Department of Radiology, Affilated Hospital of North Sichuan Medical College, NanChong, 637000, Sichuan, China.
Ann Clin Transl Neurol. 2024 May;11(5):1148-1159. doi: 10.1002/acn3.52030. Epub 2024 Mar 3.
Abnormalities in the gray matter structure of cerebral small vessel disease (CSVD) have been observed throughout the brain. However, whether cortico-cortical connections exist between regions of gray matter atrophy in patients with CSVD has not been fully elucidated. This question was tested by comparing the gray matter covariance networks in CSVD patients with and without cognitive impairment (CI).
We performed multivariate modeling of the gray matter volume measurements of 61 patients with CI (CSVD-CI), 85 patients without CI (CSVD-NC), and 108 healthy controls using source-based morphological analysis (SBM) to obtain gray matter structural covariance networks at the population level. Then, correlations between structural covariance networks and cognitive functions were analyzed in CSVD patients. Finally, a support vector machine (SVM) classifier was used with the gray matter covariance network as a classification feature to identify CI among the CSVD population.
The results of the analysis of all the subjects showed that compared with healthy controls, the expression of the thalamic covariance network, cerebellum covariance network, and calcarine cortex covariance network was reduced in patients with CSVD. Moreover, CSVD-CI patients showed a significant reduction in the expression of the thalamic covariance network, encompassing the thalamus and the parahippocampal gyrus, relative to CSVD-NC patients, which persisted after excluding CSVD patients with thalamic lacunes. In patients with CSVD, cognitive functions were positively correlated with measures of the thalamic covariance network. More than 80% of CSVD patients with CI were correctly identified by the SVM classifier.
Our findings provide new evidence to explain the distribution state of gray matter reduction in CSVD patients, and the thalamic covariance network is the core region for early gray matter reduction during the development of CSVD disease, which is related to cognitive deficits. Reduced expression of thalamic covariance networks may provide a neuroimaging biomarker for the early identification of cognitive impairment in CSVD patients.
脑小血管病（CSVD）患者的脑灰质结构异常已在全脑范围内观察到。然而，CSVD 患者的灰质萎缩区域之间是否存在皮质-皮质连接尚未完全阐明。通过比较伴有和不伴有认知障碍（CI）的 CSVD 患者的灰质协方差网络，对这一问题进行了测试。
我们对 61 名伴有 CI（CSVD-CI）的患者、85 名不伴有 CI（CSVD-NC）的患者和 108 名健康对照者的灰质体积测量值进行了基于源的形态学分析（SBM）的多元建模，以获得人群水平的灰质结构协方差网络。然后，分析了 CSVD 患者的结构协方差网络与认知功能之间的相关性。最后，使用支持向量机（SVM）分类器，将灰质协方差网络作为分类特征，对 CSVD 人群中的 CI 进行识别。
对所有受试者的分析结果表明，与健康对照组相比，CSVD 患者的丘脑协方差网络、小脑协方差网络和距状皮层协方差网络的表达减少。此外，CSVD-CI 患者的丘脑协方差网络表达显著减少，包括丘脑和海马旁回，与 CSVD-NC 患者相比，这一现象在排除了 CSVD 患者的丘脑腔隙后仍然存在。在 CSVD 患者中，认知功能与丘脑协方差网络的测量值呈正相关。超过 80%的 CSVD 伴有 CI 的患者可以通过 SVM 分类器正确识别。
我们的研究结果提供了新的证据，解释了 CSVD 患者灰质减少的分布状态，丘脑协方差网络是 CSVD 疾病发展过程中早期灰质减少的核心区域，与认知缺陷有关。丘脑协方差网络表达减少可能为 CSVD 患者认知障碍的早期识别提供神经影像学生物标志物。
