Stephan Hamann has just written an interesting piece on mapping emotion to the brain (here). His conclusion is that
“Although neuroimaging studies have identified consistent neural correlates associated with basic emotions and other emotion models, they have ruled out simple one-to-one mappings between emotions and brain regions, pointing to the need for more complex, network-based representations of emotion.”
I also think that “networks” is the right approach, and have written a short commentary that makes the following points (for refs, please see the commentary):
1) Given the extensive interactions among brain regions, the emphasis shifts from attempting to understand the brain one region at a time, to understanding how coalitions of regions support the mind-brain. Insofar as brain regions are not the unit of interest, they should not be viewed as “cognitive” or “emotional.” Traditionally, however, regions whose function involves homeostatic processes and/or bodily representations have been frequently viewed as “emotional,” whereas regions whose function is less aligned with such processes have been viewed as “cognitive.”
2) The architectural features of the brain are such that they provide massive opportunity for cognitive-emotional interactions (Modha & Singh 2010). These interactions are suggested to involve all brain territories. For example, extensive communication between the amygdala and visual cortex exists, and efferent amygdala projections reach nearly all levels of the visual cortex (Amaral et al. 2003). Thus, visual processing takes place within a context that is defined by signals occurring in the amygdala (as well as the orbitofrontal cortex, pulvinar, and other regions), including those linked to affective significance (Pessoa & Adolphs 2010). Therefore, vision is never pure vision, but is affective vision – even at the level of primary visual cortex (Damaraju et al. 2009; Padmala & Pessoa 2008). Cognitive-emotional interactions also abound in the prefrontal cortex, which is thought to be involved in abstract computations that are farthest from the sensory periphery. More generally, given inter-region interactivity, and the fact that networks intermingle signals of diverse origin, although a characterization of brain function in terms of networks is needed, the networks themselves are best conceptualized as neither “cognitive” nor “emotional.”
3) Regions that are important for affective processing appear to be exceedingly well connected (e.g., Petrovich et al. 2001; Swanson 2000). This suggests that these regions have important “quasi-global” roles and that this is an important feature of this class of region. However, regions traditionally described as “emotional” are not the only ones that are highly connected. Highly connected regions are encountered throughout the brain, including in the occipital, temporal, parietal, and frontal lobes, in addition to the insula, cingulate, thalamus, and regions at the base of the brain (Modha & Singh 2010).
4) Emphasizing only interactions between brain regions that are supported by direct, robust structural connections is misleading. For one, the strength of functional connectivity is equally important, and at times will deviate from the strength of the structural connection (Honey et al. 2007). Architectural features guarantee the rapid integration of information even when robust structural connections are not present, and support functional interactions that are strongly context dependent. This is illustrated, for example, by the “one-step” property of amygdala–prefrontal connectivity – amygdala signals reach nearly all prefrontal regions within a single connectivity step (see Averbeck & Seo 2008).
5) Taken together, these considerations suggest that the mind-brain is not decomposable in terms of emotion and cognition. In other words, the neural basis of emotion and cognition should be viewed as governed less by properties that are intrinsic to specific sites and more by interactions among multiple brain regions. In this sense, emotion and cognition are functionally integrated systems, namely, they more or less continuously impact each other’s operations (Bechtel & Richardson 2010). As suggested by Bechtel and Richardson, “The problem is then not one of isolating the localized mechanisms, but of exhibiting the organization and the constituent functions. . . [A]n explanation in terms of organization supplants direct localization” (p. 151).