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Neuroimaging Sample Size Calculator

Simulate neuroimaging sample size planning with effect-size maps and pipeline corrections.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Standard power calculators dramatically overestimate neuroimaging study power because they ignore spatial correlation, massive multiple comparisons, attrition, and the full inference pipeline; this Skill encodes the suite of simulation-based steps needed to plan and justify sample sizes using pilot maps or published effect-size distributions while flagging assumptions that require independent verification.

Core Features & Use Cases

  • Pilot-driven simulation pipeline: Generate synthetic datasets from pilot maps or noise models, apply the planned smoothing and analysis pipeline, and compute voxel-, ROI-, or cluster-level power across a range of candidate Ns.
  • Correction-aware reporting: Estimate power under cluster-based, voxelwise FWE, TFCE, or FDR thresholds, deflate small-study effect sizes, and include attrition buffers so grant and registered-report analyses remain defensible.
  • Research planning guardrails: Prompt researchers to state their hypothesis, justify methods, declare expected outcomes, and document assumptions before running the simulation-based plan; ideal for preparing registered reports, grant proposals, or power justifications in reviews.

Quick Start

Ask the skill to simulate power for your planned fMRI contrast using pilot effect-size maps and report the minimum N required for 80% power with the planned correction method.

Frequently Asked Questions about Neuroimaging Sample Size Calculator

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

FAQPage Schema
How do I calculate sample size for an fMRI study using pilot data?▼

Estimate fMRI sample size by generating synthetic datasets from pilot effect-size maps, applying planned smoothing and multiple comparison correction, and computing voxel or cluster-level power across candidate Ns.

Why does standard power analysis overestimate neuroimaging study power?▼

Standard power analysis overestimates neuroimaging power because it ignores spatial correlation, massive multiple comparisons, attrition, and the full inference pipeline. Simulation-based approaches model these factors to produce defensible power estimates.

Can I estimate power for ROI-based analyses instead of whole-brain fMRI?▼

Yes, the simulation computes power for voxel, ROI, or cluster-level analyses using pilot maps or published effect-size distributions for fMRI, EEG, and MEG studies.

Does sample size simulation support cluster-based, FWE, TFCE, and FDR corrections?▼

Yes, the simulation estimates power under cluster-based, voxelwise FWE, TFCE, or FDR thresholds while deflating small-study effect sizes and including attrition buffers.

What do I need to prepare a registered report power justification for a grant?▼

State your hypothesis, justify methods, declare expected outcomes, and document assumptions before running simulation-based planning to generate correction-aware, attrition-buffered power reports for registered reports and grants.

Can I use published effect-size maps instead of pilot data for MEG power analysis?▼

Yes, the simulation pipeline accepts published effect-size maps or noise models to generate synthetic datasets for MEG and EEG sample size planning across candidate Ns.

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