ArticleBrain imaging and behavior2021
Machine learning-based estimation of cognitive performance using regional brain MRI markers: the Northern Manhattan Study.
Article in Brain imaging and behavior, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Article
- Prediction of cognitive test scores: a comparison of brain structure, health, demographic, and cognitive data across adulthood.GeroScience · 2026Article
- Pubertal Hormones and the Early Adolescent Female Brain: A Multimodality Brain MRI Study.Human brain mapping · 2026Article
- TSP-GNN: a novel neuropsychiatric disorder classification framework based on task-specific prior knowledge and graph neural network.Frontiers in neuroscience · 2023Article
- Deep learning identifies brain structures that predict cognition and explain heterogeneity in cognitive aging.NeuroImage · 2022Article
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Authors and funding
9 authors.
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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 R
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