Figure 6 : Difference in functional connectivity patterns with dACC pattern classifiers for pain and rejection.

From: Separate neural representations for physical pain and social rejection

Figure 6

(a) The multivariate pattern classifiers for pain and rejection within the dorsal anterior cingulate cortex (dACC) region-of-interest (ROI) (a top). The dACC ROI was defined by the searchlight analysis results presented in Fig. 5, which showed that the dACC ROI contained information for both pain and rejection conditions, but the patterns were distinct and non-transferrable. Using the dACC ROI, we trained linear SVMs to discriminate pain and rejection from their respective control conditions, and tested on out-of-sample participants. The pattern weights were uncorrelated with each other, r=−0.04. The cross-validated (leave-one-subject-out) accuracy in two-choice classification tests demonstrated separate modifiability of the pattern classifiers (a bottom). The dotted line indicates the chance level (=50%), and the error bars represent standard error of the mean across subjects. **P<0.01, ***P<0.001, binomial test. (b) Seed-based functional connectivity with dACC pattern classifiers for pain and rejection. Here seeds were pattern expression values (the dot-product of a vectorized activation map and SVM weights within dACC) for pain and rejection. The functional connectivity for each condition was calculated with independent resting-state fMRI data (n=91). These maps were thresholded at family-wise error rate (FWER)<0.05 using Bonferroni correction. Here we used Bonferroni correction instead of false discovery rate (FDR) because the latter provided too liberal thresholds for these functional connectivity patterns (uncorrected P<0.03) and therefore yielded non-sensible maps. (c) Paired t-test results between two seed-based functional connectivity patterns. The results were thresholded at FDR<0.05.