Journal article
B. Xu, L. Dall’Aglio, J. Flournoy, G. Bortsova, B. Tervo-Clemmens, P. Collins, Marleen de Bruijne, M. Luciana, A. Marquand, H. Wang, H. Tiemeier, R. Muetzel
medRxiv, 2023
medRxiv, 2023
Semantic Scholar
DOI
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APA
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Xu, B., Dall’Aglio, L., Flournoy, J., Bortsova, G., Tervo-Clemmens, B., Collins, P., … Muetzel, R. (2023). Multivariate brain-based dimensions of child psychiatric problems: degrees of generalizability. MedRxiv.
Chicago/Turabian
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Xu, B., L. Dall’Aglio, J. Flournoy, G. Bortsova, B. Tervo-Clemmens, P. Collins, Marleen de Bruijne, et al. “Multivariate Brain-Based Dimensions of Child Psychiatric Problems: Degrees of Generalizability.” medRxiv (2023).
MLA
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Xu, B., et al. “Multivariate Brain-Based Dimensions of Child Psychiatric Problems: Degrees of Generalizability.” MedRxiv, 2023.
BibTeX Click to copy
@article{b2023a,
title = {Multivariate brain-based dimensions of child psychiatric problems: degrees of generalizability},
year = {2023},
journal = {medRxiv},
author = {Xu, B. and Dall’Aglio, L. and Flournoy, J. and Bortsova, G. and Tervo-Clemmens, B. and Collins, P. and de Bruijne, Marleen and Luciana, M. and Marquand, A. and Wang, H. and Tiemeier, H. and Muetzel, R.}
}
Abstract
Multivariate machine learning techniques are a promising set of tools for identifying complex brain-behavior associations. However, failure to replicate results from these methods across samples has hampered their clinical relevance. This study aimed to delineate dimensions of brain functional connectivity that are associated with child psychiatric symptoms in two large and independent cohorts: the Adolescent Brain Cognitive Development (ABCD) Study and the Generation R Study (total n=8,605). Using sparse canonical correlations analysis, we identified three brain-behavior dimensions in ABCD: attention problems, aggression and rule-breaking behaviors, and withdrawn behaviors. Importantly, out-of-sample generalizability of these dimensions was consistently observed in ABCD, suggesting robust multivariate brain-behavior associations. Despite this, out-of-study generalizability in Generation R was limited. These results highlight that the degree of generalizability can vary depending on the external validation methods employed as well as the datasets used, emphasizing that biomarkers will remain elusive until models generalize better in true external settings.
