Dynamic integration and segregation of amygdala subregional functional circuits linking to physiological arousal

• Published in: NeuroImage (Orlando, Fla.) Vol. 238; p. 118224
• Main Authors: Zeng, Yimeng, Tao, Fuxiang, Cui, Zaixu, Wu, Liyun, Xu, Jiahua, Dong, Wenshan, Liu, Chao, Yang, Zhi, Qin, Shaozheng
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.09.2021; Elsevier Limited; Elsevier
• Subjects: Amygdala; Arousal; Brain; Brain architecture; Brain mapping; Cognition; Conductance; Dynamic states; Emotion; Emotional behavior; Emotions; fMRI; Functional magnetic resonance imaging; Functional morphology; Integration; Learning algorithms; Machine learning; Medical imaging; Neural networks; Neuroimaging; Physiology; Skin; fMRI; Emotion; Amygdala; Dynamic states; Arousal
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2021.118224

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.