Estimated Brain Age in Healthy Aging and Across Multiple Neurological Disorders

Area of Science:

• Neuroimaging
• Artificial Intelligence in Medicine
• Gerontology

Background:

• Brain aging mechanisms in the general population and neurological disorders remain unclear.
• Understanding brain aging is crucial for diagnosing and managing neurological conditions.
• Accelerated brain aging may indicate underlying pathological processes.

Purpose of the Study:

• To characterize brain aging patterns in healthy individuals and patients with neurological disorders.
• To investigate the clinical relevance of brain aging metrics.
• To identify differences in brain age gap (BAG) across various neurological conditions.

Main Methods:

• Retrospective analysis of 3D T1-weighted MRI scans from 2913 healthy controls and patients with MS, NMOSD, AD, PD, and cSVD.
• Utilized a 3D convolutional neural network model to estimate brain age.
• Calculated brain age gap (BAG) and correlated it with MRI metrics, cognitive, and physical measures.

Main Results:

• Healthy individuals with accelerated brain age (BAG ≥ 5 years) showed increased white matter hyperintensity (WMH) and reduced regional brain volumes.
• Significantly higher BAG was observed in MS, NMOSD, AD, PD, and cSVD compared to healthy controls.
• Increased BAG correlated with brain atrophy, WMH burden, cognitive impairment, and physical disability in MS.

Conclusions:

• Accelerated brain aging in healthy individuals is associated with WMH and regional brain volume loss.
• Neurological disorders exhibit distinct patterns of accelerated brain aging.
• Accelerated brain aging is clinically relevant, correlating with cognitive and physical functional impairments.