README.md

metadata

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 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