  We show that fMRI analysis using machine learning tools are sufficient to
distinguish valence (i.e., positive or negative) of freely retrieved
autobiographical memories in a cross-participant setting. Our methodology uses
feature selection (ReliefF) in combination with boosting methods, both applied
directly to data represented in voxel space. In previous work using the same
data set, Nawa and Ando showed that whole-brain based classification could
achieve above-chance classification accuracy only when both training and
testing data came from the same individual. In a cross-participant setting,
classification results were not statistically significant. Additionally, on
average the classification accuracy obtained when using ReliefF is
substantially higher than previous results - 81% for the within-participant
classification, and 62% for the cross-participant classification. Furthermore,
since features are defined in voxel space, it is possible to show brain maps
indicating the regions of that are most relevant in determining the results of
the classification. Interestingly, the voxels that were selected using the
proposed computational pipeline seem to be consistent with current
neurophysiological theories regarding the brain regions actively involved in
autobiographical memory processes.
