Alzheimer's disease: connecting findings from graph theoretical studies of brain networks
Abstract
The interrelationships between pathological processes and emerging clinical phenotypes in Alzheimer's disease (AD) are important yet complicated to study, because the brain is a complex network where local disruptions can have widespread effects. Recently, properties in brain networks obtained with neuroimaging techniques have been studied in AD with tools from graph theory. However, the interpretation of graph alterations remains unclear, because the definition of connectivity depends on the imaging modality used. Here we examined which graph properties have been consistently reported to be disturbed in AD studies, using a heuristically defined "graph space" to investigate which theoretical models can best explain graph alterations in AD. Findings from structural and functional graphs point to a loss of highly connected areas in AD. However, studies showed considerable variability in reported group differences of most graph properties. This suggests that brain graphs might not be isometric, which complicates the interpretation of graph measurements. We highlight confounding factors such as differences in graph construction methods and provide recommendations for future research.
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Inconsistencies in connectome findings are a well-known issue in the literature and often reflect differences in methodology such as imaging modality and/or choice of thresholding method.
“Inconsistencies in connectome findings are a well-known issue in the literature (for reviews, see Dai & He, 2014; Tijms, Wink et al., 2013) and often reflect differences in methodology such as imaging modality and/or choice of thresholding method.”
From Functional and structural connectome properties in the 5XFAD transgenic mouse model of Alzheimer’s disease (2018) · cites this paper as Tijms, Wink et al., 2013
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- Authors
- Tijms, B. M., Wink, A. M., de Haan, W., van der Flier, W. M., Stam, C. J., Scheltens, P., & Barkhof, F.
- Affiliations
- Amsterdam UMC Location Vrije Universiteit Amsterdam (NL)
- Corresponding author
- Tijms, Betty M.
- Venue
- Neurobiology of Aging
- Publisher
- Elsevier BV
- Published
- 2013-08
- Volume
- 34
- Issue
- 8
- Pages
- 2023-2036
- ISSN
- 0197-4580 (ISSN-L)
- OpenAlex
- W2087938247
- Cited by
- 416
- References
- 135
- Citations by year
- 2026: 17 · 2025: 23 · 2024: 29 · 2023: 18 · 2022: 27 · 2021: 39 · 2020: 33 · 2019: 30 · 2018: 49 · 2017: 41 · 2016: 38 · 2015: 36 · 2014: 30 · 2013: 5 · 2012: 1
- FWCI
- 3.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neurobiology of Aging (publishedVersion)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- Alzheimer Nederland and Stichting VUmc fonds
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- Alzheimer's disease, brain networks, graph theory, structural connectivity, functional connectivity, graph space, graph construction, network topology, clinical phenotype
- MeSH terms
- Alzheimer Disease, Brain, Electroencephalography, Humans, Magnetic Resonance Imaging, Models, Theoretical, Nerve Net, Magnetoencephalography, Neuroimaging
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