Extracting functional connectivity signatures in substance use disorder using energy landscape analysis

Abstract

Substance Use Disorder (SUD) is a complex condition with profound effects on brain function. Understanding the altered functional connectivity patterns in the brains of SUD patients is crucial for unraveling the neurological underpinnings of this disorder. This study employs Energy Landscape Analysis, an energy-based machine learning technique, to investigate whole brain Regions of Interest (ROI) functional connectivity differences between SUD patients and healthy controls. The challenge with Energy Landscape Analysis lies in selecting the appropriate ROI from the extensive brain atlas. In this study, seed-based connectivity was utilized to identify relevant ROIs, overcoming the limitation of analyzing only a limited number of ROIs. The dataset comprised 53 cocaine users and 52 age- and sex-matched healthy controls, with fMRI data preprocessed using the CONN toolbox. ROI-ROI seed-based pair connectivity was derived through first and second level analyses. The identified sub-ROIs were categorized into default CONN network affiliations and bundled into Superior Temporal Gyrus (STG), Inferior Temporal Gyrus, temporooccipital part (toITG), Visual Primary (VIS-P), Auditory (AUD), Cerebellum, Basal Ganglia (BSL), and Thalamus (THL). Significance testing revealed eight connectivity states among all above regions with p-values that satisfy Bonferroni correction between controls and patients. Notably, the connectivity states with the lowest p-values revealed a distinctive pattern: STG (auditory attention) toITG were disconnected from the rest of the networks. This finding underscores the importance of investigating specific network disruptions in SUD, shedding light on potential neural mechanisms underlying the disorder. In summary, our study utilizes Energy Landscape Analysis to explore whole brain ROI functional connectivity in SUD, revealing disrupted connectivity patterns that may have implications for understanding the neural basis of this disorder. These findings may ultimately inform targeted interventions and treatment strategies for individuals with SUD.