Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population‐wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how mu...

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Bibliographic Details
Published in:	Human brain mapping Vol. 44; no. 5; pp. 1913 - 1933
Main Authors:	Yeung, Hon Wah, Stolicyn, Aleks, Buchanan, Colin R., Tucker‐Drob, Elliot M., Bastin, Mark E., Luz, Saturnino, McIntosh, Andrew M., Whalley, Heather C., Cox, Simon R., Smith, Keith
Format:	Journal Article
Language:	English
Published:	Hoboken, USA John Wiley & Sons, Inc 01.04.2023
Subjects:	Accuracy Algorithms Brain Brain - diagnostic imaging Brain - pathology Cognition Cognitive ability Complexity Connectome - methods Decision making Deep learning diffusion tensor imaging general psychopathology Humans Learning algorithms Machine Learning Magnetic resonance imaging Mathematical models Medical imaging Mental disorders Mental Health Neural networks Neuroimaging Phenotypes Predictions Psychopathology Sex Statistical methods structural connectomes deep learning diffusion tensor imaging cognition structural connectomes general psychopathology
ISSN:	1065-9471, 1097-0193, 1097-0193
Online Access:	Get full text
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