Distinguishing schizophrenia and bipolar disorder through a Multiclass Classification model based on multimodal neuroimaging data

Author:

Chen MingORCID,Xia Xiaowei,Kang Zhuang,Li Zhinan,Dai Jiamin,Wu Junyan,Chen Cai,Qiu Yong,Liu Tong,Liu Yanxi,Zhang Ziyi,Shen Qingni,Tao Sichu,Deng Zixin,Lin YingORCID,Wei Qinling

Funder

National Natural Science Foundation of China

Natural Science Foundation of Guangdong Province

BP

Sun Yat-sen University

Science and Technology Planning Project of Fuzhou

Guangzhou Municipal Science and Technology Project

Department of Trade and Industry

Publisher

Elsevier BV

Reference77 articles.

1. Aberrant striatal coupling with default mode and central executive network relates to self-reported avolition and anhedonia in schizophrenia;Brakowski;J. Psychiatr. Res.,2022

2. Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis;Bruin;Psychol. Med.,2023

3. Investigating the predictive value of different resting-state functional MRI parameters in obsessive-compulsive disorder;Bu;Transl. Psychiatry,2019

4. DPARSF: a MATLAB toolbox for "pipeline" data analysis of resting-state fMRI;Chao-Gan;Front. Syst. Neurosci.,2010

5. A diffusion tensor imaging study of structural dysconnectivity in never-medicated, first-episode schizophrenia;Cheung;Psychol. Med.,2008

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