Using parametric regressors to disentangle properties of multi-feature processes

Abstract

FMRI data observed under a given experimental condition may be decomposed into two parts: the average effect and the deviation of single replications from this average effect. The average effect is represented by the mean activation over a specific condition. The deviation from this average effect may be decomposed into two components as well: systematic variation due to known empirical factors and pure measurement error. In most fMRI designs deviations from mean activation may be treated as measurement error. Nevertheless, often deviation from the average also may contain systematic variation that can be distinguished from simple measurement error. In these cases, the average fMRI signal may provide only a coarse picture of real brain activation. The larger the variation within-condition, the coarser the average effect and the more relevant is the impact of deviations from it. Systematic deviation from the mean activation may be examined by defining a set of parametric regressors. Here, the applicability of parametric methods to refine the evaluation of fMRI studies is discussed with special emphasis on (i) examination of the impact of continuous predictors on the fMRI signal, (ii) control for variation within each experimental condition and (iii) isolation of specific contributions by different features of a single complex stimulus, especially in the case of a sampled stimulus. The usefulness and applicability of this method are discussed and an example with real data is presented.

A simple solution for model comparison in bold imaging: the special case of reward prediction error and reward outcomes

“…However, all of these regions seemed to respond to RO and RPE if their correlation was not taken into account. Recently, more studies have begun to apply similar analysis techniques (e.g., Bornstein and Daw, 2012) and this method can be applicable to other areas of cognitive neuroscience such as numerical cognition where multicolinearity is a problem in identifying the neural correlates of parametric regressors (Wood et al, 2008). Consequently, Rohe et al (2012) have provided a simple and elegant solution to the model comparison issue that can be applied to many experiments.…”

Sustaining attention to simple tasks: A meta-analytic review of the neural mechanisms of vigilant attention.

“…In particular, hierarchical orthogonalization of the parametric regressors may have resulted in the first modulator (reflecting time on task) explaining variance potentially shared between short- and long-term VA (cf. Wood, Nuerk, Sturm, & Willmes, 2008). This, in turn, might have biased the analysis of brain activity related to short-term VA duration toward subprocesses that are sensitive to energetic short-term but not long-term modulation (e.g., implicit temporal preparation; cf.…”

Abnormal Coupling Between Default Mode Network and Delta and Beta Band Brain Electric Activity in Psychotic Patients

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