Free SKILL.md scraped from GitHub. Clone the repo or copy the file directly into your Claude Code skills directory.
npx versuz@latest install hiyenwong-ai-collection-collection-skills-brain-dit-fmri-foundation-model-v4git clone https://github.com/hiyenwong/ai_collection.gitcp ai_collection/SKILL.MD ~/.claude/skills/hiyenwong-ai-collection-collection-skills-brain-dit-fmri-foundation-model-v4/SKILL.md---
name: brain-dit-fmri-foundation-model-v4
description: Brain-DiT universal multi-state fMRI foundation model methodology. Metadata-conditioned diffusion pretraining with DiT on 349,898 sessions across 24 datasets spanning resting, task, naturalistic, disease, and sleep states. Activation: brain-dit, fmri foundation model, diffusion transformer brain, metadata-conditioned pretraining, multi-state fmri, brain diffusion
---
# Brain-DiT: Universal Multi-state fMRI Foundation Model
Based on: *Brain-DiT: A Universal Multi-state fMRI Foundation Model with Metadata-Conditioned Pretraining* (Xia et al., 2026, arXiv:2604.12683)
## Core Contribution
Brain-DiT is a **universal multi-state fMRI foundation model** that adopts **metadata-conditioned diffusion pretraining** with a Diffusion Transformer (DiT), pretrained on **349,898 sessions from 24 datasets** spanning resting, task, naturalistic, disease, and sleep states.
## Key Findings
1. **Diffusion-based generative pretraining is stronger** than masked reconstruction or alignment for fMRI representation learning
2. **Metadata-conditioned pretraining** improves downstream performance by disentangling intrinsic neural dynamics from population-level variability
3. **Downstream tasks exhibit distinct preferences for representational scale**:
- ADNI classification benefits more from global semantic representations
- Age/sex prediction relies more on fine-grained local structure
4. Multi-scale representations capture both fine-grained functional structure and global semantics
## Architecture
```
fMRI Input → Metadata Conditioning → Diffusion Transformer (DiT) → Multi-scale Representations
├── Fine-grained local structure
└── Global semantics
```
## Pretraining Strategy
| Aspect | Detail |
|--------|--------|
| Method | Metadata-conditioned diffusion pretraining |
| Data | 349,898 sessions from 24 datasets |
| Brain States | Resting, task, naturalistic, disease, sleep |
| Model | Diffusion Transformer (DiT) |
## Comparison with Prior Methods
| Method | Pretraining Objective | Limitation |
|--------|----------------------|------------|
| Prior fMRI FMs | Masked reconstruction (raw/latent) | Limited brain states, mismatched tasks |
| Brain-DiT | Metadata-conditioned diffusion | Multi-state, multi-scale, disentangled |
## Downstream Task Scale Preferences
| Task | Preferred Scale | Rationale |
|------|----------------|-----------|
| ADNI classification | Global semantic | Disease patterns are distributed |
| Age prediction | Fine-grained local | Regional developmental changes |
| Sex prediction | Fine-grained local | Structural dimorphism patterns |
## Implementation
- **Code**: https://github.com/REDMAO4869/Brain-DiT
- **PDF**: https://arxiv.org/pdf/2604.12683
## Pitfalls
1. **Scale mismatch**: Different downstream tasks need different representational scales — don't use a single-scale model
2. **Metadata quality**: Metadata-conditioned pretraining requires well-structured, consistent metadata
3. **Data heterogeneity**: 24 datasets with different acquisition protocols need careful harmonization
## Use Cases
1. fMRI foundation model pretraining with diffusion
2. Multi-state brain representation learning
3. Metadata-conditioned neural representation disentanglement
4. Cross-dataset fMRI analysis
## Related Skills
- `neural-dynamics-universal-translator`: Cross-model neural dynamics translation
- `brain-foundation-model-batch-effects`: Batch effects in brain FMs
- `brain-network-controllability`: Network control analysis