{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T06:55:44Z","timestamp":1790751344278,"version":"4.1.0"},"reference-count":15,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Schizophrenia (SZ) and bipolar disorder (BD) exhibit significant overlap in clinical symptoms, posing substantial challenges to accurate diagnosis. This study proposes an innovative multi-modal fusion framework based on convolutional neural networks (CNNs), integrating structural and functional MRI biomarkers-gray matter volume (GMV) and amplitude of low-frequency fluctuation (ALFF).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Total 31 patients with schizophrenia, 48 patients with bipolar disorder, and 38 healthy controls (HC) were enrolled and underwent resting-state fMRI scans. The framework comprises three core components: Gaussian data augmentation to mitigate the limitation of small sample sizes; a dedicated CNN architecture for processing GMV and ALFF features to generate class probability outputs for SZ, BD, and HC; and DempsterShafer (D-S) evidence theory for robust multi-modal decision fusion.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Extensive 5-fold cross-validation confirms the framework's efficacy: it achieves superior performance metrics (Accuracy: 0.9656 \u00b1 0.0324, Precision: 0.9698 \u00b1 0.0319, Recall: 0.9656 \u00b1 0.0324, F1-Score: 0.9661 \u00b1 0.0324) and outperforms traditional machine learning methods (SVM, Random Forest, XGBoost, KNN).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The findings demonstrate that the proposed multi-modal fusion approach enhances the ability to differentiate among SZ, BD, and HC compared to unimodal classification methods, providing a reliable technical support for the auxiliary diagnosis of psychiatric disorders.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fncom.2026.1835559","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T06:01:44Z","timestamp":1790748104000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["An innovative framework based on GMV-ALFF multi-modal MRI and Gaussian data augmentation for psychiatric disorder classification"],"prefix":"10.3389","volume":"20","author":[{"given":"Wenjing","family":"Zhu","sequence":"first","affiliation":[{"name":"Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoying","family":"Jiang","sequence":"additional","affiliation":[{"name":"Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"key":"B1","first-page":"28","article-title":"Diagnostic stability in bipolar disorder: a systematic review","volume":"48","author":"Cano-Ruiz","year":"2020","journal-title":"Actas Esp Psiquiatr"},{"key":"B2","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.jpsychires.2024.02.024","article-title":"Distinguishing schizophrenia and bipolar disorder through a Multiclass Classification model based on multimodal neuroimaging data","volume":"172","author":"Chen","year":"2024","journal-title":"J Psychiatr Res"},{"key":"B3","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1038\/s41537-025-00564-7","article-title":"Classification of schizophrenia, bipolar disorder and major depressive disorder with comorbid traits and deep learning algorithms","volume":"11","author":"Chen","year":"2025","journal-title":"Schizophrenia (Heidelb)"},{"key":"B4","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1145\/3388440.3412478","article-title":"deep learning fusion model for brain disorder classification: Application to distinguishing schizophrenia and autism spectrum disorder","volume":"2020","author":"Du","year":"2020","journal-title":"ACM BCB"},{"key":"B5","doi-asserted-by":"publisher","first-page":"109596","DOI":"10.1016\/j.jneumeth.2022.109596","article-title":"M\u00fcnir K. 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