---
license: cc-by-nc-4.0
tags:
- fmri
- brain
- subcortex
- neuroscience
metrics:
- pearsonr
model-index:
- name: tribev2-subcortical-v3-lahner
  results:
  - task:
      type: regression
      name: fMRI voxel prediction (subcortical)
    dataset:
      name: Lahner2024Bold (BOLD Moments)
      type: lahner2024bold
      split: test
    metrics:
    - name: Pearson r
      type: pearsonr
      value: 0.165
      verified: false
language:
- en
base_model:
- facebook/tribev2
pipeline_tag: graph-ml
---

# TRIBE v2 Subcortical Head (Lahner)

This model provides **subcortical fMRI prediction weights** for the TRIBE v2 architecture.  
It predicts BOLD activity in subcortical brain regions (e.g. hippocampus, amygdala, pallidum) from multimodal inputs (video, audio, text).


![image](https://cdn-uploads.huggingface.co/production/uploads/69cfeb2e504736d71118dec9/ELQVk9bMyGlbJFgIPshcR.png)

---


## Model Details

- **Developed by:** Logan Fernandez  
- **Model type:** Multimodal fMRI encoding model (regression)  
- **Base model:** TRIBE v2  
- **License:** CC BY-NC 4.0  

---

## Uses

- Predict deep brain activity from naturalistic stimuli  
- Study stimulus → brain response relationships  
- Analyze emotional response to stimuli
- Make the brain watch reels (See Github repo)

---

## Limitations

- Subject-specific behavior for Lahner-style subjects, tuned to short-form media
- Correlation, not causation
---

## Training

- Dataset: Lahner2024Bold (BOLD Moments)  
- Features: video (V-JEPA), audio (Wac2Vert), text (Qwen3B)
- Projection: subcortical mask  

---

## Evaluation

- **Metric:** Pearson correlation  
- **Result:** `r = 0.165` (test split)

This reflects the difficulty of predicting subcortical activity, which is noisier and lower resolution than cortical signals.

---

## Repository

https://github.com/Enzyme0/homunculus

---

## Contact

Logan Fernandez
[loganfe@outlook.com](mailto:loganfe@outlook.com)


## Acknowledgements

This model builds on TRIBE-V2 and is intended as a downstream or derived component built from that work.

## Citation

Please cite both this model and TRIBE-V2 when relevant:

```bibtex
@article{dAscoli2026TribeV2,
  title={A foundation model of vision, audition, and language for in-silico neuroscience},
  author={d'Ascoli, St{\'e}phane and Rapin, J{\'e}r{\'e}my and Benchetrit, Yohann and Brookes, Teon and Begany, Katelyn and Raugel, Jos{\'e}phine and Banville, Hubert and King, Jean-R{\'e}mi},
  year={2026}
}