For this set of analyses, subjects' parameter estimates for each regressor were extracted using MARSBAR (http://marsbar.sourceforge.net/), from the 5 ROIs described above, respectively, the left and right lateral ROIs, the left and right dorsomedial ROIs, and the ventromedial ROI.
In the present study, we analyzed PPI regressor estimates for early learning trials (SRO-Rep2 and SRO-Rep3) and late learning trials (SRO-Rep7 and SRO-Rep8).
Subsequently, parameter estimates of the HRF regressor for each of the different conditions were calculated from the least-mean-squares fit of the model to the time series.
Voxel-wise parameter estimates for these regressors were obtained by restricted maximum-likelihood estimation, using a temporal high-pass filter (cut-off 128 s) to remove low-frequency drifts, and modeling temporal autocorrelation across scans with an AR(1) process.
Voxel-wise parameter estimates for these regressors were obtained by restricted maximum-likelihood estimation (ReML), using a temporal high-pass filter (cut-off 128 secs) to remove low-frequency drifts, and modelling temporal autocorrelation across scans with an Auto-regression (1) process.
Again, events within each block were estimated using 4 regressor functions, 1 for each condition.
Contrast images were then calculated by applying appropriate linear contrasts to the parameter estimates for the regressors of interest.
These features at different superpixel scales are then fused for learning a regressor that estimates the rating for each plant image.
Activation coefficients for the experimental conditions were estimated for each individual in within-subjects models (including motion parameters as regressors) and compared statistically with t-contrasts in between-subjects GLM designs.
Standard errors of regression coefficients were estimated for each QOL domains by bootstrapping since the equations included the generated regressor (interpersonal trust score) from PCA.
