Functional atlas of emotional faces processing: a voxel-based meta-analysis of 105 functional magnetic resonance imaging studies

Abstract

Background: Most of our social interactions involve perception of emotional information from the faces of other people. Furthermore, such emotional processes are thought to be aberrant in a range of clinical disorders, including psychosis and depression. However, the exact neurofunctional maps underlying emotional facial processing are not well defined.

Methods: Two independent researchers conducted separate comprehensive PubMed (1990 to May 2008) searches to find all functional magnetic resonance imaging (fMRI) studies using a variant of the emotional faces paradigm in healthy participants. The search terms were: "fMRI AND happy faces," "fMRI AND sad faces," "fMRI AND fearful faces," "fMRI AND angry faces," "fMRI AND disgusted faces" and "fMRI AND neutral faces." We extracted spatial coordinates and inserted them in an electronic database. We performed activation likelihood estimation analysis for voxel-based meta-analyses.

Results: Of the originally identified studies, 105 met our inclusion criteria. The overall database consisted of 1785 brain coordinates that yielded an overall sample of 1600 healthy participants. Quantitative voxel-based meta-analysis of brain activation provided neurofunctional maps for 1) main effect of human faces; 2) main effect of emotional valence; and 3) modulatory effect of age, sex, explicit versus implicit processing and magnetic field strength. Processing of emotional faces was associated with increased activation in a number of visual, limbic, temporoparietal and prefrontal areas; the putamen; and the cerebellum. Happy, fearful and sad faces specifically activated the amygdala, whereas angry or disgusted faces had no effect on this brain region. Furthermore, amygdala sensitivity was greater for fearful than for happy or sad faces. Insular activation was selectively reported during processing of disgusted and angry faces. However, insular sensitivity was greater for disgusted than for angry faces. Conversely, neural response in the visual cortex and cerebellum was observable across all emotional conditions.

Limitations: Although the activation likelihood estimation approach is currently one of the most powerful and reliable meta-analytical methods in neuroimaging research, it is insensitive to effect sizes.

Conclusion: Our study has detailed neurofunctional maps to use as normative references in future fMRI studies of emotional facial processing in psychiatric populations. We found selective differences between neural networks underlying the basic emotions in limbic and insular brain regions.

Fig. 1

Neurofunctional network subserving processing of human faces. The areas in yellow represent activation likelihood estimation neurofunctional maps of brain areas activated during processing of human faces (human faces > fixation, false discovery rate = 0.01, cluster p < 0.001).

Fig. 2

Brain maps of neural activation in response to happy, sad, angry, fearful and disgusted human faces compared with neutral faces (false discovery rate = 0.001, cluster p < 0.001). Amy = amygdala; FG = fusiform gyrus; MFG = medial frontal gyrus.

Fig. 3

Neural correlates of explicit versus implicit facial emotional processing (yellow clusters: explicit > implicit; blue clusters: implicit > explicit; false discovery rate = 0.01 cluster p < 0.001).

Fig. 4

Age modulation of human emotional processing (yellow clusters: old > young; blue clusters: young > old; false discovery rate = 0.01, cluster p < 0.001).

Fig. 5

Differential neurofunctional response to emotions. (A) Increased activation during processing of happy faces compared with sad faces. (B) Differential activation between processing fearful and happy faces (yellow: happy > fearful; blue: fearful > happy). (C) Differential activation between processing angry and disgusted faces (yellow: angry > disgust; blue: disgust > angry). False discovery rate = 0.001, cluster p < 0.001.