Keywords: Diagnosis/Prediction, Multimodal
Motivation: The relationship between multimodal MRI neuroimaging and future cognitive performance of children from low-socioeconomic status backgrounds remains incompletely understood.
Goal(s): We aimed to predict cognitive performance at age 9 using multimodal MRI data of the same children at age 7.
Approach: We implemented 10-fold cross validated support vector machines and regression modelling on a combination of structural, diffusion, and spectroscopic MRI to predict continuous scores and categories of cognitive performance.
Results: We could predict whether children would fall into a poorer or better scoring category at age 9 with 76% accuracy, 81% specificity, and 72% sensitivity.
Impact: We demonstrate the ability to predict overall cognitive performance at age 9 from neuroimaging 2 years earlier. This could facilitate identification of at-risk children who may benefit the most from earlier targeted interventions.
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