# Omni-fMRI: A Universal Atlas-Free fMRI Foundation Model

arXiv: https://arxiv.org/abs/2601.23090 · HTML: https://mlsanity.com/paper/2601.23090/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2026.

## Built on by (most cited first, 11 of 11)

- [Brain-DiT: A Universal Multi-state fMRI Foundation Model with Metadata-Conditioned Pretraining](https://mlsanity.com/paper/2604.12683/) (2026)
- [FlexiBrain: Resolution-Agnostic Voxel-Level Encoding for Native fMRI](https://mlsanity.com/paper/2606.11500/) (2026)
- [BrainWorld: A Structural-Prior-Conditioned Generative Model for Whole-Brain 4D fMRI Dynamics](https://mlsanity.com/paper/2606.17742/) (2026)
- [Pretraining Induces a Reusable Spectral Basis for Downstream Task Adaptation](https://mlsanity.com/paper/2605.07302/) (2026)
- [Beyond Single-Source Cognitive Taskonomy:Multi-Source Task Relations through fMRI Transfer Learning](https://mlsanity.com/paper/2606.26279/) (2026)
- [BrainTaskonomy: Learning How to Pretrain and What to Transfer in fMRI Foundation Models](https://mlsanity.com/paper/2609.10518/) (2026)
- [PIMSM: Physics-Informed Multi-Scale Mamba for Stable Neural Representations under Distribution Shift](https://mlsanity.com/paper/2605.16351/) (2026)
- [Omni-Sleep: A Sleep Foundation Model via Hierarchical Contrastive Learning of CNS-ANS Dynamics](https://mlsanity.com/paper/2607.07720/) (2026)
- [A Scaling Study for fMRI Foundation Models](https://mlsanity.com/paper/2609.27232/) (2026)
- [FlatClip: Reusing Image Foundation Models for fMRI Representation Learning via Cortical Flatmaps](https://mlsanity.com/paper/2609.31204/) (2026)
- [Natural Image Autoencoder-Based fMRI Representations for Trait and State Prediction](https://mlsanity.com/paper/2609.34167/) (2026)

## Abstract (arXiv)

Self-supervised fMRI foundation models have shown promising transfer performance, yet most rely on predefined region-level parcellations that discard fine-grained voxel information and introduce atlas-dependent biases. We propose Omni-fMRI, an atlas-free foundation model that operates directly on voxel-level signals. To enable scalable pretraining on 49,497 fMRI sessions across nine datasets, Omni-fMRI introduces a dynamic patching mechanism that substantially reduces computational cost while preserving informative spatial structure. To support reproducibility and fair comparison, we establish a comprehensive benchmark suite spanning 11 datasets and a diverse set of resting-state and task-based fMRI tasks. Experimental results demonstrate that Omni-fMRI consistently outperforms existing foundation models, providing a scalable and reproducible framework for atlas-free brain representation learning. Code and logs are available.