Yi Ting, Wei Weian, Ma Di, Wu Yali, Cai Qifang, Jin Ke, Gao Xin
Department of Radiology, Hunan Children's Hospital, Changsha, China.
College of Information Science and Technology, Nanjing Forestry University, Nanjing, China.
Front Neurosci. 2022 Jun 28;16:952067. doi: 10.3389/fnins.2022.952067. eCollection 2022.
Structural magnetic resonance imaging (sMRI) reveals abnormalities in patients with autism spectrum syndrome (ASD). Previous connectome studies of ASD have failed to identify the individual neuroanatomical details in preschool-age individuals. This paper aims to establish an individual morphological connectome method to characterize the connectivity patterns and topological alterations of the individual-level brain connectome and their diagnostic value in patients with ASD.
Brain sMRI data from 24 patients with ASD and 17 normal controls (NCs) were collected; participants in both groups were aged 24-47 months. By using the Jensen-Shannon Divergence Similarity Estimation (JSSE) method, all participants's morphological brain network were ascertained. Student's -tests were used to extract the most significant features in morphological connection values, global graph measurement, and node graph measurement.
The results of global metrics' analysis showed no statistical significance in the difference between two groups. Brain regions with meaningful properties for consensus connections and nodal metric features are mostly distributed in are predominantly distributed in the basal ganglia, thalamus, and cortical regions spanning the frontal, temporal, and parietal lobes. Consensus connectivity results showed an increase in most of the consensus connections in the frontal, parietal, and thalamic regions of patients with ASD, while there was a decrease in consensus connectivity in the occipital, prefrontal lobe, temporal lobe, and pale regions. The model that combined morphological connectivity, global metrics, and node metric features had optimal performance in identifying patients with ASD, with an accuracy rate of 94.59%.
The individual brain network indicator based on the JSSE method is an effective indicator for identifying individual-level brain network abnormalities in patients with ASD. The proposed classification method can contribute to the early clinical diagnosis of ASD.
结构磁共振成像（sMRI）显示自闭症谱系障碍（ASD）患者存在异常。先前关于ASD的连接组研究未能识别学龄前个体的个体神经解剖学细节。本文旨在建立一种个体形态连接组方法，以表征个体水平脑连接组的连接模式和拓扑改变及其在ASD患者中的诊断价值。
收集了24例ASD患者和17名正常对照（NCs）的脑部sMRI数据；两组参与者年龄均为24 - 47个月。通过使用詹森 - 香农散度相似性估计（JSSE）方法，确定了所有参与者的形态学脑网络。采用学生t检验提取形态连接值、全局图测量和节点图测量中最显著的特征。
全局指标分析结果显示两组之间的差异无统计学意义。具有共识连接和节点指标特征的有意义属性的脑区大多分布在基底神经节、丘脑以及跨越额叶、颞叶和顶叶的皮质区域。共识连接结果显示，ASD患者额叶、顶叶和丘脑区域的大多数共识连接增加，而枕叶、前额叶、颞叶和苍白球区域的共识连接减少。结合形态连接性、全局指标和节点指标特征的模型在识别ASD患者方面具有最佳性能，准确率为94.59%。
基于JSSE方法的个体脑网络指标是识别ASD患者个体水平脑网络异常的有效指标。所提出的分类方法有助于ASD的早期临床诊断。
