Computational protocol: Reduced cerebellar brain activity during reward processing in adolescent binge drinkers

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Protocol publication

[…] Image preprocessing followed conventional procedures previously described in other reports (; ). Analysis of Functional NeuroImages (AFNI) was used for all image preprocessing (). Briefly, following image reconstruction, anatomical masks were skull-stripped to remove non-brain skull and tissue. First, functional data were subjected to slice timing correction, identification of movement artifact, and realignment of TRs to the volume requiring the least amount of adjustment of rigid body head motion following a least squares algorithm (). Then, TRs that required more than 2.5 mm or 2.5 degrees of adjustment were censored prior to further analyses to limit artifact induced by head motion or other noise. Functional data were blurred with a 6 mm full width half maximum Gaussian kernel to increase signal-to-noise ratio, fractionized to the anatomical image, and normalized to convert values to relative percent signal change. The two runs of the WOF task were then concatenated as were the six motion regressors from each run of the task. The hemodynamic response function (HRF) was modeled with duration of the event defined as the length of each phase of the trial, while modeling delays in the HRF. Regressors of interest were represented by Wins and No Wins (as defined above), while other task-related regressors, including risky, safe, and chance decisions, and risky, safe, and chance anticipations were also modeled (as defined above), but were not examined for the current analyses. AFNI’s baseline model included the six motion regressors of non-interest, unmodeled fixation, linear drift, and the average BOLD signal from the entire timecourse of the task. Data were re-sampled into 3 mm3 voxels and transformed to standardized Talairach space (). [...] To illustrate significant WOF task-related activity, regardless of group status and study visit, we conducted a conjunction analysis, voxel thresholded at p<0.05 (), which confirmed expected reward-related and task-positive brain regions activated by the task in the Win vs. No Win contrast, including the ventral striatum, occipital cortex, and fronto-parietal regions. The Win vs. No Win contrast appears to be a valid measure of reward processing, as opposed to more general incentive salience processing, as areas activated in the current study closely resemble those in previous adolescent studies of reward vs. no reward brain activity, which show distinct patterns from loss vs. no loss BOLD response (; ).For group-level analyses, an a priori region of interest (ROI) analysis of the ventral striatum was conducted by using the Talairach Daemon atlas in AFNI, and applying 4-mm radius masks (10 voxels each) of the left and right ventral striatum with peak coordinates at 12, −8, −8 (right ventral striatum) and −12, −8, −8 (left ventral striatum). The ventral striatal masks were resampled to 3 mm3 to match the functional data. Percent signal change for the Win vs. No Win contrast was extracted from left and right ventral striatal masks for each participant and mixed model ANOVAs examined the effect of group, time, and group-by-time interactions for the Win vs. No Win contrast in SPSS.Next, for the whole-brain analyses examining differences in reward processing between binge drinkers and controls, one sample t-tests were voxel thresholded at p<0.05 for each group and added together to form a map of task-related brain activity at the second study visit. AFNI’s 3dttest++ was used to compare groups on differences in brain activity during Wins vs. No Wins, restricted to the pre-defined task-related activity mask. Results of this analysis were multiple comparison corrected using Monte Carlo simulation with both a voxel and cluster threshold (p/α< 0.05) (), yielding a minimum cluster size of 102 voxels. Percent signal change from the cluster in which significant group differences emerged at the second study visit were extracted with 3dROIstats, and signal from the baseline study visit was also extracted from this region. Hierarchical regressions in IBM SPSS Version 20.0 () tested whether group differences remained significant after accounting for brain activity in this cluster at the first study visit, age at revisit, and time between scans. Significant findings from this analysis were overlaid on a Talairach brain template in AFNI, while bar graphs created in GraphPad Prism version 5.00 for Windows, GraphPad Software, San Diego California, USA,, were used to illustrate percent signal change in each group at baseline and second study visits in both the significant contrast of interest (Win vs. No Win) and for Win vs. baseline, and No Win vs. baseline brain activity.In order to examine whether group differences in brain activity were present between youth who emerged into binge drinking and controls, an identical whole-brain analysis to that described above was conducted at baseline (controlling for baseline differences in head movement; p/α< 0.05, ≥94 voxels). Furthermore, two-way ANOVAs examined effects of group, sex, and group-by-sex interactions in clusters where significant differences in BOLD response were present between binge drinkers and controls at revisit. […]

Pipeline specifications

Software tools AFNI, SPSS
Applications Miscellaneous, Functional magnetic resonance imaging
Organisms Homo sapiens
Diseases Brain Diseases, Neurotoxicity Syndromes
Chemicals Ethanol