Abstract
Background
Congenital heart disease (CHD) affects about 1% of births and is linked to differences in thinking and learning. Understanding how birth, genetic, clinical, and environmental factors together explain cognitive variability can inform monitoring and care. This study builds a multivariate model predicting cognition across multiple domains in adolescents and young adults with CHD.
Methods
We studied 89 adolescents and young adults (AYAs; mean age 16 years) with CHD who completed structural and diffusion MRI and fifteen neurocognitive tests across seven domains. Using an enhanced forward-inclusion and backward-elimination strategy with cross-validation, we built multivariate models incorporating biological, socioeconomic, clinical, genetic, and brain imaging features. Performance was evaluated using Pearson correlation (\(r\)) between observed and inferred scores, mean absolute error (MAE), and inverse inferability score (IIS).
Results
Here we show that models infer scores with moderate accuracy (\(r\) = 0.245–0.648; MAE = 1.6–12.0 points; mean MAE = 6.3). Highest correlations include Digit Span (\(r\) = 0.65; p < 0.001), Verbal Comprehension Index (\(r\) = 0.594; p < 0.001), and Matrix Reasoning (\(r\) = 0.574; p < 0.001). Domain ranking by IIS shows the best (lowest) scores for general intelligence (0.0886), followed by working memory (0.7100), and a higher (worse) score for perceptual reasoning (1.9199).
Conclusions
A multivariate approach combining brain imaging with genetic, clinical, and environmental factors provides clinically meaningful inference of individual cognitive performance in AYAs with CHD. These findings suggest complementary roles of brain, genetic, and contextual factors in shaping cognitive variability and motivate validation in larger cohorts.
Plain Language Summary
Children born with congenital heart disease can have differences in thinking and learning. We aimed to learn which factors, brain structure, genetics, medical history, and family environment, explain these differences in teens and young adults. We combined brain scans with health, family, and genetic information and used computer models to estimate scores on standard tests. The models reasonably estimated individual scores; some abilities, such as general thinking skills, working memory, and processing speed, were estimated more accurately than others. These findings suggest that brain scans, together with medical and family information, can provide a clearer picture of thinking skills. With further testing in larger, diverse groups, this approach could help guide follow‑up care tailored to each person.
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Data availability
The source data underlying Fig. 2 are provided in Supplementary Data 1–3. Specifically, Fig. 2a data are available in Supplementary Data 1, Fig. 2b in Supplementary Data 2, and Fig. 2c-q in Supplementary Data 3. The datasets used for model training and validation contain controlled-access human subject data, including brain MRI, genomic (whole-exome/genome–derived variants), demographic, and socioeconomic information from participants enrolled in the PCGC CHD Brain and Genes study (ClinicalTrials.gov: NCT03070197). These data are not publicly available due to ethical, legal, and privacy considerations associated with identifiable and potentially re-identifiable participant information, as governed by the informed consent, institutional review board (IRB) approvals, and NIH data-sharing policies. Conditions of access. Access to the controlled datasets may be granted to qualified investigators for legitimate academic research purposes, subject to (i) approval by the data-holding institution(s), (ii) verification of IRB or equivalent ethics approval at the requesting institution (or determination of exemption, as applicable), and (iii) execution of an appropriate Data Use Agreement (DUA). Timeframe for response. Data access requests will be acknowledged within 2 weeks of receipt, and a decision will typically be communicated within 4–8 weeks, depending on the completion of institutional review and DUA processing. Restrictions on data use. Approved data may be used only for the purposes described in the approved request and in compliance with the DUA. Restrictions include but are not limited to: (a) no attempts at participant re-identification; (b) no redistribution or sharing of the data with third parties; (c) secure data storage and access limited to authorized personnel; and (d) destruction or return of the data upon completion of the approved research, as specified in the DUA. Request process. Access requests should be directed to Prof. Yangming Ou, PhD (yangming.ou@childrens.harvard.edu) or Prof. Sarah U. Morton, MD, PhD (sarah.morton@childrens.harvard.edu), who are responsible for coordinating institutional review and responding to data access requests.
Code availability
The source codes of our enhanced forward inclusion and backward elimination (FIBE)62 approach used in this manuscript are available at https://github.com/i3-research/fibe and at Zenodo62.
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Acknowledgements
We are deeply grateful to the families and participants whose time and commitment made this research possible. We also thank the clinical coordinators, research staff, and collaborators who supported data collection and processing including Somer Bishop, Henry Buswell, Christopher Cannistraci, Johanna Calderon, Victor Chen, Lauren Christopher, Todd Constable, Nancy Cross, Cecelia DeSoto, Lazar Fleysher, John Foxe, Ed Freedman, Borjan Gagoski, Anne Snow Gallagher, Judith Geva, Emily Griffin, Dorota Gruber, Abha Gupta, Brandi Henson, Rick Kim, Alex Kolevzon, Linda Lambert, Kristen Lanzilotta, Brande Latney, Christina Layton, Derek Lundahl, Shannon Lundy, Stacy Lurie, Meghan MacNeal, Laura Ment, Julith S. Miller, Leona Oakes, Sharon O’Neill, Minhui Ouyang, Emily Richardson, Angela Romano-Adesman, Kelly Sadamitsu, Hedy Sarofin, Anjali Sadhwani, Dustin Scheinost, Zoey Shaw, Paige Siper, Deepak Srivastava, Sherin Stahl, Eileen Taillie, Allison Thomas, Alexandra Thompson, Nhu Tran, Marti Tristani, Henry Wang, Ting Wang, Wing Wang, Sarah Winter, Julie Wolf, Han Yin, Duan Xu, Amy Young, Yensy Zetino, and Brandon Zielinski for their contributions to the study. This work is supported by the National Institutes of Health – 1066 [5U01HL131003-09 (subaward number: OS00000958)]. The content is solely the responsibility of the authors and does not necessarily represent the official views of any funding agencies.
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S.U.M. and Y.O. jointly supervised the research. M.A.H. and Y.O. conceptualized and designed the study. M.A.H. developed the machine learning algorithm. M.A.H., S.U.M., and Y.O. conducted the data analysis and interpreted the results. D.C.B., W.K.C., D.X., Y.S., J.W.N., and P.E.G. offered senior mentorship and insightful comments that shaped the study design and interpretation. S.H. generated the brain age deviation data. H.R.A. assisted in organizing the neurocognitive functions into their respective broad cognitive domains. E.A., M.B., J.C., B.D.G., E.G., D.J.H., H.H., P.M.Q., T.A.M., A.N.B., G.A.P., N.T., M.E.T., and J.W.N. contributed to the collection and structured organization of data from participating centers. M.A.H., S.U.M., and Y.O. contributed to drafting the initial manuscript. All authors reviewed, revised, and approved the final version of the manuscript.
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Hussain, M.A., He, S., Adams, H.R. et al. Machine learning to infer neurocognitive testing scores among adolescents and young adults with congenital heart disease. Commun Med (2026). https://doi.org/10.1038/s43856-026-01417-9
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DOI: https://doi.org/10.1038/s43856-026-01417-9
