Neuroimaging Sample Size Calculator

Simulate statistical power for fMRI studies by inputting pilot data, unthresholded maps, or meta-analytic maps. The simulation applies effect-size deflation and multiple comparison corrections to estimate the required sample size accurately.

Traditional power analysis fails for neuroimaging because it ignores multiple comparisons, spatial correlation, and multi-level inference. Simulation-based power estimation solves this by modeling these spatial dependencies directly during the sample size calculation.

Yes, this simulation workflow supports planning ROI-based analyses for EEG or MEG. It provides specific ROI-based shortcuts and templates to adapt to your paradigm when full simulations are impractical.

To estimate statistical power for whole-brain analyses, you need pilot data, unthresholded statistical maps, or meta-analytic maps. These inputs drive the simulation to determine required sample sizes and apply multiple comparison corrections.

Account for multiple comparisons when planning a neuroimaging study by using simulation-based power estimation. This approach inherently models multiple comparison corrections and spatial correlations to provide clear power reporting.