ArticleAnnals of neurosciences2025
Graph Theoretical Measures for Alzheimer's, MCI, and Normal Controls: A Comparative Study Using MRI Data.
Article in Annals of neurosciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Reliable resting-state EEG connectivity measures reveal brain network destabilization and cognitive decoupling following prolonged extreme working conditions.Cognitive neurodynamics · 2026Article
- Multiscale characterization and classification of Alzheimer's disease via integration of brain fingerprint radiomics and graph‑theoretical network metrics.Neuroradiology · 2026Article
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Authors and funding
2 authors.
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Abstract
Background: The Graph theory provides the platform that could be used to model complex brain networks mathematically, and it could play a significant role in the diagnosis of various neurodegenerative diseases such as Alzheimer's. Purpose: The main aim of our study is to perform a comparative analysis in terms of various graph theoretic measures of structural brain networks. In particular, the paper evaluates graph theoretical measures by first forming graphs using magnetic resonance imaging (MRI) data. Method: In this paper, we study and evaluate graph theoretical measures using MRI data, namely characteristic path length, global efficiency, strength, and clustering coefficient, in a cohort of normal controls ( Results: It is observed that these measures may be used to differentiate Alzheimer's from normal. In our study, we observed that a very random and disrupted network is obtained in the case of Alzheimer's in comparison with the normal and MCI cases. The other observations in terms of graph theoretic measures are an increase in characteristic path length, a decrease in global efficiency, a decrease in strength, and a reduction in values of the clustering coefficient in the case of Alzheimer's. Conclusion: The findings suggest that graph theoretical measures and alterations in network topology could be used as quantitative biomarkers of AD.
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