[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126690-en":3,"doc-seo-126690-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126690,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder","Obsessive-compulsive disorder (OCD) is a prevalent and impairing mental condition driven by distressing obsessions and compulsive behaviors. As clinical rating scales alone do not capture the full complexity of neurological dysfunction, research increasingly uses neuroimaging to link brain changes with OCD. This systematic review surveys recent advances in artificial intelligence and machine learning applied to neuroimaging modalities, emphasizing how these methods improve representation, analysis, and interpretation of neurobiological patterns associated with OCD.","TYPE Systematic Review PUBLISHED 01 November 2023 DOI 10.3389/fnhum.2023.1280512  \nOPEN ACCESS  \nEDITED BY  \nChen Li,  \nNortheastern University, China  \nREVIEWED BY  \nJiawei Zhang,  \nNational University of Defense Technology, China  \nHaoyuan Chen, ShanghaiTech University, China Zhang Baihua,  \nNingbo University, China  \n*CORRESPONDENCE  \nQiang Kang  \n [kangqiang2023@163.com](kangqiang2023@163.com)  \nRECEIVED 20 August 2023  \nACCEPTED 18 October 2023  \nPUBLISHED 01 November 2023  \nCITATION  \nLi X, Kang Q and Gu H (2023) A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder.  \nFront. Hum. Neurosci. 17:1280512 .  \ndoi: 10.3389/fnhum.2023.1280512  \nCOPYRIGHT  \n© 2023 Li, Kang and Gu. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nA comprehensive review for machine learning on neuroimaging in  \nobsessive-compulsive disorder  \nXuanyi Li 1, Qiang Kang 2* and Hanxing Gu3  \n1 Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China,  \n2 Department of Radiology, Xing’an League People’s Hospital of Inner Mongolia, Mongolia, China,  \n3 Department of Geriatric Psychiatry, Qingdao Mental Health Center, Qingdao, Shandong, China  \nObsessive-compulsive disorder (OCD) is a common mental disease, which can exist as a separate disease or become one of the symptoms of other mental diseases. With the development of society, statistically, the incidence rate of obsessive-compulsive disorder has been increasing year by year. At present, in the diagnosis and treatment of OCD, The clinical performance of patients measured by scales is no longer the only quantitative indicator. Clinical workers and researchers are committed to using neuroimaging to explore the relationship between changes in patient neurological function and obsessive-compulsive disorder. Through machine learning and artificial learning, medical information in neuroimaging can be better displayed. In this article, we discuss recent advancements in artificial intelligence related to neuroimaging in the context of Obsessive-Compulsive Disorder.  \nKEYWORDS  \nneuroimaging, MRI, machine learning, obsessive-compulsive disorder, AI  \n1. Introduction  \n1.1. The current situation of obsessive-compulsive disorder  \n1.1.1. Epidemiology of obsessive-compulsive disorder  \nObsessive-Compulsive Disorder (OCD) is a severe mental disorder characterized primarily by frequent, uncontrollable obsessive thoughts and compulsive behaviors. These obsessive thoughts are often referred to as obsessions, while the corresponding compulsive behaviors are seen as coping mechanisms aimed at alleviating the distress caused by these thoughts. These obsessions and compulsions can significantly disrupt a patient’s daily life, affecting their social interactions, academic performance, work, and family life (Fontenelle et al., 2006) . The exact pathogenesis of obsessive-compulsive disorder (OCD) remains incompletely understood, but research suggests that it involves complex neurobiological and genetic factors. Many studies emphasize the role of imbalances in neurotransmitters such as glutamate, serotonin, and dopamine in the onset of OCD. Additionally, environmental factors, life events, and cognitive factors may also play a role in the development of OCD. OCD is a global issue, affecting a substantial portion of the population. According to statistical data, the prevalence of OCD worldwide is estimated to be around 2–3%, with no significant differences observed between different countries and regions (Robbins et al., 2019) . The onset of OCD typi","cbCaijNVJbhEEcXK","https://ap.wps.com/l/cbCaijNVJbhEEcXK","pdf",1912088,1,16,"English","en",105,"# Introduction\n## Epidemiology of obsessive-compulsive disorder\n# Diagnosis and treatment context\n## Neuroimaging and artificial intelligence developments","[{\"question\":\"What is the core focus of this review?\",\"answer\":\"The review focuses on recent advances in machine learning methods applied to neuroimaging for obsessive-compulsive disorder (OCD).\"},{\"question\":\"Why are neuroimaging approaches important in OCD research?\",\"answer\":\"Neuroimaging helps explore the relationship between changes in neurological function and OCD, going beyond the limits of scale-based clinical measurements.\"},{\"question\":\"How is OCD typically diagnosed and treated in clinical practice?\",\"answer\":\"Diagnosis relies on clinical assessment using criteria such as WHO and DSM, while treatment commonly includes cognitive behavioral therapy and, for more severe cases, medication such as SSRIs and other antidepressants.\"}]","A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder | 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