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

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...

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Veröffentlicht in:	Human brain mapping Jg. 41; H. 5; S. 1119 - 1135
Hauptverfasser:	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
Sprache:	Englisch
Veröffentlicht:	Hoboken, USA John Wiley & Sons, Inc 01.04.2020
Schlagworte:	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
ISSN:	1065-9471, 1097-0193, 1097-0193
Online-Zugang:	Volltext
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