Journal article

Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan

Raymond Pomponio, Guray Erus, Mohamad Habes, Jimit Doshi, Dhivya Srinivasan, Elizabeth Mamourian, Vishnu Bashyam, Ilya M Nasrallah, Theodore D Satterthwaite, Yong Fan, Lenore J Launer, Colin L Masters, Paul Maruff, Chuanjun Zhuo, Henry Voelzke, Sterling C Johnson, Jurgen Fripp, Nikolaos Koutsouleris, Daniel H Wolf, Raquel Gur Show all

NeuroImage | ACADEMIC PRESS INC ELSEVIER SCIENCE | Published : 2020

Abstract

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age tren..

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Grants

Awarded by National Institute on Aging


Awarded by National Institute of Mental Health


Awarded by National Institutes of Health


Awarded by National Multiple Sclerosis Society


Awarded by National Institute of Neurological Disorders and Stroke


Awarded by National Heart, Lung, and Blood Institute (NHLBI)


Awarded by NIA


Awarded by NHLBI


Funding Acknowledgements

This work was supported by the National Institute on Aging (grant number 1RF1AG054409), the National Institute of Mental Health (grant numbers 5R01MH112070; R01MH120482; R01MH112847), and the National Institutes of Health (grant number 75N95019C00022). MH was supported in part by The Allen H. and Selma W. Berkman Charitable Trust (Accelerating Research on Vascular Dementia) and the National Institutes of Health (grant number R01HL127659-04S1). TDS was supported in part by the National Institute of Mental Health (grant numbers R01MH120482, R01MH112847). DHW was supported in part by the National Institute of Mental Health (grant number R01MH113565). DAW was supported in part by the National Institute on Aging (grant numbers AG010124; R01AG055005). RTS was supported in part by the National Multiple Sclerosis Society (grant number RG170728586) and National Institute of Neurological Disorders and Stroke (grant number R01NS060910). The Coronary Artery Risk Development in Young Adults Study (CARDIA) is supported by contracts HHSN268201800003I, HHSN268201800004I, HHSN268201800005I, HHSN268201800006I, and HHSN268201800007I from the National Heart, Lung, and Blood Institute (NHLBI). CARDIA was also partially supported by the Intramural Research Program of the National Institute on Aging (NIA) and an intra-agency agreement between NIA and NHLBI (AG0005). The Baltimore Longitudinal Study of Aging (BLSA) is supported by the Intramural Research Program, National Institute on Aging, NIH. This research has been conducted using the UK Biobank Resource under Application Number 35148. The Australian Imaging Biomakers and Lifestyle (AIBL) study was supported by funding from the Science and Industry Endowment Fund, the Dementia Collaborative Research Centres, the McCusker Alzheimer's Research Foundation, the National Health and Medical Research Council (AUS), and the Yulgilbar Foundation, plus numerous commercial interactions supporting data collection. Details of the AIBL consortium can be found at www.AIBL.csiro.au and a list of the researchers of AIBL is provided at http://aibl.csiro.au/.