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This study applies Energy Landscape Analysis (ELA), an energy-based machine learning framework, to compare whole-brain connectivity and temporal dynamics between 53 cocaine users and 52 matched healthy controls using CONN toolbox preprocessing. Seed-based connectivity guides ROI selection, yielding group-specific low-energy connectivity states and state visitation patterns. Results suggest cocaine addiction weakens adaptive, protective “guardian” connectivity rather than increasing persistence in maladaptive states.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/resting-state-fmri-analysis-of-functional-connectivity-and-temporal-dynamics-differences-between-cocaine-users-and-healthy-controls/443938/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/resting-state-fmri-analysis-of-functional-connectivity-and-temporal-dynamics-differences-between-cocaine-users-and-healthy-controls/443938.png","ImageObject",300,407,{"name":92,"@type":93},"Quinn Holloway","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-30","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What method is used to study connectivity differences in this research?","Question",{"text":112,"@type":113},"The study uses Energy Landscape Analysis (ELA), an energy-based machine learning approach, to characterize whole-brain functional connectivity and its temporal dynamics.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were regions of interest (ROIs) selected for ELA in this study?",{"text":117,"@type":113},"ROI selection used seed-based connectivity analysis to identify task-relevant ROIs from comprehensive brain atlases, avoiding reliance on a limited predefined subset.",{"name":119,"@type":110,"acceptedAnswer":120},"What key group differences were observed between cocaine users and healthy controls?",{"text":121,"@type":113},"Healthy controls showed stronger positive connectivity between cerebellar and visual regions, while cocaine users showed stronger positive connectivity between the cerebellum and the inferior temporal gyrus; ELA also identified seven low-energy states distinguishing the groups.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},443938,1790806115,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},2336474466712,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Neuroimage: Reports 6 (2026) 100304  \nContents lists available at ScienceDirect  \nNeuroimage: Reports  \njournal [homepage:](homepage: www.sciencedirect.com/journal/neuroimage-reports)[ www.sciencedirect.com/journal/neuroimage-reports](homepage: www.sciencedirect.com/journal/neuroimage-reports)  \nResting-state fMRI analysis of functional connectivity and temporal dynamics differences between cocaine users and healthy controls  \nSravani Varanasi a,* , Tianye Zhaib, Hong Gu b , Betty Jo Salmeron b, Yihong Yang b, Fow-Sen Choaa  \na Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, 21250, USA  \nb Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Bethesda, MD, 28092, USA  \n\n| A B S T R A C T |\n| --- |\n| Understanding alterations in functional connectivity among individuals with substance use disorder (SUD) is critical for elucidating the neural mechanisms underlying addiction. In this study, we applied Energy Landscape Analysis (ELA), an energy-based machine learning method, to examine whole-brain functional connectivity differences between SUD patients and healthy controls (HCs). A key methodological challenge in ELA lies in the selection of appropriate Regions of Interest (ROIs) from comprehensive brain atlases. To address this, we employed seed-based connectivity analysis to identify task-relevant ROIs, thereby overcoming the limitation of focusing on a restricted subset of regions. The dataset comprised 53 cocaine users (CUs) and 52 age-and sex-matched HCs, with functional MRI data preprocessed using the CONN toolbox. ROI-to-ROI seed-based connectivity was computed through first-and second-level analyses. ELA revealed that HCs exhibited stronger positive connectivity between cerebellar and visual regions, whereas CUs showed stronger positive connectivity between the cerebellum and the inferior temporal gyrus (temporooccipital part; toITG). Seven low-energy connectivity states were identified that differentiated the two groups. In these states, the cerebellum and toITG demonstrated antagonistic activation patterns, while the cerebellum and visual cortex co-activated in HCs. Temporal dynamics analyses further indicated that HCs visited these low-energy states more frequently, driven by shorter dwell times but higher transition rates. These findings suggest that cocaine addiction may reflect a weakening of adaptive, protective (“guardian”) connectivity patterns, rather than an increased propensity to remain in maladaptive connectivity states. Collectively, these results highlight key network-level distinctions between HCs and CUs and offer new insights into the neurobiological mechanisms of cocaine addiction. |\n\n1. Introduction  \nComputational analysis of functional magnetic resonance imaging (fMRI) data has become an indispensable tool for studying neural mechanisms underlying various clinical conditions, including substance use disorders such as cocaine addiction. Resting-state functional magnetic resonance imaging (rs-fMRI) has been central to revealing systemslevel abnormalities, reporting disrupted functional connectivity within and between the default mode network (DMN), salience network (SN), executive control network (ECN), mesocorticolimbic circuits, and thalamostriatal pathways in individuals with cocaine use compared with healthy controls (HCs). (Xu et al., 2024; Geng et al., 2017; Liang et al., 2015; Ray et al., 2016). The ability to detect subtle alterations in how brain regions coordinate activity—particularly at rest—offers profound insights into the neurobiology of drug dependence, beyond what can be revealed through task-based imaging alone (Yang and Lewis, 2021; Yuste and Fairhall, 2015).  \nOne crucial advance in this field is the study of temporal dynamics  \nusing resting-state fMRI data (Yuste and Fairhall, 2015). Beyond static connectivity, the human brain at rest exhibits r","cbCainPtIrrVWDtv","https://ap.wps.com/l/cbCainPtIrrVWDtv","pdf",3252455,"English","# Introduction\n## Temporal dynamics in resting-state fMRI\n## Energy Landscape Analysis (ELA)","[{\"question\":\"What method is used to study connectivity differences in this research?\",\"answer\":\"The study uses Energy Landscape Analysis (ELA), an energy-based machine learning approach, to characterize whole-brain functional connectivity and its temporal dynamics.\"},{\"question\":\"How were regions of interest (ROIs) selected for ELA in this study?\",\"answer\":\"ROI selection used seed-based connectivity analysis to identify task-relevant ROIs from comprehensive brain atlases, avoiding reliance on a limited predefined subset.\"},{\"question\":\"What key group differences were observed between cocaine users and healthy controls?\",\"answer\":\"Healthy controls showed stronger positive connectivity between cerebellar and visual regions, while cocaine users showed stronger positive connectivity between the cerebellum and the inferior temporal gyrus; ELA also identified seven low-energy states distinguishing the groups.\"}]","Resting-state fMRI analysis of functional connectivity and temporal dynamics differences between cocaine users and healthy controls | PDF",1790706147,23]