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BrainAgeEngine: MRI-Based Brain Age Prediction, Brain Age Gap Analysis, and Neurodegeneration Biomarker Correlation
Brain age prediction from neuroimaging data provides a biomarker of brain health, with brain age gap (BAG = predicted - chronological age) reflecting accelerated or decelerated aging. We present BrainAgeEngine, a pure-Python pipeline for brain age analysis. The engine implements MRI feature extraction (cortical thickness, subcortical volumes, white matter), brain age prediction (ridge regression), BAG calculation, neurodegeneration biomarker correlation, and lifestyle factor analysis. Applied to 500 subjects (20-90 years), the pipeline achieves prediction r=0.879, MAE=8.2 years, and BAG-MMSE r=−0.807.
Introduction
Brain age is estimated from structural MRI features. Brain age gap (BAG) = predicted age - chronological age. Positive BAG indicates accelerated aging; negative BAG indicates preserved brain health. BAG correlates with cognitive decline and neurodegeneration.
Methods
Feature Extraction
Cortical thickness (68 regions), subcortical volumes (14 structures), white matter FA (20 tracts).
Brain Age Prediction
Ridge regression: age = Σ β_i × feature_i + ε. Cross-validated.
BAG
BAG = predicted_age - chronological_age.
Results
Prediction r=0.879. MAE=8.2y. BAG-MMSE r=−0.807.
Code Availability
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