# Interpretable brain age prediction using linear latent variable models of functional connectivity

## TL;DR

This paper introduces an interpretable method for predicting brain age using linear latent variable models on functional connectivity data, enabling both accurate age estimation and insights into age-related brain connectivity changes.

## Contribution

It presents a novel two-step framework combining PCA-based connectivity networks with linear regression for brain age prediction, emphasizing interpretability and generalizability.

## Key findings

- Models accurately predict brain age across datasets.
- Identified reproducible functional connectivity networks related to aging.
- Demonstrated generalization to independent datasets.

## Abstract

Neuroimaging-driven prediction of brain age, defined as the predicted biological age of a subject using only brain imaging data, is an exciting avenue of research. In this work we seek to build models of brain age based on functional connectivity while prioritizing model interpretability and understanding. This way, the models serve to both provide accurate estimates of brain age as well as allow us to investigate changes in functional connectivity which occur during the ageing process. The methods proposed in this work consist of a two-step procedure: first, linear latent variable models, such as PCA and its extensions, are employed to learn reproducible functional connectivity networks present across a cohort of subjects. The activity within each network is subsequently employed as a feature in a linear regression model to predict brain age. The proposed framework is employed on the data from the CamCAN repository and the inferred brain age models are further demonstrated to generalize using data from two open-access repositories: the Human Connectome Project and the ATR Wide-Age-Range.