Improved Assessment of Significant Activation in Functional Magnetic Resonance Imaging (fMRI): Use of a Cluster‐Size Threshold

TLDR

Functional MRI studies involve over 10,000 statistical comparisons, and reducing per‑pixel false‑positive rates to control false positives inevitably reduces detection power. The authors propose an alternative method that exploits the spatial contiguity of true neural activity to improve detection. By modeling the probability distribution of cluster sizes as a function of per‑pixel false‑positive probability, cluster‑size thresholds can be applied to reject false positives independently. Monte Carlo simulations and human fMRI experiments demonstrate that this cluster‑size approach can increase statistical power by up to fivefold compared to per‑pixel thresholding alone.

Abstract

The typical functional magnetic resonance (fMRI) study presents a formidable problem of multiple statistical comparisons (i.e., > 10,000 in a 128 x 128 image). To protect against false positives, investigators have typically relied on decreasing the per pixel false positive probability. This approach incurs an inevitable loss of power to detect statistically significant activity. An alternative approach, which relies on the assumption that areas of true neural activity will tend to stimulate signal changes over contiguous pixels, is presented. If one knows the probability distribution of such cluster sizes as a function of per pixel false positive probability, one can use cluster-size thresholds independently to reject false positives. Both Monte Carlo simulations and fMRI studies of human subjects have been used to verify that this approach can improve statistical power by as much as fivefold over techniques that rely solely on adjusting per pixel false positive probabilities.