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Scientific Publications

Embracing Computational Errors to Create More Predictive and Generalizable Biomarkers

September 21, 2021

Exploring how computational errors can be leveraged to improve models of brain networks.


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. 

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Tagged with: Science and Research
Gregory Kiar, PhD
Gregory Kiar, PhD, is a research scientist at the Child Mind Institute. Throughout his degrees in Biomedical Engineering, Greg has … Read Bio