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Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging

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Title
Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging
Author(s)
Benkarim, Oualid; Paquola, Casey; Bo-Yong Park; Kebets, Valeria; Seok-Jun Hong; Vos de Wael, Reinder; Zhang, Shaoshi; Yeo, B T Thomas; Eickenberg, Michael; Ge, Tian; Poline, Jean-Baptiste; Bernhardt, Boris C.; Bzdok, Danilo
Publication Date
2022-04
Journal
PLoS biology, v.20, no.4, pp.421 - +
Publisher
NLM (Medline)
Abstract
Brain imaging research enjoys increasing adoption of supervised machine learning for single-participant disease classification. Yet, the success of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population variation. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in 2 separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n = 297) and the Healthy Brain Network (HBN, n = 551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially in regions part of the default mode network. Collectively, our findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.
URI
https://pr.ibs.re.kr/handle/8788114/11696
DOI
10.1371/journal.pbio.3001627
ISSN
1544-9173
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > 1. Journal Papers (저널논문)
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