title: Detection of time-varying signals in event-related fMRI designs.
abstract: In neuroimaging research on attention, cognitive control, decision-making, and other areas where response time (RT) is a critical variable, the temporal variability associated with the decision is often assumed to be inconsequential to the hemodynamic response (HDR) in rapid event-related designs. On this basis, the majority of published studies model brain activity lasting less than 4 s with brief impulses representing the onset of neural or cognitive events, which are then convolved with the hemodynamic impulse response function (HRF). However, electrophysiological studies have shown that decision-related neuronal activity is not instantaneous, but in fact, often lasts until the motor response. It is therefore possible that small differences in neural processing durations, similar to human RTs, will produce noticeable changes in the HDR, and therefore in the results of regression analyses. In this study we compare the effectiveness of traditional models that assume no temporal variance with a model that explicitly accounts for the duration of very brief epochs of neural activity. Using both simulations and fMRI data, we show that brief differences in duration are detectable, making it possible to dissociate the effects of stimulus intensity from stimulus duration, and that optimizing the model for the type of activity being detected improves the statistical power, consistency, and interpretability of results.
authors.affiliation: Program in Imaging and Cognitive Sciences, Columbia University, New York, New York 10032, USA. jg2269@columbia.edu
authors.forename: Jack
authors.initials: J
authors.lastname: Grinband
authors.forename: Tor D
authors.initials: TD
authors.lastname: Wager
authors.forename: Martin
authors.initials: M
authors.lastname: Lindquist
authors.forename: Vincent P
authors.initials: VP
authors.lastname: Ferrera
authors.forename: Joy
authors.initials: J
authors.lastname: Hirsch
mesh_terms: D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D012680:Sensitivity and Specificity