fmriprep

name	fmriprep
description	Preprocess task-based or resting-state fMRI data with fMRIPrep — a robust, BIDS-App preprocessing pipeline built on FSL, ANTs, FreeSurfer, AFNI, and Nilearn. Use this skill whenever the user asks to preprocess fMRI/BOLD data, run fMRIPrep on a BIDS dataset, set up Docker/Singularity/Apptainer containers for fMRIPrep, choose output spaces (MNI152NLin2009cAsym, fsaverage, fsLR/CIFTI), configure susceptibility distortion correction (SDC), extract or interpret the confounds table, resample to surface/grayordinates, cite fMRIPrep and its dependencies, or debug fMRIPrep crashes and hangs.
version	1.0.0
authors	["Claude (AI-assisted)"]
review_status	ai-generated

fMRIPrep: Robust fMRI Preprocessing Pipeline

Purpose

This skill encodes complete operational knowledge of fMRIPrep — the NiPreps BIDS-App for preprocessing task and resting-state fMRI. It covers installation (Docker/Apptainer/pip), the full CLI, workflow internals, output layout, the confounds table, output spaces / TemplateFlow, susceptibility distortion correction, BIDS filters, troubleshooting, and how to cite. It is derived from the fMRIPrep 25.2.x (LTS) source tree (https://github.com/nipreps/fmriprep).

When to Use This Skill

Activate when the user:

• Wants to run fMRIPrep on a BIDS dataset (Docker, Singularity/Apptainer, pip)
• Asks how to install fMRIPrep or set up dependencies (FreeSurfer license, TemplateFlow)
• Needs help choosing --output-spaces, --cifti-output, or surface targets (fsaverage, fsLR)
• Needs help with SDC / fieldmap issues (--use-syn-sdc, --force syn-sdc, IntendedFor, B0FieldIdentifier)
• Asks about the confounds file (~_desc-confounds_timeseries.tsv), aCompCor/tCompCor, motion parameters
• Debugs a crashing/hanging fMRIPrep run, FreeSurfer IsRunning errors, or race conditions
• Wants to reuse pre-computed derivatives (--derivatives), an existing FreeSurfer subject dir, or a partial run
• Needs a BIDS filter file (--bids-filter-file) or wants to select tasks/sessions/echoes
• Needs a citation/boilerplate for a paper or grant proposal
• Wants to interpret the HTML visual report

Reference Files (Progressive Disclosure)

Topic	File	When to Read
Installation (Docker, Apptainer, pip, wrapper, FreeSurfer license)	references/installation.md	User asks how to install/set up fMRIPrep, or hits license/permission errors
Complete CLI reference (every flag, defaults, choices)	references/cli-reference.md	User asks "what does flag X do?" or needs the full option table
Practical invocation examples (single subject, batch, HPC SLURM, Docker)	references/usage-examples.md	User asks "how do I actually run it on my data?"
Workflow internals (anat + BOLD stages, HMC, BBR, SDC, surface, CIFTI)	references/workflows.md	User asks what fMRIPrep does under the hood, or wants a step-by-step methods section
Output layout and derivatives naming	references/outputs.md	User asks what files fMRIPrep produces, or how to find a specific derivative
Confounds table (aCompCor, tCompCor, motion, FD, DVARS, spike regressors)	references/confounds.md	User asks which columns of the confounds TSV to use for denoising
Output spaces / TemplateFlow (MNI152NLin2009cAsym, fsLR, fsaverage, custom)	references/spaces.md	User asks how to specify --output-spaces, use custom templates, or pre-fetch templates
Susceptibility distortion correction (fieldmaps, PEPOLAR, phase-diff, SyN)	references/sdc-fieldmaps.md	User has fieldmaps, wants fieldmap-less SDC, or SDC-related errors
BIDS filter file syntax + PyBIDS queries	references/bids-filter.md	User needs to select specific sessions/tasks or non-BIDS entities
FAQ, troubleshooting, hangs, HPC, TemplateFlow offline	references/troubleshooting-faq.md	User's run is crashing/hanging or they're on an HPC without Internet
Citation boilerplate, DOIs, BibTeX, dependency papers	references/citation.md	User writes a paper or grant that uses fMRIPrep
Pinned dependency versions (25.2.x LTS)	references/versions-dependencies.md	User asks which version of FreeSurfer/ANTs/FSL/AFNI ships with fMRIPrep

What fMRIPrep Is

fMRIPrep is a BIDS-App: a container that consumes a BIDS-organized dataset and emits a BIDS-Derivatives dataset plus a per-subject HTML report. It performs minimal preprocessing — motion correction, field unwarping, normalization, bias-field correction, brain extraction, coregistration, and optional surface resampling — and deliberately does not smooth or denoise. Downstream denoising is enabled by the confounds table.

• Homepage: https://fmriprep.org
• Repo: https://github.com/nipreps/fmriprep
• Container image: nipreps/fmriprep:<version> on Docker Hub
• Latest release (of this snapshot): 25.2.5 (2026-03-10) — 25.2.x is the LTS track, supported through October 2029
• License: Apache-2.0 (since 21.0.x)
• RRID: SCR_016216

Command-Line Structure (BIDS-App)

fmriprep <bids_dir> <output_dir> <analysis_level> [OPTIONS]

• bids_dir — root of the BIDS dataset (contains sub-*/ folders and dataset_description.json)
• output_dir — where derivatives + reports go (must NOT be inside bids_dir)
• analysis_level — only participant is supported

Same structure applies to the container form:

docker run --rm -it <mounts> nipreps/fmriprep:<version> <bids_dir> <output_dir> participant [OPTIONS]

Pipeline Overview

                       ┌──────────────────────────────────────┐
                       │  BIDS Dataset (T1w/T2w + BOLD + fmap)│
                       └──────────────────┬───────────────────┘
                                          │
                    ┌─────────────────────┼─────────────────────┐
                    ▼                     ▼                     ▼
        ┌───────────────────┐   ┌───────────────────┐   ┌───────────────────┐
        │  Anatomical WF    │   │  Fieldmap WF      │   │  BOLD WF (per run)│
        │  (smriprep)       │   │  (SDCFlows)       │   │  (fmriprep)       │
        │  ───────────────  │   │  ───────────────  │   │  ─────────────    │
        │  • Conform T1w    │   │  • PEPOLAR/TOPUP  │   │  • Reference img  │
        │  • N4 bias corr   │   │  • Phase-diff     │   │  • HMC (mcflirt)  │
        │  • Skull-strip    │   │  • Phase encoding │   │  • STC (3dTShift) │
        │    (antsBrainExt) │   │  • SyN-SDC (t1w)  │   │  • SDC apply      │
        │  • FAST segment   │   │                   │   │  • BBR to T1w     │
        │  • antsRegistrat  │   │                   │   │  • Multi-echo T2* │
        │    → MNI templates│   │                   │   │  • Resample to    │
        │  • recon-all      │   │                   │   │    output spaces  │
        │  • MSM-Sulc       │   │                   │   │  • Surface + CIFTI│
        │  • Curv/Sulc/Thick│   │                   │   │  • Confounds      │
        └─────────┬─────────┘   └──────────┬────────┘   └──────────┬────────┘
                  │                        │                       │
                  └────────────────────────┴───────────────────────┘
                                          │
                                          ▼
                     ┌────────────────────────────────────────┐
                     │  BIDS-Derivatives + sub-XX.html report │
                     └────────────────────────────────────────┘

Read references/workflows.md for the full method description and node names.

Quick Start

The fastest path for a new user with Docker:

# 1. Install the wrapper (Python 3.10+)
python -m pip install --user fmriprep-docker

# 2. Get a FreeSurfer license (free): https://surfer.nmr.mgh.harvard.edu/registration.html
#    Save the file, e.g. as ~/.licenses/freesurfer/license.txt
export FS_LICENSE=$HOME/.licenses/freesurfer/license.txt

# 3. Pre-fetch TemplateFlow templates (avoids network needs mid-run)
python -m pip install --user templateflow
python -c "from templateflow.api import get; \
  get(['MNI152NLin2009cAsym','MNI152NLin6Asym','OASIS30ANTs','fsaverage','fsLR'])"

# 4. Run — replace paths with yours
fmriprep-docker /path/to/bids /path/to/derivatives participant \
    --participant-label 01 \
    --output-spaces MNI152NLin2009cAsym:res-2 fsaverage:den-10k \
    --nthreads 8 --omp-nthreads 4 --mem 12000 \
    -w /path/to/work

Equivalent bare Docker invocation (what the wrapper produces):

docker run --rm -it \
    -v /path/to/bids:/data:ro \
    -v /path/to/derivatives:/out \
    -v /path/to/work:/scratch \
    -v $FS_LICENSE:/opt/freesurfer/license.txt:ro \
    -v $HOME/.cache/templateflow:/home/fmriprep/.cache/templateflow \
    nipreps/fmriprep:25.2.5 \
    /data /out participant \
    --participant-label 01 \
    --output-spaces MNI152NLin2009cAsym:res-2 fsaverage:den-10k \
    --nthreads 8 --omp-nthreads 4 --mem 12000 \
    -w /scratch

Equivalent Apptainer/Singularity invocation:

apptainer run --cleanenv \
    -B /path/to/bids:/data:ro \
    -B /path/to/derivatives:/out \
    -B /path/to/work:/work \
    -B $FS_LICENSE:/opt/freesurfer/license.txt:ro \
    -B $HOME/.cache/templateflow:/opt/templateflow \
    --env TEMPLATEFLOW_HOME=/opt/templateflow \
    fmriprep-25.2.5.sif \
    /data /out participant \
    --participant-label 01 \
    --fs-license-file /opt/freesurfer/license.txt \
    -w /work

Key Concepts (cheat-sheet)

Concept	Meaning
BIDS-App	Container/exec with fixed 3-positional CLI (bids_dir output_dir participant). fMRIPrep only supports participant.
BIDS Derivatives	Output format — sub-folders per subject, sidecar JSON per NIfTI, standardized entity keys (space-, desc-, hemi-, from-, to-).
NiPreps	The umbrella (www.nipreps.org). fMRIPrep depends on sister packages: smriprep (anat), sdcflows (SDC), niworkflows (utilities), nireports (reports), templateflow (templates).
TemplateFlow	Registry of standard neuroimaging templates fetched on demand. Home dir: $TEMPLATEFLOW_HOME (default ~/.cache/templateflow).
Working directory (-w)	Nipype's scratch space. Enables resume-on-crash. Not inside bids_dir.
--output-spaces	Which coordinate systems to resample BOLD/T1w to. Space KEY[:cohort-X][:res-Y][:den-Z]. Default: MNI152NLin2009cAsym:res-native. Read references/spaces.md.
--level	minimal (transforms only), resampling (adds mid-way NIfTIs), full (default — every derivative).
--ignore	Skip preprocessing aspects. Choices: fieldmaps, slicetiming, sbref, t2w, flair, fmap-jacobian.
--force	Override auto-choices. Choices: bbr, no-bbr, syn-sdc, fmap-jacobian.
--derivatives	Reuse precomputed derivatives from another package (BIDS-Derivatives-compliant); replaces the older --anat-derivatives.
--use-syn-sdc	Fieldmap-less SDC using anatomical prior; if no fmap and unable → error (default) or warn.
--cifti-output	Enable grayordinate output. Choices: 91k (2 mm, 91282 grayordinates), 170k (1.6 mm, 170494). Implies MNI152NLin6Asym.
--fs-no-reconall	Skip FreeSurfer surface reconstruction. Disables surface/CIFTI outputs.
--fs-license-file	Path to your FreeSurfer license (required even if recon-all is not run — some FS binaries need it).
Confounds	Nuisance regressors written to ~_desc-confounds_timeseries.tsv. Read references/confounds.md before including in a design matrix.
HMC	Head-Motion Correction (FSL mcflirt). Reference-image based.
STC	Slice-Timing Correction (AFNI 3dTShift). Runs only if SliceTiming present in metadata and ≥5 usable volumes.
BBR	Boundary-Based Registration (FreeSurfer bbregister when recon-all is on, else FSL flirt --schedule=bbr.sch).
CompCor	Component-based noise correction. Anatomical (aCompCor) uses eroded WM/CSF/combined masks; temporal (tCompCor) uses top-variance voxels.
Boilerplate	Auto-generated methods paragraph in the report — copy verbatim into papers (public-domain, CC0).

Sensible Defaults

fMRIPrep is designed so a bare invocation "just works". If you have no strong preferences, this is the recommended starting point:

• --output-spaces MNI152NLin2009cAsym:res-2 fsaverage:den-10k anat — one volumetric standard space, a light surface space, and native anatomical
• --nthreads = number of CPU cores allocated to the container
• --omp-nthreads ≤ nthreads (typically nthreads / 2 when running multiple subjects; single subject: 8 is a common cap)
• --mem-mb at least 8000 (Docker wrapper warns below 8 GB); 16 GB is comfortable
• Keep the FS license mounted; keep TEMPLATEFLOW_HOME mounted; keep -w on a fast local disk (not NFS if avoidable)
• Process one subject per container instance (see FAQ)

Common Pitfalls

1. Output dir == input dir: fMRIPrep will refuse to run. Put derivatives in a separate path (e.g. bids/derivatives/fmriprep-25.2.5/).
2. Working directory inside bids_dir: also refused. Put -w /scratch/work outside the input.
3. --output-spaces MNI152NLin6Asym:res-2 ≠ 2 mm always — res- is a TemplateFlow index, not millimeters. Verify with the template JSON.
4. Race conditions on parallel subjects: never share the same -w across subjects. Give each subject its own working directory, or launch each subject in its own container.
5. FreeSurfer IsRunning.lh+rh error after a crash: delete IsRunning.* files in <output>/sourcedata/freesurfer/sub-XX/scripts/ and re-run.
6. Slice-timing correction is referenced to the middle slice by default (--slice-time-ref 0.5), which shifts effective volume onsets by 0.5 TR — adjust your GLM accordingly, or set --slice-time-ref 0 to disable the shift.
7. Never include all confounds columns in a design matrix — pick a strategy (24P+aCompCor, ICA-AROMA, scrubbing) and use the corresponding subset.
8. No skull-stripped T1w inputs — fMRIPrep expects raw T1w. If skull-stripped, use --skull-strip-t1w skip (understand the risks) or revert to originals.
9. Version pinning: process an entire study with the same fMRIPrep version+container build. Do not upgrade mid-study.
10. Datasets with lesions: place sub-XX/anat/sub-XX_label-lesion_roi.nii.gz and add *lesion_roi.nii.gz to .bidsignore — enables lesion cost-function masking during registration.
11. Apple Silicon Docker: run under --platform linux/amd64 (or DOCKER_DEFAULT_PLATFORM=linux/amd64 when using fmriprep-docker); the container is only built for linux-64.
12. Multi-echo two-echo data: rejected since 25.2.4 — fMRIPrep requires ≥3 echoes for tedana T2* estimation.
13. --use-syn-sdc requires anatomical space templates to be present locally (MNI152NLin2009cAsym); pre-fetch or allow Internet.
14. HPC + Singularity: the FS license and a writeable TEMPLATEFLOW_HOME must be bind-mounted; login-node fetches templates before batch submission (compute nodes are usually offline).

What This Skill Does Not Cover

• Group-level statistics — fMRIPrep is participant-level only. Use FitLins, Nilearn, SPM, FSL FEAT, etc. downstream.
• Denoising strategy selection — see Ciric 2017 and Parkes 2018; fMRIPrep only supplies regressors.
• DWI, MEG, EEG — different NiPreps apps (dMRIPrep, MEGPrep, planned).
• Custom Nipype workflow edits — for extension, read the API docs at https://fmriprep.readthedocs.io/en/latest/api.html.

Where to Get Help

• Community Q&A: https://neurostars.org/tag/fmriprep (canonical support channel)
• Bug reports: https://github.com/nipreps/fmriprep/issues
• Full docs: https://fmriprep.org / https://fmriprep.readthedocs.io
• Sample HTML report: https://fmriprep.org/en/latest/_static/SampleReport/sample_report.html

Verify Installation

fmriprep --version                    # bare-metal
docker run --rm nipreps/fmriprep:25.2.5 --version   # Docker
apptainer run fmriprep-25.2.5.sif --version         # Apptainer

Expected output form: fMRIPrep vXX.Y.Z.