Department of Ophthalmology & Visual Science, The Chinese University of Hong Kong, Hong Kong.
Department of Ophthalmology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China.
Neuroreport. 2022 Aug 2;33(12):526-533. doi: 10.1097/WNR.0000000000001813. Epub 2022 Jul 5.
Objective of the study is to investigate the altered intrinsic functional hubs in patients with comitant exotropia (CE) using the voxel-wise degree centrality (DC) analysis method. A total of 28 CE patients and 28 healthy controls (HCs) similarly matched in sex, age, and education level were recruited in this study. All subjects underwent a resting-state functional MRI scan, the voxel-wise DC method was applied to evaluate brain network hubs alterations in CE patients. Then, the DC maps between two groups were chosen to be classification features to distinguish patients with CE from HCs based on the support vector machine (SVM) model. The algorithm performance was evaluated by a permutation test. Compared with HCs, CE patients exhibited significant enhanced DC value in the left cerebelum 8 and the right cerebelum 3; and remarkably decreased DC value in the right precentral gyrus, right anterior cingulated, and paracingulate gyri (two-tailed, voxel level: P < 0.01; GRF correction, cluster level: P < 0.05). However, no relationship was found between the observed average DC of the different brain regions and the clinical features ( P > 0.05). In addition, the SVM model showed an accuracy of 83.93% to clarify CE patients from HCs using the DC maps as a classification feature. CE patients displayed altered functional network hubs in multiple brain areas associated with cognition and motor control, and the DC variability could classify patients from HCs with high accuracy. These findings may assist to understand the neuropathological mechanism for the disease.
本研究旨在使用体素水平度中心度(degree centrality,DC)分析方法探讨共同性外斜视(concomitant exotropia,CE)患者的固有功能中枢改变。本研究共纳入 28 例 CE 患者和 28 例性别、年龄和受教育程度相匹配的健康对照者(healthy controls,HCs)。所有受试者均行静息态功能磁共振成像(functional magnetic resonance imaging,fMRI)扫描,采用体素水平 DC 方法评估 CE 患者脑网络中枢的改变。然后,选择两组之间的 DC 图谱作为分类特征,基于支持向量机(support vector machine,SVM)模型将 CE 患者与 HCs 区分开来。通过置换检验评估算法性能。与 HCs 相比,CE 患者左侧小脑 8 和右侧小脑 3 的 DC 值显著升高,右侧中央前回、右侧前扣带回和旁扣带回的 DC 值显著降低(双侧,体素水平:P < 0.01;GRF 校正,簇水平:P < 0.05)。然而,未发现观察到的不同脑区的平均 DC 值与临床特征之间存在相关性(P > 0.05)。此外,SVM 模型使用 DC 图谱作为分类特征,区分 CE 患者和 HCs 的准确率为 83.93%。CE 患者在与认知和运动控制相关的多个脑区显示出功能网络中枢改变,DC 变异性可以以较高的准确率将患者与 HCs 区分开来。这些发现可能有助于了解该疾病的神经病理学机制。