Brain age gap difference between healthy and mild dementia subjects: Functional network connectivity analysis.
Summary
The brain age gap, calculated using functional network connectivity, can help identify Alzheimer's disease progression. This novel biomarker shows significant differences between healthy individuals and those with mild dementia.
Area of Science:
- Neuroscience
- Medical Imaging
- Biomarkers
Background:
- Brain age gap, the difference between predicted and chronological age, is a biomarker for brain aging and disease.
- Previous studies primarily used structural MRI; functional network connectivity (FNC) from fMRI for brain age prediction is less explored.
- Understanding brain age in relation to Alzheimer's disease (AD) progression is crucial for early detection and intervention.
Purpose of the Study:
- To predict brain age using FNC derived from functional MRI (fMRI).
- To investigate the association between FNC-based brain age gap and Alzheimer's disease progression.
- To establish the brain age gap as a potential biomarker for mild dementia.
Main Methods:
- Trained a support vector regression (SVR) model using FNC data from 951 cognitively normal individuals (aged 42-95).
- Validated the model on two independent datasets comprising cognitively normal and mild dementia subjects (aged 50-80, N=70).
- Calculated and compared the brain age gap between normal cognitive function (NCF) and mild dementia (MD) groups.
Main Results:
- The trained SVR model successfully predicted brain age from FNC.
- The mean brain age gap was -2.25 for the NCF group and 2.08 for the MD group.
- A significant difference in brain age gap was observed between NCF and MD groups, indicating accelerated aging in dementia.
Conclusions:
- The brain age gap estimated from FNC is a promising biomarker for Alzheimer's disease progression.
- FNC-based brain age prediction offers a novel approach to assessing neurodegenerative changes.
- This method could aid in the early identification and monitoring of mild dementia.
