Family Environment Modulates Linkage of Transdiagnostic Psychiatric Phenotypes and Dissociable Brain Features in the Developing Brain.

BACKGROUND: Family environment has long been known for shaping brain function and psychiatric phenotypes, especially during childhood and adolescence. Accumulating neuroimaging evidence suggests that across different psychiatric disorders, common phenotypes may share common neural bases, indicating latent brain-behavior relationships beyond diagnostic categories. However, the influence of family environment on the brain-behavior relationship from a transdiagnostic perspective remains unknown. METHODS: We included a community-based sample of 699 participants (ages 5-22 years) and applied partial least squares regression analysis to determine latent brain-behavior relationships from whole-brain functional connectivity and comprehensive phenotypic measures. Comparisons were made between diagnostic and nondiagnostic groups to help interpret the latent brain-behavior relationships. A moderation model was introduced to examine the potential moderating role of family factors in the estimated brain-behavior associations. RESULTS: Four significant latent brain-behavior pairs were identified that reflected the relationship of dissociable brain network and general behavioral problems, cognitive and language skills, externalizing problems, and social dysfunction, respectively. The group comparisons exhibited interpretable variations across different diagnostic groups. A warm family environment was found to moderate the brain-behavior relationship of core symptoms in internalizing disorders. However, in neurodevelopmental disorders, family factors were not found to moderate the brain-behavior relationship of core symptoms, but they were found to affect the brain-behavior relationship in other domains. CONCLUSIONS: Our findings leveraged a transdiagnostic analysis to investigate the moderating effects of family factors on brain-behavior associations, emphasizing the different roles that family factors play during this developmental period across distinct diagnostic groups.

A spatio-temporal decomposition framework for dynamic functional connectivity in the human brain.

Much recent attention has been directed toward investigating the spatial and temporal organization of brain dynamics, but the rules which constrain the variation of spatio-temporal organization in functional connectivity under different brain states remain unclear. Here, we developed a novel computational approach based on tensor decomposition and regularization to represent dynamic functional connectivity as a linear combination of dynamic modules and time-varying weights. In this approach, dynamic modules represent co-activating functional connectivity patterns, and time-varying weights represent the temporal expression of dynamic modules. We applied this dynamic decomposition model (DDM) on a resting-state fMRI dataset and found that whole-brain dynamic functional connectivity can be decomposed as a linear combination of eight dynamic modules which we summarize as 'high order modules' and 'primary-high order modules', according to their spatial attributes and correspondence with existing intrinsic functional brain networks. By clustering the time-varying weights, we identified five brain states including three major states and two minor states. We found that state transitions mainly occurred between the three major states, and that temporal variation of dynamic modules may contribute to brain state transitions. We then conceptualized the variability of weights as the flexibility of the corresponding dynamic modules and found that different dynamic modules exhibit different amounts of flexibility and contribute to different cognitive measures. Finally, we applied DDM to a schizophrenia resting-state fMRI dataset and found that atypical flexibility of dynamic modules correlates with impaired cognitive flexibility in schizophrenia. Overall, this work provides a quantitative framework that characterizes temporal variation in the topology of dynamic functional connectivity.

Gut microbiome diversity mediates the association between right dorsolateral prefrontal cortex and anxiety level.

Despite the fast growing interest in the impact of microbiome-gut-brain interaction on regulating emotional behavior in animals, the underlying mechanisms on how brain anatomy together with gut microbiotic condition jointly influence emotional state in healthy human volunteers remain largely unknown and hypothetic. Here, high-resolution structural magnetic resonance imaging data, stool samples, and psychological assessment results on anxiety level were collected from 61 healthy adults. Voxel-based morphometry was used to assess gray matter (GM) volumes, whereas 16s rRNA gene sequencing was used for bacterial classification. Correlation and mediation analysis were conducted to quantify the relationships among regional GM volume, gut microbiome diversity, and anxiety level. We observed that anxiety level was negatively correlated with GM volume in the right dorsolateral prefrontal cortex and alpha diversity index of gut microbiome. Additional mediation analysis revealed the indirect effect of dorsolateral prefrontal cortex GM volume on anxiety level via gut microbiome diversity. Our findings provide potential evidence of the microbiome-gut-brain interactions and their association with anxiety, highlighting gut microbiome diversity as a mediator that influences the relationship between brain morphometry and anxiety level.