Hypothesis testing for selective, differential, and conjoined brain activation.

Abstract

Hypothesis testing in functional neuroimaging studies relies heavily on the computation of categorical contrasts in which brain activation associated with one experimental condition is assessed relative to brain activation associated with a different experimental condition. Often, multiple pair-wise contrasts are computed and reported independently. Here we describe an approach to hypothesis testing that logically combines multiple pair-wise contrasts to distinguish among selective, differential and conjoined brain activation patterns. Using a sample dataset in which participants viewed objects, visual noise patterns or a fixation cross, we demonstrate that selective and differential brain activation patterns are often confounded with current approaches to hypothesis testing but that the logical combination approach can distinguish between these two types of data patterns. Specifically, we show that brain regions that respond selectively to an object recognition task relative to viewing visual noise or a fixation cross (selective activation) are mutually exclusive from brain regions that show a graded response to object viewing, noise viewing and visual fixation (differential activation). We thus show that the logical combination approach sufficiently constrains the results of categorical contrasts to reflect only the data pattern that would be predicted from the cognitive processing account under investigation.