Alzheimer's Disease Detection via a Surrogate Brain Age Prediction Task using 3D Convolutional Neural Networks

Structural magnetic resonance imaging (MRI) studies demonstrated that Alzheimer's Disease (AD) causes not only local but also whole-brain level neural degenerative changes. To assess such changes, convolutional neural networks (CNN) are a popular approach as they are very capable automated feature extractors. In this work, due to the lack of segmentation that highlights brain degenerative changes, CNN-based brain age prediction is used as a surrogate task for training a feature extractor. Using the 3D TI-weighted MRI data of cognitive normal (CN) subjects from the OASIS-3 dataset, lightweight 3D CNN-based models are trained to predict brain age. The extracted features are then used in the binary classification of CN vs AD patients from their brain MRI scans. To established a baseline, we used support vector machines and random forest classifier as base classifiers. Our results suggest that the 3D MRI driven CNN brain age prediction surrogate task approach can learn AD-relevant features with high discriminative power without a complicated pipeline of preprocessing or data augmentation. Highlighting the novelty of the approach: the train-test-split is carefully performed on subject-level to avoid data leakage; the spatial information of 3D volumetric data is fully utilised; the robustness is proven by not using complicated data preprocessing and augmentation techniques.