Classification of Schizophrenia Patients and Healthy Controls Using ICA of Complex-Valued fMRI Data and Convolutional Neural Networks

Yue Qiu, Qiu Hua Lin, Li Dan Kuang, Wen Da Zhao, Xiao Feng Gong, Fengyu Cong, Vince D. Calhoun

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

4 Scopus citations


Deep learning has contributed greatly to functional magnetic resonance imaging (fMRI) analysis, however, spatial maps derived from fMRI data by independent component analysis (ICA), as promising biomarkers, have rarely been directly used to perform individualized diagnosis. As such, this study proposes a novel framework combining ICA and convolutional neural network (CNN) for classifying schizophrenia patients (SZs) and healthy controls (HCs). ICA is first used to obtain components of interest which have been previously implicated in schizophrenia. Functionally informative slices of these components are then selected and labelled. CNN is finally employed to learn hierarchical diagnostic features from the slices and classify SZs and HCs. We use complex-valued fMRI data instead of magnitude fMRI data, in order to obtain more contiguous spatial activations. Spatial maps estimated by ICA with multiple model orders are employed for data argumentation to enhance the training process. Evaluations are performed using 82 resting-state complex-valued fMRI datasets including 42 SZs and 40 HCs. The proposed method shows an average accuracy of 72.65% in the default mode network and 78.34% in the auditory cortex for slice-level classification. When performing subject-level classification based on majority voting, the result shows 91.32% and 98.75% average accuracy, highlighting the potential of the proposed method for diagnosis of schizophrenia and other neurological diseases.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings
EditorsHuchuan Lu, Huajin Tang, Zhanshan Wang
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783030228071
StatePublished - 2019
Event16th International Symposium on Neural Networks, ISNN 2019 - Moscow, Russian Federation
Duration: Jul 10 2019Jul 12 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11555 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Symposium on Neural Networks, ISNN 2019
Country/TerritoryRussian Federation


  • Data argumentation
  • Deep learning
  • ICA
  • Model order
  • Schizophrenia
  • fMRI

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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