984 Functional imaging was performed using a SENSE- PRESTO scan protocol (Neggers et al. 2008; scan param- eters: TR 22.5 ms; TE 33.2 ms; flip angle =10°; FOV 224× 256 ×160; matrix 56× 64 × 40; voxel size 4.0 mm isotropic; scan time 0.6075 s; 40 slices; sagittal orientation, 1,182 volumes). A high-contrast volume with a flip angle of 27° (FA27) was scanned for registration purposes. Before the functional imaging run, a high-resolution whole brain anatomical scan was performed (scan parameters: TR 9.4 ms; TE 4.7 ms; flip angle = 8°; FOV 220.8 ×240× 159.6; matrix 368 ×400 × 113; voxel size 0.6 ×0.6× 0.6 mm, 266 slices; sagittal orientation). Analysis Behavioral and physiological measures VAS scores were corrected for baseline values and analyzed using repeated-measures ANOVA with drug and time as within-subject factors (Van Hell et al. 2011; Zuurman et al. 2008). Mean heart rate during the MID task was calculated for placebo and THC sessions separately. Task performance Reward task performance was measured using reaction times (RT) on neutral and rewarding trials. A repeated- measures analysis with drug (two levels: THC and placebo) and condition (two levels: reward and neutral) was performed to analyze differences between THC and placebo, and rewarding and neutral trials. fMRI Functional MRI data were preprocessed and analyzed using SPM5 (Wellcome Trust Centre for Neuroimaging, London, UK). Preprocessing of data consisted of realignment of functional images and coregistration with the anatomical volume using the FA27 volume. After realignment, func- tional scans were spatially normalized into standard MNI space and smoothed (FWHM =8 mm) to reduce the effect of between-subject spatial variability in activation. For each individual subject, regression-coefficients for each voxel were obtained from a general linear model regression analysis using a factor matrix that contained factors representing event-related changes time-locked to anticipation and feedback of neutral and reward trials (hits and misses modelled separately). All factors were con- volved with a canonical hemodynamic response function. For anticipation, the variable anticipation period was used as the expected duration of brain activity. For feedback, a fixed period (duration of feedback period) was used as the expected duration of brain activity. To reduce the presence Psychopharmacology (2012) 219:981–990 of slow trends in the signal, a high-pass filter with a cut-off frequency of 0.007 Hz was applied to the data. As this is the first study exploring the effects of THC administration on reward processing in the brain, we chose to perform region of interest (ROI) analyses on areas that were involved in this particular task. This approach has been described previously as a powerful approach to explore data in a complex design (see Poldrack (2007)). Group activation maps were created for placebo and THC sessions separately. ROIs were calculated based on two contrasts that were sensitive for signal changes related to reward. The first group map contrasted anticipation of rewarding targets versus antic- ipation of neutral targets (denoted as “ANT”). The second group map contrasted feedback of rewarded targets versus feedback of missed targets (denoted as “FB”). ROIs were constructed by clustering neighbouring voxels that reached threshold in either the placebo or the THC session (ANT thresholded at t > 3.2, p < 0.005; FB thresholded at t > 4.1, p < 0.001). Constructing the ROIs based on the highest values in either the THC or the placebo session prevents bias towards our hypothesis (Kriegeskorte et al. 2009; Vul et al. 2009). Mean signal change for each ROI, each subject and each condition were based on beta values averaged over voxels in each ROI, extracted using Marsbar (Brett et al. 2002). All hypothesis tests were performed using SPSS 15. To measure THC effects on anticipation, an overall repeated- measures MANOVA was performed on ANT ROIs with drug (two levels: THC and placebo), condition (two levels: reward and neutral), and ROI (fourteen levels) as within- subjects factors. Follow-up ANOVA analyses were per- formed for every ROI separately with drug and condition as within-subjects factors. To measure effects of THC on reward feedback activity, repeated-measures MANOVA were performed on FB ROIs, for neutral and reward trials separately, with drug (two levels), condition (two levels: hits and misses), and ROI (ten levels) as within-subjects factors. Follow-up ANOVA analyses were again performed for every ROI. Results Behavioral results THC plasma concentration reached a maximum of 60.1 ± 33.7 ng/ml 5 min after inhalation of 6 mg THC and decreased rapidly thereafter (also see Van Hell et al. (2011)). Repeated-measures ANOVA with drug (two levels) as within-subject factor showed that THC administration increased subjective scores of “feeling high” (F(1,10) = 10.4, p < 0.01) and heart rate (F(1,10) = 8.0, p < 0.02). THC