Effective and Interpretable fMRI Analysis via Functional Brain Network Generation

Overall framework of our end-to-end fMRI analysis pipeline with functional brain network generation.
Publication
ICML 2021 Workshop on Interpretable Machine Learning in Healthcare

Recent studies in neuroscience show great potential of functional brain networks constructed from fMRI data for popularity modeling and clinical predictions. However, existing functional brainnetworks are noisy and unaware of down stream 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.

Hejie Cui
Hejie Cui
Doctoral Student in CS

My research interests include graph machine learning and knowledge graphs.

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