Altered Dynamics and Characterization of Functional Networks in Cocaine Use Disorder: A Coactivation Pattern Analysis of Resting-State fMRI data

Abstract

BackgroundCocaine Use Disorder (CUD) poses significant neurobiological and neuropsychiatric challenges, often resulting in severe cognitive and behavioral impairments. This study aims to explore the neural dynamics of CUD using a dynamic coactivation pattern (CAP) analysis approach to provide a deeper understanding of the transient neurobiological mechanisms of the disorder.MethodsResting-state functional MRI data (SUDMEX_CONN) from 56 CUD patients and 57 healthy controls (HC) were analyzed. CAP analysis was employed to capture transient brain states and their coactivation patterns. Temporal dynamic metrics such as Fraction of Time, Persistence (PST), and Counts were computed to assess differences between groups. Stationary functional connectivity (sFC) was also examined, and meta-analytic term mapping from the Neurosynth database was used to characterize functional associations.ResultsCAP analysis revealed six distinct coactivation patterns, with five showing high spatial similarity between CUD and HC groups. Notable differences were observed in State 6, which displayed inverse activation patterns between the groups. CUD individuals exhibited significantly reduced PST across all brain states and altered transition probabilities, particularly increased transitions from the default mode network (DMN) to the somatomotor network and decreased transitions from DMN to attentional/executive networks. Clinical correlations indicated that prolonged cocaine use was associated with altered PST in specific brain states. sFC analysis identified significant alterations in regions such as the right supramarginal gyrus, left superior frontal gyrus, right precentral gyrus, and right lingual gyrus, each linked to distinct cognitive and behavioral functions.ConclusionsThis study highlights the utility of CAP analysis in capturing the dynamic neural underpinnings of CUD. The findings provide insights into the neurobiological mechanisms of the disorder, suggesting potential biomarkers for CUD. These results have implications for developing an enhanced approach for substance use disorders, as well as improving our understanding and management of CUD.