# Neuroimaging Power Guide

> Sample-size planning for fMRI/EEG studies using effect-size benchmarks and simulation-based power

- Skill: `neuroaihub/neuroimaging-power-guide` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add neuroaihub/neuroimaging-power-guide`
- Raw SKILL.md: https://api.skillmd.com/api/skills/neuroaihub/neuroimaging-power-guide/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: neuroaihub (https://skillmd.com/u/neuroaihub)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/neuroaihub/neuroimaging-power-guide

---


# Neuroimaging Power Guide

## Purpose

Statistical power in neuroimaging is fundamentally different from power in behavioral research. The massive multiple comparisons problem (testing ~100,000 voxels simultaneously), spatial correlation structure, and non-standard test statistics mean that standard power formulas underestimate required sample sizes. Meanwhile, the field has historically been severely underpowered: the median fMRI study has only ~20% power to detect a typical effect (Button et al., 2013).

A competent programmer without neuroimaging training would apply standard power calculations (e.g., G*Power for a t-test) without accounting for multiple comparison correction, would not know typical effect sizes in neuroimaging, and would dramatically underestimate the sample sizes needed. This skill encodes the domain-specific knowledge for neuroimaging power analysis.

## When to Use This Skill

- Planning sample size for a new fMRI, EEG, or MEG study
- Estimating power for grant applications or registered reports
- Determining whether a published study was adequately powered
- Choosing between ROI-based and whole-brain analysis based on power constraints
- Evaluating the reliability implications of sample size choices

## Research Planning Protocol

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

1. **State the research question** — What specific question is this analysis/paradigm addressing?
2. **Justify the method choice** — Why is this approach appropriate? What alternatives were considered?
3. **Declare expected outcomes** — What results would support vs. refute the hypothesis?
4. **Note assumptions and limitations** — What does this method assume? 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](https://github.com/HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills/issues).

## Why Neuroimaging Power Is Different

Standard power analysis assumes a single statistical test. Neuroimaging involves:

| Challenge | Impact on Power | Source |
|---|---|---|
| Massive multiple comparisons | ~100,000 voxels tested; correction reduces sensitivity by orders of magnitude | Nichols & Hayasaka, 2003 |
| Spatial smoothness | Adjacent voxels are correlated, reducing effective number of independent tests but complicating power calculation | Worsley et al., 1996 |
| Multi-level inference | Subject-level estimation + group-level test; both levels contribute noise | Mumford & Nichols, 2008 |
| Effect size variability | Effects vary across voxels, regions, and subjects; no single "effect size" characterizes a study | Poldrack et al., 2017 |
| Threshold-dependent power | Power depends heavily on the statistical threshold (corrected vs. uncorrected) and correction method | Hayasaka et al., 2007 |

**Key implication**: A standard G*Power calculation for a two-sample t-test will dramatically overestimate the power of a whole-brain fMRI analysis because it ignores multiple comparison correction (Mumford & Nichols, 2008).

## Typical Effect Sizes in Neuroimaging

### fMRI Effect Sizes

| Analysis Type | Typical Effect Size | Unit | Source |
|---|---|---|---|
| Task activation (voxel-level) | Cohen's d = **0.5-1.0** | Standardized mean difference | Poldrack et al., 2017 |
| Task activation (ROI-level) | Cohen's d = **0.5-1.5** | Standardized mean difference | Poldrack et al., 2017 |
| Between-group difference (voxel) | Cohen's d = **0.3-0.8** | Standardized mean difference | Poldrack et al., 2017 |
| Functional connectivity (correlation) | r = **0.2-0.5** | Pearson correlation | Marek et al., 2022 |
| Brain-behavior association | r = **0.1-0.3** | Pearson correlation | Marek et al., 2022 |
| Brain-wide association (replicable) | r < **0.05** at N < 1000 | Pearson correlation | Marek et al., 2022 |

**Critical finding**: Marek et al. (2022) demonstrated that brain-behavior correlations in typical neuroimaging samples (N < 100) are severely inflated. Replicable brain-behavior associations require **N > 2,000** for whole-brain analyses.

### EEG/ERP Effect Sizes

| Analysis Type | Typical Effect Size | Source |
|---|---|---|
| ERP component amplitude (e.g., N400, P300) | Cohen's d = **0.3-0.8** | Boudewyn et al., 2018 |
| ERP latency differences | Cohen's d = **0.2-0.5** | Luck, 2014 |
| EEG oscillatory power | Cohen's d = **0.3-0.6** | Cohen, 2014 |
| EEG connectivity (coherence/PLV) | Cohen's d = **0.2-0.5** | Cohen, 2014 |

## Sample Size Benchmarks

### fMRI Sample Size Recommendations

| Design | Minimum N | Recommended N | Assumptions | Source |
|---|---|---|---|---|
| Within-subject task activation | **20** | **25-30** | Large effect (d > 0.8), lenient correction | Desmond & Glover, 2002 |
| Between-group comparison (large effect, d = 0.8) | **20 per group** | **25-30 per group** | Whole-brain, cluster-corrected | Thirion et al., 2007 |
| Between-group comparison (medium effect, d = 0.5) | **40 per group** | **50+ per group** | Whole-brain, cluster-corrected | Thirion et al., 2007; Poldrack et al., 2017 |
| Resting-state individual differences | **25+** | **50+** (much more for replicability) | Depends on reliability of measure | Marek et al., 2022 |
| Brain-behavior correlations | **100+** | **N > 2,000** for replicable whole-brain | Large-scale only | Marek et al., 2022 |
| ROI-based analysis (a priori) | **15-20** | **25+** | Single ROI, no whole-brain correction | Desmond & Glover, 2002 |

### EEG/ERP Sample Size Recommendations

| Design | Minimum per Condition | Recommended per Condition | Source |
|---|---|---|---|
| ERP trials per condition per subject | **30** | **40-60** | Boudewyn et al., 2018 |
| ERP between-group (medium d = 0.5) | **34 per group** | **50+ per group** | Boudewyn et al., 2018 |
| ERP within-subject (medium d = 0.5) | **25 subjects** | **30+ subjects** | Luck, 2014 |
| Time-frequency analysis | **40 trials** | **60+ trials** | Cohen, 2014 |

### Power at Common Sample Sizes

| N (per group) | Power for d = 0.5 (uncorrected) | Power for d = 0.5 (corrected, whole-brain) | Power for d = 0.8 (corrected) |
|---|---|---|---|
| 10 | ~26% | < 10% | ~25% |
| 20 | ~50% | ~20% | ~50% |
| 30 | ~70% | ~35% | ~70% |
| 40 | ~82% | ~50% | ~85% |
| 60 | ~94% | ~70% | ~95% |

Values are approximate, based on simulations from Mumford & Nichols (2008) and Desmond & Glover (2002). Exact power depends on design, smoothness, effect spatial extent, and correction method.

## Power Decision Tree

```
What type of analysis are you planning?
 |
 +-- Whole-brain voxelwise analysis
 | |
 | +-- Within-subject (one-sample t-test)
 | | --> Minimum N = 20; aim for N = 25-30
 | | (Desmond & Glover, 2002)
 | |
 | +-- Between-group comparison
 | | |
 | | +-- Large expected effect (d > 0.8)
 | | | --> N = 20-25 per group (Thirion et al., 2007)
 | | |
 | | +-- Medium expected effect (d = 0.5)
 | | | --> N = 40-50 per group (Poldrack et al., 2017)
 | | |
 | | +-- Small expected effect (d = 0.3)
 | | --> N = 80+ per group; consider ROI approach
 | |
 | +-- Brain-behavior correlation
 | --> N = 100+ minimum; N > 2,000 for replicability
 | (Marek et al., 2022)
 |
 +-- ROI-based analysis (a priori regions)
 | --> Use standard power formulas (G*Power) with expected
 | effect size from literature or pilot data.
 | No multiple comparison correction needed for single ROI.
 | N = 15-30 typical for medium-large effects.
 |
 +-- ERP analysis
 |
 +-- Between-group
 | --> 30-50 per group for medium effects
 | (Boudewyn et al., 2018)
 |
 +-- Within-subject
 --> 25-30 subjects, 30+ trials per condition
 (Boudewyn et al., 2018; Luck, 2014)
```

## Simulation-Based Power Approaches

### fMRIpower (Mumford & Nichols, 2008)

Estimates power using pilot group-level activation maps:

1. Run a pilot study (or use published results) to obtain group-level statistical maps
2. Estimate effect sizes at each voxel from the pilot data
3. Simulate new datasets with varying N by resampling from the estimated effect size and variance
4. Apply the full statistical pipeline (including multiple comparison correction) to each simulation
5. Power = proportion of simulations that detect the effect at a given ROI or voxel

**Requirements**: Pilot data from at least **10-15 subjects** for stable variance estimates (Mumford & Nichols, 2008)

### NeuroPowerTools (Durnez et al., 2016)

Web-based tool for peak-based power estimation:

1. Upload an unthresholded statistical map from a pilot or published study
2. The tool fits a mixture model to the peak distribution (null + alternative)
3. Estimates the proportion of truly active voxels and their average effect size
4. Computes power for new studies with varying N and thresholds

**Advantage**: Does not require individual subject data; can use published group maps
**URL**: https://neuropowertools.org

### Permutation-Based Power (Hayasaka et al., 2007)

1. Generate simulated datasets under the alternative hypothesis using effect size maps from pilot data
2. For each simulated dataset, run a full permutation test (5,000+ permutations)
3. Compute power as the proportion of simulations in which the permutation test rejects the null

**Advantage**: Fully nonparametric; accounts for the exact multiple comparison correction used
**Disadvantage**: Computationally expensive (requires running thousands of permutation tests per power estimate)

### PowerMap (Joyce & Hayasaka, 2012)

Simulation-based power using parametric assumptions:

1. Specify effect size map (from pilot data or assumed values)
2. Specify noise model (based on residuals from pilot data)
3. Simulate datasets with varying N
4. Apply parametric statistical testing with specified correction method
5. Estimate power at each voxel

## Multiple Comparison Correction Impact on Power

The choice of correction method dramatically affects required sample size:

| Correction Method | Effective Alpha per Voxel | Relative Power | Source |
|---|---|---|---|
| None (p < 0.001 uncorrected) | 0.001 | **Highest** (but invalid inference) | -- |
| FDR q < 0.05 | ~0.0001-0.001 (data-dependent) | Moderate-High | Genovese et al., 2002 |
| Cluster-based (CDT p < 0.001) | Depends on cluster size | Moderate-High for large effects | Eklund et al., 2016 |
| Voxelwise FWE (RFT, p < 0.05) | ~0.00000005 | Low | Worsley et al., 1996 |
| TFCE + permutation | Varies | Moderate | Smith & Nichols, 2009 |

**Domain insight**: Switching from voxelwise FWE to cluster-based or FDR correction can increase power by **50-200%** for the same sample size, because these methods exploit the spatial extent of true activations (Nichols & Hayasaka, 2003).

## Test-Retest Reliability and Power

For individual differences designs (correlating brain measures with behavior), reliability of the brain measure is critical (Elliott et al., 2020):

| Measure | Typical ICC | Implication | Source |
|---|---|---|---|
| Task fMRI activation (ROI) | **0.3-0.6** | Poor to moderate reliability | Elliott et al., 2020 |
| Resting-state connectivity | **0.3-0.7** | Moderate reliability; depends on scan duration | Elliott et al., 2020 |
| ERP amplitude | **0.5-0.8** | Moderate to good | Cassidy et al., 2012 |
| EEG oscillatory power | **0.6-0.9** | Good to excellent | Cohen, 2014 |

**Critical formula**: The maximum detectable correlation between brain and behavior is bounded by the reliabilities of both measures:

```
r_observed_max = r_true * sqrt(reliability_brain * reliability_behavior)
```

With brain ICC = 0.5 and behavior reliability = 0.8, even a true correlation of r = 0.5 would appear as r = 0.5 * sqrt(0.5 * 0.8) = **0.32** on average (Elliott et al., 2020). This attenuation means far larger samples are needed.

**Recommendation**: For individual differences designs, collect longer scan sessions (at least **20-30 minutes** of resting-state data; Birn et al., 2013) or use multi-session data to improve reliability.

## Practical Power Calculation Workflow

### For a New fMRI Study

1. **Define the primary analysis**: Whole-brain voxelwise or ROI-based?
2. **Estimate effect size**:
 - From pilot data (preferred): extract effect sizes from pilot activation maps
 - From literature: find the most comparable published study; correct for publication bias by assuming the true effect is **~50-75%** of the published estimate (Button et al., 2013)
 - From meta-analysis: use NeuroSynth or BrainMap to estimate typical activation strength
3. **Choose the power analysis tool**:
 - ROI-based: Standard power calculation (G*Power) using the estimated effect size at the ROI
 - Whole-brain: fMRIpower, NeuroPowerTools, or simulation
4. **Set target power**: **80%** (conventional) or **90%** (recommended for costly neuroimaging studies)
5. **Account for attrition**: Add **10-20%** to planned N for participant exclusions due to excessive motion, incomplete data, or technical failures
6. **Report**: Effect size source, power tool used, correction method, target power, final N

### For a New EEG/ERP Study

1. **Estimate effect size**: From pilot data or published ERP studies (see effect size table above)
2. **Determine trial count**: At least **30 trials per condition** post-rejection (Boudewyn et al., 2018)
3. **Plan for trial attrition**: Assume **20-30%** trial rejection rate; collect accordingly
4. **Subject-level power**: Use G*Power with the estimated within- or between-subject effect size
5. **Account for subject attrition**: Add **15-20%** for exclusions due to excessive artifacts

## Common Pitfalls

1. **Using uncorrected power estimates for whole-brain analyses**: A study with 80% power at p < 0.001 uncorrected has far less than 80% power after FWE or FDR correction (Mumford & Nichols, 2008)
2. **Ignoring effect size inflation in pilot studies**: Small pilot studies produce inflated effect sizes due to the "winner's curse." Assume the true effect is **50-75%** of the pilot estimate (Button et al., 2013)
3. **Applying behavioral power formulas to neuroimaging**: Standard t-test power calculations dramatically overestimate power for whole-brain analyses because they ignore multiple comparison correction
4. **Not accounting for participant attrition**: In fMRI, **10-20%** of participants may be excluded due to motion, scanner artifacts, or incomplete data. Over-recruit accordingly
5. **Ignoring reliability for individual differences**: Brain measures with ICC < 0.5 attenuate correlations, requiring much larger samples than traditional power analysis suggests (Elliott et al., 2020)
6. **Assuming published sample sizes are adequate**: Most published fMRI studies are underpowered (median power ~20%; Button et al., 2013). Do not use published N as a benchmark
7. **Neglecting the impact of design efficiency**: An optimized event-related design can be 2-3x more efficient than a suboptimal one (Dale, 1999), effectively increasing power without adding subjects

## Minimum Reporting Checklist

- [ ] Target effect size and its source (pilot data, literature, meta-analysis)
- [ ] Effect size metric used (Cohen's d, r, partial eta-squared)
- [ ] Power analysis method (analytical, simulation-based, tool used)
- [ ] Target power level (typically 80% or 90%)
- [ ] Statistical test assumed (one-sample t, two-sample t, correlation, ANOVA)
- [ ] Multiple comparison correction method and parameters
- [ ] Planned N and justification
- [ ] Attrition allowance (expected exclusion rate)
- [ ] For simulation-based: number of simulations, pilot data source, software
- [ ] For reliability-dependent designs: reliability estimates and their source

## References

- Birn, R. M., Molloy, E. K., Patriat, R., et al. (2013). The effect of scan length on the reliability of resting-state fMRI connectivity estimates. *NeuroImage*, 83, 550-558.
- Boudewyn, M. A., Luck, S. J., Farrens, J. L., & Kappenman, E. S. (2018). How many trials does it take to get a significant ERP effect? *Psychophysiology*, 55(6), e13049.
- Button, K. S., Ioannidis, J. P. A., Mokrysz, C., et al. (2013). Power failure: Why small sample size undermines the reliability of neuroscience. *Nature Reviews Neuroscience*, 14(5), 365-376.
- Cassidy, S. M., Robertson, I. H., & O'Connell, R. G. (2012). Retest reliability of event-related potentials: Evidence from a variety of paradigms. *Psychophysiology*, 49(5), 659-664.
- Cohen, M. X. (2014). *Analyzing Neural Time Series Data: Theory and Practice*. MIT Press.
- Dale, A. M. (1999). Optimal experimental design for event-related fMRI. *Human Brain Mapping*, 8(2-3), 109-114.
- Desmond, J. E., & Glover, G. H. (2002). Estimating sample size in functional MRI (fMRI) neuroimaging studies. *Journal of Neuroscience Methods*, 118(2), 115-128.
- Durnez, J., Degryse, J., Moerkerke, B., et al. (2016). Power and sample size calculations for fMRI studies based on the prevalence of active peaks. *bioRxiv*, 049429.
- Eklund, A., Nichols, T. E., & Knutsson, H. (2016). Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates. *PNAS*, 113(28), 7900-7905.
- Elliott, M. L., Knodt, A. R., Ireland, D., et al. (2020). What is the test-retest reliability of common task-functional MRI measures? *Biological Psychiatry*, 87(11), 934-948.
- Genovese, C. R., Lazar, N. A., & Nichols, T. (2002). Thresholding of statistical maps in functional neuroimaging using the false discovery rate. *NeuroImage*, 15(4), 870-878.
- Hayasaka, S., Peiffer, A. M., Hugenschmidt, C. E., & Laurienti, P. J. (2007). Power and sample size calculation for neuroimaging studies by non-central random field theory. *NeuroImage*, 37(3), 721-730.
- Joyce, K. E., & Hayasaka, S. (2012). Development of PowerMap: A software package for statistical power calculation in neuroimaging studies. *Neuroinformatics*, 10(4), 351-365.
- Luck, S. J. (2014). *An Introduction to the Event-Related Potential Technique* (2nd ed.). MIT Press.
- Marek, S., Tervo-Clemmens, B., Calabro, F. J., et al. (2022). Reproducible brain-wide association studies require thousands of individuals. *Nature*, 603(7902), 654-660.
- Mumford, J. A., & Nichols, T. E. (2008). Power calculation for group fMRI studies accounting for arbitrary design and temporal autocorrelation. *NeuroImage*, 39(1), 261-268.
- Nichols, T. E., & Hayasaka, S. (2003). Controlling the familywise error rate in functional neuroimaging: A comparative review. *Statistical Methods in Medical Research*, 12(5), 419-446.
- Poldrack, R. A., Baker, C. I., Durnez, J., et al. (2017). Scanning the horizon: Towards transparent and reproducible neuroimaging research. *Nature Reviews Neuroscience*, 18(2), 115-126.
- Smith, S. M., & Nichols, T. E. (2009). Threshold-free cluster enhancement. *NeuroImage*, 44(1), 83-98.
- Thirion, B., Pinel, P., Meriaux, S., et al. (2007). Analysis of a large fMRI cohort: Statistical and methodological issues for group analyses. *NeuroImage*, 35(1), 105-120.
- Worsley, K. J., Marrett, S., Neelin, P., et al. (1996). A unified statistical approach for determining significant signals in images of cerebral activation. *Human Brain Mapping*, 4(1), 58-73.

See `references/` for detailed simulation examples and effect size lookup tables.