Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Comput Methods Programs Biomed. 2024 Jun;250:108196. doi: 10.1016/j.cmpb.2024.108196. Epub 2024 Apr 24.
People with autism spectrum disorder (ASD) often have cognitive impairments. Effective connectivity between different areas of the brain is essential for normal cognition. Electroencephalography (EEG) has been widely used in the detection of neurological diseases. Previous studies on detecting ASD with EEG data have focused on frequency-related features. Most of these studies have augmented data by splitting the dataset into time slices or sliding windows. However, such approaches to data augmentation may cause the testing data to be contaminated by the training data. To solve this problem, this study developed a novel method for detecting ASD with EEG data.
This study quantified the functional connectivity of the subject's brain from EEG signals and defined the individual to be the unit of analysis. Publicly available EEG data were gathered from 97 and 92 subjects with ASD and typical development (TD), respectively, while they were at rest or performing a task. Time-series maps of brain functional connectivity were constructed, and the data were augmented using a deep convolutional generative adversarial network. In addition, a combined network for ASD detection, based on convolutional neural network (CNN) and long short-term memory (LSTM), was designed and implemented.
Based on functional connectivity, the network achieved classification accuracies of 81.08% and 74.55% on resting state and task state data, respectively. In addition, we found that the functional connectivity of ASD differed from TD primarily in the short-distance functional connectivity of the parietal and occipital lobes and in the distant connections from the right temporoparietal junction region to the left posterior temporal lobe.
This paper provides a new perspective for better utilizing EEG to understand ASD. The method proposed in our study is expected to be a reliable tool to assist in the diagnosis of ASD.
自闭症谱系障碍（ASD）患者常存在认知障碍。大脑不同区域之间的有效连接对正常认知至关重要。脑电图（EEG）已广泛应用于神经系统疾病的检测。先前使用 EEG 数据检测 ASD 的研究主要集中在与频率相关的特征上。这些研究大多通过将数据集划分为时间片或滑动窗口来扩充数据。然而，这种数据扩充方法可能会导致测试数据受到训练数据的污染。为了解决这个问题，本研究开发了一种使用 EEG 数据检测 ASD 的新方法。
本研究从 EEG 信号中量化了受试者大脑的功能连接，并将个体定义为分析单位。从分别处于休息或执行任务状态的 97 名 ASD 患者和 92 名典型发育（TD）患者中收集了公开可用的 EEG 数据。构建了大脑功能连接的时间序列图，并使用深度卷积生成对抗网络对数据进行扩充。此外，还设计并实现了一种基于卷积神经网络（CNN）和长短时记忆（LSTM）的 ASD 联合检测网络。
基于功能连接，该网络在静息态和任务态数据上的分类准确率分别为 81.08%和 74.55%。此外，我们发现 ASD 的功能连接与 TD 主要在顶叶和枕叶的短距离功能连接以及从右侧颞顶联合区到左侧颞后叶的远距离连接上存在差异。
本文为更好地利用 EEG 理解 ASD 提供了新的视角。我们研究中提出的方法有望成为辅助 ASD 诊断的可靠工具。
