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The dynamical organization of brain networks is essential to support human cognition and emotion for rapid adaption to ever-changing environment. As the core nodes of emotion-related brain circuitry, the basolateral amygdala (BLA) and centromedial amygdala (CMA) as two major amygdalar nuclei, are recognized to play distinct roles in affective functions and internal states, via their unique connections with cortical and subcortical structures in rodents. However, little is known how the dynamical organization of emotion-related brain circuitry reflects internal autonomic responses in humans. Using resting-state functional magnetic resonance imaging (fMRI) with K-means clustering approach in a total of 79 young healthy individuals (cohort 1: 42; cohort 2: 37), we identified two distinct states of BLA- and CMA-based intrinsic connectivity patterns, with one state (integration) showing generally stronger BLA- and CMA-based intrinsic connectivity with multiple brain networks, while the other (segregation) exhibiting weaker yet dissociable connectivity patterns. In an independent cohort 2 of fMRI data with concurrent recording of skin conductance, we replicated two similar dynamic states and further found higher skin conductance level in the integration than segregation state. Moreover, machine learning-based Elastic-net regression analyses revealed that time-varying BLA and CMA intrinsic connectivity with distinct network configurations yield higher predictive values for spontaneous fluctuations of skin conductance level in the integration than segregation state. Our findings highlight dynamic functional organization of emotion-related amygdala nuclei circuits and networks and its links to spontaneous autonomic arousal in humans.

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Many studies have reported abnormalities in the volume of subcortical structures in individuals with autism spectrum disorder (ASD), and many of these change with age. However, most studies that have investigated subcortical structures were cross-sectional and did not accurately segment the subcortical structures. In this study, we used volBrain, an automatic and reliable quantitative analysis tool, and a longitudinal design to examine developmental changes in the volume of subcortical structures in ASD, and quantified the relation between subcortical volume development and clinical correlates. Nineteen individuals with ASD (16 males; age: 12.53 ± 2.34 years at baseline; interval: 2.33 years) and 14 typically developing controls (TDC; 12 males; age: 13.50 ± 1.77 years at baseline; interval: 2.31 years) underwent T1-weighted MRI at two time points. Bilaterally, hippocampus volume increased from baseline to follow-up in both ASD and TDC, with no difference between groups. Left caudate and right thalamus volume decreased in ASD, but did not change in TDC. The decreases in left caudate and right thalamus volume were related to ASD social score. Right amygdala volume was larger in ASD than in TDC at baseline but not at follow-up. These results confirm previous cross-sectional findings regarding the development of subcortical structures in ASD. The association between developmental changes in left caudate and right thalamus volume and ASD social score offers an explanation for the social deficits in ASD. Results also captured the different abnormality of amygdala volume between childhood and late adolescence.

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Purpose: Although alterations in resting-state functional connectivity between brain regions have been reported in children with attention-deficit/hyperactivity disorder (ADHD), the spatial organization of these changes remains largely unknown. Here, we studied frontal-parietal attention network topology in children with ADHD, and related topology to a clinical measure of disease progression. Methods: Resting-state fMRI scans were obtained from New York University Child Study Center, including 119 children with ADHD (male n = 89; female n = 30) and 69 typically developing controls (male n = 33; female n = 36). We characterized frontal-parietal functional networks using standard graph analysis (clustering coefficient and shortest path length) and the construction of a minimum spanning tree, a novel approach that allows a unique and unbiased characterization of brain networks. Results: Clustering coefficient and path length in the frontal-parietal attention network were similar in children with ADHD and typically developing controls; however, diameter was greater and leaf number, tree hierarchy, and kappa were lower in children with ADHD, and were significantly correlated with ADHD symptom score. There were significant alterations in nodal eccentricity in children with ADHD, involving prefrontal and occipital cortex regions, which are compatible with the results of previous ADHD studies. Conclusions: Our results indicate the tendency to deviate from a more centralized organization (star-like topology) towards a more decentralized organization (line-like topology) in the frontal-parietal attention network of children with ADHD. This represents a more random network that is associated with impaired global efficiency and network decentralization. These changes appear to reflect clinically relevant phenomena and hold promise as markers of disease progression.