The challenge of BWAs: Unknown unknowns in feature space and variance

Peter A Bandettini¹, Javier Gonzalez-Castillo², Dan Handwerker², Paul Taylor³, Gang Chen³, Adam Thomas⁴

Affiliations

¹ Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD 20817, USA; Functional MRI Core Facility, National Institute of Mental Health, Bethesda, MD 20817, USA. Electronic address: bandettp@mail.nih.gov.
² Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD 20817, USA.
³ Scientific and Statistical Computing Core Facility, National Institute of Mental Health, Bethesda, MD 20817, USA.
⁴ Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD 20817, USA.

PMID: 35963233
DOI: 10.1016/j.medj.2022.07.002

Free article

Comment

The challenge of BWAs: Unknown unknowns in feature space and variance

Peter A Bandettini et al. Med. 2022.

Free article

. 2022 Aug 12;3(8):526-531.

doi: 10.1016/j.medj.2022.07.002.

Authors

Peter A Bandettini¹, Javier Gonzalez-Castillo², Dan Handwerker², Paul Taylor³, Gang Chen³, Adam Thomas⁴

Affiliations

¹ Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD 20817, USA; Functional MRI Core Facility, National Institute of Mental Health, Bethesda, MD 20817, USA. Electronic address: bandettp@mail.nih.gov.
² Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD 20817, USA.
³ Scientific and Statistical Computing Core Facility, National Institute of Mental Health, Bethesda, MD 20817, USA.
⁴ Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD 20817, USA.

PMID: 35963233
DOI: 10.1016/j.medj.2022.07.002

Abstract

The recent paper by Marek et al.¹ has shown that, to capture brain-wide associations using fMRI and MRI measures, thousands of individuals are required. These results can be potentially misunderstood to imply that MRI or fMRI lack sensitivity or specificity. This commentary discusses the demonstrated sensitivity of fMRI and focuses on methodology that may allow improvements in BWA studies. While individual variation may be an ultimate constraint, refinements in acquisition, population selection, and processing may bring about higher correlations.

Published by Elsevier Inc.

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Conflict of interest statement

Declaration of interests The authors declare no competing interests.

Comment on

Reproducible brain-wide association studies require thousands of individuals.
Marek S, Tervo-Clemmens B, Calabro FJ, Montez DF, Kay BP, Hatoum AS, Donohue MR, Foran W, Miller RL, Hendrickson TJ, Malone SM, Kandala S, Feczko E, Miranda-Dominguez O, Graham AM, Earl EA, Perrone AJ, Cordova M, Doyle O, Moore LA, Conan GM, Uriarte J, Snider K, Lynch BJ, Wilgenbusch JC, Pengo T, Tam A, Chen J, Newbold DJ, Zheng A, Seider NA, Van AN, Metoki A, Chauvin RJ, Laumann TO, Greene DJ, Petersen SE, Garavan H, Thompson WK, Nichols TE, Yeo BTT, Barch DM, Luna B, Fair DA, Dosenbach NUF. Marek S, et al. Nature. 2022 Mar;603(7902):654-660. doi: 10.1038/s41586-022-04492-9. Epub 2022 Mar 16. Nature. 2022. PMID: 35296861 Free PMC article.

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The challenge of BWAs: Unknown unknowns in feature space and variance

Affiliations

The challenge of BWAs: Unknown unknowns in feature space and variance

Authors

Affiliations

Abstract

Conflict of interest statement

Comment on

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Medical