NIPreps: integrating neuroimaging preprocessing workflows across modalities, populations, and species

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Project Summary Despite the rapid advances in the neuroimaging research workflow over the last decade, the enormous variability between and within data types and specimens impedes integrated analyses. Moreover, the availability of a comprehensive portfolio of software libraries and tools has also resulted in a concerning degree of analytical variability. Generalizing the preprocessing — that is, the intermediate step between data generation by the measurement device and the subsequent statistical modeling and analysis — beyond fMRIPrep, we propose a framework called NiPreps (NeuroImaging Preprocessing toolS) that we envision as a workbench for the development of such pipelines. By exclusively addressing the preprocessing of the data, fMRIPrep has successfully allowed researchers to focus their effort and expertise on the portion most relevant to scientific inference (i.e., statistical and computational analyses) and reduce methodological variability. NiPreps expands fMRIPrep to operate on new imaging modalities (diffusion MRI, arterial spin labeling, positron emission tomography, and multi-echo functional MRI) and disciplines (e.g., preclinical imaging). Despite some remarkable analysis workflows that display end-to-end consolidation, integrations across applications (e.g., analyses of human and nonhuman data) remain exceptionally challenging. Hence, we will evolve fMRIPrep into NiPreps, a software framework integrating BIDS and following the BIDS-Apps specifications. First, the project will consolidate the NiPreps foundations, with the generalization of fMRIPrep's driving principles and methods across modalities and domains of application. Second, we will expand the portfolio of end-user NiPreps with dMRIPrep, ASLPrep, PETPrep, and better coverage of multi-echo fMRI by fMRIPrep. Finally, we will address the NiPreps community's consolidation to ensure the sustainability of the framework, converging the communities around each "-Prep" with hackathons and docusprints. In short, NIPreps will pave the way towards next-generation imaging, ultimately allowing neuroscientists to seek a unified statistical framework capable of rigorously integrating cross-application and cross-species data analysis.