G-ALPS: An index for evaluating cognitive decline and aging using diffusion tensor imaging

Area of Science:

• Neuroimaging
• Biomedical Engineering
• Neurology

Background:

• Glymphatic system dysfunction is linked to cognitive decline in neurodegenerative diseases like Alzheimer's disease (AD).
• Accurate assessment of glymphatic function is crucial for understanding and managing these conditions.

Purpose of the Study:

• To introduce and validate the G-Along Perivascular Space (G-ALPS) index, an optimized metric for assessing glymphatic system function.
• To evaluate the G-ALPS index's performance against the existing ALPS index using diffusion tensor imaging (DTI) data.

Main Methods:

• Developed the G-ALPS index using Genetic Programming, an optimized version of the ALPS index.
• Analyzed 217 diffusion tensor imaging (DTI) samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Human Connectome Project (HCP) datasets.

Main Results:

• The G-ALPS index demonstrated superior correlation with cognitive measures (MMSE, CDR, FAQ) compared to ALPS.
• G-ALPS showed significantly enhanced sensitivity in detecting aging effects across AD, MCI, and control groups.
• Improved correlation was observed between G-ALPS and sleep quality measures (MMSE, PSQI).

Conclusions:

• The G-ALPS index shows promise as an indirect quantitative metric for assessing glymphatic system function or dysfunction.
• This optimized index may offer improved insights into cognitive decline associated with neurodegeneration, aging, and sleep disorders.