neuroimaging

This paper introduces Neuro-JEPA, a foundation model that uses a latent predictive objective and Mixture-of-Experts architecture to encode brain MRI scans across T1w, T2w, and FLAIR sequences, pretrained on a large dataset of 1.55 million scans.

This paper introduces BrainSimSiam, a lightweight self-supervised framework using siamese networks to learn robust fMRI representations from positive-only pairs, achieving strong performance on downstream tasks even with limited data.

This paper uses EEG recordings to study neural dynamics when humans process AI-generated hallucinated content, revealing distinct cognitive patterns and differences between misjudged and correctly judged hallucinations.

This blog post explores the intersection of machine learning and neuroscience, specifically focusing on using multivariate classification techniques on neuroimaging data to understand brain function and behavior.