Aphasia fMRI to FC Analysis using VAE

This demo uses a Variational Autoencoder (VAE) to analyze functional connectivity patterns in the brain and their relationship to demographic variables.

Dataset Information

By default, this uses the SreekarB/OSFData dataset from HuggingFace with the following variables:

ID: Subject identifier
wab_aq: Aphasia severity score
age: Age of the subject
mpo: Months post onset
education: Years of education
gender: Subject gender
handedness: Subject handedness (ignored in the analysis)

Data Source (HF Dataset ID or Local Directory)

Latent Dimensions

8 64

Number of Epochs

100 5000

Batch Size

8 64

Use HuggingFace Dataset

Status

Analysis Results

Accuracy Metrics

Run analysis to see reconstruction accuracy metrics here

Examples

Data Source (HF Dataset ID or Local Directory)	Latent Dimensions	Number of Epochs	Batch Size	Use HuggingFace Dataset

How this works

Data Loading: The system downloads NIfTI files (P01_rs.nii format) from the SreekarB/OSFData dataset
Preprocessing: The fMRI data is processed using the Power 264 atlas and converted to functional connectivity (FC) matrices
VAE Training: A conditional VAE model learns the latent representation of brain connectivity
Predictive Modeling: The system trains a Random Forest regressor on latent features to predict WAB-AQ scores (aphasia severity)
Analysis: The system analyzes relationships between latent brain connectivity patterns and demographic variables
Visualization: Results are displayed showing original FC, reconstructed FC, generated FC, and demographic correlations

Note: This app works with the SreekarB/OSFData dataset that contains NIfTI files and demographic information.