Ensemble Manifold Regularized Multi-Modal Graph Convolutional Network for Cognitive Ability Prediction

Objective: Multi-modal functional magnetic resonance imaging (fMRI) can be used to make predictions about individual behavioral and cognitive traits based on brain connectivity networks. Methods: To take advantage of complementary information from multi-modal fMRI, we propose an interpretable multi-...

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Vydáno v:	IEEE transactions on biomedical engineering Ročník 68; číslo 12; s. 3564 - 3573
Hlavní autoři:	Qu, Gang, Xiao, Li, Hu, Wenxing, Wang, Junqi, Zhang, Kun, Calhoun, Vince, Wang, Yu-Ping
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.12.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Achievement tests Artificial neural networks Biological system modeling Biomarkers Brain Brain mapping Brain modeling Cognition Cognition & reasoning Cognitive ability Data integration Deep learning Embedding fMRI functional connectivity Functional magnetic resonance imaging graph convolutional networks interpretable deep learning Magnetic resonance imaging Manifolds Modal data multi-modal deep learning Neural networks Neuroimaging Performance prediction Predictions Predictive models Regularization Time series analysis
ISSN:	0018-9294, 1558-2531, 1558-2531
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Shrnutí:	Objective: Multi-modal functional magnetic resonance imaging (fMRI) can be used to make predictions about individual behavioral and cognitive traits based on brain connectivity networks. Methods: To take advantage of complementary information from multi-modal fMRI, we propose an interpretable multi-modal graph convolutional network (MGCN) model, incorporating both fMRI time series and functional connectivity (FC) between each pair of brain regions. Specifically, our model learns a graph embedding from individual brain networks derived from multi-modal data. A manifold-based regularization term is enforced to consider the relationships of subjects both within and between modalities. Furthermore, we propose the gradient-weighted regression activation mapping (Grad-RAM) and the edge mask learning to interpret the model, which is then used to identify significant cognition-related biomarkers. Results: We validate our MGCN model on the Philadelphia Neurodevelopmental Cohort to predict individual wide range achievement test (WRAT) score. Our model obtains superior predictive performance over GCN with a single modality and other competing approaches. The identified biomarkers are cross-validated from different approaches. Conclusion and Significance: This paper develops a new interpretable graph deep learning framework for cognition prediction, with the potential to overcome the limitations of several current data-fusion models. The results demonstrate the power of MGCN in analyzing multi-modal fMRI and discovering significant biomarkers for human brain studies.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0018-9294 1558-2531 1558-2531
DOI:	10.1109/TBME.2021.3077875