Artifactual fMRI group and condition differences driven by performance confounds.

Analysis techniques comparing groups or conditions that vary in performance are open to a possible confound driven by those performance differences, if these errors are ignored. Disproportionate numbers of errors may either introduce noise into the signal of interest or confound the signal of interest with additional signal associated with specific error-related processes. Two inhibitory task datasets were reanalysed, one comparing young and elderly groups, the other comparing high and low conflict conditions within the same group of subjects. The data were analysed twice using event-related techniques, one treating correct and error responses separately, the other treating error responses as if they were correct. It was found that the activation maps differed considerably, with the inclusion of errors leading to many false positive and false negative activation clusters. Using performance as a covariate, analyses of covariance (ANCOVA) were used to try to correct these differences without success. Data simulations that varied the number of errors included in the analyses found that surprisingly few errors could significantly alter activation maps. Consequently, brain-imaging investigations that do not accommodate error contributions to functional signals are at risk of misinterpreting activation patterns.