TY - JOUR T1 - An Omics-Inspired Elastic Net Approach Drastically Improves Out-of-Sample Prediction and Regional Inference of Task-Based fMRI JF - bioRxiv DO - 10.1101/2020.10.21.348367 SP - 2020.10.21.348367 AU - Narun Pornpattananangkul AU - Adam Bartonicek AU - Yue Wang AU - Argyris Stringaris Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/10/21/2020.10.21.348367.abstract N2 - Predicting individual differences in cognitive processes is crucial, but the ability of task-based fMRI to do so remains dubious, despite decades of costly research. We tested the ability of working-memory fMRI in predicting working-memory, using the Adolescent Brain Cognitive Development (n = 4,350). The conventionally-used mass-univariate approach led to poor out-of-sample prediction (Mean r = .1-.12). However, the multivariate ‘Elastic Net’, which draws information across brain regions, enhanced out-of-sample prediction (r = .47) by several folds. The Elastic Net also enabled us to predict cognitive performance from various tasks collected outside of the scanner, highlighting its generalizability. Moreover, using an omics-inspired approach, we combined Elastic Net with permutation, allowing us to statistically infer which brain regions contribute to individual differences while accounting for collinearity. Accordingly, our framework can build an easy-to-interpret predictive fMRI model that transfers knowledge learned from large-scale datasets to smaller samples, akin to polygenic scores in genomics.Competing Interest StatementThe authors have declared no competing interest. ER -