Resample aggregating improves the generalizability of connectome predictive modeling.

ABSTRACT: It is a longstanding goal of neuroimaging to produce reliable, generalizable models of brain behavior relationships. More recently, data driven predictive models have become popular. However, overfitting is a common problem with statistical models, which impedes model generalization. Cross validation (CV) is often used to estimate expected model performance within sample. Yet, the best way to generate brain behavior models, and apply them out-of-sample, on an unseen dataset, is unclear. As a solution, this study proposes an ensemble learning method, in this case resample aggregating, encompassing both model parameter estimation and feature selection. Here we investigate the use of resampled aggregated models when used to estimate fluid intelligence (fIQ) from fMRI based functional connecti

SUBMITTER: O'Connor D 

PROVIDER: S-EPMC8282199 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature