Microstructural mapping of neural pathways in Alzheimer's disease using macrostructure-informed normative tractometry

Area of Science:

• Neuroimaging
• Brain Microstructure Analysis
• White Matter Integrity

Background:

• Diffusion-weighted MRI (dMRI) detects brain tissue changes relevant to neurodegenerative diseases.
• Current methods often average diffusion measures, neglecting underlying white matter fiber geometry.

Purpose of the Study:

• To introduce a novel framework, macrostructure-informed normative tractometry (MINT), for joint analysis of white matter (WM) microstructure and macrostructure.
• To investigate alterations in MCI and dementia using MINT.
• To compare MINT metrics with traditional diffusion tensor imaging (DTI) metrics to understand the impact of fiber geometry.

Main Methods:

• Developed the MINT framework integrating macrostructure and microstructure.
• Employed normative models to capture healthy variability.
• Utilized variational autoencoders for tractography and fiber geometry patterns.
• Applied multivariate methods for WM microstructure and macrostructure modeling.
• Leveraged transfer learning for efficient model training.

Main Results:

• Identified consistent microstructural and macrostructural anomalies in MCI and dementia across two large cohorts (North America, India).
• Ranked the sensitivity of various diffusion metrics in detecting dementia.
• Demonstrated that changes in DTI metrics can be influenced by macroscopic alterations.

Conclusions:

• MINT enables joint modeling of tract shape and microstructure for improved interpretation of neurodegenerative disease effects.
• The framework has the potential to better disentangle and understand alterations in neural pathways.
• MINT offers a more comprehensive approach than univariate DTI analysis.