Computational Errors Could Have a Negative Impact on the Ability to Study Brain Networks

Identifying the effects that random unavoidable computing errors have on developing reliable models of brain-phenotype relationships.

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, endangering the trust in conclusions. We explored the instability of results by instrumenting a structural connectome estimation pipeline with Monte Carlo Arithmetic to introduce random noise throughout.

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