Identification and Perceived Intensity of Facial Expressions of Emotion in Bipolar Disorder and Major Depression

Identification and Perceived Intensity of Facial Expressions of Emotion in Bipolar Disorder and... Abstract Objective This study aimed to examine the accuracy in identifying facial expressions and the perceived intensity of emotions of happiness, sadness, disgust, anger, fear, surprise in bipolar disorder (BD), and major depressive disorder (MDD). Method Ninety-three adult participants (n = 30 BD, 80% female; n = 18 MDD, 72.2% female; n = 45 C, 46.7% female) completed a facial expression task. Stimuli in the task were first presented for 200 ms, followed by 500 ms, and 1000 ms. Participants were asked to identify the emotion expressed by each face and judge its intensity on a Likert scale. Accuracy and perceived intensity of expressions corresponding to each emotion were compared between groups using a repeated measures ANCOVA, with length of stimulus presentation as a within-subjects variable. Results Expressions of sadness were rated more intensely by patients with BD, and expressions of anger by those with MDD, than control participants. Subjects with BD were less accurate than control participants in identifying expressions of disgust. An interaction effect was also identified for the detection of sadness, whereby patients with BD were significantly less accurate when expressions were shown for 200 ms. Conclusions The increased intensity with which emotions were perceived by patients with BD and MDD has important repercussions for patient functioning and clinical practice. A tendency to overestimate the intensity of certain facial expressions in mood disorders may lead patients to interpret social cues erroneously and engage in dysfunctional behaviors and cognitive patterns. Future studies should focus on this variable in addition to the accuracy of emotion identification. Bipolar disorder, Depression, Emotions, Facial expressions Introduction Facial expression processing has been a major topic of study in science for over 100 years (Darwin, 1890). Over time, it also became an important issue in psychology and psychiatry, as impairments in facial expression processing began to be identified as a consistent feature of both neurological (Alonso-Recio, Serrano, & Martín, 2014; Torres et al., 2015) and psychiatric conditions (Dalili, Penton-Voak, Harmer, & Munafò, 2016; Daros, Ruocco, Reilly, Harris, & Sweeney, 2014; Hargreaves et al., 2016; Yalcin-Siedentopf et al., 2014). Facial expressions and emotional processing in more general terms have been especially studied in bipolar (BD) and major depressive disorder (MDD), both of which are characterized by persistent and dysfunctional alterations in emotion and cognition, closely associated with significant changes in mood state (American Psychiatric Association, 2013). The two conditions are also similar in that they have been found to be associated with abnormalities in frontolimbic structures associated with emotional processing. Individuals with MDD have been found to show reduced activity in the ventromedial prefrontal cortex (PFC), as well as diminished discrimination between emotional and neutral items in the activation of the amygdala, caudate, and hippocampus (Ritchey, Dolcos, Eddington, Strauman, & Cabeza, 2011). Like MDD, BD is associated with hyperactivation of limbic regions in response to emotional stimuli (Delvecchio et al., 2012). However, patients with BD also show more extensive functional and structural abnormalities in prefrontal regions such as the ventrolateral prefrontal cortex (Townsend & Altshuler, 2012) and the inferior frontal gyrus (Breakspear et al., 2015). Some studies suggest that the alterations in frontolimbic regions associated with the modulation and recognition of facial expressions in mood disorders may also influence the cognitive and emotional processes responsible for evaluative processes and social judgments in these conditions (Radaelli et al., 2014; Sagar, Dahlgren, Gönenç, & Gruber, 2013). Despite their relevance to the study of facial expression processing in mood disorders, behavioral findings in this area of study have been less conclusive. While some studies have identified significant impairments in facial expression recognition across all major emotions in BD and MDD (Kohler, Hoffman, Eastman, Healey, & Moberg, 2011), others suggest that this is not a consistent feature of BD (Robinson, Gray, Burt, Ferrier, & Gallagher, 2015), or that impairments may be present for some emotions, but not others, in MDD (Dalili et al., 2016). There are also disagreements regarding the severity of these impairments, with some studies suggesting them to be small (Dalili et al., 2016) and others describing them as moderate (Kohler et al., 2011). Only a few studies have compared patients with MDD and BD in terms of their performance on behavioral measures of facial expression recognition. The few existing investigations on the topic have not identified significant differences between these participant groups (Kohler et al., 2011; Schaefer, Baumann, Rich, Luckenbaugh, & Zarate, 2010). While these findings may suggest that there are no such differences to be found, it is also possible that between-group differences were masked by confounding factors such as education levels, which are known to have an affect on facial expression processing in BD and MDD (Dalili et al., 2016; Kohler et al., 2011) but have not been considered in comparative studies of these disorders (Schaefer et al., 2010). There is, as such, a need for additional studies of facial expression recognition in these disorders, with greater control for potential confounding variables. The majority of investigations have focused on the accuracy of identification of emotional expressions, with less emphasis being given to the perceived intensity of these emotions. While the intensity of facial expressions is often manipulated as part of experimental procedures, to determine, for instance, sensitivity thresholds for the identification of different emotions (e.g., Robinson et al., 2015), the “perceived” intensity of facial expressions has only been scarcely investigated as a dependent variable. The few studies to examine this variable have produced promising but inconclusive findings (Altamura et al., 2016; Chen et al., 2006; Schaefer et al., 2010). Given the importance of facial expressions as a nonverbal social cue, the perceived intensity of such stimuli should be further investigated, since a tendency to over- or underestimate the intensity of the emotions displayed by others may lead to erroneous social judgments and maladaptive behaviors. There have also been few studies of the influence of length of stimulus presentation on the recognition of facial expressions. The majority of studies which consider length of stimulus presentation as an independent variable focus on attentional biases in patients with anxiety disorders (e.g., Bantin, Stevens, Gerlach, & Hermann, 2016; Bradley, Mogg, Falla, & Hamilton, 1998). In unipolar depression and BD, while the length of stimulus presentation varies widely between studies (Bourke, Douglas, & Porter, 2010), it is very rarely manipulated within individual studies. The few studies to do so often fail to consider the length of stimulus presentation as a variable in itself, and only vary the duration to avoid habituation (Gohier et al., 2014; Jerram, Lee, Negreira, & Gansler, 2013). The only study to date which evaluated the recognition of facial expressions of emotion shown for different lengths of time did not find a significant association between these variables (Shiroma, Thuras, Johns, & Lim, 2014). However, there is a clear need for additional investigations of this topic, especially in light of findings regarding the affect of stimulus duration on attentional biases in depression (Donaldson, Lam, & Mathews, 2007) and on emotional processing (Palumbo, D’Ascenzo, Quercia, & Tommasi, 2017) using non-facial stimuli. Such findings would also be relevant to the differentiation between perceptual and cognitive/interpretational issues in facial expression processing. According to the literature, cognitive functions such as attention may be impaired in patients with BD or MDD even in the absence of a mood episode (Cotrena, Branco, Shansis, & Fonseca, 2016; Cullen et al., 2016; Evans et al., 2013; Lam, Kennedy, McIntyre, & Khullar, 2014). These abilities are directly related to facial expression processing. The interpretation of briefly displayed expressions requires both attentional focus to the stimuli and its features, to gather information on which to base a judgment, as well as processing speed, to ensure the interpretation is formulated in a timely manner (David, Soeiro-de-Souza, Moreno, & Bio, 2014). While this may not be an issue in self-paced assessment tasks, it is decidedly relevant for social functioning, because microexpressions may last for as little as 500 ms (Yan, Wu, Liang, Chen, & Fu, 2013). As such, performance in facial expression tasks must consider both interpretational issues, such as whether the individual can correctly identify different expressions of emotion, as well as the influence of attentional processes on the ability to perceive the stimulus in the first place (David et al., 2014; Davies et al., 2016). As such, the aim of the present study was to examine the accuracy in identifying facial expressions and the perceived intensity of these emotions in patients with MDD and BD, as compared to control participants. It was hypothesized that patients with MDD and, especially, subjects with BD would show lower accuracy in emotional recognition relative to control subjects. We expected these differences to be larger for faces shown for shorter periods of time, but to still reach significance faces shown for 500 ms or longer. Methods Participants The sample size was determined based on a previous study of facial expression processing which also examined both accuracy and perceived intensity in patients with BD (Altamura et al., 2016). The study in question identified significant differences between a group of 20 control participants and 16 patients with BD. In the present study, the sample consisted of 30 patients with BD (n = 17 with BD type I and n = 13 with BD type II), 18 patients with MDD as well as 45 healthy adults with no mood disorders. Patients were recruited from the mood disorders outpatient unit of a psychiatric hospital, a university teaching clinic, and private practice. Control participants were selected by convenience from work and university settings, as well as the community at large. All patients were at least 18 years old, and had at least 1 year of formal education. The following exclusion criteria were applied to the sample: uncorrected sensory impairments which would interfere with task performance, neurological conditions, and pregnancy or lactation. Patients with psychotic symptoms at the time of testing or who reported substance abuse within the previous month were also excluded from participation. The control group was selected using the same criteria, and was screened for mood disorders according to DSM-5 criteria, cognitive impairment, and intellectual disability. Procedures and Instruments All participants provided written consent for participation, and the present study was approved by the Research Ethics Committee of the institution where it was conducted. Subjects were evaluated in individual sessions, and the presence of mood disorders was examined using DSM-5 criteria (American Psychiatric Association, 2013). All diagnoses were confirmed by consensus with a clinical psychologist with expertise in mood disorders who is also a coauthor of this study (C.C.). All subjects took part in at least two assessment sessions lasting approximately one and a half hours each. Inclusion and exclusion criteria were first investigated using a sociocultural and health questionnaire (Cotrena, Branco & Fonseca, manuscript in preparation). Participants were then administered the Mini-Mental State Examination (MMSE; Folstein, Folstein, & McHugh, 1975), adapted by Chaves and Izquierdo (1992) for the local population. Subjects also completed the Block Design and Vocabulary Subtests from the Wechsler Adult Intelligence Scales (WAIS-III) (Nascimento, 2004), whose scores were converted to estimated IQ using the tables provided by Jeyakumar, Warriner, Raval, and Ahmad (2004). Clinical assessments were performed using the Mini International Neuropsychiatric Interview (MINI) (Amorim, 2000). Symptoms of depression and mania were investigated using the Hamilton Depression Rating Scale (HDRS) (Gorenstein, Andrade, & Zuardi, 2000; Hamilton, 1960) and the Young Mania Rating Scale (YMRS) (Vilela & Loureiro, 2000; Young, Biggs, Ziegler, & Meyer, 1978), respectively. Prior to completing the facial expression assessment, participants were also administered a sustained attention test (Sisto, Noronha, Lamounier, Bartholomeu, & Rueda, 2006). The task provides a concentration score which was used as a statistical control for individual differences in attention which may influence the ability to visualize and interpret facial expressions in the subsequent task. Lastly, participants were administered a Facial Expression Recognition Task in which they were asked to identify the emotion expressed by different individuals, and provide a subjective rating of the intensity of the emotion on a Likert scale of 1–5. The task was divided into three sections. In Section 1, a series of 26 facial expressions were displayed for 200 ms each. After each image was displayed, the participant was shown a list of the six basic emotions (happiness, sadness, disgust, anger, fear, and surprise) as well as the word “neutral,” and asked to identify which word best describes the expression they had seen. They then provided a rating of the intensity of that emotion on a scale of 1–5. The images depicted four actors (two females and two males) of varied ethnic backgrounds making the facial expressions corresponding to each of the basic emotions. The 26 images shown in each section of the task corresponded to the six expressions made by each actor, plus two images of the actors showing a neutral facial expression. The two subsequent blocks of the task were similar to the first. However, the second block presented the images for 500 ms, while the third presented each for 1 s. This task has been validated in a nonclinical population by a previous study (Vasconcellosa et al., 2014). Over the course of the task, participants identify and rate the intensity of a total of 78 facial expressions (26 per block). However, in the present study, each of the sections was analyzed separately, to allow for an assessment of between-group differences in the identification and interpretation of different expressions as a function of the time for which they were exposed to each stimulus. As a result, for each block of 26 faces, the number of expressions correctly identified for each emotion was calculated for every patient in the sample. The maximum possible score on this measure was four, since each block contained four renditions of each basic emotion (each emotion was displayed by all four actors). Then, the mean intensity assigned to the correctly identified emotions was calculated. This procedure yielded an accuracy and intensity score per emotion per block. These values were then used for subsequent analysis. Data Analysis Descriptive analyses of patient characteristics were first performed. These values were compared between groups using Student’s t-tests or chi-square tests, as appropriate. Performance on the facial expression task was then compared between participant groups using a mixed Analysis of Covariance (ANCOVA). The model included the number of accurately identified expressions in each section of the task as a within-subjects factor and diagnosis (MDD, BD, or no mood disorders) as a between-subjects factor. Patient age and scores on the HDRS, YMRS, and the sustained attention test were entered in the model as covariates. This analysis was performed separately for each of the six basic emotions (facial expressions corresponding to happiness, sadness, disgust, anger, surprise, and fear). The mean intensity assigned by participants to the correctly identified expressions corresponding to each emotion were also analyzed. These variables were entered into a similar mixed ANCOVA model as that described for the accuracy judgments. The covariates and between-subject variables did not differ between these analyses, but mean intensity ratings for each emotion were entered as within-subject variables rather than the accuracy for each emotion. These analyses were corrected by Greenhouse–Geisser estimates of sphericity, when appropriate. All analyses were followed by Bonferroni post-hoc tests. Significant interactions were analyzed using Student’s t-tests for paired samples. All data were analyzed using the Statistical Package for the Social Sciences (SPSS), v. 21. Results were considered significant at p < .05. Missing data accounted for less than 2% of observations, and was handled using pairwise deletion. Results All 93 patients completed the assessment. Participants with BD type I and BD type II did not significantly differ with regard to any demographic or clinical characteristics, including mood symptoms and medication use, and as such, were collapsed into a single group. To ensure this procedure did not mask any significant differences in facial expression processing between the two groups, we conducted separate analyses to determine whether patients with the two conditions differed on any variables in the facial expression task. No significant findings were identified. Additionally, no differences were identified between patients with and without a history of psychotic symptoms or comorbidities. The demographic and clinical characteristics of control participants and patients with mood disorders are displayed in Table 1. Table 1. Participant demographic and clinical characteristics. BD (n = 30) MDD (n = 18) C (n = 45) F or x2 p ηp2 Post hoc Age* 42.90 (13.12) 32.00 (12.33) 26.16 (10.13) 18.82 <.001 .295 BD > C,MDD Education*a 13.23 (6.58) 13.86 (3.41) 15.31 (3.00) 2.03 .138 .043 — SES* 26.03 (6.81) 28.17 (5.49) 30.33 (7.17) 3.67 .029 .075 BD < C HDRS* 13.30 (9.62) 8.83 (8.26) 2.13 (3.68) 23.49 <.001 .343 BD > C,MDD YMRS* 2.50 (3.19) 1.11 (1.88) 0.79 (1.61) 5.09 .008 .104 BD > C IQ* 104.07 (14.72) 116.44 (10.87) 120.76 (9.96) 17.51 <.001 .287 BD < C,MDD MMSE* 27.66 (2.47) 28.33 (1.65) 29.51 (.84) 11.44 <.001 .204 C > MDD,BD Gender (F; n)b 24 (80.0%) 13 (72.2%) 21 (46.7%) 9.45 .009 — — Suicide attempts 9 (30.0%) 1 (5.6%) — 4.08 .067 — — Depression severityc  None 10 (33.3%) 10 (55.6%) — 2.36 .498 —  Mild 10 (33.3%) 4 (22.2%) — —  Moderate 6 (20.0%) 3 (16.7%) —  Severe 4 (13.3%) 1 (5.6%) — History of psychosis 13 (46%) 1 (5.6%) — 8.65 .003 — Psychiatric comorbidities 15 (50%) 11 (61.1%) — 0.56 .454 — Medication use  Antipsychotics 2 (6.7%) 1 (5.6%) — 0.02 1.000 —  Anticonvulsants 7 (23.3%) 1 (5.6%) — 2.56 .229 —  Antidepressants 15 (46.7%) 7 (38.9%) — 0.28 .765 —  Mood stabilizers 3 (10.0%) 2 (11.1%) — 0.02 1.000 —  Benzodiazepines 4 (13.3%) 3 (16.7%) — 0.10 1.000 —  Psychostimulants 3 (10.0%) 1 (5.6%) — 0.59 1.000 — BD (n = 30) MDD (n = 18) C (n = 45) F or x2 p ηp2 Post hoc Age* 42.90 (13.12) 32.00 (12.33) 26.16 (10.13) 18.82 <.001 .295 BD > C,MDD Education*a 13.23 (6.58) 13.86 (3.41) 15.31 (3.00) 2.03 .138 .043 — SES* 26.03 (6.81) 28.17 (5.49) 30.33 (7.17) 3.67 .029 .075 BD < C HDRS* 13.30 (9.62) 8.83 (8.26) 2.13 (3.68) 23.49 <.001 .343 BD > C,MDD YMRS* 2.50 (3.19) 1.11 (1.88) 0.79 (1.61) 5.09 .008 .104 BD > C IQ* 104.07 (14.72) 116.44 (10.87) 120.76 (9.96) 17.51 <.001 .287 BD < C,MDD MMSE* 27.66 (2.47) 28.33 (1.65) 29.51 (.84) 11.44 <.001 .204 C > MDD,BD Gender (F; n)b 24 (80.0%) 13 (72.2%) 21 (46.7%) 9.45 .009 — — Suicide attempts 9 (30.0%) 1 (5.6%) — 4.08 .067 — — Depression severityc  None 10 (33.3%) 10 (55.6%) — 2.36 .498 —  Mild 10 (33.3%) 4 (22.2%) — —  Moderate 6 (20.0%) 3 (16.7%) —  Severe 4 (13.3%) 1 (5.6%) — History of psychosis 13 (46%) 1 (5.6%) — 8.65 .003 — Psychiatric comorbidities 15 (50%) 11 (61.1%) — 0.56 .454 — Medication use  Antipsychotics 2 (6.7%) 1 (5.6%) — 0.02 1.000 —  Anticonvulsants 7 (23.3%) 1 (5.6%) — 2.56 .229 —  Antidepressants 15 (46.7%) 7 (38.9%) — 0.28 .765 —  Mood stabilizers 3 (10.0%) 2 (11.1%) — 0.02 1.000 —  Benzodiazepines 4 (13.3%) 3 (16.7%) — 0.10 1.000 —  Psychostimulants 3 (10.0%) 1 (5.6%) — 0.59 1.000 — Note: BDI = Bipolar Disorder type I; BDII = Bipolar Disorder type II; MDD = major depressive disorder; C = control participants; SES = socioeconomic status; HDRS = Hamilton Depression Rating Scale; YMRS = Young Mania Rating Scale; IQ = Intelligence quotient; MMSE = Mini-Mental State examination. *Data presented as mean and standard deviation. aYears of formal education. bAbsolute and relative frequency of female participants. cClassified according to Zimmermann et al. (2013). Table 1. Participant demographic and clinical characteristics. BD (n = 30) MDD (n = 18) C (n = 45) F or x2 p ηp2 Post hoc Age* 42.90 (13.12) 32.00 (12.33) 26.16 (10.13) 18.82 <.001 .295 BD > C,MDD Education*a 13.23 (6.58) 13.86 (3.41) 15.31 (3.00) 2.03 .138 .043 — SES* 26.03 (6.81) 28.17 (5.49) 30.33 (7.17) 3.67 .029 .075 BD < C HDRS* 13.30 (9.62) 8.83 (8.26) 2.13 (3.68) 23.49 <.001 .343 BD > C,MDD YMRS* 2.50 (3.19) 1.11 (1.88) 0.79 (1.61) 5.09 .008 .104 BD > C IQ* 104.07 (14.72) 116.44 (10.87) 120.76 (9.96) 17.51 <.001 .287 BD < C,MDD MMSE* 27.66 (2.47) 28.33 (1.65) 29.51 (.84) 11.44 <.001 .204 C > MDD,BD Gender (F; n)b 24 (80.0%) 13 (72.2%) 21 (46.7%) 9.45 .009 — — Suicide attempts 9 (30.0%) 1 (5.6%) — 4.08 .067 — — Depression severityc  None 10 (33.3%) 10 (55.6%) — 2.36 .498 —  Mild 10 (33.3%) 4 (22.2%) — —  Moderate 6 (20.0%) 3 (16.7%) —  Severe 4 (13.3%) 1 (5.6%) — History of psychosis 13 (46%) 1 (5.6%) — 8.65 .003 — Psychiatric comorbidities 15 (50%) 11 (61.1%) — 0.56 .454 — Medication use  Antipsychotics 2 (6.7%) 1 (5.6%) — 0.02 1.000 —  Anticonvulsants 7 (23.3%) 1 (5.6%) — 2.56 .229 —  Antidepressants 15 (46.7%) 7 (38.9%) — 0.28 .765 —  Mood stabilizers 3 (10.0%) 2 (11.1%) — 0.02 1.000 —  Benzodiazepines 4 (13.3%) 3 (16.7%) — 0.10 1.000 —  Psychostimulants 3 (10.0%) 1 (5.6%) — 0.59 1.000 — BD (n = 30) MDD (n = 18) C (n = 45) F or x2 p ηp2 Post hoc Age* 42.90 (13.12) 32.00 (12.33) 26.16 (10.13) 18.82 <.001 .295 BD > C,MDD Education*a 13.23 (6.58) 13.86 (3.41) 15.31 (3.00) 2.03 .138 .043 — SES* 26.03 (6.81) 28.17 (5.49) 30.33 (7.17) 3.67 .029 .075 BD < C HDRS* 13.30 (9.62) 8.83 (8.26) 2.13 (3.68) 23.49 <.001 .343 BD > C,MDD YMRS* 2.50 (3.19) 1.11 (1.88) 0.79 (1.61) 5.09 .008 .104 BD > C IQ* 104.07 (14.72) 116.44 (10.87) 120.76 (9.96) 17.51 <.001 .287 BD < C,MDD MMSE* 27.66 (2.47) 28.33 (1.65) 29.51 (.84) 11.44 <.001 .204 C > MDD,BD Gender (F; n)b 24 (80.0%) 13 (72.2%) 21 (46.7%) 9.45 .009 — — Suicide attempts 9 (30.0%) 1 (5.6%) — 4.08 .067 — — Depression severityc  None 10 (33.3%) 10 (55.6%) — 2.36 .498 —  Mild 10 (33.3%) 4 (22.2%) — —  Moderate 6 (20.0%) 3 (16.7%) —  Severe 4 (13.3%) 1 (5.6%) — History of psychosis 13 (46%) 1 (5.6%) — 8.65 .003 — Psychiatric comorbidities 15 (50%) 11 (61.1%) — 0.56 .454 — Medication use  Antipsychotics 2 (6.7%) 1 (5.6%) — 0.02 1.000 —  Anticonvulsants 7 (23.3%) 1 (5.6%) — 2.56 .229 —  Antidepressants 15 (46.7%) 7 (38.9%) — 0.28 .765 —  Mood stabilizers 3 (10.0%) 2 (11.1%) — 0.02 1.000 —  Benzodiazepines 4 (13.3%) 3 (16.7%) — 0.10 1.000 —  Psychostimulants 3 (10.0%) 1 (5.6%) — 0.59 1.000 — Note: BDI = Bipolar Disorder type I; BDII = Bipolar Disorder type II; MDD = major depressive disorder; C = control participants; SES = socioeconomic status; HDRS = Hamilton Depression Rating Scale; YMRS = Young Mania Rating Scale; IQ = Intelligence quotient; MMSE = Mini-Mental State examination. *Data presented as mean and standard deviation. aYears of formal education. bAbsolute and relative frequency of female participants. cClassified according to Zimmermann et al. (2013). As can be seen in Table 1, the groups differed significantly in their scores on the HDRS and YMRS. Since mood is known to exert a potential influence on the processing of facial expressions, these two variables were entered as covariates into the mixed ANCOVA model for the accuracy and perceived intensity of facial expressions. Age also differed significantly between groups, and, as such, it was entered as a covariate in the model due to its possible affect on cognition and, consequently, on task comprehension and performance. Exposure to medication did not differ between participant groups. Happiness The accuracy with which happiness was detected, and the perceived intensity of corresponding facial expressions, was not influenced by patient diagnosis, length of stimulus presentation (task section), or the interaction between these two factors. Sadness The accuracy with which sadness was detected was not influenced by patient diagnosis or the length of stimulus presentation (task section). However, it was influenced by the interaction of task section and diagnosis, F(1.71, 142.33) = 2.84, p = .031, ηp2 = .067. Paired samples t-tests showed that, for patients with BD, the number of correctly identified expressions in the first section of the task (M = 2.03; SD = 1.22) was lower than that identified in the second (M = 3.03; SD = 1.27) and third (M = 3.10; SD = 1.01) block of the task, at p < .001 in both cases. In patients with MDD, the number of accurately detected expressions in the second block of the task (M = 3.00; SD = 0.91) differed only from block three (M = 3.44; SD = 0.86), at p = .028. Lastly, for control participants, the number of correctly identified expressions in the first section of the task (M = 2.67; SD = 0.90) was lower than that identified in the second (M = 3.29; SD = 0.89) and third (M = 3.40; SD = 0.81) blocks of the task, at p < .001 in both cases. The accuracy of each participant group in the identification of expressions of sadness in each block of the task is shown in Fig. 1a. Fig. 1. View largeDownload slide Recognition and perceived intensity of facial expressions of sadness, disgust, and anger by patients with bipolar disorder, major depression, and control subjects. Fig. 1. View largeDownload slide Recognition and perceived intensity of facial expressions of sadness, disgust, and anger by patients with bipolar disorder, major depression, and control subjects. The perceived intensity of expressions of sadness was not influenced by task section or the interaction between diagnosis and length of exposure. However, it did suffer a main effect of diagnosis, F(3, 78) = 4.02, p = .022, ηp2 = 0.097. Pairwise comparisons showed that control participants rated expressions of sadness as being significantly less intense (M = 2.65; SE = 0.11) than did patients with BD (M = 3.26; SE = 0.16), p = .020. Patients with MDD did not significantly differ from either group. This data is shown in Fig. 1d. Disgust The accuracy with which disgust was detected was not influenced by length of stimulus presentation (task section) or the interaction between this variable and diagnosis. However, it did suffer a main effect of patient diagnosis, F(2, 79) = 3.69, p = .029, ηp2 = .085. Pairwise comparisons showed that control participants were significantly more accurate in identifying expressions of disgust (M = 3.11; SE = 0.11) than patients with BD (M = 2.56; SE = 0.14), p = .025. Patients with MDD did not significantly differ from either group. The perceived intensity of expressions of disgust was not influenced by patient diagnosis, length of stimulus presentation (task section), or the interaction between these factors. Data pertaining to the recognition and perceived intensity of disgust is shown in Fig. 1b and e, respectively. Anger The accuracy with which anger was detected was not influenced by patient diagnosis, length of stimulus presentation (task section), or the interaction between these two factors. Similarly, the perceived intensity of expressions of anger was not influenced by task section or the interaction between diagnosis and length of exposure. However, it did suffer a main effect of diagnosis, F(2, 78) = 6.30, p = .003, ηp2 = .141. Pairwise comparisons showed that control participants rated expressions of anger as being significantly less intense (M = 3.13; SE = 0.12) than did patients with MDD (M = 3.85; SE = 0.17), p = .003. Patients with BD did not significantly differ from either group on this measure. These data are shown in Fig. 1c and f. Surprise The accuracy with which surprise was detected, and the perceived intensity of these expressions, were not influenced by patient diagnosis, length of stimulus presentation (task section), or the interaction between these two factors. Fear The accuracy with which fear was detected, and the perceived intensity of corresponding facial expressions, was not influenced by patient diagnosis, length of stimulus presentation (task section), or the interaction between these two factors. Discussion The aim of the present study was to investigate the accuracy of patients with BD and MDD in identifying emotional expressions, and analyze the intensity with which these expressions are perceived. Our hypothesis that patients with MDD and BD would be less accurate than control subjects in emotion recognition was only corroborated for disgust, in the case of patients with BD. The accuracy with which happiness, surprise or fear were identified did not differ between groups. The hypothesis that differences in identification accuracy would be more pronounced when facial expressions were shown for shorter periods of time was corroborated for patients with BD in the detection of sadness. An interaction effect was observed, whereby these individuals were significantly less accurate when exposed to the facial expression for a period of 200 ms. Additionally, some interesting differences in the perceived intensity of facial expressions were found between patients with BD, MDD, and control participants. Expressions of anger were judged as more intense by patients with MDD than control participants, while a similar pattern was observed for disgust in BD. The difference between control participants and patients with BD in the identification of disgust is partially corroborated by the previous literature. Although some studies have reported enhanced perception of this emotion in patients with BD relative to control subjects (Harmer, Grayson, & Goodwin, 2002; Summers, Papadopoulou, Bruno, Cipolotti, & Ron, 2006), a recent meta-analysis concluded that overall, patients with BD do show impairments in the detection of disgust (Kohler et al., 2011). The absence of differences between participant groups on the remaining variables investigated is also partly corroborated by the literature. BD and MDD have not been found to differ with regards to facial expression recognition according to a meta-analytic study which combined the findings of 51 previous investigations (Kohler et al., 2011). The absence of differences between the MDD and control groups may be explained by the fact that emotion recognition impairments in this disorder had too small an effect size to be detectable in the present sample (Dalili et al., 2016), and the fact that the vast majority of patients were receiving pharmacological treatment for their condition. According to the literature, this may have a normalizing effect on frontolimbic connections and the activation of prefrontal and subcortical structures associated with emotional processing (Delaveau et al., 2011). Therefore, it is possible that any impairments present, already small to begin with, may have been reduced by the use of medication, resulting in the lack of significant findings in the present study. The absence of differences between patients with BD and healthy adult subjects, however, is still a matter of debate. While several previous studies have reported significant impairments in facial expression recognition both in mood episodes (Daros et al., 2014) and euthymia (Lawlor-Savage, Sponheim, & Goghari, 2014; Yalcin-Siedentopf et al., 2014), others have identified no significant differences in emotion recognition between control participants and individuals with BD (Robinson et al., 2015). The fact that most patients with BD in the present study were either euthymic or presenting with only mild symptoms of depression may also have influenced our findings, because many of the impairments in emotion recognition in BD may be attributable to cognitive or attentional biases associated with mood symptoms (Rocca, Heuvel, Caetano, & Lafer, 2009). The absence of significant symptoms of mania or depression in this sample may explain the lack of differences in the accuracy of emotion recognition between patients with BD and control subjects in the majority of emotions investigated. In addition to sample characteristics, variations between these findings may also be attributed to methodological differences between studies, including the intensity of facial expressions and the length of stimulus presentation. The present study addressed these issues by involving unambiguous facial expressions rather than subtle or morphed images, and using different lengths of stimulus presentation. Since the accuracy of emotion recognition is known to improve with increasing expression intensity, both in healthy participants and subjects with BD (Van Rheenen & Rossell, 2014), the use of intense, unambiguous expressions in the present study may have had a “facilitating” effect on emotion recognition for patients with BD and MDD. While this may have masked milder impairments in facial expression recognition, it also confirmed that, when presented with unambiguous facial expressions, patients with mood disorders may not always differ from control subjects in their ability to identify the underlying emotion (Robinson et al., 2015). The fact that three different lengths of stimulus presentation were used in the present study—200 ms, 500 ms, and 1 s—also confer an advantage to these findings over others in the literature. The shorter duration of stimulus presentation increase the ecological validity of the task relative to other studies which are either self-paced (e.g., Lawlor-Savage, Sponheim, & Goghari, 2014), or show each facial expression for a longer period of time, such as 2000 ms (Van Rheenen & Rossell, 2014). At the same time, to ensure performance was not influenced by an inability to perceive the stimulus when it was only flashed briefly on the screen, analyses were also conducted after showing the expressions for 500 and 1000 ms. While this variable did not have a significant influence on the recognition of most facial expressions, it did affect the identification of sadness. These findings suggest that participants’ accuracy may be influenced by the length of stimulus presentation, and as such, rather than using a single time frame for stimulus presentation, studies should explore the possibility of examining facial expression processing when stimuli are shown for different periods of time. In addition to explaining the discrepancy between the present findings and those of other previous studies, these observations may also explain why the current literature on facial expression processing in mood disorders exhibits such a wide variability in its findings and conclusions. Variations in stimulus intensity, length of presentation, and modes of response (e.g., dichotomous—happy/not happy, or multiple-choice—happy/sad/neutral) preclude the comparison between studies and prevent more comprehensive conclusions regarding facial expression processing in mood disorders. The current literature may benefit greatly from the standardization of stimulus presentation and assessment parameters. The present study also makes a novel contribution to the literature by evaluating the perceived intensity of facial expressions by patients with both BD and MDD relative to control subjects. This analysis produced some interesting findings, revealing significant differences between BD and control subjects for sadness, and MDD and controls subjects for anger. Although intensity ratings have not been widely investigated in studies of facial expression processing, a recent study has found that individuals with BD rate facial expressions as more intense than do control participants (Altamura et al., 2016). Studies of facial expression processing in MDD have also suggested that these individuals may experience greater arousal (Yoon, Joormann, & Gotlib, 2009) and attach more salience (Liu, Huang, Wang, Gong, & Chan, 2012) to facial expressions of anger relative to control subjects. This may be a manifestation of the alterations in emotional control identified in patients with BD and MDD. Neuroimaging studies suggest that individuals with this condition show weaker top-down frontolimbic connectivity in response to emotional stimuli (de Almeida & Phillips, 2013; Groenewold et al., 2015; Townsend et al., 2013). This may lead patients with mood disorders to respond more strongly to facial expressions of emotion, and thus perceive them as more intense, than individuals without the condition. The present findings, together with other studies which identified emotion-specific differences between control subjects and patients with BD and MDD on facial expression tasks, suggest that these disorders may be associated with alterations in the interpretation of certain specific emotions, rather than the decoding of facial expressions in general. Such a finding would have significant implication for social functioning, and as such, must be further investigated by additional studies. The present findings must be interpreted in light of some limitations. These include the fact that the study may have been underpowered to detect impairments in facial expression processing in MDD (Dalili et al., 2016), and did not control for the effects of medication use, which may affect facial expression recognition in patients with BD (Bilderbeck, Atkinson, Geddes, Goodwin, & Harmer, 2016). A small sample size is also associated with a higher false-discovery rate (FDR), suggesting that some findings identified as significant may not actually reflect an underlying difference between groups (Button et al., 2013; Colquhoun, 2014). Our statistical control of HDRS and YMRS scores may also have been insufficient to account for the influence of mood symptoms on the task. Although previous studies have identified no associations between facial expression recognition and number of previous episodes in BD (Derntl, Seidel, Kryspin-Exner, Hasmann, & Dobmeier, 2009; Martino, Strejilevich, Fassi, Marengo, & Igoa, 2011), or the duration of illness in a mixed sample of patients with MDD and BD (Kohler et al., 2011), the fact that these variables were not controlled in the present study may be seen as a limitation. Lastly, we did not control for the effects of gender or diagnostic subtype in the BD group on facial expression processing. However, it must be noted that, although differences between patients with BD I and II were identified by Derntl and colleagues (2009) in a widely cited study of facial recognition in BD, several studies published since have not identified significant differences between these patient groups (Bilderbeck et al., 2016; Martino et al., 2011; Martino, Samamé, & Strejilevich, 2016; Van Rheenen & Rossell, 2014; Van Rheenen, Meyer, & Rossell, 2014). Similarly, gender differences in facial expression recognition in studies of both BD and MDD have generally found these effects to be nonsignificant (Derntl et al., 2009; Schaefer et al., 2010; Vaskinn et al., 2007). A few words are also in order regarding our option not to control for significant between-group differences in IQ scores when analyzing our findings. Firstly, we point out that the IQ scores obtained by our patients with MDD as well as the control group are quite similar to those identified in previous studies (e.g., Schaefer, Baumann, Rich, Luckenbaugh, & Zarate, 2010). Secondly, while we see how this could be a concern if IQ were to have a significant influence on facial expression processing, several previous studies—including a recent systematic review (e.g., Van Rheenen & Rossell, 2014)—have identified no significant association between IQ and facial expression recognition in BD. Other studies have reached a similar conclusion after findings remained unchanged upon the inclusion of IQ as a covariate in between-group comparisons (Daros et al., 2014; Schaefer et al., 2010). As such, we believe this does not limit the generalizability of our findings. Nevertheless, this investigation also has a number of strengths. These include the control of variables such as mood symptoms, age, and attention, with the latter, especially, being an important but rarely considered factor in studies of facial expression processing. The importance of controlling these variables is underscored by the effect size of between-group differences in these variables. Depression symptoms and age were associated with the largest effect sizes in the comparison between participant groups. As such, though the statistical control of these variables may not have entirely eliminated their influence on our findings, it was important in minimizing their affect on results. The assessment of perceived intention of facial expressions in addition to accuracy is also an important field of study, with important repercussions on patient functioning and clinical practice. Lastly, the interaction between length of exposure and accuracy of identification of facial expression in patients with BD suggests that future studies may wish to include the duration of stimulus presentation as a variable of interest in their analysis. These results make an important contribution to the literature on facial expression processing in mood disorders and suggest several promising avenues for future research, whose findings may bring significant benefits to both patients and practitioners working with mood disorders. Future studies should investigate differences in the intensity of perceived emotion in patients with BD and MDD, as well as changes in facial expression processing during euthymia, mania, and depression, given the association between mood and cognitive bias in the perception of emotional stimuli. Funding The authors would like to thank the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for their support to this study. Conflict of interest None declared. References Alonso-Recio , L. , Serrano , J. M. , & Martín , P. ( 2014 ). Selective attention and facial expression recognition in patients with Parkinson’s disease . Archives of Clinical Neuropsychology: The Official Journal of the National Academy of Neuropsychologists , 29 , 374 – 384 . https://doi.org/10.1093/arclin/acu018. Google Scholar CrossRef Search ADS Altamura , M. , Padalino , F. A. , Stella , E. , Balzotti , A. , Bellomo , A. , Palumbo , R. , et al. . ( 2016 ). Facial emotion recognition in bipolar disorder and healthy aging . Journal of Nervous & Mental Disease , 204 , 188 – 193 . https://doi.org/10.1097/NMD.0000000000000453. Google Scholar CrossRef Search ADS American Psychiatric Association . ( 2013 ). 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Identification and Perceived Intensity of Facial Expressions of Emotion in Bipolar Disorder and Major Depression

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Abstract

Abstract Objective This study aimed to examine the accuracy in identifying facial expressions and the perceived intensity of emotions of happiness, sadness, disgust, anger, fear, surprise in bipolar disorder (BD), and major depressive disorder (MDD). Method Ninety-three adult participants (n = 30 BD, 80% female; n = 18 MDD, 72.2% female; n = 45 C, 46.7% female) completed a facial expression task. Stimuli in the task were first presented for 200 ms, followed by 500 ms, and 1000 ms. Participants were asked to identify the emotion expressed by each face and judge its intensity on a Likert scale. Accuracy and perceived intensity of expressions corresponding to each emotion were compared between groups using a repeated measures ANCOVA, with length of stimulus presentation as a within-subjects variable. Results Expressions of sadness were rated more intensely by patients with BD, and expressions of anger by those with MDD, than control participants. Subjects with BD were less accurate than control participants in identifying expressions of disgust. An interaction effect was also identified for the detection of sadness, whereby patients with BD were significantly less accurate when expressions were shown for 200 ms. Conclusions The increased intensity with which emotions were perceived by patients with BD and MDD has important repercussions for patient functioning and clinical practice. A tendency to overestimate the intensity of certain facial expressions in mood disorders may lead patients to interpret social cues erroneously and engage in dysfunctional behaviors and cognitive patterns. Future studies should focus on this variable in addition to the accuracy of emotion identification. Bipolar disorder, Depression, Emotions, Facial expressions Introduction Facial expression processing has been a major topic of study in science for over 100 years (Darwin, 1890). Over time, it also became an important issue in psychology and psychiatry, as impairments in facial expression processing began to be identified as a consistent feature of both neurological (Alonso-Recio, Serrano, & Martín, 2014; Torres et al., 2015) and psychiatric conditions (Dalili, Penton-Voak, Harmer, & Munafò, 2016; Daros, Ruocco, Reilly, Harris, & Sweeney, 2014; Hargreaves et al., 2016; Yalcin-Siedentopf et al., 2014). Facial expressions and emotional processing in more general terms have been especially studied in bipolar (BD) and major depressive disorder (MDD), both of which are characterized by persistent and dysfunctional alterations in emotion and cognition, closely associated with significant changes in mood state (American Psychiatric Association, 2013). The two conditions are also similar in that they have been found to be associated with abnormalities in frontolimbic structures associated with emotional processing. Individuals with MDD have been found to show reduced activity in the ventromedial prefrontal cortex (PFC), as well as diminished discrimination between emotional and neutral items in the activation of the amygdala, caudate, and hippocampus (Ritchey, Dolcos, Eddington, Strauman, & Cabeza, 2011). Like MDD, BD is associated with hyperactivation of limbic regions in response to emotional stimuli (Delvecchio et al., 2012). However, patients with BD also show more extensive functional and structural abnormalities in prefrontal regions such as the ventrolateral prefrontal cortex (Townsend & Altshuler, 2012) and the inferior frontal gyrus (Breakspear et al., 2015). Some studies suggest that the alterations in frontolimbic regions associated with the modulation and recognition of facial expressions in mood disorders may also influence the cognitive and emotional processes responsible for evaluative processes and social judgments in these conditions (Radaelli et al., 2014; Sagar, Dahlgren, Gönenç, & Gruber, 2013). Despite their relevance to the study of facial expression processing in mood disorders, behavioral findings in this area of study have been less conclusive. While some studies have identified significant impairments in facial expression recognition across all major emotions in BD and MDD (Kohler, Hoffman, Eastman, Healey, & Moberg, 2011), others suggest that this is not a consistent feature of BD (Robinson, Gray, Burt, Ferrier, & Gallagher, 2015), or that impairments may be present for some emotions, but not others, in MDD (Dalili et al., 2016). There are also disagreements regarding the severity of these impairments, with some studies suggesting them to be small (Dalili et al., 2016) and others describing them as moderate (Kohler et al., 2011). Only a few studies have compared patients with MDD and BD in terms of their performance on behavioral measures of facial expression recognition. The few existing investigations on the topic have not identified significant differences between these participant groups (Kohler et al., 2011; Schaefer, Baumann, Rich, Luckenbaugh, & Zarate, 2010). While these findings may suggest that there are no such differences to be found, it is also possible that between-group differences were masked by confounding factors such as education levels, which are known to have an affect on facial expression processing in BD and MDD (Dalili et al., 2016; Kohler et al., 2011) but have not been considered in comparative studies of these disorders (Schaefer et al., 2010). There is, as such, a need for additional studies of facial expression recognition in these disorders, with greater control for potential confounding variables. The majority of investigations have focused on the accuracy of identification of emotional expressions, with less emphasis being given to the perceived intensity of these emotions. While the intensity of facial expressions is often manipulated as part of experimental procedures, to determine, for instance, sensitivity thresholds for the identification of different emotions (e.g., Robinson et al., 2015), the “perceived” intensity of facial expressions has only been scarcely investigated as a dependent variable. The few studies to examine this variable have produced promising but inconclusive findings (Altamura et al., 2016; Chen et al., 2006; Schaefer et al., 2010). Given the importance of facial expressions as a nonverbal social cue, the perceived intensity of such stimuli should be further investigated, since a tendency to over- or underestimate the intensity of the emotions displayed by others may lead to erroneous social judgments and maladaptive behaviors. There have also been few studies of the influence of length of stimulus presentation on the recognition of facial expressions. The majority of studies which consider length of stimulus presentation as an independent variable focus on attentional biases in patients with anxiety disorders (e.g., Bantin, Stevens, Gerlach, & Hermann, 2016; Bradley, Mogg, Falla, & Hamilton, 1998). In unipolar depression and BD, while the length of stimulus presentation varies widely between studies (Bourke, Douglas, & Porter, 2010), it is very rarely manipulated within individual studies. The few studies to do so often fail to consider the length of stimulus presentation as a variable in itself, and only vary the duration to avoid habituation (Gohier et al., 2014; Jerram, Lee, Negreira, & Gansler, 2013). The only study to date which evaluated the recognition of facial expressions of emotion shown for different lengths of time did not find a significant association between these variables (Shiroma, Thuras, Johns, & Lim, 2014). However, there is a clear need for additional investigations of this topic, especially in light of findings regarding the affect of stimulus duration on attentional biases in depression (Donaldson, Lam, & Mathews, 2007) and on emotional processing (Palumbo, D’Ascenzo, Quercia, & Tommasi, 2017) using non-facial stimuli. Such findings would also be relevant to the differentiation between perceptual and cognitive/interpretational issues in facial expression processing. According to the literature, cognitive functions such as attention may be impaired in patients with BD or MDD even in the absence of a mood episode (Cotrena, Branco, Shansis, & Fonseca, 2016; Cullen et al., 2016; Evans et al., 2013; Lam, Kennedy, McIntyre, & Khullar, 2014). These abilities are directly related to facial expression processing. The interpretation of briefly displayed expressions requires both attentional focus to the stimuli and its features, to gather information on which to base a judgment, as well as processing speed, to ensure the interpretation is formulated in a timely manner (David, Soeiro-de-Souza, Moreno, & Bio, 2014). While this may not be an issue in self-paced assessment tasks, it is decidedly relevant for social functioning, because microexpressions may last for as little as 500 ms (Yan, Wu, Liang, Chen, & Fu, 2013). As such, performance in facial expression tasks must consider both interpretational issues, such as whether the individual can correctly identify different expressions of emotion, as well as the influence of attentional processes on the ability to perceive the stimulus in the first place (David et al., 2014; Davies et al., 2016). As such, the aim of the present study was to examine the accuracy in identifying facial expressions and the perceived intensity of these emotions in patients with MDD and BD, as compared to control participants. It was hypothesized that patients with MDD and, especially, subjects with BD would show lower accuracy in emotional recognition relative to control subjects. We expected these differences to be larger for faces shown for shorter periods of time, but to still reach significance faces shown for 500 ms or longer. Methods Participants The sample size was determined based on a previous study of facial expression processing which also examined both accuracy and perceived intensity in patients with BD (Altamura et al., 2016). The study in question identified significant differences between a group of 20 control participants and 16 patients with BD. In the present study, the sample consisted of 30 patients with BD (n = 17 with BD type I and n = 13 with BD type II), 18 patients with MDD as well as 45 healthy adults with no mood disorders. Patients were recruited from the mood disorders outpatient unit of a psychiatric hospital, a university teaching clinic, and private practice. Control participants were selected by convenience from work and university settings, as well as the community at large. All patients were at least 18 years old, and had at least 1 year of formal education. The following exclusion criteria were applied to the sample: uncorrected sensory impairments which would interfere with task performance, neurological conditions, and pregnancy or lactation. Patients with psychotic symptoms at the time of testing or who reported substance abuse within the previous month were also excluded from participation. The control group was selected using the same criteria, and was screened for mood disorders according to DSM-5 criteria, cognitive impairment, and intellectual disability. Procedures and Instruments All participants provided written consent for participation, and the present study was approved by the Research Ethics Committee of the institution where it was conducted. Subjects were evaluated in individual sessions, and the presence of mood disorders was examined using DSM-5 criteria (American Psychiatric Association, 2013). All diagnoses were confirmed by consensus with a clinical psychologist with expertise in mood disorders who is also a coauthor of this study (C.C.). All subjects took part in at least two assessment sessions lasting approximately one and a half hours each. Inclusion and exclusion criteria were first investigated using a sociocultural and health questionnaire (Cotrena, Branco & Fonseca, manuscript in preparation). Participants were then administered the Mini-Mental State Examination (MMSE; Folstein, Folstein, & McHugh, 1975), adapted by Chaves and Izquierdo (1992) for the local population. Subjects also completed the Block Design and Vocabulary Subtests from the Wechsler Adult Intelligence Scales (WAIS-III) (Nascimento, 2004), whose scores were converted to estimated IQ using the tables provided by Jeyakumar, Warriner, Raval, and Ahmad (2004). Clinical assessments were performed using the Mini International Neuropsychiatric Interview (MINI) (Amorim, 2000). Symptoms of depression and mania were investigated using the Hamilton Depression Rating Scale (HDRS) (Gorenstein, Andrade, & Zuardi, 2000; Hamilton, 1960) and the Young Mania Rating Scale (YMRS) (Vilela & Loureiro, 2000; Young, Biggs, Ziegler, & Meyer, 1978), respectively. Prior to completing the facial expression assessment, participants were also administered a sustained attention test (Sisto, Noronha, Lamounier, Bartholomeu, & Rueda, 2006). The task provides a concentration score which was used as a statistical control for individual differences in attention which may influence the ability to visualize and interpret facial expressions in the subsequent task. Lastly, participants were administered a Facial Expression Recognition Task in which they were asked to identify the emotion expressed by different individuals, and provide a subjective rating of the intensity of the emotion on a Likert scale of 1–5. The task was divided into three sections. In Section 1, a series of 26 facial expressions were displayed for 200 ms each. After each image was displayed, the participant was shown a list of the six basic emotions (happiness, sadness, disgust, anger, fear, and surprise) as well as the word “neutral,” and asked to identify which word best describes the expression they had seen. They then provided a rating of the intensity of that emotion on a scale of 1–5. The images depicted four actors (two females and two males) of varied ethnic backgrounds making the facial expressions corresponding to each of the basic emotions. The 26 images shown in each section of the task corresponded to the six expressions made by each actor, plus two images of the actors showing a neutral facial expression. The two subsequent blocks of the task were similar to the first. However, the second block presented the images for 500 ms, while the third presented each for 1 s. This task has been validated in a nonclinical population by a previous study (Vasconcellosa et al., 2014). Over the course of the task, participants identify and rate the intensity of a total of 78 facial expressions (26 per block). However, in the present study, each of the sections was analyzed separately, to allow for an assessment of between-group differences in the identification and interpretation of different expressions as a function of the time for which they were exposed to each stimulus. As a result, for each block of 26 faces, the number of expressions correctly identified for each emotion was calculated for every patient in the sample. The maximum possible score on this measure was four, since each block contained four renditions of each basic emotion (each emotion was displayed by all four actors). Then, the mean intensity assigned to the correctly identified emotions was calculated. This procedure yielded an accuracy and intensity score per emotion per block. These values were then used for subsequent analysis. Data Analysis Descriptive analyses of patient characteristics were first performed. These values were compared between groups using Student’s t-tests or chi-square tests, as appropriate. Performance on the facial expression task was then compared between participant groups using a mixed Analysis of Covariance (ANCOVA). The model included the number of accurately identified expressions in each section of the task as a within-subjects factor and diagnosis (MDD, BD, or no mood disorders) as a between-subjects factor. Patient age and scores on the HDRS, YMRS, and the sustained attention test were entered in the model as covariates. This analysis was performed separately for each of the six basic emotions (facial expressions corresponding to happiness, sadness, disgust, anger, surprise, and fear). The mean intensity assigned by participants to the correctly identified expressions corresponding to each emotion were also analyzed. These variables were entered into a similar mixed ANCOVA model as that described for the accuracy judgments. The covariates and between-subject variables did not differ between these analyses, but mean intensity ratings for each emotion were entered as within-subject variables rather than the accuracy for each emotion. These analyses were corrected by Greenhouse–Geisser estimates of sphericity, when appropriate. All analyses were followed by Bonferroni post-hoc tests. Significant interactions were analyzed using Student’s t-tests for paired samples. All data were analyzed using the Statistical Package for the Social Sciences (SPSS), v. 21. Results were considered significant at p < .05. Missing data accounted for less than 2% of observations, and was handled using pairwise deletion. Results All 93 patients completed the assessment. Participants with BD type I and BD type II did not significantly differ with regard to any demographic or clinical characteristics, including mood symptoms and medication use, and as such, were collapsed into a single group. To ensure this procedure did not mask any significant differences in facial expression processing between the two groups, we conducted separate analyses to determine whether patients with the two conditions differed on any variables in the facial expression task. No significant findings were identified. Additionally, no differences were identified between patients with and without a history of psychotic symptoms or comorbidities. The demographic and clinical characteristics of control participants and patients with mood disorders are displayed in Table 1. Table 1. Participant demographic and clinical characteristics. BD (n = 30) MDD (n = 18) C (n = 45) F or x2 p ηp2 Post hoc Age* 42.90 (13.12) 32.00 (12.33) 26.16 (10.13) 18.82 <.001 .295 BD > C,MDD Education*a 13.23 (6.58) 13.86 (3.41) 15.31 (3.00) 2.03 .138 .043 — SES* 26.03 (6.81) 28.17 (5.49) 30.33 (7.17) 3.67 .029 .075 BD < C HDRS* 13.30 (9.62) 8.83 (8.26) 2.13 (3.68) 23.49 <.001 .343 BD > C,MDD YMRS* 2.50 (3.19) 1.11 (1.88) 0.79 (1.61) 5.09 .008 .104 BD > C IQ* 104.07 (14.72) 116.44 (10.87) 120.76 (9.96) 17.51 <.001 .287 BD < C,MDD MMSE* 27.66 (2.47) 28.33 (1.65) 29.51 (.84) 11.44 <.001 .204 C > MDD,BD Gender (F; n)b 24 (80.0%) 13 (72.2%) 21 (46.7%) 9.45 .009 — — Suicide attempts 9 (30.0%) 1 (5.6%) — 4.08 .067 — — Depression severityc  None 10 (33.3%) 10 (55.6%) — 2.36 .498 —  Mild 10 (33.3%) 4 (22.2%) — —  Moderate 6 (20.0%) 3 (16.7%) —  Severe 4 (13.3%) 1 (5.6%) — History of psychosis 13 (46%) 1 (5.6%) — 8.65 .003 — Psychiatric comorbidities 15 (50%) 11 (61.1%) — 0.56 .454 — Medication use  Antipsychotics 2 (6.7%) 1 (5.6%) — 0.02 1.000 —  Anticonvulsants 7 (23.3%) 1 (5.6%) — 2.56 .229 —  Antidepressants 15 (46.7%) 7 (38.9%) — 0.28 .765 —  Mood stabilizers 3 (10.0%) 2 (11.1%) — 0.02 1.000 —  Benzodiazepines 4 (13.3%) 3 (16.7%) — 0.10 1.000 —  Psychostimulants 3 (10.0%) 1 (5.6%) — 0.59 1.000 — BD (n = 30) MDD (n = 18) C (n = 45) F or x2 p ηp2 Post hoc Age* 42.90 (13.12) 32.00 (12.33) 26.16 (10.13) 18.82 <.001 .295 BD > C,MDD Education*a 13.23 (6.58) 13.86 (3.41) 15.31 (3.00) 2.03 .138 .043 — SES* 26.03 (6.81) 28.17 (5.49) 30.33 (7.17) 3.67 .029 .075 BD < C HDRS* 13.30 (9.62) 8.83 (8.26) 2.13 (3.68) 23.49 <.001 .343 BD > C,MDD YMRS* 2.50 (3.19) 1.11 (1.88) 0.79 (1.61) 5.09 .008 .104 BD > C IQ* 104.07 (14.72) 116.44 (10.87) 120.76 (9.96) 17.51 <.001 .287 BD < C,MDD MMSE* 27.66 (2.47) 28.33 (1.65) 29.51 (.84) 11.44 <.001 .204 C > MDD,BD Gender (F; n)b 24 (80.0%) 13 (72.2%) 21 (46.7%) 9.45 .009 — — Suicide attempts 9 (30.0%) 1 (5.6%) — 4.08 .067 — — Depression severityc  None 10 (33.3%) 10 (55.6%) — 2.36 .498 —  Mild 10 (33.3%) 4 (22.2%) — —  Moderate 6 (20.0%) 3 (16.7%) —  Severe 4 (13.3%) 1 (5.6%) — History of psychosis 13 (46%) 1 (5.6%) — 8.65 .003 — Psychiatric comorbidities 15 (50%) 11 (61.1%) — 0.56 .454 — Medication use  Antipsychotics 2 (6.7%) 1 (5.6%) — 0.02 1.000 —  Anticonvulsants 7 (23.3%) 1 (5.6%) — 2.56 .229 —  Antidepressants 15 (46.7%) 7 (38.9%) — 0.28 .765 —  Mood stabilizers 3 (10.0%) 2 (11.1%) — 0.02 1.000 —  Benzodiazepines 4 (13.3%) 3 (16.7%) — 0.10 1.000 —  Psychostimulants 3 (10.0%) 1 (5.6%) — 0.59 1.000 — Note: BDI = Bipolar Disorder type I; BDII = Bipolar Disorder type II; MDD = major depressive disorder; C = control participants; SES = socioeconomic status; HDRS = Hamilton Depression Rating Scale; YMRS = Young Mania Rating Scale; IQ = Intelligence quotient; MMSE = Mini-Mental State examination. *Data presented as mean and standard deviation. aYears of formal education. bAbsolute and relative frequency of female participants. cClassified according to Zimmermann et al. (2013). Table 1. Participant demographic and clinical characteristics. BD (n = 30) MDD (n = 18) C (n = 45) F or x2 p ηp2 Post hoc Age* 42.90 (13.12) 32.00 (12.33) 26.16 (10.13) 18.82 <.001 .295 BD > C,MDD Education*a 13.23 (6.58) 13.86 (3.41) 15.31 (3.00) 2.03 .138 .043 — SES* 26.03 (6.81) 28.17 (5.49) 30.33 (7.17) 3.67 .029 .075 BD < C HDRS* 13.30 (9.62) 8.83 (8.26) 2.13 (3.68) 23.49 <.001 .343 BD > C,MDD YMRS* 2.50 (3.19) 1.11 (1.88) 0.79 (1.61) 5.09 .008 .104 BD > C IQ* 104.07 (14.72) 116.44 (10.87) 120.76 (9.96) 17.51 <.001 .287 BD < C,MDD MMSE* 27.66 (2.47) 28.33 (1.65) 29.51 (.84) 11.44 <.001 .204 C > MDD,BD Gender (F; n)b 24 (80.0%) 13 (72.2%) 21 (46.7%) 9.45 .009 — — Suicide attempts 9 (30.0%) 1 (5.6%) — 4.08 .067 — — Depression severityc  None 10 (33.3%) 10 (55.6%) — 2.36 .498 —  Mild 10 (33.3%) 4 (22.2%) — —  Moderate 6 (20.0%) 3 (16.7%) —  Severe 4 (13.3%) 1 (5.6%) — History of psychosis 13 (46%) 1 (5.6%) — 8.65 .003 — Psychiatric comorbidities 15 (50%) 11 (61.1%) — 0.56 .454 — Medication use  Antipsychotics 2 (6.7%) 1 (5.6%) — 0.02 1.000 —  Anticonvulsants 7 (23.3%) 1 (5.6%) — 2.56 .229 —  Antidepressants 15 (46.7%) 7 (38.9%) — 0.28 .765 —  Mood stabilizers 3 (10.0%) 2 (11.1%) — 0.02 1.000 —  Benzodiazepines 4 (13.3%) 3 (16.7%) — 0.10 1.000 —  Psychostimulants 3 (10.0%) 1 (5.6%) — 0.59 1.000 — BD (n = 30) MDD (n = 18) C (n = 45) F or x2 p ηp2 Post hoc Age* 42.90 (13.12) 32.00 (12.33) 26.16 (10.13) 18.82 <.001 .295 BD > C,MDD Education*a 13.23 (6.58) 13.86 (3.41) 15.31 (3.00) 2.03 .138 .043 — SES* 26.03 (6.81) 28.17 (5.49) 30.33 (7.17) 3.67 .029 .075 BD < C HDRS* 13.30 (9.62) 8.83 (8.26) 2.13 (3.68) 23.49 <.001 .343 BD > C,MDD YMRS* 2.50 (3.19) 1.11 (1.88) 0.79 (1.61) 5.09 .008 .104 BD > C IQ* 104.07 (14.72) 116.44 (10.87) 120.76 (9.96) 17.51 <.001 .287 BD < C,MDD MMSE* 27.66 (2.47) 28.33 (1.65) 29.51 (.84) 11.44 <.001 .204 C > MDD,BD Gender (F; n)b 24 (80.0%) 13 (72.2%) 21 (46.7%) 9.45 .009 — — Suicide attempts 9 (30.0%) 1 (5.6%) — 4.08 .067 — — Depression severityc  None 10 (33.3%) 10 (55.6%) — 2.36 .498 —  Mild 10 (33.3%) 4 (22.2%) — —  Moderate 6 (20.0%) 3 (16.7%) —  Severe 4 (13.3%) 1 (5.6%) — History of psychosis 13 (46%) 1 (5.6%) — 8.65 .003 — Psychiatric comorbidities 15 (50%) 11 (61.1%) — 0.56 .454 — Medication use  Antipsychotics 2 (6.7%) 1 (5.6%) — 0.02 1.000 —  Anticonvulsants 7 (23.3%) 1 (5.6%) — 2.56 .229 —  Antidepressants 15 (46.7%) 7 (38.9%) — 0.28 .765 —  Mood stabilizers 3 (10.0%) 2 (11.1%) — 0.02 1.000 —  Benzodiazepines 4 (13.3%) 3 (16.7%) — 0.10 1.000 —  Psychostimulants 3 (10.0%) 1 (5.6%) — 0.59 1.000 — Note: BDI = Bipolar Disorder type I; BDII = Bipolar Disorder type II; MDD = major depressive disorder; C = control participants; SES = socioeconomic status; HDRS = Hamilton Depression Rating Scale; YMRS = Young Mania Rating Scale; IQ = Intelligence quotient; MMSE = Mini-Mental State examination. *Data presented as mean and standard deviation. aYears of formal education. bAbsolute and relative frequency of female participants. cClassified according to Zimmermann et al. (2013). As can be seen in Table 1, the groups differed significantly in their scores on the HDRS and YMRS. Since mood is known to exert a potential influence on the processing of facial expressions, these two variables were entered as covariates into the mixed ANCOVA model for the accuracy and perceived intensity of facial expressions. Age also differed significantly between groups, and, as such, it was entered as a covariate in the model due to its possible affect on cognition and, consequently, on task comprehension and performance. Exposure to medication did not differ between participant groups. Happiness The accuracy with which happiness was detected, and the perceived intensity of corresponding facial expressions, was not influenced by patient diagnosis, length of stimulus presentation (task section), or the interaction between these two factors. Sadness The accuracy with which sadness was detected was not influenced by patient diagnosis or the length of stimulus presentation (task section). However, it was influenced by the interaction of task section and diagnosis, F(1.71, 142.33) = 2.84, p = .031, ηp2 = .067. Paired samples t-tests showed that, for patients with BD, the number of correctly identified expressions in the first section of the task (M = 2.03; SD = 1.22) was lower than that identified in the second (M = 3.03; SD = 1.27) and third (M = 3.10; SD = 1.01) block of the task, at p < .001 in both cases. In patients with MDD, the number of accurately detected expressions in the second block of the task (M = 3.00; SD = 0.91) differed only from block three (M = 3.44; SD = 0.86), at p = .028. Lastly, for control participants, the number of correctly identified expressions in the first section of the task (M = 2.67; SD = 0.90) was lower than that identified in the second (M = 3.29; SD = 0.89) and third (M = 3.40; SD = 0.81) blocks of the task, at p < .001 in both cases. The accuracy of each participant group in the identification of expressions of sadness in each block of the task is shown in Fig. 1a. Fig. 1. View largeDownload slide Recognition and perceived intensity of facial expressions of sadness, disgust, and anger by patients with bipolar disorder, major depression, and control subjects. Fig. 1. View largeDownload slide Recognition and perceived intensity of facial expressions of sadness, disgust, and anger by patients with bipolar disorder, major depression, and control subjects. The perceived intensity of expressions of sadness was not influenced by task section or the interaction between diagnosis and length of exposure. However, it did suffer a main effect of diagnosis, F(3, 78) = 4.02, p = .022, ηp2 = 0.097. Pairwise comparisons showed that control participants rated expressions of sadness as being significantly less intense (M = 2.65; SE = 0.11) than did patients with BD (M = 3.26; SE = 0.16), p = .020. Patients with MDD did not significantly differ from either group. This data is shown in Fig. 1d. Disgust The accuracy with which disgust was detected was not influenced by length of stimulus presentation (task section) or the interaction between this variable and diagnosis. However, it did suffer a main effect of patient diagnosis, F(2, 79) = 3.69, p = .029, ηp2 = .085. Pairwise comparisons showed that control participants were significantly more accurate in identifying expressions of disgust (M = 3.11; SE = 0.11) than patients with BD (M = 2.56; SE = 0.14), p = .025. Patients with MDD did not significantly differ from either group. The perceived intensity of expressions of disgust was not influenced by patient diagnosis, length of stimulus presentation (task section), or the interaction between these factors. Data pertaining to the recognition and perceived intensity of disgust is shown in Fig. 1b and e, respectively. Anger The accuracy with which anger was detected was not influenced by patient diagnosis, length of stimulus presentation (task section), or the interaction between these two factors. Similarly, the perceived intensity of expressions of anger was not influenced by task section or the interaction between diagnosis and length of exposure. However, it did suffer a main effect of diagnosis, F(2, 78) = 6.30, p = .003, ηp2 = .141. Pairwise comparisons showed that control participants rated expressions of anger as being significantly less intense (M = 3.13; SE = 0.12) than did patients with MDD (M = 3.85; SE = 0.17), p = .003. Patients with BD did not significantly differ from either group on this measure. These data are shown in Fig. 1c and f. Surprise The accuracy with which surprise was detected, and the perceived intensity of these expressions, were not influenced by patient diagnosis, length of stimulus presentation (task section), or the interaction between these two factors. Fear The accuracy with which fear was detected, and the perceived intensity of corresponding facial expressions, was not influenced by patient diagnosis, length of stimulus presentation (task section), or the interaction between these two factors. Discussion The aim of the present study was to investigate the accuracy of patients with BD and MDD in identifying emotional expressions, and analyze the intensity with which these expressions are perceived. Our hypothesis that patients with MDD and BD would be less accurate than control subjects in emotion recognition was only corroborated for disgust, in the case of patients with BD. The accuracy with which happiness, surprise or fear were identified did not differ between groups. The hypothesis that differences in identification accuracy would be more pronounced when facial expressions were shown for shorter periods of time was corroborated for patients with BD in the detection of sadness. An interaction effect was observed, whereby these individuals were significantly less accurate when exposed to the facial expression for a period of 200 ms. Additionally, some interesting differences in the perceived intensity of facial expressions were found between patients with BD, MDD, and control participants. Expressions of anger were judged as more intense by patients with MDD than control participants, while a similar pattern was observed for disgust in BD. The difference between control participants and patients with BD in the identification of disgust is partially corroborated by the previous literature. Although some studies have reported enhanced perception of this emotion in patients with BD relative to control subjects (Harmer, Grayson, & Goodwin, 2002; Summers, Papadopoulou, Bruno, Cipolotti, & Ron, 2006), a recent meta-analysis concluded that overall, patients with BD do show impairments in the detection of disgust (Kohler et al., 2011). The absence of differences between participant groups on the remaining variables investigated is also partly corroborated by the literature. BD and MDD have not been found to differ with regards to facial expression recognition according to a meta-analytic study which combined the findings of 51 previous investigations (Kohler et al., 2011). The absence of differences between the MDD and control groups may be explained by the fact that emotion recognition impairments in this disorder had too small an effect size to be detectable in the present sample (Dalili et al., 2016), and the fact that the vast majority of patients were receiving pharmacological treatment for their condition. According to the literature, this may have a normalizing effect on frontolimbic connections and the activation of prefrontal and subcortical structures associated with emotional processing (Delaveau et al., 2011). Therefore, it is possible that any impairments present, already small to begin with, may have been reduced by the use of medication, resulting in the lack of significant findings in the present study. The absence of differences between patients with BD and healthy adult subjects, however, is still a matter of debate. While several previous studies have reported significant impairments in facial expression recognition both in mood episodes (Daros et al., 2014) and euthymia (Lawlor-Savage, Sponheim, & Goghari, 2014; Yalcin-Siedentopf et al., 2014), others have identified no significant differences in emotion recognition between control participants and individuals with BD (Robinson et al., 2015). The fact that most patients with BD in the present study were either euthymic or presenting with only mild symptoms of depression may also have influenced our findings, because many of the impairments in emotion recognition in BD may be attributable to cognitive or attentional biases associated with mood symptoms (Rocca, Heuvel, Caetano, & Lafer, 2009). The absence of significant symptoms of mania or depression in this sample may explain the lack of differences in the accuracy of emotion recognition between patients with BD and control subjects in the majority of emotions investigated. In addition to sample characteristics, variations between these findings may also be attributed to methodological differences between studies, including the intensity of facial expressions and the length of stimulus presentation. The present study addressed these issues by involving unambiguous facial expressions rather than subtle or morphed images, and using different lengths of stimulus presentation. Since the accuracy of emotion recognition is known to improve with increasing expression intensity, both in healthy participants and subjects with BD (Van Rheenen & Rossell, 2014), the use of intense, unambiguous expressions in the present study may have had a “facilitating” effect on emotion recognition for patients with BD and MDD. While this may have masked milder impairments in facial expression recognition, it also confirmed that, when presented with unambiguous facial expressions, patients with mood disorders may not always differ from control subjects in their ability to identify the underlying emotion (Robinson et al., 2015). The fact that three different lengths of stimulus presentation were used in the present study—200 ms, 500 ms, and 1 s—also confer an advantage to these findings over others in the literature. The shorter duration of stimulus presentation increase the ecological validity of the task relative to other studies which are either self-paced (e.g., Lawlor-Savage, Sponheim, & Goghari, 2014), or show each facial expression for a longer period of time, such as 2000 ms (Van Rheenen & Rossell, 2014). At the same time, to ensure performance was not influenced by an inability to perceive the stimulus when it was only flashed briefly on the screen, analyses were also conducted after showing the expressions for 500 and 1000 ms. While this variable did not have a significant influence on the recognition of most facial expressions, it did affect the identification of sadness. These findings suggest that participants’ accuracy may be influenced by the length of stimulus presentation, and as such, rather than using a single time frame for stimulus presentation, studies should explore the possibility of examining facial expression processing when stimuli are shown for different periods of time. In addition to explaining the discrepancy between the present findings and those of other previous studies, these observations may also explain why the current literature on facial expression processing in mood disorders exhibits such a wide variability in its findings and conclusions. Variations in stimulus intensity, length of presentation, and modes of response (e.g., dichotomous—happy/not happy, or multiple-choice—happy/sad/neutral) preclude the comparison between studies and prevent more comprehensive conclusions regarding facial expression processing in mood disorders. The current literature may benefit greatly from the standardization of stimulus presentation and assessment parameters. The present study also makes a novel contribution to the literature by evaluating the perceived intensity of facial expressions by patients with both BD and MDD relative to control subjects. This analysis produced some interesting findings, revealing significant differences between BD and control subjects for sadness, and MDD and controls subjects for anger. Although intensity ratings have not been widely investigated in studies of facial expression processing, a recent study has found that individuals with BD rate facial expressions as more intense than do control participants (Altamura et al., 2016). Studies of facial expression processing in MDD have also suggested that these individuals may experience greater arousal (Yoon, Joormann, & Gotlib, 2009) and attach more salience (Liu, Huang, Wang, Gong, & Chan, 2012) to facial expressions of anger relative to control subjects. This may be a manifestation of the alterations in emotional control identified in patients with BD and MDD. Neuroimaging studies suggest that individuals with this condition show weaker top-down frontolimbic connectivity in response to emotional stimuli (de Almeida & Phillips, 2013; Groenewold et al., 2015; Townsend et al., 2013). This may lead patients with mood disorders to respond more strongly to facial expressions of emotion, and thus perceive them as more intense, than individuals without the condition. The present findings, together with other studies which identified emotion-specific differences between control subjects and patients with BD and MDD on facial expression tasks, suggest that these disorders may be associated with alterations in the interpretation of certain specific emotions, rather than the decoding of facial expressions in general. Such a finding would have significant implication for social functioning, and as such, must be further investigated by additional studies. The present findings must be interpreted in light of some limitations. These include the fact that the study may have been underpowered to detect impairments in facial expression processing in MDD (Dalili et al., 2016), and did not control for the effects of medication use, which may affect facial expression recognition in patients with BD (Bilderbeck, Atkinson, Geddes, Goodwin, & Harmer, 2016). A small sample size is also associated with a higher false-discovery rate (FDR), suggesting that some findings identified as significant may not actually reflect an underlying difference between groups (Button et al., 2013; Colquhoun, 2014). Our statistical control of HDRS and YMRS scores may also have been insufficient to account for the influence of mood symptoms on the task. Although previous studies have identified no associations between facial expression recognition and number of previous episodes in BD (Derntl, Seidel, Kryspin-Exner, Hasmann, & Dobmeier, 2009; Martino, Strejilevich, Fassi, Marengo, & Igoa, 2011), or the duration of illness in a mixed sample of patients with MDD and BD (Kohler et al., 2011), the fact that these variables were not controlled in the present study may be seen as a limitation. Lastly, we did not control for the effects of gender or diagnostic subtype in the BD group on facial expression processing. However, it must be noted that, although differences between patients with BD I and II were identified by Derntl and colleagues (2009) in a widely cited study of facial recognition in BD, several studies published since have not identified significant differences between these patient groups (Bilderbeck et al., 2016; Martino et al., 2011; Martino, Samamé, & Strejilevich, 2016; Van Rheenen & Rossell, 2014; Van Rheenen, Meyer, & Rossell, 2014). Similarly, gender differences in facial expression recognition in studies of both BD and MDD have generally found these effects to be nonsignificant (Derntl et al., 2009; Schaefer et al., 2010; Vaskinn et al., 2007). A few words are also in order regarding our option not to control for significant between-group differences in IQ scores when analyzing our findings. Firstly, we point out that the IQ scores obtained by our patients with MDD as well as the control group are quite similar to those identified in previous studies (e.g., Schaefer, Baumann, Rich, Luckenbaugh, & Zarate, 2010). Secondly, while we see how this could be a concern if IQ were to have a significant influence on facial expression processing, several previous studies—including a recent systematic review (e.g., Van Rheenen & Rossell, 2014)—have identified no significant association between IQ and facial expression recognition in BD. Other studies have reached a similar conclusion after findings remained unchanged upon the inclusion of IQ as a covariate in between-group comparisons (Daros et al., 2014; Schaefer et al., 2010). As such, we believe this does not limit the generalizability of our findings. Nevertheless, this investigation also has a number of strengths. These include the control of variables such as mood symptoms, age, and attention, with the latter, especially, being an important but rarely considered factor in studies of facial expression processing. The importance of controlling these variables is underscored by the effect size of between-group differences in these variables. Depression symptoms and age were associated with the largest effect sizes in the comparison between participant groups. As such, though the statistical control of these variables may not have entirely eliminated their influence on our findings, it was important in minimizing their affect on results. The assessment of perceived intention of facial expressions in addition to accuracy is also an important field of study, with important repercussions on patient functioning and clinical practice. Lastly, the interaction between length of exposure and accuracy of identification of facial expression in patients with BD suggests that future studies may wish to include the duration of stimulus presentation as a variable of interest in their analysis. These results make an important contribution to the literature on facial expression processing in mood disorders and suggest several promising avenues for future research, whose findings may bring significant benefits to both patients and practitioners working with mood disorders. Future studies should investigate differences in the intensity of perceived emotion in patients with BD and MDD, as well as changes in facial expression processing during euthymia, mania, and depression, given the association between mood and cognitive bias in the perception of emotional stimuli. Funding The authors would like to thank the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for their support to this study. Conflict of interest None declared. References Alonso-Recio , L. , Serrano , J. M. , & Martín , P. ( 2014 ). Selective attention and facial expression recognition in patients with Parkinson’s disease . Archives of Clinical Neuropsychology: The Official Journal of the National Academy of Neuropsychologists , 29 , 374 – 384 . https://doi.org/10.1093/arclin/acu018. Google Scholar CrossRef Search ADS Altamura , M. , Padalino , F. A. , Stella , E. , Balzotti , A. , Bellomo , A. , Palumbo , R. , et al. . ( 2016 ). Facial emotion recognition in bipolar disorder and healthy aging . Journal of Nervous & Mental Disease , 204 , 188 – 193 . https://doi.org/10.1097/NMD.0000000000000453. Google Scholar CrossRef Search ADS American Psychiatric Association . ( 2013 ). 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Archives of Clinical NeuropsychologyOxford University Press

Published: Sep 14, 2017

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