Andrea Duggento

This paper introduces ES-GC, a nonlinear Granger causality estimator using echo-state networks, showing improved detection of causal interactions in biological networks.

This paper develops a deep learning architecture for fusing multiple 3D neuroimaging modalities into informative latent embeddings, demonstrating their ability to cluster subjects into separable phenotypic strata.

This paper develops a parameter-efficient deep learning model to predict conversion from mild cognitive impairment to Alzheimer’s disease using multimodal data, achieving high accuracy and specificity.