Reproducible, generalizable brain models of affective processes

Abstract

Recent years have seen dramatic advancement in the measurement of biology at a systems level. In humans, neuroimaging can be used to probe the brain bases of affect and emotion in increasingly sophisticated ways, but the complexity of these measures presents new challenges in maintaining scientific transparency and reproducibility. We describe several new models of the brain bases of affective processes, including models that predict the intensity of pain, negative affect, autonomic responses, and prosocial emotions including empathic care and distress. These models reduce complex, brain-wide neuroimaging data to measures that can be readily replicated and generalized across laboratories, and they can yield correlates of affective behavior that are substantially stronger than those based on single regions from standard brain maps. They can also be used as mechanistic targets for interventions, allowing comparisons across diverse treatments. Most importantly, they can teach us about the brain representations that underlie various forms of affect, in part by providing information about the necessary and sufficient brain bases for predicting affective states and behaviors. The results across the series of studies discussed here indicate that different forms of affect have reliably different brain representations. For example, somatic pain, romantic rejection, vicarious pain, and empathic care all have differentiable brain substrates. The latter processes are particularly important for empathy and prosocial behavior, and the chapter includes an extended example of how multivariate brain measures can inform us about how we might recognize others’ suffering and take action to help.