First episodes of affective disorders often emerge during adolescence and young adulthood. Alterations in resting-state functional connectivity (RSFC) have been reported in affective disorders, yet findings are heterogeneous and associations of RSFC with subclinical affective symptoms in community samples remain limited. A better understanding of these underlying neurobiological mechanisms may aid in identifying early vulnerability markers of affective disorders. We examined associations between affective symptom severity and both static and dynamic RSFC using resting-state fMRI data from 512 adolescents and young adults (aged 14–23) drawn from an age- and sex-stratified population-based sample. Group independent component analysis was used to derive RSFC measures. Associations with depressive and manic symptom severity were assessed while controlling for age and sex. Dynamic RSFC was analyzed using a sliding-window approach. Static RSFC showed significant effects of age and sex but no associations with affective symptoms. Dynamic RSFC analysis identified four connectivity states. In one state, manic symptom severity and its interaction with depressive symptom severity were associated with connectivity between the postcentral gyrus and the right superior temporal gyrus. Additionally, the dynamic index fraction of time showed interactions of affective symptoms with age and sex. Overall, RSFC measures demonstrated limited sensitivity to subclinical affective symptom variation in community youth, with only a single state-specific association observed. These findings suggest that subtle alterations in somatomotor-default mode network connectivity may reflect early vulnerability-related processes, though replications and further research are required. Key limitations include the use of very brief symptom measures and developmental heterogeneity across the sample.
Background Early-life parental separation due to rural labor migration represents a prevalent form of early-life social adversity; however, its neural correlates in adulthood remain insufficiently understood, particularly from the perspective of dynamic brain network organization. Methods In this study, resting-state f...
Bo-Yan Cui, Lu-Qing Lan, Chang-Juan Xie et al.· Frontiers in Psychiatry· 0 citations
AIM
This study aimed to identify the resting-state functional connectivity (rsFC) of insula subregions associated with nonspecific somatic symptoms in mood disorders and to examine whether these connections are state-dependent.
METHODS
In Phase 1, seed-based rsFC analyses were performed using 12 insula subregions fro...
Shin Hattori, T. Kamishikiryo, Alan S. R. Fermin et al.· Psychiatry and Clinical Neur...· 0 citations
The findings suggest that the level of pubertal maturation is associated with the longitudinal stability of large-scale brain networks in girls, and a subnetwork positively associated with Tanner breast stage is identified.
Susumu Morita, K. Kasai, Masahiro Kiyota et al.· Neurosciences research· 0 citations
The overall pattern is compatible with hyperarousal, but both increased and decreased connectivity were reported; diagnostic definitions, sex, symptom severity and duration, medication exposure, acquisition conditions, and seed-based versus data-driven analyses may moderate direction and magnitude.
Ya-Ni Zhang, Yi-Wen Zhang, Cai-Li Shi et al.· Nature and Science of Sleep· 0 citations
Abstract Background Early motor and language milestones are key indicators of neurodevelopment, yet their associations with later autism traits and attention-deficit/hyperactivity disorder (ADHD) symptoms remain poorly understood. Neurobiological mechanisms linking early developmental delays to these conditions are not...
Umer Jon Ganai, Braj Bhushan· Psychological Medicine· 0 citations
Worry is an important contributor to anxiety and depressive disorders, particularly in adolescence. Underlying changes in brain networks are thought to drive development of pathological worry, yet the degree to which neural correlates of worry vary by age is unknown. Here, we use multivariate pattern analysis (MVPA) to...
Cecilia A. Westbrook, H. Karim, C. Andreescu et al.· medRxiv· 0 citations
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