Abstract
The ABCD Neurocognitive Prediction Challenge (ABCD-NP-Challenge 2019) made available T1-weighted structural scans for children alongside their fluid intelligence scores. The goal of the challenge was to use this anatomical brain data to train a model that could be successful in predicting fluid intelligence scores from held-out T1-weighted structural scans taken of other children. Functional magnetic resonance imaging (fMRI) has been moderately successful at identifying neural correlates of cognitive functioning, including intelligence. This study sought to leverage anatomical metrics within functionally defined regions, convolutional neural networks, and regression models to predict fluid intelligence. The proposed model performed competitively on the ABCD-NP-Challenge, and significantly outperformed a non deep-learning approach for behavior prediction based on the LASSO.
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Chiang, J.N., Reggente, N., Dell’Italia, J., Zheng, Z.S., Lutkenhoff, E.S. (2019). Predicting Fluid Intelligence Using Anatomical Measures Within Functionally Defined Brain Networks. In: Pohl, K., Thompson, W., Adeli, E., Linguraru, M. (eds) Adolescent Brain Cognitive Development Neurocognitive Prediction. ABCD-NP 2019. Lecture Notes in Computer Science(), vol 11791. Springer, Cham. https://doi.org/10.1007/978-3-030-31901-4_17
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