Cross-Disease Classification of Depression, Bipolar Disorder, and Schizophrenia Using Multiscale Spatiotemporal Network Fusion Features From fNIRS.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	Cross-Disease Classification of Depression, Bipolar Disorder, and Schizophrenia Using Multiscale Spatiotemporal Network Fusion Features From fNIRS.
Συγγραφείς:	Meng Y; Mental Health Center West China Hospital of Sichuan University Chengdu 610041 China wchscu.cn., Yang Y; Mental Health Center West China Hospital of Sichuan University Chengdu 610041 China wchscu.cn., Li Q; Mental Health Center West China Hospital of Sichuan University Chengdu 610041 China wchscu.cn., Fu Q; Mental Health Center West China Hospital of Sichuan University Chengdu 610041 China wchscu.cn.; College of Biomedical Engineering Sichuan University Chengdu 610065 China scu.edu.cn., Gao M; College of Biomedical Engineering Sichuan University Chengdu 610065 China scu.edu.cn., He L; College of Biomedical Engineering Sichuan University Chengdu 610065 China scu.edu.cn., Zhang J; College of Biomedical Engineering Sichuan University Chengdu 610065 China scu.edu.cn., Qiu C; Mental Health Center West China Hospital of Sichuan University Chengdu 610041 China wchscu.cn.
Πηγή:	Depression and anxiety [Depress Anxiety] 2026 Oct 06; Vol. 2026, pp. 9921579. Date of Electronic Publication: 2026 Oct 06 (Print Publication: 2026).
Τύπος έκδοσης:	Journal Article
Γλώσσα:	English
Στοιχεία περιοδικού:	Publisher: Wiley Country of Publication: United States NLM ID: 9708816 Publication Model: eCollection Cited Medium: Internet ISSN: 1520-6394 (Electronic) Linking ISSN: 10914269 NLM ISO Abbreviation: Depress Anxiety Subsets: MEDLINE
Imprint Name(s):	Original Publication: New York, NY : Wiley, c1997-
Ιατρικοί όροι (MeSH):	Schizophrenia/diagnosis , Schizophrenia/classification , Schizophrenia/physiopathology , Schizophrenia/diagnostic imaging , Bipolar Disorder/diagnosis , Bipolar Disorder/classification , Bipolar Disorder/physiopathology , Bipolar Disorder/diagnostic imaging , Major Depressive Disorder/diagnosis , Major Depressive Disorder/classification , Major Depressive Disorder/physiopathology , Major Depressive Disorder/diagnostic imaging , Functional Neuroimaging*/methods, Spectroscopy, Near-Infrared/methods ; Humans ; Female ; Adult ; Male ; Middle Aged
Περίληψη:	Background: Major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) are highly prevalent and disabling psychiatric conditions with increasing incidence, significantly affecting individuals' daily functioning, social relationships, and overall quality of life. Current diagnostic practices largely rely on clinical interviews and symptom-based rating scales, which are inherently subjective and time-consuming-posing major obstacles to large-scale screening. Methods: To address this, we propose a novel multiscale spatiotemporal network fusion (MSNF) feature extraction method based on functional near-infrared spectroscopy (fNIRS) combined with the verbal fluency test (VFT). This method is designed to enable objective, efficient, and automated differentiation among multiple psychiatric disorders (MDD, BD, and SCZ), as well as between patient groups and healthy controls (HCs). Leveraging the practical advantages of fNIRS, including its portability, motion tolerance, and balanced spatiotemporal resolution, the proposed MSNF fuses temporal dynamics with graph-theoretical topology, capturing both local hemodynamic dynamics and hierarchy connectivity patterns. Results: In this study, a total of 414 participants were recruited, including individuals diagnosed with MDD, BD, SCZ, and HCs. Classification experiments using machine learning (ML) models demonstrate the proposed method's robustness and superior diagnostic performance in binary and multiclass settings, outperforming traditional unidimensional feature approaches. Additionally, group-level functional connectivity (FC) analyses reveal distinct neurobiological signatures among psychiatric groups. Conclusions: These findings underscore the translational potential of MSNF-enhanced fNIRS as a scalable neuroimaging-based biomarker tool for early psychiatric screening and differential diagnosis. (Copyright © 2026 Yajing Meng et al. Depression and Anxiety published by John Wiley & Sons Ltd.)
Competing Interests:	The authors declare no conflicts of interest.
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Contributed Indexing:	Keywords: functional near–infrared spectroscopy; machine learning; neuroimaging biomarkers; verbal fluency task
Entry Date(s):	Date Created: 20261007 Date Completed: 20261007 Latest Revision: 20261008
Update Code:	20261008
PubMed Central ID:	PMC13639845
DOI:	10.1155/da/9921579
PMID:	42840883
Βάση Δεδομένων:	MEDLINE

Περιγραφή
ISSN:	1520-6394
DOI:	10.1155/da/9921579