Dataset Information

BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis.

ABSTRACT: Understanding which brain regions are related to a specific neurological disorder or cognitive stimuli has been an important area of neuroimaging research. We propose BrainGNN, a graph neural network (GNN) framework to analyze functional magnetic resonance images (fMRI) and discover neurological biomarkers. Considering the special property of brain graphs, we design novel ROI-aware graph convolutional (Ra-GConv) layers that leverage the topological and functional information of fMRI. Motivated by the need for transparency in medical image analysis, our BrainGNN contains ROI-selection pooling layers (R-pool) that highlight salient ROIs (nodes in the graph), so that we can infer which ROIs are important for prediction. Furthermore, we propose regularization terms-unit loss, topK pooling (TPK) loss and group-level consistency (GLC) loss-on pooling results to encourage reasonable ROI-selection and provide flexibility to encourage either fully individual- or patterns that agree with group-level data. We apply the BrainGNN framework on two independent fMRI datasets: an Autism Spectrum Disorder (ASD) fMRI dataset and data from the Human Connectome Project (HCP) 900 Subject Release. We investigate different choices of the hyper-parameters and show that BrainGNN outperforms the alternative fMRI image analysis methods in terms of four different evaluation metrics. The obtained community clustering and salient ROI detection results show a high correspondence with the previous neuroimaging-derived evidence of biomarkers for ASD and specific task states decoded for HCP. Our code is available at https://github.com/xxlya/BrainGNN_Pytorch.

SUBMITTER: Li X

PROVIDER: S-EPMC9916535 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Publications

BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis.

Li Xiaoxiao X Zhou Yuan Y Dvornek Nicha N Zhang Muhan M Gao Siyuan S Zhuang Juntang J Scheinost Dustin D Staib Lawrence H LH Ventola Pamela P Duncan James S JS

Medical image analysis 20210912

Understanding which brain regions are related to a specific neurological disorder or cognitive stimuli has been an important area of neuroimaging research. We propose BrainGNN, a graph neural network (GNN) framework to analyze functional magnetic resonance images (fMRI) and discover neurological biomarkers. Considering the special property of brain graphs, we design novel ROI-aware graph convolutional (Ra-GConv) layers that leverage the topological and functional information of fMRI. Motivated b ...[more]

PMID: 34655865

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Dataset Information

BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis.

Publications

BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis.

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