TRACTOGRAPHY DENSITY AND NETWORK MEASURES IN ALZHEIMER'S DISEASE.

Area of Science:

• Neuroscience
• Medical Imaging
• Network Science

Background:

• Alzheimer's disease (AD) is characterized by declining brain connectivity, affecting both function and structure.
• Diffusion-based tractography provides novel methods for mapping brain networks and understanding AD's impact.

Purpose of the Study:

• To identify which brain network measures best differentiate Alzheimer's patients from controls.
• To investigate how fiber density in tractography influences these network measures and diagnostic distinctions.

Main Methods:

• Computed brain networks from diffusion-weighted images (DWI) in 110 subjects across diagnostic groups (controls, early/late MCI, AD).
• Utilized orientation distribution function (ODF) lookup tables and accelerated tractography for high-density fiber extraction and connectivity mapping.
• Analyzed global efficiency, transitivity, path length, mean degree, density, modularity, small world, and assortativity.

Main Results:

• Mean nodal degree was the most effective measure for distinguishing between diagnostic groups.
• High-density fiber matrices significantly improved the detection of subtle clinical differences, such as between mild cognitive impairment (MCI) and normal cognition, and between early and late MCI subtypes.

Conclusions:

• Mean nodal degree is a sensitive indicator of brain network alterations in Alzheimer's disease and related cognitive impairments.
• The density of fibers extracted during tractography critically influences the ability of network measures to detect clinical differences, necessitating careful consideration in diagnostic analyses.