众所周知，自闭症谱系障碍 (ASD) 尽管患病率很高，但仍难以诊断。现有研究已转向使用神经影像数据来提高临床适用性和诊断结果的有效性。然而，扫描神经图像所需的时间和财力限制了数据集的规模，进一步削弱了统计结果的泛化能力。此外，由多个全球机构收集的多站点数据集由于其异质性而难以应用机器学习方法。我们提出了一种深度学习方法，结合 F 分数特征选择方法，使用功能磁共振成像 (fMRI) 数据集进行 ASD 诊断。所提出的方法在全球 fMRI 数据集上进行了评估，ABIDE（自闭症脑成像数据交换）。使用我们的方法选择的 fMRI 功能连接特征可以在站点内数据集上达到 64.53% 的平均准确率，在整个 ABIDE 数据集上达到 70.9% 的准确率。此外，基于所选特征，网络拓扑分析显示 ASD 中的路径长度和聚类系数显着降低，表明小世界架构对随机网络的损失。改变后的大脑网络可以深入了解 ASD 的潜在病理，我们的方法选择的功能连接特征可以作为生物标志物。基于所选特征，网络拓扑分析显示 ASD 中的路径长度和聚类系数显着降低，表明小世界架构丢失到随机网络。改变后的大脑网络可以深入了解 ASD 的潜在病理，我们的方法选择的功能连接特征可以作为生物标志物。基于所选特征，网络拓扑分析显示 ASD 中的路径长度和聚类系数显着降低，表明小世界架构丢失到随机网络。改变后的大脑网络可以深入了解 ASD 的潜在病理，我们的方法选择的功能连接特征可以作为生物标志物。

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Autism spectrum disorder (ASD) is notoriously difficult to diagnose despite having a high prevalence. Existing studies have shifted toward using neuroimaging data to enhance the clinical applicability and the effectiveness of the diagnostic results. However, the time and financial resources required to scan neuroimages restrict the scale of the datasets and further weaken the generalization ability of the statistical results. Furthermore, multi-site datasets collected by multiple worldwide institutions make it difficult to apply machine learning methods due to their heterogeneity. We propose a deep learning approach combined with the F-score feature selection method for ASD diagnosis using a functional magnetic resonance imaging (fMRI) dataset. The proposed method is evaluated on the worldwide fMRI dataset, known as ABIDE (Autism Brain Imaging Data Exchange). The fMRI functional connectivity features selected using our method can achieve an average accuracy of 64.53% on intra-site datasets and an accuracy of 70.9% on the whole ABIDE dataset. Moreover, based on the selected features, the network topology analysis showed a significant decrease in the path length and the cluster coefficient in ASD, indicating a loss of small-world architecture to a random network. The altered brain network may provide insight into the underlying pathology of ASD, and the functional connectivity features selected by our method may serve as biomarkers.