Disrupted Dynamic Interactions Between Large-Scale Brain Networks in Cocaine Users Are Associated With Dependence Severity

• Published in: Biological psychiatry : cognitive neuroscience and neuroimaging Vol. 8; no. 6; pp. 672 - 679
• Main Authors: Zhai, Tianye, Gu, Hong, Salmeron, Betty Jo, Stein, Elliot A., Yang, Yihong
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.06.2023
• Subjects: Addiction; Brain; Brain Mapping - methods; Brain states; Cocaine; Cocaine-Related Disorders; Dependence severity; Humans; Magnetic Resonance Imaging - methods; Network interaction; Resting-state fMRI; Temporal dynamics; Network interaction; Resting-state fMRI; Dependence severity; Temporal dynamics; Addiction; Brain states
• ISSN: 2451-9022; 2451-9030; 2451-9030
• DOI: 10.1016/j.bpsc.2022.08.010

Substance use disorder is conceptualized as a neuropsychiatric disease with multifaceted phenotypic manifestations including disrupted interactions between brain networks. While the current understanding of brain network interactions is mostly based on static functional connectivity, accumulating evidence suggests that temporal dynamics of these network interactions may better reflect brain function and disease-related dysfunction. We thus investigated brain dynamics in cocaine use disorder and assessed their relationship with cocaine dependence severity. Using a time frame analytical approach on resting-state functional magnetic resonance imaging data of 54 cocaine users and 54 age- and sex-matched healthy control participants, we identified temporally recurring brain network configuration patterns, termed brain states. With Menon’s triple network model as a guide, we characterized these state dynamics by quantifying their occurrence rate and transition probability. Group differences in the state dynamics and their association with cocaine dependence were assessed. Three recurrent brain states with spatial patterns resembling the default mode, salience, and executive control networks were identified. Compared with healthy control subjects, cocaine users showed a higher default mode state occurrence rate and higher probability of transitioning from the salience state to the default mode state, with the former being attributed to the latter. A composite state transition probability negatively correlated with cocaine dependence severity. Our results provide novel evidence supporting the triple network model. While confirming hyperactivity of default mode network in cocaine users, our findings indicate the failure of salience network in toggling between default mode and executive control networks in cocaine use disorder.