A deep learning fusion model for brain disorder classification: Application to distinguishing schizophrenia and autism spectrum disorder

Yuhui Du, Bang Li, Yuliang Hou, Vince D. Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Deep learning has shown a great promise in classifying brain disorders due to its powerful ability in learning optimal features by nonlinear transformation. However, given the high-dimension property of neuroimaging data, how to jointly exploit complementary information from multimodal neuroimaging data in deep learning is difficult. In this paper, we propose a novel multilevel convolutional neural network (CNN) fusion method that can effectively combine different types of neuroimage-derived features. Importantly, we incorporate a sequential feature selection into the CNN model to increase the feature interpretability. To evaluate our method, we classified two symptom-related brain disorders using large-sample multi-site data from 335 schizophrenia (SZ) patients and 380 autism spectrum disorder (ASD) patients within a cross-validation procedure. Brain functional networks, functional network connectivity, and brain structural morphology were employed to provide possible features. As expected, our fusion method outperformed the CNN model using only single type of features, as our method yielded higher classification accuracy (with mean accuracy >85%) and was more reliable across multiple runs in differentiating the two groups. We found that the default mode, cognitive control, and subcortical regions contributed more in their distinction. Taken together, our method provides an effective means to fuse multimodal features for the diagnosis of different psychiatric and neurological disorders.

Original languageEnglish (US)
Title of host publicationProceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450379649
DOIs
StatePublished - Sep 21 2020
Event11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020 - Virtual, Online, United States
Duration: Sep 21 2020Sep 24 2020

Publication series

NameProceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020

Conference

Conference11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020
CountryUnited States
CityVirtual, Online
Period9/21/209/24/20

Keywords

  • Classification
  • Deep learning
  • Fusion
  • Multimodal neuroimaging

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Biomedical Engineering
  • Health Informatics

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