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

> **NIH NIH RF1** · STANFORD UNIVERSITY · 2021 · $1,445,753

## Abstract

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.

## Key facts

- **NIH application ID:** 10260312
- **Project number:** 1RF1MH121867-01A1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Oscar Esteban
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,445,753
- **Award type:** 1
- **Project period:** 2021-07-19 → 2025-07-18

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10260312

## Citation

> US National Institutes of Health, RePORTER application 10260312, NIPreps: integrating neuroimaging preprocessing workflows across modalities, populations, and species (1RF1MH121867-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10260312. Licensed CC0.

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