We propose the NeuroImaging Preprocessing workflows ("NIPreps") framework, a workbench for the development of workflows and visualization tools for the preprocessing neuroimaging data. NiPreps expands our fMRIPrep software to operate on new imaging modalities 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. Next-generation neuroimaging workflows require harmonizing measures of wide-ranging phenomena (morphometry, functional activity, and connectivity) from multiple sources (e.g., scanning centers or modalities). Hence, we will evolve fMRIPrep into NiPreps, a software framework integrating BIDS and following the BIDS-Apps specifications. First, the project will consolidate the foundations of the NiPreps integration, 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 diffusion, arterial spin labeling (ASL), and molecular imaging "-Preps." Finally, we will address the consolidation of the NiPreps community to ensure the sustainability of the framework, converging the communities around fMRIPrep, dMRIPrep, ASLPrep, and PETPrep with hackathons and docusprints. In short, NIPreps will pave the way towards next-generation imaging, ultimately allowing neuroscientists to seek for a unified statistical framework that is capable of rigorously integrating cross-application and cross-species data analysis.

Next-generation neuroimaging analyses will require integrating measures (structural morphometry, functional activity, and connectivity) across disciplines (e.g., brain development, animal models, neuropsychiatric disorders, etc.). Leveraging the support of the BIDS ecosystem of standards, we will develop NIPreps, a collection of BIDS-Apps that generalize the currently existing image preprocessing core of fMRIPrep across data types. By addressing current bottlenecks in the neuroimaging research workflow that impede integration, e.g., consistent multimodal analyses of inhomogeneous data, NiPreps opens new avenues for integrative and reproducible analysis, ultimately enabling the kinds of translation that are essential to maximizing the benefits of the investments in research on the brain and mental health disorders.