Anger-sensitive networks: characterizing neural systems recruited during aggressive social interactions using data-driven analysis.

Social neuroscience uses increasingly complex paradigms to improve ecological validity, as investigating aggressive interactions with functional magnetic resonance imaging (fMRI). Standard analyses for fMRI data typically use general linear models (GLM), which require a priori models of task effects on neural processes. These may inadequately model non-stimulus-locked or temporally overlapping cognitive processes, as mentalizing about other agents. We used the data-driven approach of independent component analysis (ICA) to investigate neural processes involved in a competitive interaction. Participants were confronted with an angry-looking opponent while having to anticipate the trial outcome and the opponent's behaviour. We show that several spatially distinctive neural networks with associated temporal dynamics were modulated by the opponent's facial expression. These results dovetail and extend the main effects observed in the GLM analysis of the same data. Additionally, the ICA approach identified effects of the experimental condition on neural systems during inter-trial intervals. We demonstrate that cognitive processes during aggressive interactions are poorly modelled by simple stimulus onset/duration variables and instead have more complex temporal dynamics. This highlights the utility of using data-driven analyses to elucidate the distinct cognitive processes recruited during complex social paradigms.