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Related Experiment Video

Updated: Jul 23, 2026

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Graph Theoretical Measures for Alzheimer's, MCI, and Normal Controls: A Comparative Study Using MRI Data.

Rakhi Sharma1, Shiv Dutt Joshi1

  • 1Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India.

Annals of Neurosciences
|February 28, 2025
PubMed
Summary

Graph theory analysis reveals distinct brain network disruptions in Alzheimer's disease (AD). These findings highlight network topology alterations as potential quantitative biomarkers for diagnosing AD and mild cognitive impairment (MCI).

Keywords:
AlzheimerMRIbrain connectivitymild cognitive impairment (MCI)

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Area of Science:

  • Neuroscience
  • Graph Theory
  • Medical Imaging Analysis

Background:

  • Graph theory offers a mathematical framework for modeling complex brain networks.
  • This approach holds significant potential for diagnosing neurodegenerative diseases like Alzheimer's disease (AD).

Purpose of the Study:

  • To conduct a comparative analysis of structural brain network properties using graph theory measures.
  • To evaluate the efficacy of graph theoretical measures derived from magnetic resonance imaging (MRI) data for differentiating between normal controls, mild cognitive impairment (MCI), and Alzheimer's disease (AD) cohorts.

Main Methods:

  • Structural brain networks were constructed from MRI data of 30 normal controls, 30 MCI patients, and 30 AD patients.
  • Key graph theoretical measures including characteristic path length, global efficiency, strength, and clustering coefficient were calculated.
  • Receiver operating characteristic (ROC) analysis was employed to validate the diagnostic performance of these measures.

Main Results:

  • Alzheimer's disease brains exhibited significantly more random and disrupted network topology compared to normal and MCI groups.
  • Characteristic path length increased, while global efficiency, strength, and clustering coefficient decreased in AD patients.
  • Graph theoretical measures demonstrated potential in differentiating AD from normal cognitive states.

Conclusions:

  • Alterations in brain network topology, quantifiable through graph theoretical measures, show promise as biomarkers for Alzheimer's disease.
  • These findings support the use of network analysis as a quantitative tool for AD diagnosis and potentially for tracking disease progression.