Unsupervised classification reveals consistency and degeneracy in neural network patterns of emotion

In the present study, we used an unsupervised classification algorithm to reveal both consistency and degeneracy in neural network connectivity during anger and anxiety. Degeneracy refers to the ability of different biological pathways to produce the same outcomes (Edelman & Gally, 2001; Tononi et al., 1999). Previous research is suggestive of degeneracy in emotion, but little research has explicitly examined whether degenerate functional connectivity patterns exist for emotion categories such as anger and anxiety. Twenty-four subjects underwent fMRI while listening to unpleasant music and self-generating experiences of anger and anxiety. A data-driven model building algorithm with unsupervised classification (S-GIMME; Gates et al., 2017) identified patterns of connectivity amongst 11 intrinsic networks that were associated with anger vs. anxiety. As predicted, degenerate functional connectivity patterns existed within these overarching consistent patterns. Degenerate patterns were not attributable to differences in emotion experience or other individual-level factors. These findings are consistent with the constructionist account that emotions emerge from flexible functional neuronal assemblies and that emotion categories such as anger and anxiety each describe populations of highly variable instances.