fMRI Data Acquisition and Analysis
Brain activation of participants was measured using a 3 Tesla magnetic resonance scanner (Magnetom Trio A, Tim System, Siemens Medical Systems; Erlangen, Germany). For the functional analysis, 110 volumes of echo-planar-images were acquired using a T2*-weighted sequence (TE = 30 ms, flip angle = 90°, matrix = 64 x 64, FOV = 192 mm, TR = 2.7 s). Each volume comprised 40 axial slices with 3 mm thickness and an interslice space of one millimeter creating a voxel size of 3 x 3 x 3 mm. In addition, an anatomical scan with high resolution was acquired using a T1-weighted MPRAGE sequence with a voxel size of 1 x 1 x 1 mm. For data preprocessing, the first four volumes were discarded to secure steady-state tissue magnetization. Preprocessing and data analysis was performed using the software SPM8 (Wellcome Trust Centre for Neuroimaging, University College London). Data were realigned to minimize effects of body movement. Realigned data then were normalized and transposed to the Talairach space (Talairach and Tournoux, 1988) using the anatomical image that was co-registered with a T1-template and the mean image of the realigned data. After normalization, the images were smoothed with a Gaussian kernel of 8 mm full width at half-minimum (FWHM). Preprocessed data were used for first level analysis where the onset and duration of the decision-screens were taken to assess the activation during the three different stimulus conditions (high, low, and mixed risk). In addition, we added two parameters in the parametric SPM model for every stimulus condition for the potential gain and the potential loss. These parameters were calculated as the sum of the potential wins the sum of potential losses of these two options. For the first-level analysis, contrasts of predictor estimates (beta-weights) were defined for each risk level (i.e.: high risk, mixed risk, and low risk). The expected blood oxygen level-dependent (BOLD) signal changes were modeled using a canonical hemodynamic response function. The resulting contrast images of each participant were used for second-level ANOVA calculations (group level). At the end we got group level predictor estimates (beta-weights) which we used for calculating contrasts for comparison between risk and ambiguity, mixed-risk trials in comparison to high- and low-risk trials with and without ambiguity and for the parametrical results of potential gains or losses. The analysis focused on the time of the decision-making. Results of the analysis within each Region of Interest (ROI) were regarded as statistically significant when t-values were < 0.001 uncorrected. ROIs were defined using the Talairach client software (Talairach Project, International Consortium for Brain Mapping). In order to prevent false positive activations, results are reported only for brain areas which showed z-value higher than 3.09 – i.e., p < 0.001, uncorrected – and a volume greater than 180 mm³ - i.e., five voxels with a spatial resolution of 3 x 3 x 4 mm (here we opted for four millimeters, because the thickness of measured slices was three millimeter plus a distance between slices of one millimeter).