Automated High-Definition MRI Processing Routine Robustly Detects Longitudinal Morphometry Changes in Alzheimer's...

Area of Science:

• Neuroimaging
• Neurodegenerative Diseases
• Biostatistics

Background:

• Longitudinal MRI studies are crucial for tracking neurodegenerative diseases and drug efficacy.
• Manual assessments are time-consuming and often yield suboptimal results.

Purpose of the Study:

• To develop and validate a precise, automated analysis pipeline for longitudinal MRI studies.
• To enhance the detection of subtle neurodegenerative changes in small cohorts.

Main Methods:

• Automated image processing including surface and voxel-based morphometry with detailed brain atlases (HCP MMP 1.0).
• Statistical analysis using a multiplicative model of annual percent change (APC).
• Multiple testing correction adapted from genome-wide association studies.

Main Results:

• The pipeline identified 22 significant morphometric changes (cortical volume, area, thickness) using surface-based morphometry (SBM) in a small Alzheimer's cohort.
• Compared to VBM, SBM detected more significant changes, highlighting its sensitivity.
• A 1-year decrease in brain morphometry correlated with increased clinical disability and cognitive decline.

Conclusions:

• Automated MRI analysis with precise morphometry and statistical correction yields significant outcomes even in small study cohorts.
• The proposed pipeline is reliable and suitable for routine use in clinical trials for neurodegenerative diseases.