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File-based localization of numerical perturbations in data analysis pipelines.

Author(s): Salari A, Kiar G, Lewis L, Evans AC, Glatard T

BACKGROUND: Data analysis pipelines are known to be affected by computational conditions, presumably owing to the creation and propagation of numerical errors. While this process could play a major role in the current reproducibility crisis, the precise cau...

Article GUID: 33269388
Comparing perturbation models for evaluating stability of neuroimaging pipelines.

Author(s): Kiar G, de Oliveira Castro P, Rioux P, Petit E, Brown ST, Evans AC, Glatard T

With an increase in awareness regarding a troubling lack of reproducibility in analytical software tools, the degree of validity in scientific derivatives and their downstream results has become unclear. The nature of reproducibility issues may vary across ...

Article GUID: 32831546
A Quantitative Comparison of Overlapping and Non-Overlapping Sliding Windows for Human Activity Recognition Using Inertial Sensors.

Author(s): Dehghani A, Sarbishei O, Glatard T, Shihab E

Sensors (Basel). 2019 Nov 18;19(22): Authors: Dehghani A, Sarbishei O, Glatard T, Shihab E

Article GUID: 31752158
Cyberinfrastructure for Open Science at the Montreal Neurological Institute.

Author(s): Das S, Glatard T, Rogers C, Saigle J, Paiva S, MacIntyre L, Safi-Harab M, Rousseau ME, Stirling J, Khalili-Mahani N, MacFarlane D, Kostopoul...

Front Neuroinform. 2016;10:53 Authors: Das S, Glatard T, Rogers C, Saigle J, Paiva S, MacIntyre L, Safi-Harab M, Rousseau ME, Stirling J, Khalili-Mahani N, MacFarlane D, Kostopoulos P, Rioux P, Ma...

Article GUID: 28111547
Best practices in data analysis and sharing in neuroimaging using MRI.

Author(s): Nichols TE, Das S, Eickhoff SB, Evans AC, Glatard T, Hanke M, Kriegeskorte N, Milham MP, Poldrack RA, Poline JB, Proal E, Thirion B, Van Ess...

Nat Neurosci. 2017 Feb 23;20(3):299-303 Authors: Nichols TE, Das S, Eickhoff SB, Evans AC, Glatard T, Hanke M, Kriegeskorte N, Milham MP, Poldrack RA, Poline JB, Proal E, Thirion B, Van Essen DC, ...

Article GUID: 28230846
The first MICCAI challenge on PET tumor segmentation.

Author(s): Hatt M, Laurent B, Ouahabi A, Fayad H, Tan S, Li L, Lu W, Jaouen V, Tauber C, Czakon J, Drapejkowski F, Dyrka W, Camarasu-Pop S, Cervenansky...

Med Image Anal. 2018 02;44:177-195 Authors: Hatt M, Laurent B, Ouahabi A, Fayad H, Tan S, Li L, Lu W, Jaouen V, Tauber C, Czakon J, Drapejkowski F, Dyrka W, Camarasu-Pop S, Cervenansky F, Girard P...

Article GUID: 29268169
Boutiques: a flexible framework to integrate command-line applications in computing platforms.

Author(s): Glatard T, Kiar G, Aumentado-Armstrong T, Beck N, Bellec P, Bernard R, Bonnet A, Brown ST, Camarasu-Pop S, Cervenansky F, Das S, Ferreira da...

Gigascience. 2018 05 01;7(5): Authors: Glatard T, Kiar G, Aumentado-Armstrong T, Beck N, Bellec P, Bernard R, Bonnet A, Brown ST, Camarasu-Pop S, Cervenansky F, Das S, Ferreira da Silva R, Flandin...

Article GUID: 29718199
Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure.

Author(s): Commowick O, Istace A, Kain M, Laurent B, Leray F, Simon M, Pop SC, Girard P, Améli R, Ferré JC, Kerbrat A, Tourdias T, Cervenansky F, Glata...

Sci Rep. 2018 Sep 12;8(1):13650 Authors: Commowick O, Istace A, Kain M, Laurent B, Leray F, Simon M, Pop SC, Girard P, Améli R, Ferré JC, Kerbrat A, Tourdias T, Cervenansky F, Glatard T,...

Article GUID: 30209345
A Serverless Tool for Platform Agnostic Computational Experiment Management.

Author(s): Kiar G, Brown ST, Glatard T, Evans AC

Front Neuroinform. 2019;13:12 Authors: Kiar G, Brown ST, Glatard T, Evans AC

Article GUID: 30890927
Title:Comparing perturbation models for evaluating stability of neuroimaging pipelines.
Authors:Kiar G,  de Oliveira Castro P,  Rioux P,  Petit E,  Brown ST,  Evans AC,  Glatard T
Link:https://www.ncbi.nlm.nih.gov/pubmed/32831546
DOI:10.1177/1094342020926237
Category:Int J High Perform Comput Appl
PMID:32831546
Dept Affiliation: IMAGING
1 Department of Biomedical Engineering, McGill University, Montreal, Canada.
2 Department of Computer Science, University of Versailles, Versailles, France.
3 Exascale Computing Lab, Intel, Paris, France.
4 Department of Computer Science, Concordia University, Montreal, Canada.

Description:

Comparing perturbation models for evaluating stability of neuroimaging pipelines.

Int J High Perform Comput Appl. 2020 Sep; 34(5):491-501

Authors: Kiar G, de Oliveira Castro P, Rioux P, Petit E, Brown ST, Evans AC, Glatard T

Abstract

With an increase in awareness regarding a troubling lack of reproducibility in analytical software tools, the degree of validity in scientific derivatives and their downstream results has become unclear. The nature of reproducibility issues may vary across domains, tools, data sets, and computational infrastructures, but numerical instabilities are thought to be a core contributor. In neuroimaging, unexpected deviations have been observed when varying operating systems, software implementations, or adding negligible quantities of noise. In the field of numerical analysis, these issues have recently been explored through Monte Carlo Arithmetic, a method involving the instrumentation of floating-point operations with probabilistic noise injections at a target precision. Exploring multiple simulations in this context allows the characterization of the result space for a given tool or operation. In this article, we compare various perturbation models to introduce instabilities within a typical neuroimaging pipeline, including (i) targeted noise, (ii) Monte Carlo Arithmetic, and (iii) operating system variation, to identify the significance and quality of their impact on the resulting derivatives. We demonstrate that even low-order models in neuroimaging such as the structural connectome estimation pipeline evaluated here are sensitive to numerical instabilities, suggesting that stability is a relevant axis upon which tools are compared, alongside more traditional criteria such as biological feasibility, computational efficiency, or, when possible, accuracy. Heterogeneity was observed across participants which clearly illustrates a strong interaction between the tool and data set being processed, requiring that the stability of a given tool be evaluated with respect to a given cohort. We identify use cases for each perturbation method tested, including quality assurance, pipeline error detection, and local sensitivity analysis, and make recommendations for the evaluation of stability in a practical and analytically focused setting. Identifying how these relationships and recommendations scale to higher order computational tools, distinct data sets, and their implication on biological feasibility remain exciting avenues for future work.

PMID: 32831546 [PubMed - as supplied by publisher]