Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
J Neural Eng. 2022 Oct 13;19(5). doi: 10.1088/1741-2552/ac86a4.
Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with the main symptoms of social communication disabilities. ASD is more than four times more common among males than females. The diagnosis of ASD is currently a subjective process by experts the same for males and females. Various studies have suggested the use of brain connectivity features for the diagnosis of ASD. Also, sex-related biological factors have been shown to play a role in ASD etiology and influence the brain connectivity. Therefore, proposing an accurate computer-aided diagnosis system (CADS) for ASD which considers the sex of subjects seems necessary. In this study, we present a sex-dependent connectivity-based CADS for ASD using resting-state functional magnetic resonance imaging. The proposed CADS classifies ASD males from normal males, and ASD females from normal females.After data preprocessing, group independent component analysis (GICA) was applied to obtain the resting-state networks (RSNs) followed by applying dual-regression to obtain the time course of each RSN for each subject. Afterwards, functional connectivity measures of full correlation and partial correlation and the effective connectivity measure of bivariate Granger causality were computed between time series of RSNs. To consider the role of sex differences in the classification process, male, female, and mixed groups were taken into account, and feature selection and classification were designed for each sex group separately. At the end, the classification accuracy was computed for each sex group.In the female group, a classification accuracy of 93.3% was obtained using full correlation while in the male group, a classification accuracy of 86.7% was achieved using both full correlation and bivariate Granger causality. Also, in the mixed group, a classification accuracy of 83.3% was obtained using full correlation.This supports the importance of considering sex in diagnosing ASD patients from normal controls.
自闭症谱系障碍 (ASD) 是一种常见的神经发育障碍，主要症状为社交沟通障碍。自闭症在男性中的发病率比女性高四倍以上。目前，自闭症的诊断是一个由专家进行的主观过程，无论男女都一样。多项研究表明，大脑连接特征可用于自闭症的诊断。此外，已有研究表明，与性别相关的生物学因素在自闭症的发病机制中发挥作用，并影响大脑连接。因此，提出一种考虑受试者性别的准确计算机辅助诊断系统 (CADS) 对于自闭症的诊断似乎是必要的。在这项研究中，我们使用静息态功能磁共振成像 (rs-fMRI) 提出了一种基于连接的性别依赖的自闭症 CADS。该 CADS 可将自闭症男性与正常男性区分开，将自闭症女性与正常女性区分开。在数据预处理之后，应用组独立成分分析 (GICA) 获得静息态网络 (RSN)，然后应用双回归获得每个受试者每个 RSN 的时间序列。之后，计算了全相关和偏相关的功能连接测量以及二元格兰杰因果关系的有效连接测量，以获得 RSN 时间序列之间的功能连接测量。为了在分类过程中考虑性别差异的作用，考虑了男性、女性和混合组，并分别为每个性别组设计了特征选择和分类。最后，计算了每个性别组的分类准确率。在女性组中，使用全相关可获得 93.3%的分类准确率，而在男性组中，使用全相关和二元格兰杰因果关系可分别获得 86.7%和 86.7%的分类准确率。此外，在混合组中，使用全相关可获得 83.3%的分类准确率。这支持了在从正常对照中诊断自闭症患者时考虑性别的重要性。
