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This article reviews the evaluation and optimization of the preprocessing steps for blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI). This technique indirectly measures changes in local neuronal firing rates by measuring associated changes in deoxy-hemoglobin concentrations in nearby blood vessels. Based on the existing literature, it is impossible to make conclusive statements about the optimal algorithm and software implementations for any single preprocessing step, let alone entire pipelines. The author believes that the present focus on the technological testing of preprocessing steps should be balanced by approaches that test the pipeline. This should include all interactions measured using metrics that are closely linked to research and diagnostic questions addressed at the end of the processing pipeline. The goal is to avoid single expedient or default pipelines by developing a framework capable of potentially testing thousands of possible pipeline implementations per dataset. To achieve this goal, researchers depend on recent developments in software tools for managing neuroimaging workflows.