The DOI matches its Crossref record; 134 of the 135 references it cites were found in a registry or an index.
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 (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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BackgroundComplex brain networks: graph theoretical analysis of structural and functional systems
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Modern network science of neurological disorders
Stam, C. J. (2014) · Nature reviews. Neuroscience · doi:10.1038/nrn3801 · Cited by 1,327
Self-citationWilson, J. E., Mart, M. F., Cunningham, C. M., Shehabi, Y., Girard, T. D., MacLullich, A. M. J., Slooter, A. J. C., & Ely, E. W. (2020) · Nature Reviews Disease Primers · doi:10.1038/s41572-020-00223-4 · Cited by 1,371
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Self-citationFunctional connectivity and graph theory in preclinical Alzheimer's disease
Brier, M. R., Thomas, J., Fagan, A. M., Hassenstab, J. J., Holtzman, D. M., Benzinger, T. L. S., Morris, J. C., & Ances, B. M. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.10.081 · Cited by 394
Injured Brains and Adaptive Networks: The Benefits and Costs of Hyperconnectivity
Hillary, F. G., & Grafman, J. H. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.03.003 · Cited by 388
Metadata
- 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.
- ORCID
- 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
- 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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