Zhao Lei, Sun Yun-Kai, Xue Shao-Wei, Luo Hong, Lu Xiao-Dong, Zhang Lan-Hua
Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.
Institute of Psychological Science, Hangzhou Normal University, Hangzhou, China.
Front Neuroinform. 2022 Feb 22;16:761942. doi: 10.3389/fninf.2022.761942. eCollection 2022.
An increasing number of resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used functional connections as discriminative features for machine learning to identify patients with brain diseases. However, it remains unclear which functional connections could serve as highly discriminative features to realize the classification of autism spectrum disorder (ASD). The aim of this study was to find ASD-related functional connectivity patterns and examine whether these patterns had the potential to provide neuroimaging-based information to clinically assist with the diagnosis of ASD by means of machine learning. We investigated the whole-brain interregional functional connections derived from R-fMRI. Data were acquired from 48 boys with ASD and 50 typically developing age-matched controls at NYU Langone Medical Center from the publicly available Autism Brain Imaging Data Exchange I (ABIDE I) dataset; the ASD-related functional connections identified by the Boruta algorithm were used as the features of support vector machine (SVM) to distinguish patients with ASD from typically developing controls (TDC); a permutation test was performed to assess the classification performance. Approximately, 92.9% of participants were correctly classified by a combined SVM and leave-one-out cross-validation (LOOCV) approach, wherein 95.8% of patients with ASD were correctly identified. The default mode network (DMN) exhibited a relatively high network degree and discriminative power. Eight important brain regions showed a high discriminative power, including the posterior cingulate cortex (PCC) and the ventrolateral prefrontal cortex (vlPFC). Significant correlations were found between the classification scores of several functional connections and ASD symptoms ( < 0.05). This study highlights the important role of DMN in ASD identification. Interregional functional connections might provide useful information for the clinical diagnosis of ASD.
越来越多的静息态功能磁共振神经成像（R-fMRI）研究使用功能连接作为机器学习的判别特征，以识别患有脑部疾病的患者。然而，尚不清楚哪些功能连接可以作为高度判别性特征来实现自闭症谱系障碍（ASD）的分类。本研究的目的是找到与ASD相关的功能连接模式，并检查这些模式是否有可能通过机器学习提供基于神经成像的信息，以临床辅助ASD的诊断。我们研究了从R-fMRI得出的全脑区域间功能连接。数据来自纽约大学朗格尼医学中心公开可用的自闭症脑成像数据交换I（ABIDE I）数据集的48名患有ASD的男孩和50名年龄匹配的典型发育对照；由Boruta算法识别的与ASD相关的功能连接被用作支持向量机（SVM）的特征，以区分患有ASD的患者与典型发育对照（TDC）；进行置换检验以评估分类性能。通过SVM与留一法交叉验证（LOOCV）相结合的方法，大约92.9%的参与者被正确分类，其中95.8%的ASD患者被正确识别。默认模式网络（DMN）表现出相对较高的网络度和判别力。八个重要脑区显示出较高的判别力,包括后扣带回皮质（PCC）和腹外侧前额叶皮质（vlPFC）。在几个功能连接的分类分数与ASD症状之间发现了显著相关性（<0.05）。本研究强调了DMN在ASD识别中的重要作用。区域间功能连接可能为ASD临床诊断提供有用信息。
