Journal article
Addressing artifactual bias in large, automated MRI analyses of brain development
Nature neuroscience, Vol.28(8), pp.1787-1796
08/01/2025
DOI: 10.1038/s41593-025-01990-7
PMCID: PMC12912710
PMID: 40595447
Abstract
Large, population-based magnetic resonance imaging (MRI) studies of adolescents promise transformational insights into neurodevelopment and mental illness risk. However, youth MRI studies are especially susceptible to motion and other artifacts that introduce non-random noise. After visual quality control of 11,263 T1 MRI scans obtained at age 9-10 years through the Adolescent Brain Cognitive Development study, we uncovered bias in measurements of cortical thickness and surface area in 55.1% of the samples with suboptimal image quality. These biases impacted analyses relating structural MRI and clinical measures, resulting in both false-positive and false-negative associations. Surface hole number, an automated index of topological complexity, reproducibly identified lower-quality scans with good specificity, and its inclusion as a covariate partially mitigated quality-related bias. Closer examination of high-quality scans revealed additional topological errors introduced during image preprocessing. Correction with manual edits reproducibly altered thickness measurements and strengthened age-thickness associations. We demonstrate here that inadequate quality control undermines advantages of large sample size to detect meaningful associations. These biases can be mitigated through additional automated and manual interventions.
Details
- Title: Subtitle
- Addressing artifactual bias in large, automated MRI analyses of brain development
- Creators
- Safia Elyounssi - Harvard UniversityKeiko Kunitoki - Harvard UniversityJacqueline A. Clauss - University of Maryland, BaltimoreEline Laurent - Harvard UniversityKristina A. Kane - Harvard UniversityDylan E. Hughes - University of California, Los AngelesCasey E. Hopkinson - Harvard UniversityOren Bazer - Harvard UniversityRachel Freed Sussman - Harvard UniversityAlysa E. Doyle - Harvard UniversityHang Lee - Harvard UniversityBrenden Tervo-Clemmens - Harvard UniversityHamdi Eryilmaz - Harvard UniversityRandy L. Hirschtick - Harvard UniversityDeanna M. Barch - Washington University in St. LouisTheodore D. Satterthwaite - University of PennsylvaniaKevin F. Dowling - Harvard UniversityJoshua L. Roffman - Massachusetts General Hospital
- Resource Type
- Journal article
- Publication Details
- Nature neuroscience, Vol.28(8), pp.1787-1796
- DOI
- 10.1038/s41593-025-01990-7
- PMID
- 40595447
- PMCID
- PMC12912710
- NLM abbreviation
- Nat Neurosci
- ISSN
- 1097-6256
- eISSN
- 1546-1726
- Publisher
- NATURE PORTFOLIO
- Number of pages
- 24
- Grant note
- Harvard Medical School Dupont Warren Fellowship MQ Foundation U01DA041134; U01DA050988; U01DA051039; U01DA041156; U01DA041025; U01DA041120; U01DA051038; U01DA041148; U01DA041093; U01DA041089; U24DA041123; U24DA041147 / 'NIH'; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA Louis V. Gerstner Scholar Award U01DA041048; U01DA050989; U01DA051016; U01DA041022; U01DA051018; U01DA051037; U01DA050987; U01DA041174; U01DA041106; U01DA041117; U01DA041028 / NIH; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA R01MH124694; R01MH120402; T32MH112485; R01MH113550; R01MH120482; R01MH112847; R37MH125829; R01EB022572; K23DA057486 / National Institutes of Health (NIH); United States Department of Health & Human Services; National Institutes of Health (NIH) - USA Mass General Hospital Early Brain Development Initiative
- Language
- English
- Date published
- 08/01/2025
- Academic Unit
- Psychiatry
- Record Identifier
- 9985222824002771
Metrics
3 Record Views