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

View ORCID ProfileGregory Kiar, View ORCID ProfileYohan Chatelain, View ORCID ProfileAli Salari, View ORCID ProfileAlan C. Evans, View ORCID ProfileTristan Glatard
doi: https://doi.org/10.1101/2020.12.16.423084
Gregory Kiar
1Montreal Neurological Institute, McGill University, Montreal, QC, H3A 2B4, Canada
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  • For correspondence: gregory.kiar@childmind.org
Yohan Chatelain
2Department of Computer Science and Computer Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada
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Ali Salari
2Department of Computer Science and Computer Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada
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Alan C. Evans
1Montreal Neurological Institute, McGill University, Montreal, QC, H3A 2B4, Canada
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Tristan Glatard
2Department of Computer Science and Computer Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada
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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.

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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 Statement

The authors have declared no competing interest.

Footnotes

  • Funding information: Natural Sciences and Engineering Research Council of Canada (NSERC), Award Number: CGSD3-519497-2018; In partnership with the Canadian Open Neuroscience Platform.

  • updated formatting

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted July 26, 2021.
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Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics
Gregory Kiar, Yohan Chatelain, Ali Salari, Alan C. Evans, Tristan Glatard
bioRxiv 2020.12.16.423084; doi: https://doi.org/10.1101/2020.12.16.423084
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Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics
Gregory Kiar, Yohan Chatelain, Ali Salari, Alan C. Evans, Tristan Glatard
bioRxiv 2020.12.16.423084; doi: https://doi.org/10.1101/2020.12.16.423084

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