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

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.