Diagnosing autism spectrum disorder using brain entropy: A fast entropy method

Liangliang Zhang; Xun-Heng Wang; Lihua Li

doi:10.1016/j.cmpb.2019.105240

Diagnosing autism spectrum disorder using brain entropy: A fast entropy method

Comput Methods Programs Biomed. 2020 Jul:190:105240. doi: 10.1016/j.cmpb.2019.105240. Epub 2019 Nov 27.

Authors

Liangliang Zhang¹, Xun-Heng Wang², Lihua Li³

Affiliations

¹ School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China.
² Institute of Biomedical Engineering and Instrumentation, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
³ School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; Institute of Biomedical Engineering and Instrumentation, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China. Electronic address: lilh@hdu.edu.cn.

PMID: 31806393
DOI: 10.1016/j.cmpb.2019.105240

Abstract

Background and objective: Previous resting-state fMRI-based diagnostic models for autism spectrum disorder (ASD) were based on traditional linear features. The complexity of the ASD brain remains unexplored.

Methods: To increase our understanding of the nonlinear neural mechanisms in ASD, entropy (i.e., approximate entropy (ApEn) and sample entropy (SampEn)) method was used to analyze the resting-state fMRI datasets collected from 21 ASD patients and 26 typically developing (TD) individuals. Here, a fast entropy algorithm was proposed through matrix computation. We combined entropy with a support-vector machine and selected "important entropy" as features to diagnose ASD. The classification performance of the fast entropy method was compared to the state-of-the-art functional connectivity (FC) method.

Results: The area under the receiver operating characteristic curve based on FC was 0.62. The areas under the receiver operating characteristic curves based on ApEn and SampEn were 0.79 and 0.89, respectively. The results showed that the proposed fast entropy method was more efficacious than the FC method. In addition, lower entropy was found in the ASD patients. The ApEn of the left postcentral gyrus (r_s = -0.556, p = 0.009) and the SampEn of the right lingual gyrus (r_s = -0.526, p = 0.014) were both significantly negatively related to Autism Diagnostic Observation Schedule total scores in the ASD patients. The proposed algorithm for entropy computation was faster than the traditional entropy method.

Conclusions: Our study provides a new perspective to better understand the neural mechanisms of ASD. Brain entropy based on a fast algorithm was applied to distinguish ASD patients from TD individuals. ApEn and SampEn could be potential biomarkers in ASD investigations.

Keywords: Approximate entropy; Functional magnetic resonance imaging; Sample entropy; Signal complexity; Support-vector machine.

MeSH terms

Adult
Algorithms
Autism Spectrum Disorder / diagnostic imaging*
Brain / diagnostic imaging*
Brain / physiopathology*
Entropy*
Female
Humans
Magnetic Resonance Imaging / methods
Male
Support Vector Machine
Young Adult