Cited by 416Functional Brain Connectivity StudiesDementia and Cognitive Impairment ResearchComplex Network Analysis Techniques

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Alzheimer's disease: connecting findings from graph theoretical studies of brain networks

Tijms, Betty M., Wink, Alle Meije, de Haan, Willem, van der Flier, Wiesje M., Stam, Cornelis J., Scheltens, Philip, Barkhof, Frederik·2013·Neurobiology of Aging

DOI 10.1016/j.neurobiolaging.2013.02.020

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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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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: Tijms, Betty M.
Wink, Alle Meije
de Haan, Willem
van der Flier, Wiesje M.
Stam, Cornelis J.
Scheltens, Philip
Barkhof, Frederik
Venue: Neurobiology of Aging
Publisher: Elsevier BV
Published: 2013-08
Volume: 34
Issue: 8
Pages: 2023-2036
ISSN: 0197-4580 (ISSN-L)
DOI: 10.1016/j.neurobiolaging.2013.02.020
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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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Alzheimer's disease: connecting findings from graph theoretical studies of brain networks

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