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For visual and finger tapping tasks the block functions were convolved with canonical HRF to generate the model time course for the conditions (box-car design). For the auditory ��oddball�� task the design matrix consisted of an onset vector of target stimuli, Wnt inhibitor novel stimuli and nontarget stimuli (event related design) also convolved with the canonical HRF. In addition six rigid-body motion correction parameters were entered as confounding covariates to mitigate the influence of head motion in the data. Data from each participant were combined in second (group) level mixed-effects models using FLAME with automated outlier de-weighting (Beckmann et al., 2003?and?Woolrich et al., 2004). To identify the distribution of activity in each task without influence of caffeine, scans from baseline placebo (BP, pre-dose) and baseline caffeine (BC, pre-dose) scans were combined at the second level to produce group average baseline activity maps. The effects of caffeine were identified from a paired second level analysis separately for each task: U0126 concentration visual, motor and auditory oddball. These analyses modelled the interaction of the effect of ��dosing��, namely baseline pre-dose (B) or post-dose (D), and the effect of ��drug��, namely placebo (P) or caffeine (C). The interaction is described by the contrast (DC�CBC)�C(DP�CBP) and represents caffeine's effects controlled by baseline scans. Both positive and negative interaction effects were examined. A cluster correction for multiple comparisons was used, with an initial threshold of Z?>?2.3, and a whole-brain corrected cluster threshold of p?crotamiton for the visual and motor tasks was estimated for each subject (Liu et al., 2004) following linear interpolation of the region of interest BOLD time-series data to a new sampling interval of 0.1?s and averaging across stimulus cycles. The T50 was tested for an effect of caffeine using 2-way repeated measures analysis of variance. Identification of the T50 was not feasible for the auditory task because of the smaller BOLD signal changes in response to the presentation of brief events and hence lower signal to noise ratio of the BOLD responses. Brain Vision Analyzer software (Brainproducts, Germany) was used for correction of MR gradient and ballistocardiographic (BCG) artefacts (Allen et al., 1998?and?Allen et al., 2000). Gradient artefacts were removed as implemented in Vision Analyzer software by subtracting an artefact template from the data, using a baseline-corrected sliding average of 20 consecutive volumes.