Fmriprep

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)

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)

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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:

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

Pipeline Overview

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                       ┌──────────────────────────────────────┐
                       │  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:

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# 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):

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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:

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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)

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

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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.