Abstract
High dimensional neuroimaging datasets and machine learning have been used to estimate and predict domain-specific cognition, but comparisons with simpler models composed of easy-to-measure variables are limited. Regularization methods in particular may help identify regions-of-interest related to domain-specific cognition. Using data from the Northern Manhattan Study, a cohort study of mostly Hispanic older adults, we compared three models estimating domain-specific cognitive performance: sociodemographics and APOE ε4 allele status (basic model), the basic model and MRI markers, and a model with only MRI markers. We used several machine learning methods to fit our regression models: elastic net, support vector regression, random forest, and principal components regression. Model performance was assessed with the RMSE, MAE, and R2 statistics using 5-fold cross-validation. To assess whether prediction models with imaging biomarkers were more predictive than prediction models built with randomly generated biomarkers, we refit the elastic net models using 1000 datasets with random biomarkers and compared the distribution of the RMSE and R2 in models using these random biomarkers to the RMSE and R2 from observed models. Basic models explained ~ 31–38% of the variance in domain-specific cognition. Addition of MRI markers did not improve estimation. However, elastic net models with only MRI markers performed significantly better than random MRI markers (one-sided P < .05) and yielded regions-of-interest consistent with previous literature and others not previously explored. Therefore, structural brain MRI markers may be more useful for etiological than predictive modeling.
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Acknowledgements
We would like to thank the NOMAS participants for their time and effort and our project manager, Janet De Rosa, MPH.
Funding
This work was supported by the National Institutes of Neurological Disease and Stroke (R01 NS29993, F30 NS103462) and the Evelyn F. McKnight Brain Institute.
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Author contributions included conception and study design (MRC, LW), data collection or acquisition (YKC, NA, SHL, MSVE, RLS, CBW, TR), statistical analysis (MRC, LW), interpretation of results (all authors), drafting of manuscript (MRC), revising manuscript for important intellectual content (all authors), and approval of final version (all authors).
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Participants provided written informed consent, and the institutional review boards at the University of Miami and Columbia University approved this study.
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Dr. Mitchell S.V. Elkind receives compensation for providing consultative services for the BMS-Pfizer Alliance for Eliquis, and Roche for a clinical trial of stroke prevention; has given expert legal opinions on behalf of Organon (NuvaRing and stroke litigation), Auxilium (testosterone and stroke), and LivaNova (cardiac surgery and stroke); and serves on the National, Founders Affiliate, and New York City Chapter Boards of the American Heart Association/American Stroke Association. He receives royalties from UpToDate for chapters related to stroke.
Dr. Ralph L. Sacco receives federal grant support (R01 NS 29993, CTSA UL1 TR002736), private foundation support (American Heart Association Bugher Center), and pharma research support (Boehringer Ingelheim).
Dr. Clinton B. Wright receives royalties for 2 chapters on Vascular Dementia from UpToDate.
Drs. Michelle Caunca, Lily Wang, Ying Kuen Cheung, Noam Alperin, and Tatjana Rundek and Mr. Sang Lee report no disclosures.
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Caunca, M.R., Wang, L., Cheung, Y.K. et al. Machine learning-based estimation of cognitive performance using regional brain MRI markers: the Northern Manhattan Study. Brain Imaging and Behavior 15, 1270–1278 (2021). https://doi.org/10.1007/s11682-020-00325-3
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DOI: https://doi.org/10.1007/s11682-020-00325-3