ArticleFrontiers in aging neuroscience2021
White Matter Structural Network Analysis to Differentiate Alzheimer's Disease and Subcortical Ischemic Vascular Dementia.
Article in Frontiers in aging neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Alterations of White Matter Connectivity in Adults with Essential Hypertension.International journal of general medicine · 2024Trial
- Intractable prefrontal and limbic white matter network disruption in adolescents with drug-naïve nonsuicidal self-injury.BMC psychiatry · 2025Article
- Glymphatic system impairment in cerebral small vessel disease: associations with perivascular space volume and cognition.Frontiers in aging neuroscience · 2025Article
- Cognitive impairment mediates the white matter injury load and gait disorders in subcortical ischemic vascular disease.Brain imaging and behavior · 2024Article
- Article
- Exploring research hotspots and emerging trends in neuroimaging of vascular cognitive impairment: a bibliometric and visualized analysis.Frontiers in aging neuroscience · 2024Article
- Unraveling the link: white matter damage, gray matter atrophy and memory impairment in patients with subcortical ischemic vascular disease.Frontiers in neuroscience · 2024Article
- Disrupted Gray Matter Networks Associated with Cognitive Dysfunction in Cerebral Small Vessel Disease.Brain sciences · 2023Article
- Structural alterations of the salience network in patients with insular glioma.Brain and behavior · 2023Article
- Validation of the cognitive performance scale of the interRAI-PAC and montreal cognitive assessment.Nursing open · 2023Observational
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To explore the evaluation of white matter structural network analysis in the differentiation of Alzheimer's disease (AD) and subcortical ischemic vascular dementia (SIVD), 67 participants [31 AD patients, 19 SIVD patients, and 19 normal control (NC)] were enrolled in this study. Each participant underwent 3.0T MRI scanning. Diffusion tensor imaging (DTI) data were analyzed by graph theory (GRETNA toolbox). Statistical analyses of global parameters [gamma, sigma, lambda, global shortest path length (Lp), global efficiency (E
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Registered trials
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