Disrupted structural and functional brain connectomes in mild cognitive impairment and Alzheimer’s disease
Open accessAbstract
Alzheimer's disease (AD) is the most common type of dementia, comprising an estimated 60-80% of all dementia cases. It is clinically characterized by impairments of memory and other cognitive functions. Previous studies have demonstrated that these impairments are associated with abnormal structural and functional connections among brain regions, leading to a disconnection concept of AD. With the advent of a combination of non-invasive neuroimaging (structural magnetic resonance imaging (MRI), diffusion MRI, and functional MRI) and neurophysiological techniques (electroencephalography and magnetoencephalography) with graph theoretical analysis, recent studies have shown that patients with AD and mild cognitive impairment (MCI), the prodromal stage of AD, exhibit disrupted topological organization in large-scale brain networks (i.e., connectomics) and that this disruption is significantly correlated with the decline of cognitive functions. In this review, we summarize the recent progress of brain connectomics in AD and MCI, focusing on the changes in the topological organization of large-scale structural and functional brain networks using graph theoretical approaches. Based on the two different perspectives of information segregation and integration, the literature reviewed here suggests that AD and MCI are associated with disrupted segregation and integration in brain networks. Thus, these connectomics studies open up a new window for understanding the pathophysiological mechanisms of AD and demonstrate the potential to uncover imaging biomarkers for clinical diagnosis and treatment evaluation for this disease.
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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 Dai & He, 2014
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“In contrast, Tijms et al.([28]) investigated the graph properties of a single-individual gray-matter network in AD and found decreases in clustering coefficients, characteristic path lengths, normalized clustering coeffi cients, and normalized characteristic path lengths.”
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Shu, N., Wang, X., Bi, Q., Zhao, T., & Han, Y. (2018) · Radiology · doi:10.1148/radiol.2017162696 · Cited by 135 · see citing works
Kesler, S. R., Acton, P., Rao, V., & Ray, W. J. (2018) · Network Neuroscience · doi:10.1162/netn_a_00048 · Cited by 34 · see citing works
“These mathematical models of brain networks provide measurements of information-processing efficiency and resilience to pathology, among other topological properties that are highly relevant to AD (Contreras, Goni, Risacher, Sporns, & Saykin, 2015; Dai & He, 2014; Tijms, Wink et al., 2013).”
BackgroundResultInfluentialDisrupted structural connectivity in ArcAβ mouse model of Aβ amyloidosis
Al-Amin, M. M., Grandjean, J., Klohs, J., & Kim, J. (2020) · doi:10.1101/2020.04.27.064634 · Cited by 2 · see citing works
“Connectomics provide valuable information regarding the efficiency of information processing and topological organization (Dai and He, 2014).”
Background
- Authors
- Dai, Z., & He, Y.
- Affiliations
- Beijing Normal University (CN)
- ORCID
- Dai, Zhengjia
- Venue
- Neuroscience Bulletin
- Published
- 2014-04
- Volume
- 30
- Issue
- 2
- Pages
- 217-232
- ISSN
- 1995-8218 (ISSN-L)
- 1673-7067
- PMC
- PMC5562665
- OpenAlex
- W2018593373
- Cited by
- 173
- References
- 14
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 22 · 2023: 16 · 2022: 18 · 2021: 16 · 2020: 17 · 2019: 13 · 2018: 13 · 2017: 17 · 2016: 10 · 2015: 6 · 2014: 2
- FWCI
- 1.13
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Neuroscience Bulletin (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (81225012, 2014CB846102, 31221003, 81030028)
- National Science Fund for Distinguished Young Scholars (81225012)
- 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 › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- Alzheimer's disease, mild cognitive impairment, connectomics, structural connectome, functional connectome, large-scale brain networks, graph theory, network integration and segregation, disconnection hypothesis, imaging biomarkers, cognitive decline
- MeSH terms
- Alzheimer Disease, Humans, Neural Pathways, Cognitive Dysfunction, Connectome
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