Connectivity Patterns Research Articles

Rapid dynamics of electrophysiological connectome states are heritable

Abstract Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, are a prominent feature of brain activity with broad functional implications. While infra-slow (<0.1 Hz) connectome dynamics have been extensively studied with fMRI, rapid dynamics highly relevant for cognition are poorly understood. Here, we asked whether rapid electrophysiological connectome dynamics constitute subject-specific brain traits and to what extent they are under genetic influence. Using source-localized EEG connectomes during resting-state (N = 928, 473 females), we quantified heritability of multivariate (multi-state) features describing temporal or spatial characteristics of connectome dynamics. States switched rapidly every ∼60–500 ms. Temporal features were heritable, particularly, Fractional Occupancy (in theta, alpha, beta, and gamma bands) and Transition Probability (in theta, alpha, and gamma bands), representing the duration spent in each state and the frequency of state switches, respectively. Genetic effects explained a substantial proportion of phenotypic variance of these features: Fractional Occupancy in beta (44.3%) and gamma (39.8%) bands and Transition Probability in theta (38.4%), alpha (63.3%), beta (22.6%), and gamma (40%) bands. However, we found no evidence for heritability of dynamic spatial features, specifically states’ Modularity and connectivity pattern. We conclude that genetic effects shape individuals’ connectome dynamics at rapid timescales, specifically states’ overall occurrence and sequencing.

Machine learning provides insights for spatially explicit pest management strategies by integrating information on population connectivity and habitat use in a key agricultural pest.

Insect pests have garnered increasing interest because of anthropogenic global change, and their sustainable management requires knowledge of population habitat use and spread patterns. To enhance this knowledge for the prevalent tea pest Empoasca onukii, we utilized a random forest algorithm and a bivariate map to develop and integrate models of its habitat suitability and genetic connectivity across China. Our modeling revealed heterogeneous spatial patterns in suitability and connectivity despite the common key environmental predictor of isothermality. Analyses indicated that tea cultivation in areas surrounding the Tibetan Plateau and the southern tip of China may be at low risk of population outbreaks because of their predicted low suitability and connectivity. However, regions along the middle and lower reaches of the Yangtze River should consider the high abundance and high recolonization potential of E. onukii, and thus the importance of control measures. Our results also emphasized the need to prevent dispersal from outside regions in the areas north of the Yangtze River and highlighted the effectiveness of internal management efforts in southwestern China and along the southeastern coast. Further projections under future conditions suggested the potential for increased abundance and spread in regions north of the Yangtze River and the southern tip of China, and indicated the importance of long-term monitoring efforts in these areas. These findings highlighted the significance of combining information on habitat use and spread patterns for spatially explicit pest management planning. In addition, the approaches we used have potential applications in the management of other pest systems and the conservation of endangered biological resources. © 2024 Society of Chemical Industry.

Neural adaptations to congenital deafness: enhanced tactile discrimination through cross-modal neural plasticity - an fMRI study.

This study explores the compensatory neural mechanisms associated with congenital deafness through anexamination of tactile discrimination abilities using high-resolution functional magnetic resonance imaging (fMRI). To analyze the neural substrates underlying tactile processing in congenitally deaf individuals and compare themwith hearing controls. Our participant pool included thirty-five congenitally deaf individuals and thirty-five hearing controls. All participantsengaged in tactile discrimination tasks involving the identification of common objects by touch. We utilized ananalytical suite comprising voxel-based statistics, functional connectivity multivariate/voxel pattern analysis (fc-MVPA), and seed-based connectivity analysis to examine neural activity. Our findings revealed pronounced neural activity in congenitally deaf participants within regions typically associatedwith auditory processing, including the bilateral superior temporal gyrus, right middle temporal gyrus, and rightrolandic operculum. Additionally, unique activation and connectivity patterns were observed in the right insula andbilateral supramarginal gyrus, indicating a strategic reorganization of neural pathways for tactile informationprocessing. Behaviorally, both groups demonstrated high accuracy in the tactile tasks, exceeding 90%. However, thedeaf participants outperformed their hearing counterparts in reaction times, showcasing significantly enhancedefficiency in tactile information processing. These insights into the brain's adaptability to sensory loss through compensatory neural reorganization highlight theintricate mechanisms by which tactile discrimination is enhanced in the absence of auditory input. Understandingthese adaptations can help develop strategies to harness the brain's plasticity to improve sensory processing inindividuals with sensory impairments, ultimately enhancing their quality of life through improved tactile perceptionand sensory integration.

Web Application to Enable Online Social Interactions in a Parkinson Disease Risk Cohort: Feasibility Study and Social Network Analysis.

There is evidence that social interaction has an inverse association with the development of neurodegenerative diseases. PREDICT-Parkinson Disease (PREDICT-PD) is an online UK cohort study that stratifies participants for risk of future Parkinson disease (PD). This study aims to explore the methodological approach and feasibility of assessing the digital social characteristics of people at risk of developing PD and their social capital within the PREDICT-PD platform, making hypotheses about the relationship between web-based social engagement and potential predictive risk indicators of PD. A web-based application was built to enable social interaction through the PREDICT-PD portal. Feedback from existing members of the cohort was sought and informed the design of the pilot. Dedicated staff used weekly engagement activities, consisting of PD-related research, facts, and queries, to stimulate discussion. Data were collected by the hosting platform. We examined the pattern of connections generated over time through the cumulative number of posts and replies and ego networks using social network analysis. We used network metrics to describe the bonding, bridging, and linking of social capital among participants on the platform. Relevant demographic data and Parkinson risk scores (expressed as an odd 1:x) were analyzed using descriptive statistics. Regression analysis was conducted to estimate the relationship between risk scores (after log transformation) and network measures. Overall, 219 participants took part in a 4-month pilot forum embedded in the study website. In it, 200 people (n=80, 40% male and n=113, 57% female) connected in a large group, where most pairs of users could reach one another either directly or indirectly through other users. A total of 59% (20/34) of discussions were spontaneously started by participants. Participation was asynchronous, with some individuals acting as "brokers" between groups of discussions. As more participants joined the forum and connected to one another through online posts, distinct groups of connected users started to emerge. This pilot showed that a forum application within the cohort web platform was feasible and acceptable and fostered digital social interaction. Matching participants' web-based social engagement with previously collected data at individual level in the PREDICT-PD study was feasible, showing potential for future analyses correlating online network characteristics with the risk of PD over time, as well as testing digital social engagement as an intervention to modify the risk of developing neurodegenerative diseases. The results from the pilot suggest that an online forum can serve as an intervention to enhance social connectedness and investigate whether patterns of online engagement can impact the risk of developing PD through long-term follow-up. This highlights the potential of leveraging online platforms to study the role of social capital in moderating PD risk and underscores the feasibility of such approaches in future research or interventions.

Contributions of Coping Flexibility and Associated Functional Brain Connectivity to Resilient Trajectories of Mental Health During the COVID-19 Pandemic

The COVID-19 pandemic has presented an unprecedented challenge for individual mental health. In this study, we examined the lasting impact of the pandemic on mental health among Chinese college students and tested whether different coping dimensions contributed to resilience. Using a 2-year longitudinal design with four waves, we identified four common trajectories for individuals’ levels of depression and anxiety: resilient, recovered, delayed, and chronic. With prepandemic coping and resting-state neuroimaging data as predictors, we found that coping flexibility and corresponding connectivity patterns significantly predicted increased odds ratios of resilient trajectories for depression and anxiety. However, use of specific emotion-regulation strategies (i.e., cognitive reappraisal and expressive suppression) did not predict resilient trajectories. Overall, findings help to identify heterogeneity in emotional trajectories during the pandemic and indicate that coping flexibility plays a critical role in resilience. This work has implications for clinical-risk identification and developing mental-health interventions for the current pandemic and future crises.

Integration of Semantic and Topological Structural Similarity Comparison for Entity Alignment without Pre-Training

Entity alignment (EA) is a critical task in integrating diverse knowledge graph (KG) data and plays a central role in data-driven AI applications. Traditional EA approaches rely on entity embeddings, but their effectiveness is limited by scarce KG input data and representation learning techniques. Large language models have shown promise, but face challenges such as high hardware requirements, large model sizes and computational inefficiency, which limit their applicability. To overcome these limitations, we propose an entity-alignment model that compares the similarity between entities by capturing both semantic and topological information to enable the alignment of entities with high similarity. First, we analyze descriptive information to quantify semantic similarity, including individual features such as types and attributes. Then, for topological analysis, we introduce four conditions based on graph connectivity and structural patterns to determine subgraph similarity within three hops of the entity’s neighborhood, thereby improving accuracy. Finally, we integrate semantic and topological similarity using a weighted approach that considers dataset features. Our model requires no pre-training and is designed to be compact and generalizable to different datasets. Experimental results on four standard EA datasets validate the effectiveness of our proposed model.

Analysis of functional connectivity changes in attention networks and default mode networks in patients with depression and insomnia.

Major Depressive Disorder (MDD) and Insomnia Disorder (ID) are prevalent psychiatric conditions often occurring concurrently, leading to substantial impairment in daily functioning. Understanding the neurobiological underpinnings of these disorders and their comorbidity is crucial for developing effective interventions. This study aims to analyze changes in functional connectivity within attention networks and default mode networks in patients with depression and insomnia. The functional connectivity alterations in individuals with MDD, ID, comorbid MDD and insomnia (iMDD), and healthy controls (HC) were assessed from a cohort of 174 participants. They underwent rs-fMRI scans, demographic assessments, and scale evaluations for depression and sleep quality. Functional connectivity analysis was conducted using region-of-interest (ROI) and whole-brain methods. The MDD and iMDD groups exhibited higher Hamilton Depression Scale (HAMD) scores compared to HC and ID groups (P < 0.001). Both ID and MDD groups displayed enhanced connectivity between the left and right orbital frontal cortex compared to HC (P < 0.05), while the iMDD group showed reduced connectivity compared to HC and ID groups (P < 0.05). In the left insula, reduced connectivity with the right medial superior frontal gyrus was observed across patient groups compared to HC (P < 0.05), with the iMDD group showing increased connectivity compared to MDD (P < 0.05). Moreover, alterations in functional connectivity between the left thalamus and left temporal pole were found in iMDD compared to HC and MDD (P < 0.05). Correlation analyses revealed associations between abnormal connectivity and symptom severity in MDD and ID groups. Our findings demonstrate distinct patterns of altered functional connectivity in individuals with MDD, ID, and iMDD compared to healthy controls. These findings contribute to a better understanding of the pathophysiology of depression and insomnia, which could be used as a reference for the diagnosis and treatments of these patients.

Novel bow-shaped local resonance acoustic metamaterials with ultra-wide low-frequency stopband: design, modeling, and testing

This paper proposes a novel bow-spring local resonance (LR) structure featuring an exceptionally wide low-frequency stopband. Unlike traditional methods reliant on heavy mass or stiffness adjustments, this structure effectively manipulates and amplifies the dynamic characteristics of negative stiffness solely by designing parameter values for the bow-spring set. Through finite element method analysis, an ultra-wide stopband ranging from 91 to 570 Hz is achieved within the LR structure. Further modification of the connection pattern with a perforated plate extends the upper edge to 686 Hz while reducing the lower edge to 76 Hz. Most notably, within the novel bow-spring LR structure, a stopband width of 610 Hz is attained, resulting in a gap-mid gap ratio of 160.1%. The numerical and experimental results demonstrate good agreement. These findings offer a new perspective and guidelines for developing LR structures with ultra-wide low-frequency stopbands, potentially finding applications in the field of low-frequency vibration and noise reduction.

Idiosyncratic patterns of interhemispheric connectivity in the face and scene networks of the human brain

Abstract Neuroimaging studies have revealed a network of regions in both hemispheres of the human brain that respond selectively to faces. Neural models of face processing have typically focused on functional connectivity between regions in the same hemisphere (intrahemispheric), with a particular bias toward the right hemisphere. Here, we explored the role of interhemispheric connectivity using fMRI. We used three datasets to compare functional connectivity, as shown by correlations between the time-courses of neural activity of face regions during different natural viewing paradigms. We found higher correlations of neural activity between corresponding interhemispheric regions (e.g., rFFA–lFFA) than between intrahemispheric regions (e.g., rFFA–rOFA), indicating a bias towards higher interhemispheric than intrahemispheric connectivity. A similar interhemispheric bias was evident in scene-selective regions. In contrast, we did not find an interhemispheric bias in early visual regions (V1–V3), where intrahemispheric connectivity between corresponding regions was generally higher than interhemispheric connectivity. Next, we asked whether the higher interhemispheric connectivity in the face and scene networks between corresponding regions was consistent across participants. We found that the interhemispheric bias was significantly attenuated when we compared the time-course of response across participants. This shows that interhemispheric bias in connectivity between corresponding regions in the face and scene networks is specific to the individual. These findings raise the possibility that idiosyncratic variation in interhemispheric connectivity may explain individual differences in perception.

The Balancing Act of Family and College: Reciprocity and Its Consequences for Black Students

Abstract Literature indicates the ways that young adults –especially those from advantaged backgrounds—rely on parents during college and the transition to adulthood. Little research focuses on how the Black family reaches into college and Black college students’ provision of support to parents and other kin. The nexus of family and higher education is a rich site for investigating inequalities in educational attainment and outcomes. Based on interviews with Black undergraduates, this paper analyzes variation in familial involvement during college. It shows the ways in which Black students maintain a balancing act to meet academic responsibilities and family obligations. These practices help sustain the families they value but also reproduce class inequalities. The social organization of colleges and families imposes greater costs on disadvantaged students and offers greater benefits for advantaged students. The structure of education and constructions of family diminish obligations to family, narrow family ties, and mystify aspects of dependence, especially for disadvantaged students. Student narratives highlight the broad character of family values that often compete with academic obligations and detract from college immersion. Different forms and patterns of assistance and connection by class and gender are tied to structural resources and cultural differences, such as the place of family, the meaning of self, assessments of who counts as family, and reliance on a norm of one-way giving.

Explaining deep learning-based representations of resting state functional connectivity data: focusing on interpreting nonlinear patterns in autism spectrum disorder

BackgroundResting state Functional Magnetic Resonance Imaging fMRI (rs-fMRI) has been used extensively to study brain function in psychiatric disorders, yielding insights into brain organization. However, the high dimensionality of the rs-fMRI data presents significant challenges for data analysis. Variational autoencoders (VAEs), a type of neural network, have been instrumental in extracting low-dimensional latent representations of resting state functional connectivity (rsFC) patterns, thereby addressing the complex nonlinear structure of rs-fMRI data. Despite these advances, interpreting these latent representations remains a challenge. This paper aims to address this gap by developing explainable VAE models and testing their utility using rs-fMRI data in autism spectrum disorder (ASD).MethodsOne-thousand one hundred and fifty participants (601 healthy controls [HC] and 549 patients with ASD) were included in the analysis. RsFC correlation matrices were extracted from the preprocessed rs-fMRI data using the Power atlas, which includes 264 regions of interest (ROIs). Then VAEs were trained in an unsupervised manner. Lastly, we introduce our latent contribution scores to explain the relationship between estimated representations and the original rs-fMRI brain measures.ResultsWe quantified the latent contribution scores for both the ASD and HC groups at the network level. We found that both ASD and HC groups share the top network connectivitives contributing to all estimated latent components. For example, latent 0 was driven by rsFC within ventral attention network (VAN) in both the ASD and HC. However, we found significant differences in the latent contribution scores between the ASD and HC groups within the VAN for latent 0 and the sensory/somatomotor network for latent 2.ConclusionThis study introduced latent contribution scores to interpret nonlinear patterns identified by VAEs. These scores effectively capture changes in each observed rsFC feature as the estimated latent representation changes, enabling an explainable deep learning model that better understands the underlying neural mechanisms of ASD.

The interaction effect of high social support and resilience on functional connectivity using seed-based resting-state assessed by 7-Tesla ultra-high field MRI

Recent resilience research has increasingly emphasized the importance of focusing on investigating the protective factors in mentally healthy populations, complementing the traditional focus on psychopathology. Social support has emerged as a crucial element within the complex interplay of individual and socio-environmental factors that shape resilience. However, the neural underpinnings of the relationship between social support and resilience, particularly in healthy subjects, remain largely unexplored. With advances in neuroimaging techniques, such as ultra-high field MRI at 7T and beyond, researchers can more effectively investigate the neural mechanisms underlying these factors. Thus, our study employed ultra-high field rs-fMRI to explore how social support moderates the relationship between psychological resilience and functional connectivity in a healthy cohort. We hypothesized that enhanced social support would amplify resilience-associated connectivity within neural circuits essential for emotional regulation, cognitive processing, and adaptive problem-solving, signifying a synergistic interaction where strong social networks bolster the neural underpinnings of resilience. (n = 30). Through seed-based functional connectivity analyses and interaction analysis, we aimed to uncover the neural correlates at the interplay of social support and resilience. Our findings indicate that perceived social support significantly (p&amp;lt;0.001) alters functional connectivity in the right and left FP, PCC, and left hippocampus, affirming the pivotal roles of these regions in the brain’s resilience network. Moreover, we identified significant moderation effects of social support across various brain regions, each showing unique connectivity patterns. Specifically, the right FP demonstrated a significant interaction effect where high social support levels were linked to increased connectivity with regions involved in socio-cognitive processing, while low social support showed opposite effects. Similar patterns by social support levels were observed in the left FP, with connectivity changes in clusters associated with emotional regulation and cognitive functions. The PCC’s connectivity was distinctly influenced by support levels, elucidating its role in emotional and social cognition. Interestingly, the connectivity of the left hippocampus was not significantly impacted by social support levels, indicating a unique pattern within this region. These insights highlight the importance of high social support levels in enhancing the neural foundations of resilience and fostering adaptive neurological responses to environmental challenges.

Predicting high-level visual areas in the absence of task fMRI.

The ventral visual stream is organized into units, or functional regions of interest (fROIs), specialized for processing high-level visual categories. Task-based fMRI scans ("localizers") are typically used to identify each individual's nuanced set of fROIs. The unique landscape of an individual's functional activation may rely in large part on their specialized connectivity patterns; recent studies corroborate this by showing that connectivity can predict individual differences in neural responses. We focus on the ventral visual stream and ask: how well can an individual's resting state functional connectivity localize their fROIs for face, body, scene, and object perception? And are the neural processors for any particular visual category better predicted by connectivity than others, suggesting a tighter mechanistic relationship between connectivity and function? We found, among 18 fROIs predicted from connectivity for each subject, all but one were selective for their preferred visual category. Defining an individual's fROIs based on their connectivity patterns yielded regions that were more selective than regions identified from previous studies or atlases in nearly all cases. Overall, we found that in the absence of a domain-specific localizer task, a 10-min resting state scan can be reliably used for defining these fROIs.

Whole-brain intrinsic functional connectivity predicts symptoms and functioning in early psychosis

Theories of psychotic illness suggest that abnormal intrinsic functional connectivity may explain its characteristic positive and disorganization symptoms as well as lead to impaired general functioning. Here we used resting state functional magnetic resonance imaging (fMRI) to evaluate associations between these symptoms and the degree to which global connectivity is abnormal in early psychosis (EP). Eighty-six healthy controls (HCs) and 108 individuals with EP with resting state fMRI data were included in primary analyses. The EP group included 83 participants with schizophrenia-spectrum disorders and 25 with bipolar disorder type I with psychotic features. A global intrinsic connectivity “similarity index” for each EP individual was determined by calculating its correlation with the average HC connectivity matrix extracted using Schaefer atlases of multiple parcellations (100, 200, 300, and 400 region parcellations). As hypothesized, connectivity similarity with the average HC matrix was negatively associated with Brief Psychiatric Rating Scale total score, Scale for the Assessment of Positive Symptoms total score, and disorganization symptoms. Similarity was also positively associated with Global Assessment of Functioning score. Results were not driven by sex or diagnosis effects and were consistent across parcellation schemes. These results support the hypothesis that changes in whole-brain connectivity patterns are associated with psychosis symptoms and support the use of functional connectivity as a biomarker for these symptoms in EP.

Brain Connectomics Improve the Prediction of High-Risk Depression Profiles in the First Year following Breast Cancer Diagnosis

Background. Prediction of high-risk depression trajectories in the first year following breast cancer diagnosis with fMRI-related brain connectomics is unclear. Methods. The Be Resilient to Breast Cancer (BRBC) study is a multicenter trial in which 189/232 participants (81.5%) completed baseline resting-state functional magnetic resonance imaging (rs-fMRI) and four sequential assessments of depression (T0-T3). The latent growth mixture model (LGMM) was utilized to differentiate depression profiles (high vs. low risk) and was followed by multivoxel pattern analysis (MVPA) to recognize distinct brain connectivity patterns. The incremental value of brain connectomics in the prediction model was also estimated. Results. Four depression profiles were recognized and classified into high-risk (delayed and chronic, 14.8% and 12.7%) and low-risk (resilient and recovery, 50.3% and 22.2%). Frontal medial cortex and frontal pole were identified as two important brain areas against the high-risk profile outcome. The prediction model achieved 16.82-76.21% in NRI and 12.63-50.74% in IDI when brain connectomics were included. Conclusion. Brain connectomics can optimize the prediction against high-risk depression profiles in the first year since breast cancer diagnoses.

Remote Sensing Image Classification Based on Neural Networks Designed Using an Efficient Neural Architecture Search Methodology

Successful applications of machine learning for the analysis of remote sensing images remain limited by the difficulty of designing neural networks manually. However, while the development of neural architecture search offers the unique potential for discovering new and more effective network architectures, existing neural architecture search algorithms are computationally intensive methods requiring a large amount of data and computational resources and are therefore challenging to apply for developing optimal neural network architectures for remote sensing image classification. Our proposed method uses a differentiable neural architecture search approach for remote sensing image classification. We utilize a binary gate strategy for partial channel connections to reduce the sizes of the network parameters, creating a sparse connection pattern that lowers memory consumption and NAS computational costs. Experimental results indicate that our method achieves a 15.1% increase in validation accuracy during the search phase compared to DDSAS, although slightly lower (by 4.5%) than DARTS. However, we reduced the search time by 88% and network parameter size by 84% compared to DARTS. In the architecture evaluation phase, our method demonstrates a 2.79% improvement in validation accuracy over a manually configured CNN network.

Understanding attractions’ connection patterns based on intra-destination tourist mobility: A network motif approach

Understanding attractions’ connection patterns based on intra-destination tourist mobility: A network motif approach

Differential Studies on the Structure of Lignin-Carbohydrate Complexes (LCC) in Alkali-Extracted Plant Hemicelluloses.

Hemicellulose extracted by alkali treatment is of interest because of the advantages of its intact sugar structure and high degree of polymerization. However, the hemicellulose extracted by alkali treatment contained more lignin fragments and the presence of a lignin-carbohydrate complex (LCC), which affected the isolation and purification of hemicellulose and its comprehensive utilization. Therefore, the evaluation of the LCC structure of different types of lignocellulosic resources is of great significance. In this study, the LCC structures of hardwoods and Gramineae were enriched in alkaline systems. Information on the composition, structural proportions, and connection patterns of LCC samples was discussed. The similarities and differences between the LCC structures of different units of raw materials were comparatively studied. The results indicated that the monosaccharide fractions were higher in the LCC of Gramineae compared to hardwoods. The composition of the lignin fraction was dominated by G and S units. The phenyl glycosidic (PhGlc) bond is the predominant LCC linkage under alkali-stabilized conditions. In addition, Gramineae PhGlc types are more numerous compared to hardwoods. The results of the study provide insights into the differences in the chemical composition and structural features of LCC in different plants and provide important guidance for the optimization of the process of purifying hemicellulose.

Negative family and interpersonal relationship are associated with centromedial amygdala functional connectivity alterations in adolescent depression.

The amygdala, known for its functional heterogeneity, plays a critical role in the neural mechanism of adolescent major depressive disorder (aMDD). However, changes in its subregional functional networks in relation to stressful factors remain unclear. We recruited 78 comorbidity-free, medication-naive aMDD patients and 40 matched healthy controls (HC) to explore changes in resting-state functional connectivity (FC) across four amygdala subregions: the centromedial nucleus (CM), the basolateral nucleus (LB), the superficial nucleus (SF), and the amygdalostriatal transition area (Astr). Then, we performed partial correlation analysis to investigate the relationship between amygdala subregional FC and stressful factors as measured by the Chinese Version of Family Environment Scale (FES-CV) and the Adolescent Self-Rated Life Events Scale (ASLEC). Compared to HC, aMDD patients demonstrated significantly decreased functional connectivity between the left CM and left precentral gyrus, as well as between left SF and left precentral gyrus, and between left LB and posterior cingulate gyrus (PCC)/precuneus. In aMDD group, left CM-precentral gyrus FC exhibited negative correlation with interpersonal relationship and punishment, and positive correlation with family cohesion and expressiveness. This study reveals distinct patterns of abnormal functional connectivity among amygdala subregions in aMDD. Our findings suggest that the CM network, in particular, may be involved in stress-related factors in aMDD, which provide a potential target for the prevention and treatment of adolescent depression.

Aberrant functional connectivity of the salience network in adult patients with tic disorders: a resting-state fMRI study.

Tic disorders (TD) are characterized by the presence of motor and/or vocal tics. Common neurophysiological frameworks suggest dysregulations of the cortico-striatal-thalamo-cortical (CSTC) brain circuit that controls movement execution. Besides the common tics, there are other "non-tic" symptoms that are primarily related to sensory perception, sensorimotor integration, attention, and social cognition. The existence of these symptoms, the sensory tic triggers and the modifying effect of attention and cognitive control mechanisms on tics may indicate the salience network's involvement in the neurophysiology of TD. Resting-state functional MRI measurements were performed in 26 participants with TD and 25 healthy controls. The group differences in resting-state functional connectivity patterns were measured based on seed-to-voxel connectivity analyses. Compared to healthy controls, patients with TD exhibited altered connectivity between the core regions of the salience network (insula, ACC and TPJ) and sensory, associative, and motor-related cortices. Furthermore, connectivity changes were observed in relation to the severity of tics in the TD group. The salience network, particularly the insula, is likely to be an important site of dysregulation in TD. Our results provide evidence for large-scale neural deviations in TD beyond the CSTC pathologies. These findings may be relevant for developing treatment targets.Significance statement Tic disorders (TD) are associated with a variety of symptoms beyond typical motor and vocal tics that affect sensory perception, attention, and social cognition. The presence of such non-tic symptoms suggests the potential involvement of the salience network in the pathophysiology of TD. While previous studies have predominantly focused on the cortico-striato-thalamo-cortical (CSTC) circuitry, which is known to underlie tic generation and expression, we conducted resting-state fMRI to investigate the functional connectivity of the salience network in TD. Notably, we observed impaired connectivity of the salience network with relations to the tic symptom severity. Our research provided important evidence that the pathophysiology of TD involves the salience network, which is highly relevant for developing treatment strategies.