Bridging the Gap between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing.

ABSTRACT: A fundamental approach in neuroscience research is to test hypotheses based on neuropsychological and behavioral measures, i.e., whether certain factors (e.g., related to life events) are associated with an outcome (e.g., depression). In recent years, deep learning has become a potential alternative approach for conducting such analyses by predicting an outcome from a collection of factors and identifying the most "informative" ones driving the prediction. However, this approach has had limited impact as its findings are not linked to statistical significance of factors supporting hypotheses. In this article, we proposed a flexible and scalable approach based on the concept of permutation testing that integrates hypothesis testing into the data-driven deep learning analysis. We apply our a

SUBMITTER: Paschali M 

PROVIDER: S-EPMC9632755 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature