Mitigating site effects in covariance for machine learning in neuroimaging data

To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi‐site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple sites. These effects have been shown to bias comparison between si...

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Bibliographic Details
Published in:	Human brain mapping Vol. 43; no. 4; pp. 1179 - 1195
Main Authors:	Chen, Andrew A., Beer, Joanne C., Tustison, Nicholas J., Cook, Philip A., Shinohara, Russell T., Shou, Haochang
Format:	Journal Article
Language:	English
Published:	Hoboken, USA John Wiley & Sons, Inc 01.03.2022
Subjects:	Alzheimer's disease Cerebral Cortex - anatomy & histology Cerebral Cortex - diagnostic imaging ComBat Correlation analysis cortical thickness Covariance Datasets as Topic harmonization Humans Image acquisition Image Processing, Computer-Assisted - methods Image Processing, Computer-Assisted - standards Learning algorithms Machine Learning Magnetic fields Medical imaging Methods Models, Theoretical Multicenter Studies as Topic - methods Multicenter Studies as Topic - standards multi‐site analysis Nervous system Neurodegenerative diseases Neuroimaging Neuroimaging - methods Neuroimaging - standards Scanners site effect Variance site effect covariance cortical thickness harmonization ComBat multi-site analysis
ISSN:	1065-9471, 1097-0193, 1097-0193
Online Access:	Get full text
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