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Neuroimaging Power Guide

Plan sample sizes for fMRI, EEG, and MEG studies with correction-aware power tables.

34|5|Updated Feb 28, 2026
Search Tags:#research-methods#sample-size#power-analysis#neuroimaging#eeg#fmri
One-click install
npx skills add https://github.com/NeuroAIHub/awesome_cognitive_and_neuroscience_skills --skill neuroimaging-power-guide-neuroaihub
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Neuroimaging Power Guide
Source: https://github.com/NeuroAIHub/awesome_cognitive_and_neuroscience_skills/tree/main/skills/neuroimaging-power-guide
Command: npx skills add https://github.com/NeuroAIHub/awesome_cognitive_and_neuroscience_skills --skill neuroimaging-power-guide-neuroaihub

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Many cognitive neuroscience studies underestimate required sample sizes because standard power formulas ignore multiple comparison correction, spatial smoothness, effect-size inflation, and measurement reliability, so this skill clarifies the neuroimaging-specific planning workflow.

Core Features & Use Cases

  • Effect-size benchmarks summarize typical fMRI, EEG, and ERP effects across paradigms while recommending conservative adjustments for publication bias.
  • Power tables and decision trees translate design choices (whole-brain vs ROI, within- vs between-subject, connectivity analyses) into minimum and recommended sample sizes plus attrition buffers.
  • Simulation and reliability guidance directs researchers to tools like fMRIpower, NeuroPowerTools, permutation procedures, and reliability-aware reporting checklists for grant-writing or preregistrations.
  • Use Case: Compare whole-brain versus ROI strategies, consult effect-size lookups, and document correction methods, target power, and attrition allowances before submitting a grant.

Quick Start

Ask Neuroimaging Power Guide to compute the required sample size for your planned voxelwise fMRI contrast using the expected effect size, correction method, and attrition allowance.

Frequently Asked Questions about Neuroimaging Power Guide

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

FAQPage Schema
How do I calculate sample size for fMRI studies with multiple comparison correction?▼

Neuroimaging sample size calculation for fMRI requires accounting for multiple comparison penalties, spatial smoothness, and effect-size inflation. This skill provides correction-aware power tables and decision trees to translate whole-brain or ROI design choices into minimum required sample sizes with attrition buffers.

What effect size should I use for EEG power analysis during grant preparation?▼

EEG power analysis effect-size benchmarks should be drawn from typical paradigm effects while applying conservative adjustments for publication bias. This skill summarizes ERP and EEG effect estimates and recommends reliability-aware reporting checklists to support accurate sample-size planning for grant submissions.

Does standard power analysis work for whole-brain voxelwise neuroimaging designs?▼

Standard power formulas fail for whole-brain voxelwise neuroimaging because they ignore multiple comparison correction and spatial smoothness. This skill clarifies the neuroimaging-specific planning workflow, directing researchers to simulation tools like fMRIpower and permutation procedures for rigorous power estimates.

How do I plan MEG connectivity analysis sample sizes for preregistration?▼

MEG connectivity analysis sample-size planning for preregistration requires correction-aware power tables and reliability-aware reporting. This skill translates connectivity paradigm design choices into minimum and recommended sample sizes plus attrition allowances to satisfy preregistration documentation needs.

What is the difference in required sample size between ROI and whole-brain fMRI approaches?▼

ROI analyses generally require smaller sample sizes than whole-brain voxelwise fMRI approaches due to reduced multiple comparison penalties. This skill provides power tables and decision trees that directly compare these strategies, translating design choices into tailored minimum sample sizes and attrition buffers.

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