---
title: Aphasia fMRI VAE Analysis
emoji: 🧠
colorFrom: blue
colorTo: pink
sdk: gradio
sdk_version: 5.20.1
app_file: app.py
pinned: false
---

# Aphasia fMRI to FC Analysis using VAE

This demo performs functional connectivity analysis on fMRI data using a Variational Autoencoder (VAE) approach. It's designed to work with aphasia patient data, analyzing brain connectivity patterns and their relationship to demographic variables.

## About the Model

This application implements a VAE model that:
1. Takes functional connectivity (FC) matrices derived from fMRI data
2. Learns a lower-dimensional latent representation of brain connectivity
3. Conditions the generation process on demographic variables (age, sex, time post-stroke, WAB scores)
4. Allows analysis of relationships between brain connectivity patterns and demographic variables

## Dataset

This demo uses the [SreekarB/OSFData](https://huggingface.co/datasets/SreekarB/OSFData) dataset from HuggingFace, which contains:

- NIfTI files in P01_rs.nii format containing fMRI data
- Demographic information directly in the dataset:
  - ID: Subject identifier 
  - wab_aq: Aphasia quotient score (severity measure)
  - age: Subject age
  - mpo: Months post onset
  - education: Years of education
  - gender: Subject gender
  - handedness: Subject handedness (ignored in this analysis)

The application processes the NIfTI files using the Power 264 atlas to create functional connectivity matrices that are then analyzed by the VAE model.

## How to Use

1. **Configure Parameters**:
   - **Data Source**: By default, it uses the SreekarB/OSFData HuggingFace dataset
   - **Latent Dimensions**: Controls the size of the latent space (default: 32)
   - **Number of Epochs**: Training iterations (default: 200 for demo)
   - **Batch Size**: Training batch size (default: 16)

2. **Start Training**:
   - Click the "Start Training" button to begin the analysis
   - The training progress will be displayed in the Status area

3. **View Results**:
   - The VAE will learn latent representations of brain connectivity
   - Results will show correlations between demographic variables and latent brain patterns
   - The visualization shows original FC, reconstructed FC, and a new FC matrix generated from specific demographic values

## Outputs

The application produces visualizations showing:
- Original FC matrix
- Reconstructed FC matrix
- Generated FC matrix (based on specific demographic inputs)
- Correlation plots between latent variables and demographic features

## Technical Details

- Framework: PyTorch
- Interface: Gradio
- Dataset: HuggingFace Datasets API
- Analysis: Custom implementation of conditional VAE with demographic conditioning