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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Vydané v:	Human brain mapping Ročník 44; číslo 5; s. 1913 - 1933
Hlavní autori:	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
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Hoboken, USA John Wiley & Sons, Inc 01.04.2023
Predmet:	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
On-line prístup:	Získať plný text
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