# FlexiBrain: Resolution-Agnostic Voxel-Level Encoding for Native fMRI

arXiv: https://arxiv.org/abs/2606.11500 · HTML: https://mlsanity.com/paper/2606.11500/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Physiological signal modeling, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 18 of its 34 references that are in the map.
- First posted on arXiv 9 Jun 2026.

## Placed in

- State-space sequence models (S4, Mamba) / [Physiological signal modeling](https://mlsanity.com/ssm/physiological-signal-modeling/)

## Builds on (most cited first, 18 of 18)

- [Taskonomy: Disentangling Task Transfer Learning](https://mlsanity.com/paper/1804.08328/) (2018)
- [Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors](https://mlsanity.com/paper/2305.18274/) (2023)
- [FlexiViT: One Model for All Patch Sizes](https://mlsanity.com/paper/2212.08013/) (2022)
- [MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data](https://mlsanity.com/paper/2403.11207/) (2024)
- [Brain-JEPA: Brain Dynamics Foundation Model with Gradient Positioning and Spatiotemporal Masking](https://mlsanity.com/paper/2409.19407/) (2024)
- [SwiFT: Swin 4D fMRI Transformer](https://mlsanity.com/paper/2307.05916/) (2023)
- [BrainMass: Advancing Brain Network Analysis for Diagnosis with Large-scale Self-Supervised Learning](https://mlsanity.com/paper/2403.01433/) (2024)
- [Towards a general-purpose foundation model for fMRI analysis](https://mlsanity.com/paper/2506.11167/) (2025)
- [Brain Harmony: A Multimodal Foundation Model Unifying Morphology and Function into 1D Tokens](https://mlsanity.com/paper/2509.24693/) (2025)
- [Omni-fMRI: A Universal Atlas-Free fMRI Foundation Model](https://mlsanity.com/paper/2601.23090/) (2026)
- [Brain-DiT: A Universal Multi-state fMRI Foundation Model with Metadata-Conditioned Pretraining](https://mlsanity.com/paper/2604.12683/) (2026)
- [SLIM-Brain: A Data- and Training-Efficient Foundation Model for fMRI Data Analysis](https://mlsanity.com/paper/2512.21881/) (2025)
- [Explainable fMRI-based Brain Decoding via Spatial Temporal-pyramid Graph Convolutional Network](https://mlsanity.com/paper/2210.05713/) (2022)
- [Uncovering cognitive taskonomy through transfer learning in masked autoencoder-based fMRI reconstruction](https://mlsanity.com/paper/2407.00033/) (2024)
- [Transfer learning to decode brain states reflecting the relationship between cognitive tasks](https://mlsanity.com/paper/2206.03950/) (2022)
- [Voxel-Level Brain States Prediction Using Swin Transformer](https://mlsanity.com/paper/2506.11455/) (2025)
- [DCA: Graph-Guided Deep Embedding Clustering for Brain Atlases](https://mlsanity.com/paper/2509.01426/) (2025)
- [Whole-brain Transferable Representations from Large-Scale fMRI Data Improve Task-Evoked Brain Activity Decoding](https://mlsanity.com/paper/2507.22378/) (2025)

## Built on by (most cited first, 6 of 6)

- [BrainWorld: A Structural-Prior-Conditioned Generative Model for Whole-Brain 4D fMRI Dynamics](https://mlsanity.com/paper/2606.17742/) (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)
- [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)

## Abstract (arXiv)

The success of large-scale deep learning models in neuroscience is fundamentally constrained by severe data heterogeneity. Native fMRI data aggregated from diverse sources exhibit substantial variation in both spatial and temporal resolutions. Consequently, most existing frameworks rely on lengthy, rigid preprocessing pipelines that enforce uniformity across datasets. This practice introduces two critical limitations: (1) potential degradation of subject-specific anatomical information; (2) significant computational overhead, often requiring hours of processing per subject. Here, we propose FlexiBrain, a resolution-agnostic voxel-level encoding framework for native fMRI based on Mamba-JEPA. FlexiBrain defines patch sizes in real-world physical units and employs a dynamic patch resizing, thereby bypassing destructive spatial standardization while enabling direct ingestion of data in native space. We instantiate the framework using an efficient Mamba-JEPA backbone to model high-dimensional 4D fMRI signals. Across five diverse downstream neuroscience tasks, FlexiBrain consistently outperforms recent state-of-the-art methods, achieving gains of up to 12 percentage points without external data augmentation. Importantly, FlexiBrain functions as a seamless plug-in module, substantially reducing preprocessing costs and accelerating the development of robust voxel-level fMRI foundation models. Code is available at https://github.com/OneMore1/FlexiBrain.