Joint multi-site domain adaptation and multi-modality feature selection for the diagnosis of psychiatric disorders

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dc.contributor.authorJi, Yixin
dc.contributor.authorSilva, Rogers F.
dc.contributor.authorAdali, Tulay
dc.contributor.authorWen, Xuyun
dc.contributor.authorZhu, Qi
dc.contributor.authorJiang, Rongtao
dc.contributor.authorZhang, Daoqiang
dc.contributor.authorQi, Shile
dc.contributor.authorCalhoun, Vince D.
dc.date.accessioned2024-05-13T19:11:18Z
dc.date.available2024-05-13T19:11:18Z
dc.date.issued2024-04-26
dc.description.abstractIdentifying biomarkers for computer-aided diagnosis (CAD) is crucial for early intervention of psychiatric disorders. Multi-site data have been utilized to increase the sample size and improve statistical power, while multi-modality classification offers significant advantages over traditional single-modality based approaches for diagnosing psychiatric disorders. However, inter-site heterogeneity and intra-modality heterogeneity present challenges to multi-site and multi-modality based classification. In this paper, brain functional and structural networks (BFNs/BSNs) from multiple sites were constructed to establish a joint multi-site multi-modality framework for psychiatric diagnosis. To do this we developed a hypergraph based multi-source domain adaptation (HMSDA) which allows us to transform source domain subjects into a target domain. A local ordinal structure based multi-task feature selection (LOSMFS) approach was developed by integrating the transformed functional and structural connections (FCs/SCs). The effectiveness of our method was validated by evaluating diagnosis of both schizophrenia (SZ) and autism spectrum disorder (ASD). The proposed method obtained accuracies of 92.2%±2.22% and 84.8%±2.68% for the diagnosis of SZ and ASD, respectively. We also compared with 6 domain adaptation (DA), 10 multi-modality feature selection, and 8 multi-site and multi-modality methods. Results showed the proposed HMSDA + LOSMFS effectively integrates multi-site and multi-modality data to enhance psychiatric diagnosis and identify disorder-specific diagnostic brain connections.
dc.description.sponsorshipThis work was supported by the National Natural Science Foundation of China (62376124, 62136004, 62276130), the Natural Science Foundation of Jiangsu Province, China (BK20220889), the National Key Research and Development Program of China (2023YFF1204803) and the Key Research and Development Plan of Jiangsu Province, China (BE2023668, BE2022842).
dc.description.urihttps://www.researchsquare.com/article/rs-4277324/v1
dc.format.extent35 pages
dc.genrejournal articles
dc.genrepreprints
dc.identifierdoi:10.13016/m2gsbn-in0g
dc.identifier.urihttps://doi.org/10.21203/rs.3.rs-4277324/v1
dc.identifier.urihttp://hdl.handle.net/11603/33954
dc.language.isoen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.rightsCC BY 4.0 DEED Attribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleJoint multi-site domain adaptation and multi-modality feature selection for the diagnosis of psychiatric disorders
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0003-0594-2796

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