Using Individualized Brain Network for Analyzing Structural Covariance of the Cerebral Cortex in Alzheimer's Patients
Open accessAbstract
Cortical thinning patterns in Alzheimer's disease (AD) have been widely reported through conventional regional analysis. In addition, the coordinated variance of cortical thickness in different brain regions has been investigated both at the individual and group network levels. In this study, we aim to investigate network architectural characteristics of a structural covariance network (SCN) in AD, and further to show that the structural covariance connectivity becomes disorganized across the brain regions in AD, while the normal control (NC) subjects maintain more clustered and consistent coordination in cortical atrophy variations. We generated SCNs directly from T1-weighted MR images of individual patients using surface-based cortical thickness data, with structural connectivity defined as similarity in cortical thickness within different brain regions. Individual SCNs were constructed using morphometric data from the Samsung Medical Center (SMC) dataset. The structural covariance connectivity showed higher clustering than randomly generated networks, as well as similar minimum path lengths, indicating that the SCNs are "small world." There were significant difference between NC and AD group in characteristic path lengths (z = -2.97, p < 0.01) and small-worldness values (z = 4.05, p < 0.01). Clustering coefficients in AD was smaller than that of NC but there was no significant difference (z = 1.81, not significant). We further observed that the AD patients had significantly disrupted structural connectivity. We also show that the coordinated variance of cortical thickness is distributed more randomly from one region to other regions in AD patients when compared to NC subjects. Our proposed SCN may provide surface-based measures for understanding interaction between two brain regions with co-atrophy of the cerebral cortex due to normal aging or AD. We applied our method to the AD Neuroimaging Initiative (ADNI) data to show consistency in results with the SMC dataset.
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Increased characteristic path length has also been observed in patients with AD using gray matter structural connectomes and EEG-based networks.
“Increased characteristic path length has also been observed in patients with AD using gray matter structural connectomes (Kim et al., 2016; Pereira et al., 2016) and electroencephalography (EEG)-based networks (Stam et al., 2007).”
From Functional and structural connectome properties in the 5XFAD transgenic mouse model of Alzheimer’s disease (2018) · cites this paper as Kim et al., 2016
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Brain structural covariance network features are robust markers of early heavy alcohol use
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- Authors
- Kim, H.-J., Shin, J.-H., Han, C. E., Kim, H. J., Na, D. L., Seo, S. W., & Seong, J.-K.
- Affiliations
- Korea University (KR)
- Samsung Medical Center (KR)
- Corresponding author
- Seo, Sang Won
- Seong, Joon-Kyung
- Venue
- Frontiers in Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2016-09-01
- Volume
- 10
- Pages
- 394-394
- ISSN
- 1662-453X (ISSN-L)
- PMC
- PMC5007703
- OpenAlex
- W2517546362
- Cited by
- 44
- References
- 69
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 4 · 2023: 5 · 2022: 4 · 2021: 5 · 2020: 4 · 2019: 6 · 2018: 8 · 2017: 3
- FWCI
- 3.61
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Neuroscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Korea Health Industry Development Institute
- Ministry of Health and Welfare
- Alzheimer's Disease Neuroimaging Initiative
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Keywords
- Alzheimer's disease, structural covariance networks, cerebral cortex, cortical thinning, cortical thickness, small-world networks, network clustering, path length, normal aging, surface-based morphometry
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