Decoding Unattended Fearful Faces with Whole-Brain Correlations: An Approach to Identify Condition-Dependent Large-Scale Functional Connectivity
Figure 3
Time series from each condition (unmasked fearful and unmasked neutral, F and N) and for N regions (R1 though RN) were segmented from each subject's whole run and concatenated (concatenation of two blocks for each condition shown in figure). There were four 20 second (10 TR) blocks of each condition; hence each example was comprised of 40 time points per condition per subject. For each of example, correlation matrices were estimated, in which each off-diagonal element contains Pearson's correlation coefficient between region i and region j. The lower triangular region of each of these matrices were used as input features in subsequent classifiers that learned to predict the example (i.e. F or N) based on their observed patterns of the correlations. Here, we used a filter feature selection based on t-scores in the training sets during each iteration of leave-two-out cross validation. The difference map consists of the set of most informative features (those that are included in the most rounds of cross-validation and have the highest SVM weights.)