Eloy Geenjaar

This paper introduces a variational autoencoder framework to interpolate functional network connectivity data across neuropsychiatric disorders, revealing continuous gradients and heterogeneity.

This study uses an ODE model to separate rest and task signals in fMRI data, showing task-specific dynamics are subsets of rest dynamics and improving reaction time predictions.

This paper introduces a deep learning model to analyze resting-state fMRI dynamics, finding that context embeddings better predict schizophrenia diagnosis and correlate with age and symptom severity.

The paper proposes a decentralized sparse federated learning approach for neuroimaging data to reduce communication overhead while maintaining data privacy.