The influence of emotional intensity on facial emotion recognition in disordered eating

Significant facial emotion recognition (FER) deficits have been observed in participants exhibiting high levels of eating psychopathology. The current study aimed to determine if the pattern of FER deficits is influenced by intensity of facial emotion and to establish if eating psychopathology is associated with a specific pattern of emotion recognition errors that is independent of other psychopathological or personality factors. Eighty females, 40 high and 40 low scorers on the Eating Disorders Inventory (EDI) were presented with a series of faces, each featuring one of five emotional expressions at one of four intensities, and were asked to identify the emotion portrayed. Results revealed that, in comparison to Low EDI scorers, high scorers correctly recognised significantly fewer expressions, particularly of fear and anger. There was also a trend for this deficit to be more evident for subtle displays of emotion (50% intensity). Deficits in anger recognition were related specifically to scores on the body dissatisfaction subscale of the EDI. Error analyses revealed that, in comparison to Low EDI scorers, high scorers made significantly more and fear-as-anger errors. Also, a tendency to label anger expressions as sadness was related to body dissatisfaction. Current findings confirm FER deficits in subclinical eating psychopathology and extend these findings to subtle expressions of emotion. Furthermore, this is the first study to establish that these deficits are related to a specific pattern of recognition errors. Impaired FER could disrupt normal social functioning and might represent a risk factor for the development of more severe psychopathology.

Facial affect processing inbipolar disorder:a magnetoencephalography study

Background: Identifying biological markers to aid diagnosis of bipolar disorder (BD) is critically important. To be considered a possible biological marker, neural patterns in BD should be discriminant from those in healthy individuals (HI). We examined patterns of neuromagnetic responses revealed by magnetoencephalography (MEG) during implicit emotion-processing using emotional (happy, fearful, sad) and neutral facial expressions, in sixteen BD and sixteen age- and gender-matched healthy individuals. Methods: Neuromagnetic data were recorded using a 306-channel whole-head MEG ELEKTA Neuromag System, and preprocessed using Signal Space Separation as implemented in MaxFilter (ELEKTA). Custom Matlab programs removed EOG and ECG signals from filtered MEG data, and computed means of epoched data (0-250ms, 250-500ms, 500-750ms). A generalized linear model with three factors (individual, emotion intensity and time) compared BD and HI. A principal component analysis of normalized mean channel data in selected brain regions identified principal components that explained 95% of data variation. These components were used in a quadratic support vector machine (SVM) pattern classifier. SVM classifier performance was assessed using the leave-one-out approach. Results: BD and HI showed significantly different patterns of activation for 0-250ms within both left occipital and temporal regions, specifically for neutral facial expressions. PCA analysis revealed significant differences between BD and HI for mild fearful, happy, and sad facial expressions within 250-500ms. SVM quadratic classifier showed greatest accuracy (84%) and sensitivity (92%) for neutral faces, in left occipital regions within 500-750ms. Conclusions: MEG responses may be used in the search for disease specific neural markers.