Discriminative analysis of schizophrenia and major depressive disorder using fNIRS.

Autor: Diao Y; School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, PR China; The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, PR China., Wang H; The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, PR China; Henan Collaborative Innovation Center of Prevention and treatment of mental disorder, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, PR China; The Second Clinical College, Xinxiang Medical University, Xinxiang, Henan 453003, PR China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, Henan 453002, PR China; Brain Institute, Henan Academy of Innovations in Medical Science, Zhengzhou 451163, PR China., Wang X; The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, PR China; The Second Clinical College, Xinxiang Medical University, Xinxiang, Henan 453003, PR China., Qiu C; The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, PR China; The Second Clinical College, Xinxiang Medical University, Xinxiang, Henan 453003, PR China., Wang Z; School of Future Technology, Xi'an JiaoTong University, Xi'an, Shanxi 710049, PR China., Ji Z; The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, PR China., Wang C; The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, PR China., Gu J; Department of Psychiatry, Chaohu Hospital of Anhui Medical University, Hefei, PR China; Department of Psychiatry, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, PR China., Liu C; The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, PR China., Wu K; School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, PR China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, PR China; Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai 980-8575, Japan. Electronic address: kaiwu@scut.edu.cn., Wang C; The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, PR China; Henan Collaborative Innovation Center of Prevention and treatment of mental disorder, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, PR China; Henan Cloud Platform and Application Research Center for Psychological Assistance, Xinxiang, Henan 453002, PR China; Henan Key Laboratory for Sleep Medicine, Xinxiang, Henan 453002, PR China. Electronic address: wangchdr@163.com.
Jazyk: angličtina
Zdroj: Journal of affective disorders [J Affect Disord] 2024 Sep 15; Vol. 361, pp. 256-267. Date of Electronic Publication: 2024 Jun 09.
DOI: 10.1016/j.jad.2024.06.013
Abstrakt: Background: Research into the shared and distinct brain dysfunctions in patients with schizophrenia (SCZ) and major depressive disorder (MDD) has been increasing. However, few studies have explored the application of functional near-infrared spectroscopy (fNIRS) in investigating brain dysfunction and enhancing diagnostic methodologies in these two conditions.
Methods: A general linear model was used for analysis of brain activation following task-state fNIRS from 131 patients with SCZ, 132 patients with MDD and 130 healthy controls (HCs). Subsequently, seventy-seven time-frequency analysis methods were used to construct new features of fNIRS, followed by the implementation of five machine learning algorithms to develop a differential diagnosis model for the three groups. This model was evaluated by comparing it to both a diagnostic model relying on traditional fNIRS features and assessments made by two psychiatrists.
Results: Brain activation analysis revealed significantly lower activation in Broca's area, the dorsolateral prefrontal cortex, and the middle temporal gyrus for both the SCZ and MDD groups compared to HCs. Additionally, the SCZ group exhibited notably lower activation in the superior temporal gyrus and the subcentral gyrus compared to the MDD group. When distinguishing among the three groups using independent validation datasets, the models utilizing new fNIRS features achieved an accuracy of 85.90 % (AUC = 0.95). In contrast, models based on traditional fNIRS features reached an accuracy of 52.56 % (AUC = 0.66). The accuracies of the two psychiatrists were 42.00 % (AUC = 0.60) and 38.00 % (AUC = 0.50), respectively.
Conclusion: This investigation brings to light the shared and distinct neurobiological abnormalities present in SCZ and MDD, offering potential enhancements for extant diagnostic systems.
Competing Interests: Declaration of competing interest All authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024. Published by Elsevier B.V.)
Databáze: MEDLINE
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