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4 April 2022 Phenotype guided interpretable graph convolutional network analysis of fMRI data reveals changing brain connectivity during adolescence
Anton Orlichenko, Gang Qu, Yu-Ping Wang
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Proceedings Volume 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging; 1203612 (2022) https://doi.org/10.1117/12.2613172
Event: SPIE Medical Imaging, 2022, San Diego, California, United States
Abstract
A novel phenotype guided interpretable graph convolutional network (PGI-GCN) for the analysis of fMRI data is proposed. We utilize PGI-GCN to predict the ages of children and young adults based on multi-paradigm fMRI data of the Philadelphia Neurodevelopmental Cohort (PNC) dataset. We show PGI-GCN to have superior predictive capability compared to a simpler deep model that uses functional connectivity plus gender without the population-level graph. A learnable mask identifies 3 important intra-network (Memory Retrieval, Dorsal Attention, and Subcortical) and 3 important inter-network (Visual-Cerebellar, Visual-Dorsal Attention, and Subcortical-Cerebellar) connectivity differences between children and young adults.
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Anton Orlichenko, Gang Qu, and Yu-Ping Wang "Phenotype guided interpretable graph convolutional network analysis of fMRI data reveals changing brain connectivity during adolescence", Proc. SPIE 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1203612 (4 April 2022); https://doi.org/10.1117/12.2613172
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KEYWORDS
Functional magnetic resonance imaging
Brain
Data modeling
Neuroimaging
Neural networks
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