106 Chapter 5 10 images from one condition (non-food, high-calorie or low-calorie). Between blocks, subjects received questions about their desire to eat and level of anxiousness. The total duration of the task was 12 minutes. Data acquisition The data were collected using the 1.5 Tesla Siemens Avanto scanner at the Amsterdam UMC (location AMC, department of radiology). To minimize exposure of DBS electrodes to the pulsed radiofrequency field, a transmit and receive head coil was used. DBS was turned off before patients entered the scanner, and the specific absorption rate was limited to 0.1 W/kg. During task performance, the blood oxygen level dependent (BOLD) MRI signal was acquired using a functional T2*-weighted two-dimensional echo-planar imaging sequence (TR = 2000ms, echo time = 30ms, flip angle = 90°, field of view = 230 × 230 mm, matrix = 64 × 64, 25 slices, slice thickness = 4mm, slice gap = 0.4mm). Data analysis The MRI data were analyzed using SPM12 (version 6685, Wellcome Trust Centre for Neuroimaging, London, UK) and MATLAB (version R2014a, The MathWorks Inc., Natick, MA, USA). fMRI data preprocessing consisted of realignment, slice-time correction, normalization to Montreal Neurological Institute (MNI) space, resampling to 2 x 2 x 2 mm3 and spatial smoothing with a Gaussian-kernel of 8 mm full width at half maximum (FWHM). A high pass filter of 1/128 Hz was applied to the data and serial correlations were accounted for using the autoregressive AR(1) modelling. A general linear model (GLM) was used to model the conditions of interest (see below), convolved with a hemodynamic response function (HRF) using 3 regressors related for either the reward task (neutral, reward, loss) or the food task (non-food, high-calorie and low-calorie food), and 6 additional regressors to account for head motion parameters. For the reward task, the conditions of interest (reward > neutral; loss > neutral) were specified in first-level modelling, including the onset and duration of anticipation cues. A second-level full factorial design was created, combining time (T-1, T2, T4), group (AN, controls) and condition (reward > neutral, loss > neutral). The interaction effects between group and time were investigated for both reward and loss anticipation separately. For the food task, the non-food, high-calorie food and low-calorie food conditions were modeled as box-car regressors at first-level, and a second-level repeated measures ANOVA design was created,combining time (T-1, T2, T4), and condition (high-calorie food > non-food, low-calorie food > non-food). The interaction effects for time and condition were investigated within subjects. The results for both tasks were masked to exclude voxels with DBS-related signal dropout in the normalized EPI scans. First, an assessment was made of significant differences between the two post-operative sessions. As we did not observe significant group by time interactions when comparing the two post-DBS timepoints (T2, T4), we created a contrast in which both post-DBS time points were equally
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