November 30, 2025Scientific ReportsOpen Access

Detection of brain network abnormalities by graph invariants in Alzheimer’s disease using MRI images

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Graph-based framework identifies brain network disruptions in Alzheimer’s disease, suggesting topological indices predict disease progression.

Classification accuracy of 89.45% using machine learning models highlights the effectiveness of topological indices.
Neural networks outperform other methods, confirming the significance of structural brain analysis in dementia.
Analysis employs the small-world model to enhance the interpretation of complex brain networks.
Topological indices emerge as potential biomarkers for Alzheimer’s disease staging, underscoring their clinical relevance.

NallappaBhavithran et al. (2025) studied this question.

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November 30, 2025Scientific ReportsOpen Access

View Full Paper

Ask AI

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Authors

Graph-based framework identifies brain network disruptions in Alzheimer’s disease, suggesting topological indices predict disease progression.

Classification accuracy of 89.45% using machine learning models highlights the effectiveness of topological indices.
Neural networks outperform other methods, confirming the significance of structural brain analysis in dementia.
Analysis employs the small-world model to enhance the interpretation of complex brain networks.
Topological indices emerge as potential biomarkers for Alzheimer’s disease staging, underscoring their clinical relevance.

NallappaBhavithran et al. (2025) studied this question.

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