Ensemble Modeling of Neurocognitive Performance Using MRI-Derived Brain Structure Volumes

Abstract

Prediction of cognitive performance from brain structural imaging data is a challenging machine learning topic. Participating in the ABCD Neurocognitive prediction challenge (2019), we implemented several machine learning models to solve this problem. Our results show superior performance from models relying on boosted decision trees and we find benefit from using two different sets of derived brain volumetric features. Lastly, across all models, we report an increase in performance by ensembling several different model types together in a final layer.

Publication
Adolescent Brain Cognitive Development Neurocognitive Prediction
Avatar
Tanner Koomar, PhD
Postdoctoral Research Scholar

My research interests include computational genetics, machine learning, and science communication