Detection of brain network abnormalities by graph invariants in Alzheimer’s disease using MRI images Article Swipe
G, NallappaBhavithran
,
R. Selvakumar
,
G, NallappaBhavithran
,
R. Selvakumar
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-26259-8
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-26259-8
Alzheimer’s disease is a major cause of dementia in older adults. It involves gradual changes in brain function that result in cognitive decline, affecting memory, reasoning, and executive skills. The accurate detection of structural abnormalities in brain networks is crucial for early diagnosis and disease staging. This study presents a graph-based framework that analyzes abnormalities in brain networks of Alzheimer’s patients using six distance-based topological indices: Szeged index, Graovac-Ghorbani index, Padmakar–Ivan index, Mostar index, Wiener index, and Normalized Graovac-Ghorbani index. These indices effectively characterize the structural properties of brain networks and identify disruptions linked to disease progression. The proposed framework first constructs brain graphs from MRI images using the Brightness Distance Matrix method, which captures the spatial relationships between pixels. Then, the constructed brain graphs are modeled using the Watts and Strogatz small-world model to normalize the topological indices. The normalized indices serve as input features for various machine learning models, including decision trees, logistic regression, support vector machines, and a multi-layer neural network. Among these models, a refined neural network model achieves the highest classification accuracy of 89.45%, confirming the value of topological indices as interpretable biomarkers for disease staging. This framework demonstrates the potential of graph-theoretic approaches for detecting Alzheimer’s-related brain network alterations and offers a scalable, interpretable, and privacy-friendly solution.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-025-26259-8
- https://www.nature.com/articles/s41598-025-26259-8.pdf
- OA Status
- gold
- References
- 35
- OpenAlex ID
- https://openalex.org/W7106715132
All OpenAlex metadata
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https://openalex.org/W7106715132Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41598-025-26259-8Digital Object Identifier
- Title
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Detection of brain network abnormalities by graph invariants in Alzheimer’s disease using MRI imagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-11-26Full publication date if available
- Authors
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G, NallappaBhavithran, R. Selvakumar, G, NallappaBhavithran, R. SelvakumarList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-025-26259-8Publisher landing page
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https://www.nature.com/articles/s41598-025-26259-8.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41598-025-26259-8.pdfDirect OA link when available
- Concepts
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Computer science, Support vector machine, Artificial intelligence, Pattern recognition (psychology), Artificial neural network, Disease, Graph, Brain disease, Cognition, Dementia, Machine learning, Logistic regression, Graph theory, Brain function, Function (biology), Deep neural networks, Brain mappingTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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35Number of works referenced by this work
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| abstract_inverted_index.scalable, | 209 |
| abstract_inverted_index.solution. | 213 |
| abstract_inverted_index.Brightness | 110 |
| abstract_inverted_index.Normalized | 78 |
| abstract_inverted_index.approaches | 199 |
| abstract_inverted_index.biomarkers | 188 |
| abstract_inverted_index.confirming | 180 |
| abstract_inverted_index.constructs | 102 |
| abstract_inverted_index.normalized | 141 |
| abstract_inverted_index.properties | 87 |
| abstract_inverted_index.reasoning, | 26 |
| abstract_inverted_index.structural | 34, 86 |
| abstract_inverted_index.alterations | 205 |
| abstract_inverted_index.constructed | 123 |
| abstract_inverted_index.disruptions | 93 |
| abstract_inverted_index.effectively | 83 |
| abstract_inverted_index.graph-based | 51 |
| abstract_inverted_index.multi-layer | 162 |
| abstract_inverted_index.regression, | 156 |
| abstract_inverted_index.small-world | 133 |
| abstract_inverted_index.topological | 65, 138, 184 |
| abstract_inverted_index.characterize | 84 |
| abstract_inverted_index.demonstrates | 194 |
| abstract_inverted_index.progression. | 97 |
| abstract_inverted_index.Alzheimer’s | 1, 60 |
| abstract_inverted_index.abnormalities | 35, 55 |
| abstract_inverted_index.interpretable | 187 |
| abstract_inverted_index.relationships | 118 |
| abstract_inverted_index.classification | 176 |
| abstract_inverted_index.distance-based | 64 |
| abstract_inverted_index.interpretable, | 210 |
| abstract_inverted_index.Padmakar–Ivan | 71 |
| abstract_inverted_index.graph-theoretic | 198 |
| abstract_inverted_index.Graovac-Ghorbani | 69, 79 |
| abstract_inverted_index.privacy-friendly | 212 |
| abstract_inverted_index.Alzheimer’s-related | 202 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |