Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics

TL;DR

Applying Monte Carlo Arithmetic to perturb connectome estimation pipelines introduces beneficial variability, enhancing the generalizability of machine learning classifiers in brain imaging studies without requiring extensive perturbations.

Contribution

This study demonstrates that controlled numerical noise via Monte Carlo Arithmetic improves classification performance in connectomics by augmenting datasets with plausible network variations.

Findings

Perturbed networks improve classifier accuracy across methods.

Minimal perturbations are sufficient for benefits.

Enhanced robustness in brain network classification.

Abstract

Machine learning models are commonly applied to human brain imaging datasets in an effort to associate function or structure with behaviour, health, or other individual phenotypes. Such models often rely on low-dimensional maps generated by complex processing pipelines. However, the numerical instabilities inherent to pipelines limit the fidelity of these maps and introduce computational bias. Monte Carlo Arithmetic, a technique for introducing controlled amounts of numerical noise, was used to perturb a structural connectome estimation pipeline, ultimately producing a range of plausible networks for each sample. The variability in the perturbed networks was captured in an augmented dataset, which was then used for an age classification task. We found that resampling brain networks across a series of such numerically perturbed outcomes led to improved performance in all tested classifiers, preprocessing strategies, and dimensionality reduction techniques. Importantly, we find that this benefit does not hinge on a large number of perturbations, suggesting that even minimally perturbing a dataset adds meaningful variance which can be captured in the subsequently designed models.