Affective mapping: An activation likelihood estimation (ALE) meta-analysis

- Quantitative meta-analyses are superior to qualitative, especially in neuroimaging.
- ALE is a robust quantitative method for pooling neuroimaging metadata.
- Brainmap is an efficient representative neuroimaging metadata database.
- We used Brainmap to search and conduct ALE meta-analyses on emotion metadata.
- We found distinct networks for each emotion category, but different from other papers.

Functional neuroimaging has the spatial resolution to explain the neural basis of emotions. Activation likelihood estimation (ALE), as opposed to traditional qualitative meta-analysis, quantifies convergence of activation across studies within affective categories. Others have used ALE to investigate a broad range of emotions, but without the convenience of the BrainMap database. We used the BrainMap database and analysis resources to run separate meta-analyses on coordinates reported for anger, anxiety, disgust, fear, happiness, humor, and sadness. Resultant ALE maps were compared to determine areas of convergence between emotions, as well as to identify affect-specific networks. Five out of the seven emotions demonstrated consistent activation within the amygdala, whereas all emotions consistently activated the right inferior frontal gyrus, which has been implicated as an integration hub for affective and cognitive processes. These data provide the framework for models of affect-specific networks, as well as emotional processing hubs, which can be used for future studies of functional or effective connectivity.