3D-CNN based discrimination of schizophrenia using resting-state fMRI

Muhammad Naveed Iqbal Qureshi; Jooyoung Oh; Boreom Lee

doi:10.1016/j.artmed.2019.06.003

3D-CNN based discrimination of schizophrenia using resting-state fMRI

Artif Intell Med. 2019 Jul:98:10-17. doi: 10.1016/j.artmed.2019.06.003. Epub 2019 Jun 22.

Authors

Muhammad Naveed Iqbal Qureshi¹, Jooyoung Oh², Boreom Lee³

Affiliations

¹ Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada; Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada; Department of Psychiatry, McGill University, Montreal, QC, Canada; Montreal Neurological Institute and Hospital, Montreal, QC, Canada.
² Department of Psychiatry, Gangnam Severance Hospital, Yonsei University Health System, Seoul, South Korea; Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, South Korea.
³ Department of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), 61005, Gwangju, South Korea. Electronic address: leebr@gist.ac.kr.

PMID: 31521248
DOI: 10.1016/j.artmed.2019.06.003

Abstract

Motivation: This study reports a framework to discriminate patients with schizophrenia and normal healthy control subjects, based on magnetic resonance imaging (MRI) of the brain. Resting-state functional MRI data from a total of 144 subjects (72 patients with schizophrenia and 72 healthy controls) was obtained from a publicly available dataset using a three-dimensional convolution neural network 3D-CNN based deep learning classification framework and ICA based features.

Results: We achieved 98.09 ± 1.01% ten-fold cross-validated classification accuracy with a p-value < 0.001 and an area under the curve (AUC) of 0.9982 ± 0.015. In addition, differences in functional connectivity between the two groups were statistically analyzed across multiple resting-state networks. The disconnection between the visual and frontal network was prominent in patients, while they showed higher connectivity between the default mode network and other task-positive/ cerebellar networks. These ICA functional network maps served as highly discriminative three-dimensional imaging features for the discrimination of schizophrenia in this study.

Conclusion: Due to the very high AUC, this research with more validation on the cross diagnosis and publicly available dataset, may be translated in future as an adjunct tool to assist clinicians in the initial screening of schizophrenia.

Keywords: 3D-CNN; 3D-group ICA; Classification; Neuroimaging; Resting-state fMRI; Schizophrenia; TensorFlow.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adult
Area Under Curve
Brain / diagnostic imaging*
Case-Control Studies
Deep Learning*
Discriminant Analysis
Female
Functional Neuroimaging
Humans
Magnetic Resonance Imaging
Male
Middle Aged
Neural Networks, Computer
Rest
Schizophrenia / diagnostic imaging*
Young Adult