Interpretable Multimodal Fusion Networks Reveal Mechanisms of Brain Cognition

Wenxing Hu, Xianghe Meng, Yuntong Bai, Aiying Zhang, Gang Qu, Biao Cai, Gemeng Zhang, Tony W. Wilson, Julia M. Stephen, Vince D. Calhoun, Yu Ping Wang

Research output: Contribution to journalArticlepeer-review

Abstract

The combination of multimodal imaging and genomics provides a more comprehensive way for the study of mental illnesses and brain functions. Deep network-based data fusion models have been developed to capture their complex associations, resulting in improved diagnosis of diseases. However, deep learning models are often difficult to interpret, bringing about challenges for uncovering biological mechanisms using these models. In this work, we develop an interpretable multimodal fusion model to perform automated diagnosis and result interpretation simultaneously. We name it Grad-CAM guided convolutional collaborative learning (gCAM-CCL), which is achieved by combining intermediate feature maps with gradient-based weights. The gCAM-CCL model can generate interpretable activation maps to quantify pixel-level contributions of the input features. Moreover, the estimated activation maps are class-specific, which can therefore facilitate the identification of biomarkers underlying different groups. We validate the gCAM-CCL model on a brain imaging-genetic study, and demonstrate its applications to both the classification of cognitive function groups and the discovery of underlying biological mechanisms. Specifically, our analysis results suggest that during task-fMRI scans, several object recognition related regions of interests (ROIs) are activated followed by several downstream encoding ROIs. In addition, the high cognitive group may have stronger neurotransmission signaling while the low cognitive group may have problems in brain/neuron development due to genetic variations.

Original languageEnglish (US)
Article number9349455
Pages (from-to)1474-1483
Number of pages10
JournalIEEE transactions on medical imaging
Volume40
Issue number5
DOIs
StatePublished - May 2021

Keywords

  • CAM
  • Interpretable
  • brain functional connectivity
  • multimodal fusion

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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