This project was developed as part of the Fundamentals of Data Analysis course examination. Inspired by the groundbreaking paper titled "Accurate brain‐age models for routine clinical MRI examinations", the work focuses on creating a predictive model using volumetric features extracted via SynthSeg (FreeSurfer) from 3D T1-w brain MRIs. It estimates brain age, detecting deviations between chronological and biological brain age, showing that Alzheimer’s Disease links to accelerated brain ageing.

Using datasets from ADNI, AIBL, and OASIS, I curated a collection of 7545 MRI scans from 2227 unique patients for analysis. These scans belong exclusively to two diagnostic categories:

• Cognitively Normal CN 68.68%
• Alzheimer's Disease AD 31.32%

A polynomial regression model was trained via Cross-Validation, achieving a MAE of 3.99 years, with age as the dependent variable and other features as predictors. Trained on CN data, it predicted brain age for CN patients, with a Brain PAD of -0.12 years, closely matching chronological age. For AD patients, however, it showed a Brain PAD of +7.48 years, indicating that AD’s association with accelerated brain ageing.