated with cognitive challenges and alterations in brain activity, investigating functional connectivity during a task can provide a better understanding of its neural characteristics. In this study, we aim to investigate functional connectivity in adult patients with ADHD by comparing them with healthy controls under task-state conditions. We used the fNIRS dataset, which comprised 75 healthy controls and 75 medication-naïve individuals with ADHD. The network characteristics of functional connectivity were compared during a verbal fluency task, specifically focusing on density, global clustering coefficient, efficiency, and average betweenness centrality. By statistical analysis between the two groups, statistical significance was observed in density (p<0.001, t = 5.39, η2 = 0.443). Additionally, various machine learning classifiers were employed to assess the potential of functional connectivity metrics in classifying the two groups. The linear support vector machine achieved accuracy and precision of 0.800, recall of 0.808, and F1-score of 0.799, representing the highest performance among five different classifiers. In conclusion, our findings reveal distinct functional connectivity patterns among the groups, highlighting the potential of fNIRS-derived functional connectivity metrics as biomarkers for ADHD.

Sensory over-responsivity (SOR), characterized by strong negative reactions to typically innocuous stimuli, is considered a symptom of autism spectrum disorder. However, SOR also affects 15-20% of children overall, including a majority of children with common psychiatric conditions. Despite its prevalence, the clinical specificity and neurobiological bases of SOR remain poorly understood. Our study aims to determine the specific clinical significance of SOR across diverse child samples and establish whether SOR is associated with distinct patterns of functional connectivity (FC). We analyzed data from 15,728 children (ages 6-17.9 years) across five datasets: three community samples, including the Adolescent Brain Cognitive Development [ABCD] and Healthy Brain Network [HBN] studies, and two autism-enriched samples. Bivariate and multivariate models examined associations between SOR and symptoms of anxiety, attention-deficit/hyperactivity disorder, depression, conduct disorder, and oppositional defiant disorder, as well as autistic traits. Analysis of resting-state functional MRI (fMRI) data from the ABCD study tested brain-wide and circuit-specific FC correlates of mild SOR, replication of effects in an independent ABCD subsample, as well as extension to severe SOR in ABCD. Multivariate analyses revealed that SOR is associated with a remarkably consistent transdiagnostic profile: greater levels of both autistic traits and anxiety symptoms and, in community samples, lower levels of conduct disorder symptoms. Across samples, SOR is not reliably associated with symptoms of any other analyzed psychiatric conditions. SOR is associated with both brain-wide and circuit-specific resting-state functional connectivity (FC) patterns that replicate across independent subsamples and highlight FC differences between cingulo-parietal network and bilateral caudate nucleus. Our results demonstrate that SOR may constitute a transdiagnostic latent trait with both specific clinical risk and protection, and with replicable neural correlates that implicate specific cortico-subcortical circuits. These findings advance our understanding of the neurobiology and clinical relevance of SOR. They may also inform clinical practice and future research aimed at understanding and supporting individuals with sensory challenges.

Sleep-related problems (SRP) in childhood are common and clinically relevant yet their underlying neural mechanisms and links to future mental health outcomes remain poorly understood. Here, we investigated how distinct dimensions of SRP relate to multimodal brain structure and function in preadolescents, and whether these neural signatures predict trajectories of mental health difficulties. We employed multivariate mapping to investigate the relationship between structural and functional brain network patterns and various dimensions of SRP in the Adolescent Brain Cognitive Development (ABCD) dataset. Moreover, we explored whether and how the identified multimodal brain signatures could predict the trajectory of internalizing and externalizing behavior difficulties over a two-year follow-up. Our multivariate analysis revealed two robust dimensions of SRP: a general sleep disturbance dimension and a hypersomnolence and parasomnia dimension. Each was associated with partially distinct patterns of brain morphology and functional connectivity, consistent with their differential alignment along the hierarchical organization of cortical neurodevelopment maps. However, both dimensions shared common disruptions in the somatosensory, attention, and default mode networks. We further observed that only these neural patterns associated with the general sleep disturbance dimension predict the longitudinal trajectories of internalizing/externalizing symptoms. Our findings enhance the understanding of the neurobiological mechanisms underlying dimensions of SRP in preadolescence and could inform brain-based intervention and treatment programs to improve sleep-related and mental health–related outcomes across development.

Previous research has indicated that individuals with depression and anxiety disorders may similarly changes in brain connectivity compared to healthy control. These patterns of altered connectivity may emerge upon disorder onset in adolescence. Establishing shared and specific patterns at this early stage can help investigate underlying biological processes, with implications for intervention, but remaining unclear whether adolescent MDD and AD are associated with similar or distinct functional connectivity patterns. To determine similarities and differences in brain connectivity neuroimaging between adolescent MDD and AD. Eligibility criteria were established following PICOS. We conducted searches using controlled vocabulary and searching syntax on PubMed, Scopus, ScienceDirect, and Web of Science databases, up to January 31, 2024. The quality of papers was assessed using PRISMA. 5137 papers were retrieved, with 38 meeting the inclusion criteria. In the synthesis of results, it was found that adolescent MDD patients exhibit increased connectivity within default mode network (DMN), especially in areas posterior cingulate cortex and medial prefrontal cortex; and salience network (SN) including anterior cingulate cortex and insula. Adolescent AD patients show hyperconnectivity and dysregulated activity in DMN, enhanced connectivity within SN including amygdala. MDD and AD both show involvement of limbic system including amygdala and hippocampus. Results suggest both adolescent MDD and AD exhibit alterations in connectivity of SN; disrupted connectivity within DMN; dysregulation in connectivity between limbic and sensorimotor system. They also differentiate in certain connectivity patterns. These could serve as biomarkers for future research on transdiagnostic features across psychiatric disorders.

Background:Tourette syndrome (TS) and attention-deficit/hyperactivity disorder (ADHD) often co-occur and are linked to emotional and behavioral difficulties. However, their shared and distinct neural underpinnings, particularly in terms of functional connectivity, remain unclear. Here, we assessed how functional connectivity differs across TS and ADHD as well as its association with emotional and behavioral difficulties.Methods:Resting-state electroencephalography (EEG) was recorded from 137 children with TS (n = 51), ADHD (n = 24), or TS + ADHD (n = 29) or from typically developing control subjects (n = 33). Functional connectivity was computed from source-reconstructed EEG data in five frequency bands (delta, theta, alpha, beta, and gamma). Behavioral and emotional problems were assessed with the Child Behavior Checklist.Results:Both TS and ADHD were independently associated with reduced functional connectivity across different brain regions, with no interaction effect. However, externalizing problems showed a TS by ADHD interaction across three frequency bands, such that distinct patterns of functional connectivity were associated with externalizing problems in children with TS + ADHD, relative to those with either TS or ADHD.Conclusions:Although TS and ADHD are associated with decreased functional connectivity in different networks, their effects appear additive rather than interactive at the neural level. However, interactions emerged when examining behavioral problems, suggesting that although TS and ADHD contribute independently to brain connectivity disruptions, their combined impact may uniquely influence emotional and behavioral functioning. This fact highlights the need to consider both shared and disorder-specific mechanisms when studying TS and ADHD.