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.
Substance Use Disorder (SUD) represents a pervasive global health crisis characterized by the compulsive and detrimental use of psychoactive substances. In this study, we explore the functional connectivity disparities between two age- and sex-matched groups comprising 53 individuals with Cocaine Use Disorder (CUD) and 52 Healthy Control (HC) subjects. We employed resting-state fMRI data, which were preprocessed using the CONN toolbox, ensuring high-quality data for subsequent analysis. The CONN toolbox has a default atlas of 164 ROIs based on the FSL-Harvard Oxford atlas and the automated Anatomical Labeling Atlas (AAL). The investigation extended into first level and second level-analysis features within the CONN toolbox to discern functional connectivity patterns between these two groups. At the group level analysis centered on contrasting CUD patients and HCs, we particularly focused on the Region-of-Interest (ROI)-ROI connectivity maps in this study. This study revealed some key findings: Firstly, we observed that HC subjects exhibited significantly stronger connectivity between the Superior Temporal Gyrus (STG) and regions of interest within the basal ganglia network (BSL), compared to individuals with CUD. Secondly, the HC group demonstrated heightened connectivity between regions of interest belonging to the visual network and the cerebellum, contrasting with the weaker connectivity observed in the CUD group. Lastly, there was a notable increase in connectivity between the Inferior Temporal Gyrus, temporooccipital part (toITG), and the cerebellum in individuals with CUD, further emphasizing the disruption in functional connectivity within this population. Understanding these functional connectivity differences may inform future interventions and diagnostic approaches in the context of cocaine use disorder.
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