Towards a Foundation Model for Brain Age Prediction using coVariance Neural Networks

TL;DR

This paper introduces NeuroVNN, a coVariance neural network-based foundation model for brain age prediction that offers interpretability and transferability across diverse neuroimaging datasets.

Contribution

NeuroVNN is a novel pre-trained model that predicts brain age with anatomical interpretability and can transfer across datasets with different brain atlases.

Findings

NeuroVNN accurately estimates brain age in various populations.

The model successfully transfers to datasets with different dimensionalities.

NeuroVNN provides biologically plausible age estimates.

Abstract

Brain age is the estimate of biological age derived from neuroimaging datasets using machine learning algorithms. Increasing brain age with respect to chronological age can reflect increased vulnerability to neurodegeneration and cognitive decline. In this paper, we study NeuroVNN, based on coVariance neural networks, as a paradigm for foundation model for the brain age prediction application. NeuroVNN is pre-trained as a regression model on healthy population to predict chronological age using cortical thickness features and fine-tuned to estimate brain age in different neurological contexts. Importantly, NeuroVNN adds anatomical interpretability to brain age and has a `scale-free' characteristic that allows its transference to datasets curated according to any arbitrary brain atlas. Our results demonstrate that NeuroVNN can extract biologically plausible brain age estimates in different populations, as well as transfer successfully to datasets of dimensionalities distinct from that for the dataset used to train NeuroVNN.