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fmriprep

Interpret fMRIPrep derivatives, HTML reports, and confounds for downstream fMRI workflow planning.

1|Updated May 16, 2026
Search Tags:#workflow planning#fmriprep#qc reports#bids derivatives#confounds#output spaces#bold preprocessing
One-click install
npx skills add https://github.com/MarvinCui/NeuroForge --skill fmriprep
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fmriprep
Source: https://github.com/MarvinCui/NeuroForge/tree/main/NeuroForge/skills/fmriprep
Command: npx skills add https://github.com/MarvinCui/NeuroForge --skill fmriprep

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

fMRIPrep output review is difficult because BOLD derivatives, confounds, spaces, and reports must be interpreted together to plan downstream fMRI analysis safely and reproducibly.

Core Features & Use Cases

  • Derivative & report interpretation: Understand what fMRIPrep generated (e.g., spaces, transforms, and derivative types) and how to read the per-subject HTML/QC reports.
  • Confounds-driven planning: Identify high-value confound artifacts and columns (e.g., motion, DVARS, framewise displacement, aCompCor) to select what to use before analysis.
  • BIDS-aware routing: Translate between BIDS inputs and fMRIPrep derivative conventions so you can route outputs into tools like Nilearn without mismatched spaces or metadata.
  • Use case example: You receive a subject-level fMRIPrep derivatives folder and need to choose which confound regressors and output space to use for a first-level GLM while avoiding space/transform mismatches.

Quick Start

Use the fmriprep skill to review an existing fMRIPrep derivatives folder and recommend what confounds and output spaces are appropriate for downstream analysis planning.

Frequently Asked Questions about fmriprep

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I interpret fMRIPrep derivatives and HTML reports for downstream analysis?▼

Interpreting fMRIPrep derivatives involves reviewing per-subject HTML QC reports alongside BOLD outputs, transforms, and confounds to safely plan downstream fMRI workflows and avoid space or metadata mismatches.

What is the best way to select confound regressors from fMRIPrep outputs for a GLM?▼

Selecting confound regressors from fMRIPrep outputs requires identifying high-value columns like motion parameters, DVARS, framewise displacement, and aCompCOR from the confounds artifacts before running a first-level GLM.

How do I route BIDS derivatives into Nilearn without space or transform mismatches?▼

Routing BIDS derivatives into Nilearn without mismatches requires translating BIDS input conventions to fMRIPrep derivative output spaces, ensuring metadata aligns with your selected spatial templates for reproducible analysis.

When do I need to check fMRIPrep output spaces before planning an fMRI workflow?▼

Checking fMRIPrep output spaces is needed before workflow planning when you must verify which spatial templates and transforms were generated, ensuring selected derivatives match the requirements of your downstream analysis tools.

Does fMRIPrep preprocessing provide all confounds needed for BOLD analysis?▼

fMRIPrep preprocessing provides extensive confound artifacts, but safe BOLD analysis requires interpreting which specific motion, aCompCor, and framewise displacement columns fit your model rather than applying all available regressors.

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