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Effective and Interpretable fMRI Analysis with Functional Brain Network Generation
Xuan Kan · Hejie Cui · Ying Guo · Carl Yang

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in 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 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.

Author Information

Xuan Kan (Emory University)
Hejie Cui (Emory University)

Hi there! This is Hejie Cui (pronounced as “He-jay Tsuee”, 崔鹤洁 in Chinese). I also go by the name Kelly. I am a second-year Ph.D. student in Computer Science at Emory University, under the supervision of Dr. Carl Yang in Emory Graph Mining Lab. I have also been working with Dr. Eugene Agichtein in Emory Intelligent Information Access Lab. Before joining Emory, I got my bachelor’s degree in Software Engineering from Tongji University, where I was working with Dr. Lin Zhang. My current research interests lie in machine learning with an emphasis on graph representation learning and its application to multi-modality data and brain network analysis.

Ying Guo (Emory University)
Carl Yang (Emory University)

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