Brain-phenotype predictions of language and executive function can survive across diverse real-world data: Dataset shifts in developmental populations.

Autor:	Adkinson BD; Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA. Electronic address: brendan.adkinson@yale.edu., Rosenblatt M; Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA., Dadashkarimi J; Department of Radiology, Athinoula. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Department of Radiology, Harvard Medical School, Boston, MA 02129, USA., Tejavibulya L; Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA., Jiang R; Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA., Noble S; Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA; Department of Bioengineering, Northeastern University, Boston, MA 02120, USA; Department of Psychology, Northeastern University, Boston, MA 02115, USA., Scheinost D; Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA; Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA; Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA; Department of Statistics & Data Science, Yale University, New Haven, CT 06520, USA; Child Study Center, Yale School of Medicine, New Haven, CT 06510, USA; Wu Tsai Institute, Yale University, New Haven, CT 06510, USA.
Jazyk:	angličtina
Zdroj:	Developmental cognitive neuroscience [Dev Cogn Neurosci] 2024 Dec; Vol. 70, pp. 101464. Date of Electronic Publication: 2024 Oct 16.
DOI:	10.1016/j.dcn.2024.101464
Abstrakt:	Predictive modeling potentially increases the reproducibility and generalizability of neuroimaging brain-phenotype associations. Yet, the evaluation of a model in another dataset is underutilized. Among studies that undertake external validation, there is a notable lack of attention to generalization across dataset-specific idiosyncrasies (i.e., dataset shifts). Research settings, by design, remove the between-site variations that real-world and, eventually, clinical applications demand. Here, we rigorously test the ability of a range of predictive models to generalize across three diverse, unharmonized developmental samples: the Philadelphia Neurodevelopmental Cohort (n=1291), the Healthy Brain Network (n=1110), and the Human Connectome Project in Development (n=428). These datasets have high inter-dataset heterogeneity, encompassing substantial variations in age distribution, sex, racial and ethnic minority representation, recruitment geography, clinical symptom burdens, fMRI tasks, sequences, and behavioral measures. Through advanced methodological approaches, we demonstrate that reproducible and generalizable brain-behavior associations can be realized across diverse dataset features. Results indicate the potential of functional connectome-based predictive models to be robust despite substantial inter-dataset variability. Notably, for the HCPD and HBN datasets, the best predictions were not from training and testing in the same dataset (i.e., cross-validation) but across datasets. This result suggests that training on diverse data may improve prediction in specific cases. Overall, this work provides a critical foundation for future work evaluating the generalizability of brain-phenotype associations in real-world scenarios and clinical settings. Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Brendan Adkinson reports a relationship with Elevation Prep LLC that includes: equity or stocks. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Databáze:	MEDLINE
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