Computational protocol: Effects of Category-Specific Costs on Neural Systems for Perceptual Decision-Making

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[…] Functional data were analyzed using SPM5 (Statistical Parametric Mapping; The first five volumes of each run were discarded to allow for T1 equilibration. Using the FieldMap toolbox (), field maps were estimated from the phase difference between the images acquired at the short and long TE. The EPI images were then realigned and unwarped using the created field map, and slice-timing correction applied to align each voxel's time series to the acquisition time of the middle slice. Each subject's T1 image was segmented into gray matter, white matter and cerebrospinal fluid (CSF), and the segmentation parameters were used to warp the T1 image to the SPM Montreal Neurological Institute template. The resulting normalization parameters were then applied to the functional data. Finally, the normalized images were spatially smoothed using an isotropic 8 mm full-width half-maximum Gaussian kernel.fMRI time series were regressed onto a composite general linear model (GLM) containing delta (stick) functions representing the onsets of the cost cue, stimulus, response, and cumulative feedback (see Supplementary Table S1). These delta functions were convolved with the canonical HRF, and low-frequency drifts were excluded with a high-pass filter (128 s cutoff). Short-term temporal autocorrelations were modeled using an AR(1) process. The stimulus delta functions were separated into three regressors dependent on the cost condition on each trial (FV, face value; NV, neutral value; and HV, house value). Each stimulus onset was parametrically modulated by two subject-specific functions. The first was the choice probability (CP) curve fitted to the out-of-scanner psychophysics data in the neutral value condition. The second was the categorical uncertainty function (U), again derived from the out-of-scanner psychophysics data, and orthogonalized with respect to choice probability (see preceding text for mathematical definitions). The cumulative feedback stick function was also modulated with the amount of money lost on the previous 10 trials. To investigate interactions of value and response hand, the response delta function was separated by cost, decision and response hand, giving a 3 (cost; FV vs. NV vs. HV) × 2 (decision; face vs. house) × 2 (response; left vs. right) factorial combination. Motion correction regressors estimated from the realignment procedure were entered as covariates of no interest. [...] Statistical significance was assessed using linear compounds of the model parameters (regression coefficients of the trial-specific stimulus functions in the preceding text) for each subject. These contrast images were then entered into a second-level random effects analysis using a one-sample t-test against zero to assess group-level significance. Cluster-based statistics () were used to define significant activations based both on their intensity and spatial extent. Clusters were defined using a height threshold of P < 0.001 and corrected for multiple comparisons across the whole brain using family-wise error correction (FWE) and a threshold of P < 0.05. Images are displayed at the cluster-defining threshold of P < 0.001 using MRIcron ( Small-volume correction (SVC) was applied to category-specific responses by using anatomical masks for fusiform and parahippocampal gyri as specified in the PickAtlas toolbox (). Percentage signal change was extracted from clusters of interest for further analysis by averaging over subjects and sessions using MarsBar (). Estimated time courses within clusters are plotted at seven TRs following stimulus onset using a finite impulse response (FIR) model. We note that time courses are plotted for illustration purposes only, inference having first been carried out using appropriate adjustments for multiple comparisons within SPM. […]

Pipeline specifications

Software tools SPM, MRIcron
Applications Magnetic resonance imaging, Functional magnetic resonance imaging
Chemicals Oxygen