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dc.contributor.authorZheng, Kaizhong
dc.contributor.authorYu, Shujian
dc.contributor.authorChen, Liangjun
dc.contributor.authorDang, Lujuan
dc.contributor.authorChen, Badong
dc.date.accessioned2024-09-09T09:19:40Z
dc.date.available2024-09-09T09:19:40Z
dc.date.issued2024-04-01
dc.description.abstractConverging evidence increasingly suggests that psychiatric disorders, such as major depressive disorder (MDD) and autism spectrum disorder (ASD), are not unitary diseases, but rather heterogeneous syndromes that involve diverse, co-occurring symptoms and divergent responses to treatment. This clinical heterogeneity has hindered the progress of precision diagnosis and treatment effectiveness in psychiatric disorders. In this study, we propose BPI-GNN, a new interpretable graph neural network (GNN) framework for analyzing functional magnetic resonance images (fMRI), by leveraging the famed prototype learning. In addition, we introduce a novel generation process of prototype subgraph to discover essential edges of distinct prototypes and employ total correlation (TC) to ensure the independence of distinct prototype subgraph patterns. BPI-GNN can effectively discriminate psychiatric patients and healthy controls (HC), and identify biological meaningful subtypes of psychiatric disorders. We evaluate the performance of BPI-GNN against 11 popular brain network classification methods on three psychiatric datasets and observe that our BPI-GNN always achieves the highest diagnosis accuracy. More importantly, we examine differences in clinical symptom profiles and gene expression profiles among identified subtypes and observe that our identified brain-based subtypes have the clinical relevance. It also discovers the subtype biomarkers that align with current neuro-scientific knowledge.en_US
dc.identifier.citationZheng, Yu, Chen, Dang, Chen. BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping. NeuroImage. 2024;292en_US
dc.identifier.cristinIDFRIDAID 2264004
dc.identifier.doi10.1016/j.neuroimage.2024.120594
dc.identifier.issn1053-8119
dc.identifier.issn1095-9572
dc.identifier.urihttps://hdl.handle.net/10037/34558
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.journalNeuroImage
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2024 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0en_US
dc.rightsAttribution-NonCommercial 4.0 International (CC BY-NC 4.0)en_US
dc.titleBPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtypingen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Med mindre det står noe annet, er denne innførselens lisens beskrevet som Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)