Discriminating schizophrenia from normal controls using resting state functional network connectivity: A deep neural network and layer-wise relevance propagation method

Weizheng Yan, Sergey Plis, Vince Daniel Calhoun, Shengfeng Liu, Rongtao Jiang, Tian Zi Jiang, Jing Sui

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

Abstract

Deep learning has gained considerable attention in the scientific community, breaking benchmark records in many fields such as speech and visual recognition [1]. Motivated by extending advancement of deep learning approaches to brain imaging classification, we propose a framework, called 'deep neural network (DNN)+ layer-wise relevance propagation (LRP)', to distinguish schizophrenia patients (SZ) from healthy controls (HCs) using functional network connectivity (FNC). 1100 Chinese subjects of 7 sites are included, each with a 50<50 FNC matrix resulted from group ICA on resting-state fMRI data. The proposed DNN+LRP not only improves classification accuracy significantly compare to four state-of-the-art classification methods (84% vs. less than 79%, 10 folds cross validation) but also enables identification of the most contributing FNC patterns related to SZ classification, which cannot be easily traced back by general DNN models. By conducting LRP, we identified the FNC patterns that exhibit the highest discriminative power in SZ classification. More importantly, when using leave-one-site-out cross validation (using 6 sites for training, 1 site for testing, 7 times in total), the cross-site classification accuracy reached 82%, suggesting high robustness and generalization performance of the proposed method, promising a wide utility in the community and great potentials for biomarker identification of brain disorders.

Original languageEnglish (US)
Title of host publication2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Proceedings
PublisherIEEE Computer Society
Pages1-6
Number of pages6
Volume2017-September
ISBN (Electronic)9781509063413
DOIs
StatePublished - Dec 5 2017
Externally publishedYes
Event2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Tokyo, Japan
Duration: Sep 25 2017Sep 28 2017

Other

Other2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017
CountryJapan
CityTokyo
Period9/25/179/28/17

Keywords

  • Deep neural network
  • Functional network connectivity
  • Layer-wise relevance propagation
  • Schizophrenia

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

Fingerprint Dive into the research topics of 'Discriminating schizophrenia from normal controls using resting state functional network connectivity: A deep neural network and layer-wise relevance propagation method'. Together they form a unique fingerprint.

  • Cite this

    Yan, W., Plis, S., Calhoun, V. D., Liu, S., Jiang, R., Jiang, T. Z., & Sui, J. (2017). Discriminating schizophrenia from normal controls using resting state functional network connectivity: A deep neural network and layer-wise relevance propagation method. In 2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Proceedings (Vol. 2017-September, pp. 1-6). IEEE Computer Society. https://doi.org/10.1109/MLSP.2017.8168179