Asymptomatic carotid stenosis is associated with both edge and network reconfigurations identified by single-subject cortical thickness networks

Jinxia Ren; Dan Xu; Hao Mei; Xiaoli Zhong; Minhua Yu; Jiaojiao Ma; Chenhong Fan; Jinfeng Lv; Yaqiong Xiao; Lei Gao; Haibo Xu

doi:10.3389/fnagi.2022.1091829

Asymptomatic carotid stenosis is associated with both edge and network reconfigurations identified by single-subject cortical thickness networks

Front Aging Neurosci. 2023 Jan 12:14:1091829. doi: 10.3389/fnagi.2022.1091829. eCollection 2022.

Authors

Jinxia Ren¹, Dan Xu², Hao Mei¹, Xiaoli Zhong¹, Minhua Yu¹, Jiaojiao Ma¹, Chenhong Fan¹, Jinfeng Lv¹, Yaqiong Xiao³, Lei Gao¹, Haibo Xu¹

Affiliations

¹ Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
² Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
³ Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China.

Abstract

Background and purpose: Patients with asymptomatic carotid stenosis, even without stroke, are at high risk for cognitive impairment, and the neuroanatomical basis remains unclear. Using a novel edge-centric structural connectivity (eSC) analysis from individualized single-subject cortical thickness networks, we aimed to examine eSC and network measures in severe (> 70%) asymptomatic carotid stenosis (SACS).

Methods: Twenty-four SACS patients and 24 demographically- and comorbidities-matched controls were included, and structural MRI and multidomain cognitive data were acquired. Individual eSC was estimated via the Manhattan distances of pairwise cortical thickness histograms.

Results: In the eSC analysis, SACS patients showed longer interhemispheric but shorter intrahemispheric Manhattan distances seeding from left lateral temporal regions; in network analysis the SACS patients had a decreased system segregation paralleling with white matter hyperintensity burden and recall memory. Further network-based statistic analysis identified several eSC and subgraph features centred around the Perisylvian regions that predicted silent lesion load and cognitive tests.

Conclusion: We conclude that SACS exhibits abnormal eSC and a less-optimized trade-off between physical cost and network segregation, providing a reference and perspective for identifying high-risk individuals.

Keywords: brain network; cortical thickness; graph theory; structural covariance; vascular cognitive impairment.