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Single-Subject Grey Matter Graphs in Alzheimer's Disease
Open accessAbstract
Coordinated patterns of cortical morphology have been described as structural graphs and previous research has demonstrated that properties of such graphs are altered in Alzheimer's disease (AD). However, it remains unknown how these alterations are related to cognitive deficits in individuals, as such graphs are restricted to group-level analysis. In the present study we investigated this question in single-subject grey matter networks. This new method extracts large-scale structural graphs where nodes represent small cortical regions that are connected by edges when they show statistical similarity. Using this method, unweighted and undirected networks were extracted from T1 weighted structural magnetic resonance imaging scans of 38 AD patients (19 female, average age 72±4 years) and 38 controls (19 females, average age 72±4 years). Group comparisons of standard graph properties were performed after correcting for grey matter volumetric measurements and were correlated to scores of general cognitive functioning. AD networks were characterised by a more random topology as indicated by a decreased small world coefficient (p = 3.53×10(-5)), decreased normalized clustering coefficient (p = 7.25×10(-6)) and decreased normalized path length (p = 1.91×10(-7)). Reduced normalized path length explained significantly (p = 0.004) more variance in measurements of general cognitive decline (32%) in comparison to volumetric measurements (9%). Altered path length of the parahippocampal gyrus, hippocampus, fusiform gyrus and precuneus showed the strongest relationship with cognitive decline. The present results suggest that single-subject grey matter graphs provide a concise quantification of cortical structure that has clinical value, which might be of particular importance for disease prognosis. These findings contribute to a better understanding of structural alterations and cognitive dysfunction in AD.
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“However, some studies have observed conflicting results, including lower characteristic path length in DTI (Daianu et al., 2013), gray matter (Tijms, Moller et al., 2013), fMRI (Sanz-Arigita et al., 2010), EEG (de Haan et al., 2009), and magneto-encephalogram (MEG; Stam et al., 2009) connectomes in patients with AD.”
From Functional and structural connectome properties in the 5XFAD transgenic mouse model of Alzheimer’s disease (2018) · cites this paper as Tijms, Moller et al., 2013
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Vermunt, L., Sutphen, C. L., Dicks, E., de Leeuw, D. M., Allegri, R., Berman, S., Cash, D. M., Chhatwal, J. P., Cruchaga, C., Day, G. S., et al. (2024) · Brain Communications · doi:10.1093/braincomms/fcae357 · Cited by 6
“40 Finally, we calculated the small-world metric for each network with the brain connectivity toolbox (https://sites.google.com/site/bctnet/ 41 ) modified for large sized networks.”
MethodResultSelf-citationProtas, H. D., Ghisays, V., Goradia, D. D., Bauer, R., Devadas, V., Chen, K., Reiman, E. M., & Su, Y. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1089134 · Cited by 4
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“have been observed in relation with Abdeposition in patients with AD [63,27,55].”
BackgroundSelf-citationChen, G., Wu, C., Liu, Y., Fang, Z., Luo, L., Lai, X., Wang, W., & Dong, L. (2022) · Brain Imaging and Behavior · doi:10.1007/s11682-022-00709-7 · Cited by 3
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Connectivity-Driven Parcellation Methods for the Human Cerebral Cortex
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“It has been common practice in recent neuroscience studies to explore several neurological [247, 77] and neuro-developmental disorders [107, 145, 60, 78] from a network perspective.”
BackgroundThe bi-phasic behaviour of grey matter networks after the first demyelinating attack
Collorone, S., Pontillo, G., Foster, M. A., Prados, F., Kanber, B., Yiannakas, M. C., Burke, A., Ogunbowale, L., Davagnanam, I., Wheeler‐Kingshott, C. A. M. G., et al. (2025) · Brain Communications · doi:10.1093/braincomms/fcaf367 · Cited by 1
“Notably, the analysis of single-subject grey matter networks in advanced multiple sclerosis, 13 like studies using the same methodology in dementia, 40 showed that cognitively impaired patients tend towards random network topology.”
Self-citationSingle-subject gray matter networks in patients with early- to late-stage Parkinson’s disease
Yoko, S., Matsuda, H., Maikusa, N., Ota, M., Kimura, Y., Maki, H., Kagaya, R., Imokawa, T., Mukai, Y., Takahashi, Y., et al. (2025) · Neurological Research · doi:10.1080/01616412.2025.2561116 · Cited by 1
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“Our findings contribute to a growing body of work suggesting that Alzheimer’s disease may reflect not only regional atrophy, but also disruptions in the coordinated structural organisation of the cortex 48,49 .”
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“11 , 18 , 21 Given that, MRI morphometric GT metrics have been surprisingly little explored as a potential biomarker for A (cid:2) positivity, especially given its clinical usefulness potential (non-invasiveness, low cost, and high accessibility compared to PET/CSF exams), at least as a preliminary…”
Quantitative Assessment of Mesoscale Cellular Order and Organization in the Mouse Hippocampus
Hein, K., Romero-Limon, H., Möckel, C., Karasinsky, A., Möllmert, S., Kayser, J., Zaccone, A., Guck, J., & Toda, T. (2026) · bioRxiv · doi:10.64898/2026.07.06.736467
“…altered graph properties of the neural network, as well as changes in the density of hippocampal tissue have been linked to cognitive decline, mem-55 ory deficits and neurological diseases such as Alzheimer’s disease (Allen et al., 2007; Wang et al., 2006; Tijms et al., 2013; Berron et al., 2021).”
MVMGNN;Multi-View Masked Graph Neural Network for Alzheimer's Disease Diagnosis using Structural MRI
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“These findings align with graph-theoretical and connectomics evidence of disrupted hub organization and efficiency in MCI/AD (Sala-Llonch et al., 2015; Tijms et al., 2013; Yu et al., 2021).”
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“Both MCI and AD affect local de Haan et al. (2012); Heringa et al. (2014) and small-world network metrics Tijms et al. (2013); Seo et al. (2013) through changes in clustering (i.e., segregation) Brier et al. (2014) and global efficiency (i.e., integration) Lo et al. (2010); Yao et al. (2010);…”
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Metadata
- Authors
- Tijms, B. M., Möller, C., Vrenken, H., 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
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-03-11
- Volume
- 8
- Issue
- 3
- Pages
- e58921
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3594199
- OpenAlex
- W2058087702
- Cited by
- 132
- References
- 102
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 12 · 2023: 8 · 2022: 11 · 2021: 15 · 2020: 11 · 2019: 6 · 2018: 18 · 2017: 11 · 2016: 10 · 2015: 12 · 2014: 7 · 2013: 3
- FWCI
- 5.85
- Type
- journal article
- Language
- en
- Update notices
- correction (2013-09-09)
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Pure Amsterdam UMC (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- Stichting Dioraphte
- Alzheimer Nederland
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Keywords
- Alzheimer's disease, structural graph, small-world property, path length, clustering coefficient, cognitive decline, cortical morphology, hippocampus, parahippocampal gyrus, precuneus, fusiform gyrus, gray matter volume, disease prognosis
- MeSH terms
- Aged, Alzheimer Disease, Brain Mapping, Electroencephalography, Female, Humans, Magnetic Resonance Imaging, Male, Psychiatric Status Rating Scales, Severity of Illness Index, Case-Control Studies, Neuroimaging
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