Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81395
Title: Interpretable learning approaches in resting-state functional connectivity analysis : the case of autism spectrum disorder
Authors: Hu, J
Cao, L
Li, T
Liao, B
Dong, S
Li, P 
Issue Date: 2020
Source: Computational and mathematical methods in medicine, 2020, 1394830, p. 1-12
Abstract: Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, the internal logics of these networks are difficult to interpret, especially with regard to how specific network architecture decisions are made. In this paper, we study an interpretable neural network model as a method to identify ASD participants from functional magnetic resonance imaging (fMRI) data and interpret results of the model in a precise and consistent manner. First, we propose an interpretable fully connected neural network (FCNN) to classify two groups, ASD versus healthy controls (HC), based on input data from resting-state functional connectivity (rsFC) between regions of interests (ROIs). The proposed FCNN model is a piecewise linear neural network (PLNN) which uses piecewise linear function LeakyReLU as its activation function. We experimentally compared the FCNN model against widely used classification models including support vector machine (SVM), random forest, and two new classes of deep neural network models in a large dataset containing 871 subjects from ABIDE I database. The results show the proposed FCNN model achieves the highest classification accuracy. Second, we further propose an interpreting method which could explain the trained model precisely with a precise linear formula for each input sample and decision features which contributed most to the classification of ASD versus HC participants in the model. We also discuss the implications of our proposed approach for fMRI data classification and interpretation.
Publisher: Hindawi Publishing Corporation
Journal: Computational and mathematical methods in medicine 
ISSN: 1748-670X
EISSN: 1748-6718
DOI: 10.1155/2020/1394830
Rights: Copyright © 2020 Jinlong Hu et al. This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/),which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following publication Hu, J., Cao, L., Li, T., Liao, B., Dong, S., & Li, P. (2020). Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder. Computational and Mathematical Methods in Medicine, 2020, 1394830, 1-12 is available at https://dx.doi.org/10.1155/2020/1394830
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