{"id":8772,"date":"2026-06-29T17:09:09","date_gmt":"2026-06-29T09:09:09","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=8772"},"modified":"2026-06-29T18:35:26","modified_gmt":"2026-06-29T10:35:26","slug":"jase-202610-33-025","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202610-33-025","title":{"rendered":"A Functional Connectivity-Based Deep Belief Network for ADHD Detection from Resting-State fMRI Data"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 33<\/a><\/p>\n<\/div>\n\n\n\n

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Hongmei Li1<\/sup> and Yang Chen2<\/sup><\/i><\/a><\/p>\n\n\n\n

1<\/sup>Information Engineering College, Hunan Applied Technology University, Changde 415000, Hunan, China<\/p>\n\n\n\n

2<\/sup>Anhui Xinhua University, Hefei 230000, Anhui, China<\/p>\n<\/div>\n\n\n\n

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Received: May 21, 2025
Accepted: January 18, 2026
Publication Date: June 29, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Each block within the feature extraction unit is applied to every 90 -region. All networks have similar weights<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202610_33.025<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Resting state fMRI has emerged as a popular neuroimaging tool for the automated identification and classification of brain disorders. Among such disorders, ADHD (attention deficit hyperactivity disorder), which is a disorder affecting children, is ill-understood regarding the causative factors, and its diagnosis is mainly based on behavioral evaluations. Here, a deep learning (DL) direct tactic for ADHD recognition is introduced. Our proposed architecture is structured into three main components: (1) a feature extraction unit, (2) a functional connectivity unit, and (3) a three-layer deep belief network. The scheme receives preprocessed time-series fMRI signals as input and produces a diagnostic output. It is trained in a direct manner utilizing backprop agation. The experimental findings obtained from the publicly accessible ADHD-200 database indicate that this novel approach surpasses the prior innovative tactics. The recommended method achieved accuracy rates of 91.84%, 91.08%, 87.73%, and 89.62% for PU, KKI, NYU, and NI databases from the ADHD-200 database. On average, the recommended method achieved an accuracy of 90.06%, a specificity of 88.37%, an F1-score of 89.93%, and a sensitivity of 91.55% in ADHD recognition from fMRI scans. Our findings demonstrate significant advancements in categorization performance compared to existing tactics, highlighting both the utility of functional connectivity as a biomarker and the effectiveness of DL in neuroimaging contexts.<\/p>\n\n\n\n

Keywords: fMRI; ADHD; DL;functional connectivity; deep belief network<\/em><\/p>\n\n\n\n

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    <\/p>\n","protected":false},"author":3,"template":"wp-custom-template-detail-4-aricles","meta":{"_uag_custom_page_level_css":""},"categories":[12,1483,6],"tags":[1639],"acf":[],"uagb_featured_image_src":[],"uagb_author_info":{"display_name":"\u6797\u923a\u6db5","author_link":"\/jase\/?author=3"},"uagb_comment_info":0,"uagb_excerpt":" Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited. Download Citation:\u00a0 BibTeX | http:\/\/dx.doi.org\/10.6180\/jase.202610_33.025\u00a0\u00a0 Download PDF Resting state fMRI has emerged as a popular neuroimaging tool for…","_links":{"self":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope\/8772"}],"collection":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope"}],"about":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/types\/tkuisotope"}],"author":[{"embeddable":true,"href":"\/jase\/index.php?rest_route=\/wp\/v2\/users\/3"}],"wp:attachment":[{"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8772"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8772"},{"taxonomy":"post_tag","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8772"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}