We propose a novel interpretable deep learning framework that learns goal-specific functional connectivity matrix directly from time series and employs a specialized graph neural network for the final classification.
Our model, DSAM, leverages:
- Temporal causal convolutional networks to capture the temporal dynamics in both low- and high-level feature representations.
- A temporal attention unit to identify important time points.
- A self-attention unit to construct the goal-specific connectivity matrix.
- A novel variant of graph neural network to capture the spatial dynamics for downstream classification.
- Specify the model under:
conf/config.yaml - All other specifications and hyperparameters can be found under:
/conf/* - Run the main model: python main.py
This repository also contains numerous baseline implementations for brain network analysis. Part of the code was adapted from the Brain Network Transformer Repository.