ArticleFrontiers in aging neuroscience2022
Asymptomatic carotid stenosis is associated with both edge and network reconfigurations identified by single-subject cortical thickness networks.
Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 8 citations in OpenAlex.
- Reorganization of gray matter networks in patients with Moyamoya disease.Scientific reports · 2025Article
- Carotid artery stenosis is related to cerebral small vessel disease magnetic resonance imaging burden.Heliyon · 2024Article
- Alterations in brain morphology and functional connectivity mediate cognitive decline in carotid atherosclerotic stenosis.Frontiers in aging neuroscience · 2024Article
- Identification of diagnostic markers for ASD: a restrictive interest analysis based on EEG combined with eye tracking.Frontiers in neuroscience · 2023Article
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Authors and funding
11 authors at 3 institutions in 1 country.
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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.
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