RT Journal Article
SR Electronic
T1 Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.12.16.423084
DO 10.1101/2020.12.16.423084
A1 Kiar, Gregory
A1 Chatelain, Yohan
A1 Salari, Ali
A1 Evans, Alan C.
A1 Glatard, Tristan
YR 2021
UL http://biorxiv.org/content/early/2021/07/26/2020.12.16.423084.abstract
AB 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.This paper demonstrates how Monte Carlo Arithmetic, a dataagnostic perturbation technique, can be used for dataset augmentation during the generation of structural connectomes and improve downstream phenotypic prediction.Competing Interest StatementThe authors have declared no competing interest.