Recent studies in neuroscience show great potential of functional brain networks constructed from fMRI data for popularity modeling and clinical predictions. However, existing functional brain networks are noisy and unaware of downstream prediction tasks, while also incompatible with recent powerful machine learning models of GNNs. In this work, we develop an end-to-end trainable pipeline to extract prominent fMRI features, generate brain networks, and make predictions with GNNs, all under the guidance of downstream prediction tasks. Preliminary experiments on the PNC fMRI data show the superior effectiveness and unique interpretability of our framework.
@article{arxiv.2107.11247,
title = {Effective and Interpretable fMRI Analysis via Functional Brain Network Generation},
author = {Xuan Kan and Hejie Cui and Ying Guo and Carl Yang},
journal= {arXiv preprint arXiv:2107.11247},
year = {2021}
}
Comments
This paper has been accepted for ICML 2021 Workshop for Interpretable Machine Learning in Healthcare