Quantifying performance of machine learning methods for neuroimaging data

Machine learning is increasingly being applied to neuroimaging data. However, most machine learning algorithms have not been designed to accommodate neuroimaging data, which typically has many more data points than subjects, in addition to multicollinearity and low signal-to-noise. Consequently, the...

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Bibliographic Details
Published in:	NeuroImage (Orlando, Fla.) Vol. 199; pp. 351 - 365
Main Authors:	Jollans, Lee, Boyle, Rory, Artiges, Eric, Banaschewski, Tobias, Desrivières, Sylvane, Grigis, Antoine, Martinot, Jean-Luc, Paus, Tomáš, Smolka, Michael N., Walter, Henrik, Schumann, Gunter, Garavan, Hugh, Whelan, Robert
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01.10.2019 Elsevier Limited
Subjects:	Algorithms Alzheimer's disease Artificial intelligence Brain - diagnostic imaging Brain research Consortia Councils Datasets Grants Humans Learning algorithms Machine Learning Magnetic resonance imaging Medical imaging Medical research Models, Theoretical Neuroimaging Neuroimaging - methods Neuroimaging - standards Neurosciences Regression algorithms Reproducibility Reproducibility of Results Neuroimaging Regression algorithms Reproducibility Machine learning
ISSN:	1053-8119, 1095-9572, 1095-9572
Online Access:	Get full text
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