Fmri Preprocessing Pipeline Guide

fMRI Preprocessing Pipeline Guide

Purpose

fMRI preprocessing transforms raw scanner data into a form suitable for statistical analysis. Unlike generic data cleaning, every preprocessing decision in fMRI involves domain-specific trade-offs: choosing the wrong step order can introduce artifacts that mimic neural signal, smoothing at the wrong scale destroys the spatial information needed for multivariate analyses, and failing to correct for susceptibility distortions misaligns brain regions by several millimeters.

A competent programmer without neuroimaging training would get many of these decisions wrong. This skill encodes the domain knowledge required to make correct preprocessing choices for different analysis goals.

When to Use This Skill

• Setting up a preprocessing pipeline for task fMRI, resting-state fMRI, or MVPA
• Choosing between preprocessing tools (fMRIPrep, FSL, SPM, AFNI)
• Deciding which steps to include, skip, or modify for a specific analysis type
• Performing quality control on preprocessed data
• Reviewing or troubleshooting an existing preprocessing pipeline
• Selecting parameters for motion correction, smoothing, or normalization

Research Planning Protocol

Before executing the domain-specific steps below, you MUST:

1. State the research question — What analysis will follow preprocessing and what does it require?
2. Justify the preprocessing choices — Why these steps in this order? What alternatives were considered?
3. Declare expected quality metrics — What motion thresholds, SNR values, and exclusion criteria will you use?
4. Note assumptions and limitations — What does this pipeline assume about the data? Where could it mislead?
5. Present the plan to the user and WAIT for confirmation before proceeding.

For detailed methodology guidance, see the research-literacy skill.

⚠️ Verification Notice

This skill was generated by AI from academic literature. All parameters, thresholds, and citations require independent verification before use in research. If you find errors, please open an issue.

Pipeline Order Decision Tree

The standard preprocessing order is well-established, but branch points exist depending on the analysis type. The canonical order is:

1. DICOM to NIfTI conversion (BIDS format)
2. Discard non-steady-state volumes
3. Slice timing correction (conditional)
4. Motion correction (rigid body)
5. Distortion correction (fieldmap-based)
6. Coregistration (functional to structural)
7. Spatial normalization (to MNI/template space)
8. Smoothing (conditional on analysis type)

Decision points:

Is your TR < 1 second (e.g., multiband)?
 |
 +-- YES --> Skip slice timing correction; use temporal derivative in GLM instead
 | (Sladky et al., 2011; HCP consortium recommendation)
 |
 +-- NO --> Apply slice timing correction before motion correction
 (AFNI and SPM convention; Sladky et al., 2011)

What is your analysis type?
 |
 +-- Task activation (univariate GLM)
 | --> Full pipeline with smoothing (FWHM = 2-3x voxel size)
 |
 +-- Resting-state connectivity
 | --> Full pipeline + aggressive confound regression; smoothing 4-6 mm
 |
 +-- MVPA / multivariate decoding
 --> Full pipeline WITHOUT smoothing (or minimal, <= 2 mm)
 Smooth only after first-level pattern extraction

Core Preprocessing Steps

Step 1: DICOM to NIfTI Conversion

Convert raw DICOM files to NIfTI format organized in BIDS (Brain Imaging Data Structure; Gorgolewski et al., 2016). BIDS standardizes file naming and metadata, enabling automated pipeline tools like fMRIPrep to detect acquisition parameters automatically.

Tools: dcm2niix (Li et al., 2016), heudiconv, BIDScoin

Step 2: Discard Non-Steady-State Volumes

The first few volumes of an fMRI run are acquired before the MR signal reaches T1 equilibrium, producing artificially high signal intensity. Discard the first 3-5 volumes (approximately 5-10 seconds) unless the scanner automatically acquired dummy scans (Poldrack et al., 2011, Ch. 5).

Domain insight: Modern scanners often acquire dummy scans that are not saved. Check the acquisition protocol. fMRIPrep detects non-steady-state volumes automatically using signal intensity changes.

Step 3: Slice Timing Correction

Within each TR, slices are acquired sequentially (not simultaneously), creating temporal offsets of up to one full TR between first and last slices. Slice timing correction (STC) interpolates each slice to a common time point (Sladky et al., 2011).

Order debate: STC before motion correction is standard in AFNI and SPM. FSL applies STC after motion correction. Both are acceptable; the optimal order depends on the level of motion and slice acquisition pattern (Parker & Razlighi, 2019). Ideally, joint correction would be applied (Roche, 2011), but this is not yet standard in major packages.

Step 4: Motion Correction

Align all volumes to a reference volume using 6-parameter rigid body (3 translation, 3 rotation) transformation (Jenkinson et al., 2002). This is the single most critical preprocessing step.

Domain insight: Motion correction is inherently imperfect because each slice within a volume was acquired at a different time. When the head moves during a TR, each slice has a slightly different rigid-body transformation, but whole-volume correction applies a single transformation. This is an unavoidable limitation (Jenkinson et al., 2002).

Output: Save the 6 motion parameters for use as confound regressors in the GLM (see fmri-glm-analysis-guide).

Step 5: Distortion Correction

EPI images suffer geometric distortions along the phase-encoding direction due to B0 field inhomogeneities. Distortions are worst near air-tissue boundaries: orbitofrontal cortex, anterior temporal lobes, and inferior temporal regions (Jezzard & Balaban, 1995).

Domain warning: If no fieldmap data were acquired, fMRIPrep can perform fieldmapless distortion correction using nonlinear registration to the T1, but this is less accurate than fieldmap-based methods. Always acquire fieldmap data when possible.

Step 6: Coregistration

Align the functional (EPI) image to the subject's structural (T1-weighted) image. This enables projecting functional results onto anatomical space and provides the bridge to template normalization.

• Method: Boundary-based registration (BBR; Greve & Fischl, 2009) is preferred over intensity-based methods for EPI-to-T1 alignment because it uses white matter boundaries, which are well-defined in both modalities
• Always visually inspect the coregistration overlay (functional edges on structural image)

Step 7: Spatial Normalization

Warp each subject's brain to a standard template space to enable group-level comparisons.

Domain warning: Always visually inspect normalization quality. Check that major sulci (central sulcus, Sylvian fissure) and subcortical structures (caudate, putamen) align with the template. Poor normalization is a common but silent source of error in group analyses.

Step 8: Spatial Smoothing

Smoothing with a Gaussian kernel increases SNR, satisfies the smoothness assumptions of Random Field Theory, and reduces inter-subject anatomical variability (Mikl et al., 2008).

Domain warning: Smoothing before MVPA is one of the most common preprocessing errors. For multivariate analyses, skip smoothing during preprocessing entirely. If group-level smoothing is needed, apply it only after the first-level pattern analysis is complete (Misaki et al., 2013).

Step 9: Confound Regression

Confound regression removes variance from non-neural sources. This step is typically performed during the statistical model (GLM) rather than as a separate preprocessing step, but the preprocessing pipeline must output the confound time series.

For detailed confound regression guidance, see the fmri-glm-analysis-guide skill.

Key confounds to extract during preprocessing:

• 6 motion parameters (from Step 4) and their temporal derivatives and squared terms (24-parameter model; Friston et al., 1996)
• Framewise displacement (FD; Power et al., 2012)
• DVARS (Power et al., 2012)
• aCompCor components from white matter and CSF (Behzadi et al., 2007)
• Global signal (optional; controversial for connectivity analyses; Murphy & Fox, 2017)

Pipeline Variants by Analysis Type