Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual

• Published in: Human brain mapping Vol. 41; no. 5; pp. 1119 - 1135
• Main Authors: Lei, Du, Pinaya, Walter H. L., Young, Jonathan, Amelsvoort, Therese, Marcelis, Machteld, Donohoe, Gary, Mothersill, David O., Corvin, Aiden, Vieira, Sandra, Huang, Xiaoqi, Lui, Su, Scarpazza, Cristina, Arango, Celso, Bullmore, Ed, Gong, Qiyong, McGuire, Philip, Mechelli, Andrea
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.04.2020
• Subjects: Abnormalities; Adult; Classification; Connectome; Covariance matrix; Data acquisition; Diagnostic imaging; Diagnostic software; Diagnostic systems; Diffusion Tensor Imaging; Feature extraction; Female; functional connectivity; Functional magnetic resonance imaging; graph theoretical analysis; Gray Matter - diagnostic imaging; Homogeneity; Humans; Image Processing, Computer-Assisted - methods; Learning algorithms; Machine Learning; Machining; Magnetic Resonance Imaging; Male; Mathematical analysis; Matrix algebra; Matrix methods; Medical imaging; Mental disorders; Middle Aged; Model accuracy; Multimodal Imaging - methods; Neural networks; Neural Pathways - diagnostic imaging; Neuroimaging; Neuroimaging - methods; Performance enhancement; Reproducibility of Results; Research facilities; Rest; Schizophrenia; Schizophrenia - diagnostic imaging; Structure-function relationships; Substantia alba; Substantia grisea; Support Vector Machine; Support vector machines; White Matter - diagnostic imaging; Young Adult; functional connectivity; schizophrenia; machine learning; graph theoretical analysis; neuroimaging

Schizophrenia is a severe psychiatric disorder associated with both structural and functional brain abnormalities. In the past few years, there has been growing interest in the application of machine learning techniques to neuroimaging data for the diagnostic and prognostic assessment of this disord...