Author
Listed:
- Scott Marek
(Washington University School of Medicine)
- Brenden Tervo-Clemmens
(Massachusetts General Hospital, Harvard Medical School
University of Pittsburgh)
- Finnegan J. Calabro
(University of Pittsburgh
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
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
University of Minnesota Medical School)
- Oscar Miranda-Dominguez
(University of Minnesota Medical School
University of Minnesota Medical School)
- Alice M. Graham
(Oregon Health and Science University)
- Eric A. Earl
(University of Minnesota Medical School
Oregon Health and Science University)
- Anders J. Perrone
(University of Minnesota Medical School
Oregon Health and Science University)
- 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
Oregon Health and Science University)
- Johnny Uriarte
(Oregon Health and Science University)
- Kathy Snider
(Oregon Health and Science University)
- Benjamin J. Lynch
(University of Minnesota Medical School
University of Minnesota)
- James C. Wilgenbusch
(University of Minnesota Medical School
University of Minnesota)
- Thomas Pengo
(University of Minnesota Informatics Institute, University of Minnesota)
- Angela Tam
(National University of Singapore
National University of Singapore
National University of Singapore
National University of Singapore)
- Jianzhong Chen
(National University of Singapore
National University of Singapore
National University of Singapore
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
Washington University in St Louis)
- 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
Washington University in St Louis
Washington University School of Medicine
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
National University of Singapore
National University of Singapore
National University of Singapore)
- Deanna M. Barch
(Washington University School of Medicine
Washington University in St Louis)
- Beatriz Luna
(University of Pittsburgh
University of Pittsburgh)
- Damien A. Fair
(University of Minnesota Medical School
University of Minnesota Medical School
University of Minnesota Medical School)
- Nico U. F. Dosenbach
(Washington University School of Medicine
Washington University in St Louis
Washington University School of Medicine
Washington University School of Medicine)
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.
Suggested Citation
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 &, 2022.
"Reproducible brain-wide association studies require thousands of individuals,"
Nature, Nature, vol. 603(7902), pages 654-660, March.
Handle:
RePEc:nat:nature:v:603:y:2022:i:7902:d:10.1038_s41586-022-04492-9
DOI: 10.1038/s41586-022-04492-9
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