One could extend this framework to apply hierarchical regression models in which the variance from the single-subject trial-level data is used to inform group-level results. This might be particularly useful when comparing groups, e.g., if patients and control subjects have similar average effects but patients have more variable responses. This approach could also be applied to fMRI data, for example using as the dependent measure the observed data from ∼6 s post-stimulus (when the hemodynamic response is expected to peak) or a beta parameter corresponding to the fit of the post-stimulus data to a canonical hemodynamic response. Although single-trial correlations have been performed with the hemodynamic response (e.g., Rissman et al., 2004; Weissman et al., 2006), multiple regression may prove more powerful for reasons highlighted in the Section “Introduction.” Finally, the regressions could be turned around, such that behavior or experimental condition is predicted from brain activity, rather than predicting brain activity from behavior and experimental condition. In this case, logistic regression should be used if predicting binary outcomes (e.g., accuracy or condition; Dixon, 2008; Jaeger, 2008).
