All Competitors

A graph foundation model for fMRI brain networks, pretrained across 27 datasets with graph and language prompts for zero/few-shot generalization to unseen disorders.

A 1.2B-parameter fMRI foundation model of brain connectomes that uses brain-environment interaction tokens and multitask learning for behavior and disease prediction.

An fMRI foundation model that trains Vision Transformers on cortical flat-map videos via masked autoencoding, exhibiting power-law scaling on brain activity reconstruction.

A parameter-efficient (5.6M) multimodal foundation model that fuses fMRI time series with diffusion-MRI structural connectivity for brain dynamics analysis.

A spatiotemporal foundation model that learns generalizable representations directly from 4D functional MRI volumes for diverse brain-imaging tasks.

A hypergraph foundation model for brain disease diagnosis, self-supervised on high-order fMRI connectivity and fine-tuned few-shot across four diseases.

A foundational fMRI model that learns brain dynamics via stochastic optimal control, pretrained self-supervised on resting-state scans from 41,072 UK Biobank subjects.

A fMRI brain-dynamics foundation model that adapts the Joint-Embedding Predictive Architecture with brain gradient positioning and spatiotemporal masking.

A region-aware masked-autoencoder framework that learns self-supervised representations directly from fMRI time-series via per-ROI embeddings and graph attention.

Self-supervised foundation model for functional brain network analysis from fMRI, pretrained on ~70K samples across 30 datasets for disorder diagnosis.

A foundation model for fMRI brain activity recordings, pretrained with masked autoencoding on ~6,700 hours of data for clinical prediction and zero-shot network discovery.