Assessing the significance of focal activations using their spatial extent

TLDR

Current methods for detecting activated brain regions rely on thresholded statistical parametric maps that identify clusters of one voxel or larger with a small probability (e.g., 0.05). The study aims to develop an approximate analysis that estimates the probability of observing one or more activated brain regions of a specified volume or larger by chance. The authors use this approximate analysis to compute spatial extent thresholds for significance, moving beyond fixed voxel‑based thresholds. The approach shows that significance can be.

Abstract

Abstract Current approaches to detecting significantly activated regions of cerebral tissue use statistical parametric maps, which are thresholded to render the probability of one or more activated regions of one voxel, or larger , suitably small (e. g., 0.05). We present an approximate analysis giving the probability that one or more activated regions of a specified volume, or larger , could have occurred by chance. These results mean that detecting significant activations no longer depends on a fixed (and high) threshold, but can be effected at any (lower) threshold, in terms of the spatial extent of the activated region. The substantial improvement in sensitivity that ensues is illustrated using a power analysis and a simulated phantom activation study. © 1994 Wiley‐Liss, Inc.