Reproducible brain-wide association studies require thousands of individuals

Scott Marek (), Brenden Tervo-Clemmens (), Finnegan J. Calabro, David F. Montez, Benjamin P. Kay, Alexander S. Hatoum, Meghan Rose Donohue, William Foran, Ryland L. Miller, Timothy J. Hendrickson, Stephen M. Malone, Sridhar Kandala, Eric Feczko, Oscar Miranda-Dominguez, Alice M. Graham, Eric A. Earl, Anders J. Perrone, Michaela Cordova, Olivia Doyle, Lucille A. Moore, Gregory M. Conan, Johnny Uriarte, Kathy Snider, Benjamin J. Lynch, James C. Wilgenbusch, Thomas Pengo, Angela Tam, Jianzhong Chen, Dillan J. Newbold, Annie Zheng, Nicole A. Seider, Andrew N. Van, Athanasia Metoki, Roselyne J. Chauvin, Timothy O. Laumann, Deanna J. Greene, Steven E. Petersen, Hugh Garavan, Wesley K. Thompson, Thomas E. Nichols, B. T. Thomas Yeo, Deanna M. Barch, Beatriz Luna, Damien A. Fair () and Nico U. F. Dosenbach ()
Additional contact information
Scott Marek: Washington University School of Medicine
Brenden Tervo-Clemmens: Massachusetts General Hospital, Harvard Medical School
Finnegan J. Calabro: University of Pittsburgh
David F. Montez: Washington University School of Medicine
Benjamin P. Kay: Washington University School of Medicine
Alexander S. Hatoum: Washington University School of Medicine
Meghan Rose Donohue: Washington University School of Medicine
William Foran: University of Pittsburgh
Ryland L. Miller: Washington University School of Medicine
Timothy J. Hendrickson: University of Minnesota Informatics Institute, University of Minnesota
Stephen M. Malone: University of Minnesota
Sridhar Kandala: Washington University School of Medicine
Eric Feczko: University of Minnesota Medical School
Oscar Miranda-Dominguez: University of Minnesota Medical School
Alice M. Graham: Oregon Health and Science University
Eric A. Earl: University of Minnesota Medical School
Anders J. Perrone: University of Minnesota Medical School
Michaela Cordova: Oregon Health and Science University
Olivia Doyle: Oregon Health and Science University
Lucille A. Moore: Oregon Health and Science University
Gregory M. Conan: University of Minnesota Medical School
Johnny Uriarte: Oregon Health and Science University
Kathy Snider: Oregon Health and Science University
Benjamin J. Lynch: University of Minnesota Medical School
James C. Wilgenbusch: University of Minnesota Medical School
Thomas Pengo: University of Minnesota Informatics Institute, University of Minnesota
Angela Tam: National University of Singapore
Jianzhong Chen: National University of Singapore
Dillan J. Newbold: Washington University School of Medicine
Annie Zheng: Washington University School of Medicine
Nicole A. Seider: Washington University School of Medicine
Andrew N. Van: Washington University School of Medicine
Athanasia Metoki: Washington University School of Medicine
Roselyne J. Chauvin: Washington University School of Medicine
Timothy O. Laumann: Washington University School of Medicine
Deanna J. Greene: University of California San Diego
Steven E. Petersen: Washington University School of Medicine
Hugh Garavan: University of Vermont
Wesley K. Thompson: University of California San Diego
Thomas E. Nichols: University of Oxford
B. T. Thomas Yeo: National University of Singapore
Deanna M. Barch: Washington University School of Medicine
Beatriz Luna: University of Pittsburgh
Damien A. Fair: University of Minnesota Medical School
Nico U. F. Dosenbach: Washington University School of Medicine

Nature, 2022, vol. 603, issue 7902, 654-660

Abstract: Abstract Magnetic resonance imaging (MRI) has transformed our understanding of the human brain through well-replicated mapping of abilities to specific structures (for example, lesion studies) and functions1–3 (for example, task functional MRI (fMRI)). Mental health research and care have yet to realize similar advances from MRI. A primary challenge has been replicating associations between inter-individual differences in brain structure or function and complex cognitive or mental health phenotypes (brain-wide association studies (BWAS)). Such BWAS have typically relied on sample sizes appropriate for classical brain mapping4 (the median neuroimaging study sample size is about 25), but potentially too small for capturing reproducible brain–behavioural phenotype associations5,6. Here we used three of the largest neuroimaging datasets currently available—with a total sample size of around 50,000 individuals—to quantify BWAS effect sizes and reproducibility as a function of sample size. BWAS associations were smaller than previously thought, resulting in statistically underpowered studies, inflated effect sizes and replication failures at typical sample sizes. As sample sizes grew into the thousands, replication rates began to improve and effect size inflation decreased. More robust BWAS effects were detected for functional MRI (versus structural), cognitive tests (versus mental health questionnaires) and multivariate methods (versus univariate). Smaller than expected brain–phenotype associations and variability across population subsamples can explain widespread BWAS replication failures. In contrast to non-BWAS approaches with larger effects (for example, lesions, interventions and within-person), BWAS reproducibility requires samples with thousands of individuals.

Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (27)

Downloads: (external link)
https://www.nature.com/articles/s41586-022-04492-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:603:y:2022:i:7902:d:10.1038_s41586-022-04492-9

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/s41586-022-04492-9

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().