Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing 210009, China.
Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing 210009, China; Network Information Center, Zhongda Hospital, Medical School of Southeast University, Nanjing 210009, China.
Psychiatry Res Neuroimaging. 2023 Dec;336:111749. doi: 10.1016/j.pscychresns.2023.111749. Epub 2023 Nov 10.
Dysfunctions of the striatum have been repeatedly observed in autism spectrum disorder (ASD). However, previous studies have explored the static functional connectivity (sFC) of the striatum in a single frequency band, ignoring the dynamics and frequency specificity of brain FC. Therefore, we investigated the dynamic FC (dFC) and sFC of the striatum in the slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) frequency bands.
Data of 47 ASD patients and 47 typically developing (TD) controls were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. A seed-based approach was used to compute the dFC and sFC. Then, a two-sample t-test was performed. For regions showing abnormal sFC and dFC, we performed clinical correlation analysis and constructed support vector machine (SVM) models.
The middle frontal gyrus (MFG), precuneus, and medial superior frontal gyrus (mPFC) showed both dynamic and static alterations. The reduced striatal dFC in the right MFG was associated with autism symptoms. The dynamic‒static FC model had a great performance in ASD classification, with 95.83 % accuracy.
The striatal dFC and sFC were altered in ASD, which were frequency specific. Examining brain activity using dynamic and static FC provides a comprehensive view of brain activity.
自闭症谱系障碍（ASD）患者的纹状体功能失调已被反复观察到。然而，之前的研究仅在单一频段内探索了纹状体的静态功能连接（sFC），而忽略了脑 FC 的动态和频率特异性。因此，我们研究了纹状体在慢-4（0.027-0.073 Hz）和慢-5（0.01-0.027 Hz）频段内的动态功能连接（dFC）和 sFC。
从自闭症脑影像数据交换（ABIDE）数据库中获取了 47 名 ASD 患者和 47 名典型发育（TD）对照者的数据。使用基于种子的方法计算 dFC 和 sFC，然后进行双样本 t 检验。对于表现出异常 sFC 和 dFC 的区域，我们进行了临床相关性分析并构建了支持向量机（SVM）模型。
中间额回（MFG）、楔前叶和内侧额上回（mPFC）同时表现出动态和静态变化。右侧 MFG 纹状体的 dFC 减少与自闭症症状相关。动态-静态 FC 模型在 ASD 分类中具有出色的性能，准确率为 95.83%。
ASD 患者的纹状体 dFC 和 sFC 发生改变，且具有频率特异性。使用动态和静态 FC 来检查大脑活动可以提供对大脑活动的全面了解。
