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Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks

Author(s): Kiar G; Chatelain Y; de Oliveira Castro P; Petit E; Rokem A; Varoquaux G; Misic B; Evans AC; Glatard T;

The analysis of brain-imaging data requires complex processing pipelines to support findings on brain function or pathologies. Recent work has shown that variability in analytical decisions, small amounts of noise, or computational environments can lead to substantial differences in the results, ...

Article GUID: 34724000


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 domains, tools, data sets, and computational infrastructures, but numerical instabilities are though ...

Article GUID: 32831546


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