Depression and anxiety · 2026
Cross-Disease Classification of Depression, Bipolar Disorder, and Schizophrenia Using Multiscale Spatiotemporal Network Fusion Features From fNIRS.
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Meng Yajing, Yang Yulu, Li Qing, Fu Qian, Gao Mingyao, He Ling, Zhang Jing, Qiu Changjian
Аннотация
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.
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.