Reproducible brain-wide association studies require thousands of individuals

Для воспроизводимых общемозговых исследований ассоциаций необходимы тысячи участников
Thomas E. Nichols, Alexander Samir Hatoum, B.T. Thomas Yeo, Nico U.F. Dosenbach, Timothy O. Laumann, Dillan J. Newbold, Roselyne J. Chauvin, Scott A. Marek, Deanna J. Greene, Angela Tam, Wesley Kurt Thompson, Steven E. Petersen, Hugh Garavan, Damien A. Fair, Beatríz Luna, Deanna M. Barch, Eric Feczko, Óscar Miranda-Domínguez, Eric Earl, Andrew N. Van, Brenden Tervo‐Clemmens, Timothy Hendrickson, Benjamin J. Lynch, Jianzhong Chen, Finnegan J. Calabro, Will Foran, William Foran, Meghan Rose Donohue, David F. Montez, Benjamin P. Kay, Ryland L. Miller, Stephen M. Malone, Sridhar Kandala, Alice M. Graham, Anders J. Perrone, Michaela Cordova, Olivia J. Doyle, Lucille A. Moore, Greg Conan, Johnny Uriarte, Kathy Snider, James C. Wilgenbusch, Thomas Pengo, Jianzhong Chen, Annie Zheng, Nicole A. Seider, Athanasia Metoki, Thomas Pengo, Angela Tam
2022-03-16

MRIbrain-wide association studiesmultivariate methodsreproducibilitysample size
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 functions 1–3 (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 mapping 4 (the median neuroimaging study sample size is about 25), but potentially too small for capturing reproducible brain–behavioural phenotype associations 5,6 . 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.
1
Brain-wide association studies showed smaller-than-expected brain–behavior effects, making typical sample sizes statistically underpowered and prone to inflated effects and replication failures.
2
More robust associations were observed for functional MRI than structural MRI, cognitive tests than mental-health questionnaires, and multivariate than univariate methods.
3
Replication rates improved and effect-size inflation decreased as BWAS sample sizes reached the thousands of individuals.
4
Reproducible BWAS require samples of thousands because small associations and population-subsample variability contribute to widespread replication failures.
5
Using three large neuroimaging datasets totaling approximately 50,000 individuals, the study quantified BWAS effect sizes and reproducibility across sample sizes.

brain-wide association studies (BWAS) relating inter-individual differences in human brain structure or function to cognitive and mental health phenotypes

the effect sizes, statistical power, replication rates, and reproducibility of BWAS associations as a function of sample size

Publication Details
Publication Date
2022-03-16
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Authors
Thomas E. Nichols
Alexander Samir Hatoum
B.T. Thomas Yeo
Nico U.F. Dosenbach
Timothy O. Laumann
Dillan J. Newbold
Roselyne J. Chauvin
Scott A. Marek
Deanna J. Greene
Angela Tam
Wesley Kurt Thompson
Steven E. Petersen
Hugh Garavan
Damien A. Fair
Beatríz Luna
Deanna M. Barch
Eric Feczko
Óscar Miranda-Domínguez
Eric Earl
Andrew N. Van
Brenden Tervo‐Clemmens
Timothy Hendrickson
Benjamin J. Lynch
Jianzhong Chen
Finnegan J. Calabro
Will Foran
William Foran
Meghan Rose Donohue
David F. Montez
Benjamin P. Kay
Ryland L. Miller
Stephen M. Malone
Sridhar Kandala
Alice M. Graham
Anders J. Perrone
Michaela Cordova
Olivia J. Doyle
Lucille A. Moore
Greg Conan
Johnny Uriarte
Kathy Snider
James C. Wilgenbusch
Thomas Pengo
Jianzhong Chen
Annie Zheng
Nicole A. Seider
Athanasia Metoki
Thomas Pengo
Angela Tam
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