2019 Volume 59 Issue 2 Pages 155-163
Objectives : Evaluating bipolar spectrum in patients experiencing episodes of depression can be challenging and must be handled with caution ; thus, the development of novel evaluation tools is an area of high demand. Herein, we report a facial expression recognition task that was conducted for elucidating characteristics of facial expression recognition by depressed patients with and without bipolar spectrum.
Subjects and Method : We evaluated patients using depression assessment scales (BDI-Ⅱ and HAM-D) and administered bipolar disorder screening questionnaires (MDQ and BSDS), Mini-International Neuropsychiatric Interview (MINI), and a facial expression recognition task (JACFEE). The subjects comprised 23 patients diagnosed with major depressive disorder who were being treated in the psychosomatic medicine department of a university hospital in Tokyo between October 2014 and December 2016. Based on assessment results, we classified the patients into the major depressive disorder group (11 patients) and the bipolar spectrum group (12 patients), and compared their performance in the facial expression recognition task.
Results : Each group×the correct response rate for each facial expression recognition was evaluated using a t-test, and we found that compared with the major depressive disorder group, the bipolar spectrum group had a significantly lower rate of correct responses for identifying expressions of “happiness” [t (21)=−2.06, p<0.05]. Furthermore, a Pearson correlation test of the correct response rate for each expression recognition and the bipolar disorder screening questionnaire revealed a significant negative correlation between the rate of correct responses for expressions of “happiness” and MDQ (r=−0.61, p<0.01).
Conclusion : Despite the same severity of depression, patients with bipolar spectrum were not able to correctly recognize expressions of “happiness.” Furthermore, the results indicated that a wide range of symptoms in patients with bipolar spectrum may be associated with more difficulty in recognizing the expression of “happiness.” This finding potentially demonstrates the possible involvement of information processing related to facial expression recognition and the possible associated features of these diseases. Furthermore, it suggests that facial expression recognition tasks can be used as potential evaluation tools for differentiating the type of diseases.
