Site effects how-to and when: An overview of retrospective techniques to accommodate site effects in multi-site neuroimaging analyses

JMM Bayer; PM Thompson; CRK Ching; M Liu; A Chen; AC Panzenhagen; N Jahanshad; A Marquand; L Schmaal; PG Sämann

Journal article

Site effects how-to and when: An overview of retrospective techniques to accommodate site effects in multi-site neuroimaging analyses

JMM Bayer, PM Thompson, CRK Ching, M Liu, A Chen, AC Panzenhagen, N Jahanshad, A Marquand, L Schmaal, PG Sämann

Frontiers in Neurology | FRONTIERS MEDIA SA | Published : 2022

DOI: 10.3389/fneur.2022.923988

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Abstract

Site differences, or systematic differences in feature distributions across multiple data-acquisition sites, are a known source of heterogeneity that may adversely affect large-scale meta- and mega-analyses of independently collected neuroimaging data. They influence nearly all multi-site imaging modalities and biomarkers, and methods to compensate for them can improve reliability and generalizability in the analysis of genetics, omics, and clinical data. The origins of statistical site effects are complex and involve both technical differences (scanner vendor, head coil, acquisition parameters, imaging processing) and differences in sample characteristics (inclusion/exclusion criteria, samp..

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Lianne Schmaal Author

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Funding Acknowledgements

PT was supported by NIH grants R01AG058854, U01AG068057, RF1AG057892, R01AG060610, R01MH116147, P41EB015922, R01MH121246, R01NS107513, R01MH123163, and U.S. Alzheimer's Association grant ZEN-20-644609. He was also supported by NIA T32AG058507 and NIH grant U54EB020403 from the Big Data to Knowledge (BD2K) Program. PT also received partial research support from Biogen, Inc. (Boston, USA) for work unrelated to the topic of this manuscript. AC was supported by the National Institute of Neurological Disorders and Stroke (grant numbers R01 NS085211 and R01 NS060910), the National Multiple Sclerosis Society (RG-1707-28586), and a seed grant from the University of Pennsylvania Center for Biomedical Image Computing and Analytics (CBICA). AM gratefully acknowledges support from the European Research Council (ERC, grant `MENTALPRECISION' 10100118), theWellcome Trust under a Digital Innovator award (`BRAINCHART,' 215698/Z/19/Z) and theDutchOrganization for Scientific Research (016.156.415). NJ was supported by NIH grants R01AG059874 and R01MH11760. LS was supported by the National Institute of Mental Health of the National Institutes of Health (R01MH117601) and by a NHMRC Career Development Fellowship (1140764).