[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125897-en":3,"doc-seo-125897-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125897,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Unveiling Functional Biomarkers in Schizophrenia - Insights from Region of Interest Analysis Using Machine Learning","The research investigates functional biomarkers for schizophrenia by leveraging machine learning on age-matched samples from an auditory oddball task. Multi-site fMRI data are analyzed with a three-stage workflow: hierarchical ROI construction across hemisphere, gyrus, lobes, and Brodmann areas; statistical evaluation of cortical activation differences using t-tests and Pearson correlation; and prediction assessment using KNN, Naive Bayes, Decision Tree, Random Forest, SVM, and Extreme Learning Machine.","Please cite the Published Version  \nChatterjee, Indranath  and Baumgärtner, Lea (2024) Unveiling Functional Biomarkers in Schizophrenia: Insights from Region of Interest Analysis Using Machine Learning. Journal of Integrative Neuroscience, 23 (9) . ISSN 1757-448X  \nDOI: [https://doi.org/10.31083/j.jin2309179](https://doi.org/10.31083/j.jin2309179)  \nPublisher: IMR Press  \nVersion: Published Version  \nDownloaded from: [https://e-space.mmu.ac.uk/636008/](https://e-space.mmu.ac.uk/636008/)  \nUsage rights:  Creative Commons: Attribution 4 .0  \nAdditional Information: This is an open access article which first appeared in Journal of Integrative Neuroscience  \nData Access Statement: The MRI dataset utilized in this study is publicly available for download from ( [http://schizconnect.org/](http://schizconnect.org/)). The data subset used in this research can be shared with interested readers upon reasonable request. Requests for data access should be directed to the corresponding author, who will facilitate the provision of the dataset, ensuring compliance with ethical and legal requirements.  \nEnquiries:  \nIf you have questions about this document, contact [openresearch@mmu.ac.uk](openresearch@mmu.ac.uk. Please)[. Please](openresearch@mmu.ac.uk. Please) include the URL of the record in e-space. If you believe that your, or a third party's rights have been compromised through this document please see our Take Down policy (available from [https://www.mmu.ac.uk/library/using-the-library/policies-and-guidelines](https://www.mmu.ac.uk/library/using-the-library/policies-and-guidelines))  \nJ. Integr. Neurosci. 2024; 23(9): 179 [https://doi.org/10.31083/j.jin2309179](https://doi.org/10.31083/j.jin2309179)  \nOriginal Research  \nUnveiling Functional Biomarkers in Schizophrenia: Insights from Region of Interest Analysis Using Machine Learning  \nIndranath Chatterjee 1 ,2 ,3 , *, Lea Baumgärtner4  \n1Department of Computing and Mathematics, Manchester Metropolitan University, M1 5GD Manchester, UK  \n2 School of Technology, Woxsen University, 502345 Hyderabad, India  \n3 Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, 140401 Punjab, India  \n4Department of Media, Hochschule der Medien, University of Applied Science, 70569 Stuttgart, Germany  \n*Correspondence: [i.chatterjee@mmu.ac.uk](i.chatterjee@mmu.ac.uk) (Indranath Chatterjee) Academic Editor: Imran Khan Niazi  \nSubmitted: 28 April 2024 Revised: 11 July 2024 Accepted: 26 July 2024 Published: 24 September 2024  \nAbstract  \nBackground: Schizophrenia is a complex and disabling mental disorder that represents one of the most important challenges for neuroimaging research. There were many attempts to understand these basic mechanisms behind the disorder, yet we know very little. By employing machine learning techniques with age-matched samples from the auditory oddball task using multi-site functional magnetic resonance imaging (fMRI) data, this study aims to address these challenges. Methods: The study employed a three-stage model to gain a better understanding of the neurobiology underlying schizophrenia and techniques that could be applied for diagnosis. At first, we constructed four-level hierarchical sets from each fMRI volume of 34 schizophrenia patients (SZ) and healthy controls (HC) individually in terms of hemisphere, gyrus, lobes, and Brodmann areas. Second, we employed statistical methods, namely, t-tests and Pearson’s correlation, to assess the group differences in cortical activation. Finally, we assessed the predictive power of the brain regions for machine learning algorithms using K-nearest Neighbor (KNN), Naive Bayes, Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVMs), and Extreme Learning Machine (ELM) . Results: Our investigation depicts promising results, obtaining an accuracy of up to 84% when applying Pearson’s correlation-selected features at lobes and Brodmann region level (81% for Gyrus)","cbCaig8dUdZKCuv5","https://ap.wps.com/l/cbCaig8dUdZKCuv5","pdf",3843021,4,1,21,"English","en",105,"# Abstract\n# Introduction\n## Background and public health impact\n## Limitations of current neuroimaging findings\n## Study aim and proposed approach\n# Methods\n## Three-stage hierarchical ROI modeling\n## Statistical testing for group differences\n## Machine learning predictive evaluation\n# Results\n## Accuracy and selected ROI feature performance\n# Conclusions","[{\"question\":\"What data and task are used to study schizophrenia in this research?\",\"answer\":\"The study uses age-matched samples from the auditory oddball task and multi-site fMRI data. It analyzes schizophrenia patients and healthy controls.\"},{\"question\":\"How are brain regions organized for analysis before machine learning?\",\"answer\":\"Each fMRI volume is converted into four-level hierarchical sets covering hemisphere, gyrus, lobes, and Brodmann areas.\"},{\"question\":\"Which methods evaluate both group differences and prediction performance?\",\"answer\":\"Group differences in cortical activation are tested using t-tests and Pearson’s correlation, and predictive power is evaluated with KNN, Naive Bayes, Decision Tree, Random Forest, SVM, and Extreme Learning Machine.\"}]","Unveiling Functional Biomarkers in Schizophrenia - Insights from Region of Interest Analysis Using Machine Learning | PDF",1785901900,53,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"unveiling-functional-biomarkers-in-schizophrenia-insights-from-region-of-interest-analysis-using-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/unveiling-functional-biomarkers-in-schizophrenia-insights-from-region-of-interest-analysis-using-machine-learning/125897/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What data and task are used to study schizophrenia in this research?","Question",{"text":76,"@type":77},"The study uses age-matched samples from the auditory oddball task and multi-site fMRI data. It analyzes schizophrenia patients and healthy controls.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are brain regions organized for analysis before machine learning?",{"text":81,"@type":77},"Each fMRI volume is converted into four-level hierarchical sets covering hemisphere, gyrus, lobes, and Brodmann areas.",{"name":83,"@type":74,"acceptedAnswer":84},"Which methods evaluate both group differences and prediction performance?",{"text":85,"@type":77},"Group differences in cortical activation are tested using t-tests and Pearson’s correlation, and predictive power is evaluated with KNN, Naive Bayes, Decision Tree, Random Forest, SVM, and Extreme Learning Machine.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]