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| 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 |