Wang Yufei, Wang Jianxin, Wu Fang-Xiang, Hayrat Rahmatjan, Liu Jin
Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon S7N 5A9, Canada.
J Neurosci Methods. 2020 Sep 1;343:108840. doi: 10.1016/j.jneumeth.2020.108840. Epub 2020 Jul 9.
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that could cause problems in social communications. Clinically, diagnosing ASD mainly relies on behavioral criteria while this approach is not objective enough and could cause delayed diagnosis. Since functional magnetic resonance imaging (fMRI) can measure brain activity, it provides data for the study of brain dysfunction disorders and has been widely used in ASD identification. However, satisfactory accuracy for ASD identification has not been achieved.
To improve the performance of ASD identification, we propose an ASD identification method based on multi-atlas deep feature representation and ensemble learning. We first calculate multiple functional connectivity based on different brain atlases from fMRI data of each subject. Then, to get the more discriminative features for ASD identification, we propose a multi-atlas deep feature representation method based on stacked denoising autoencoder (SDA). Finally, we propose multilayer perceptron (MLP) and an ensemble learning method to perform the final ASD identification task.
Our proposed method is evaluated on 949 subjects (including 419 ASDs and 530 typical control (TCs)) from the Autism Brain Imaging Data Exchange (ABIDE) and achieves accuracy of 74.52% (sensitivity of 80.69%, specificity of 66.71%, AUC of 0.8026) for ASD identification.
Compared with some previously published methods, our proposed method obtains the better performance for ASD identification.
The results suggest that our proposed method is efficient to improve the performance of ASD identification, and is promising for ASD clinical diagnosis.
自闭症谱系障碍（ASD）是一种神经发育障碍，可导致社交沟通问题。临床上，ASD的诊断主要依赖行为标准，但这种方法不够客观，可能导致诊断延迟。由于功能磁共振成像（fMRI）可以测量大脑活动，它为脑功能障碍疾病的研究提供了数据，并已广泛应用于ASD的识别。然而，尚未实现令人满意的ASD识别准确率。
为了提高ASD识别的性能，我们提出了一种基于多图谱深度特征表示和集成学习的ASD识别方法。我们首先根据每个受试者fMRI数据的不同脑图谱计算多个功能连接。然后，为了获得更具判别力的ASD识别特征，我们提出了一种基于堆叠去噪自动编码器（SDA）的多图谱深度特征表示方法。最后，我们提出多层感知器（MLP）和一种集成学习方法来执行最终的ASD识别任务。
我们提出的方法在来自自闭症脑成像数据交换（ABIDE）的949名受试者（包括419名ASD患者和530名典型对照（TC））上进行了评估，ASD识别的准确率达到74.52%（灵敏度为80.69%，特异性为66.71%，AUC为0.8026）。
与一些先前发表的方法相比，我们提出的方法在ASD识别方面取得了更好的性能。
结果表明，我们提出的方法能有效提高ASD识别的性能，在ASD临床诊断中具有应用前景。
