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

Peter A Bandettini; Javier Gonzalez-Castillo; Dan Handwerker; Paul Taylor; Gang Chen; Adam Thomas

doi:10.1016/j.medj.2022.07.002

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

Med. 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.

Publication types

Research Support, N.I.H., Intramural
Comment

MeSH terms

Brain / diagnostic imaging
Brain Mapping* / methods
Humans
Magnetic Resonance Imaging* / methods