Stenosis and occlusion caused by carotid atherosclerosis is an important cause of ischemic stroke. In recent years, with the continuous development of magnetic resonance imaging (MRI) technology and the introduction of complex network theory, brain network analysis can be used not only to explain the clinical symptoms and cognitive dysfunction in patients with carotid stenosis caused by changes in network topological properties of different brain regions, but also to explore the imaging markers of carotid stenosis, thus providing important reference data for the diagnosis of early asymptomatic carotid stenosis, the selection of individualized intervention programs, and the assessment of efficacy. Brain network analysis has been used as a powerful tool. In this paper, we review the studies of structural and functional brain networks in patients with carotid stenosis, and introduce the definitions of brain network nodes and edges and important topological properties of complex networks. We also analyze the current research on brain network in patients with carotid stenosis, and discuss the challenges and outlook of existing imaging techniques and network construction methodologies in this field.

Objective To investigate the relationship between cerebrovascular reserve (CVR) capacity and white matter lesions in elderly people. Methods We included 315 participants aged ≥ 60 years in Jinan area of Shandong Province from May 2018 to July 2019. They underwent transcranial Doppler ultrasonography for assessing CVR, breath holding index (BHI), and arterial pulsatility index (PI). According to CVR capacity, they were divided into normal CVR group (CVR ≥ 20%, n = 206) and impaired CVR group (CVR < 20%, n = 109). Magnetic resonance imaging was performed to evaluate periventricular, subcortical, and total white matter hyperintensity (WMH) volumes and Fazekas scores. Results Compared with the normal CVR group, the impaired CVR group showed significantly higher volumes of periventricular, subcortical, and total WMHs and significantly higher proportions of Fazekas scores ≥ 2 (P < 0.01). Periventricular, subcortical, and total WMH volumes were negatively correlated with CVR (r = −0.70, −0.66, −0.73, P < 0.01) and BHI (r = −0.64, −0.65, −0.68, P < 0.01) and positively correlated with PI (r = 0.60, 0.65, 0.65, P < 0.01). After adjusting for confounding factors, periventricular, subcortical, and total WMH volumes were still negatively correlated with CVR and BHI (P < 0.01) and positively correlated with PI (P < 0.01). The logistic regression analysis showed that the risks of periventricular, subcortical, and total Fazekas score ≥ 2 in the impaired CVR group were 1.96 times (95% confidence interval [CI]: 1.17−3.27, P < 0.01), 1.84 times (95% CI: 1.11−3.05, P < 0.05), and 2.33 times (95% CI: 1.30−4.18, P < 0.01) that of the normal CVR group, respectively. Conclusion Impaired CVR is an independent risk factor for white matter lesions in the elderly.