Pedro F. da Costa

The paper introduces an active learning approach to efficiently approximate multiverse analyses in neuroimaging, demonstrating its ability to identify optimal analysis pipelines for predicting participant ages.

This study applies deep autoencoders to structural neuroimaging data from Alzheimer's and mild cognitive impairment patients to quantify deviations from healthy norms and compare performance with traditional classifiers.