Schizophrenia Research 160 (2014) 150–156
Contents lists available at ScienceDirect
Schizophrenia Research journal homepage: www.elsevier.com/locate/schres
Facial emotion identification in early-onset psychosis Sophie J. Barkl a,b,c, Suncica Lah a,c, Jean Starling d, Cassandra Hainsworth e, Anthony W.F. Harris b,f, Leanne M. Williams b,g,⁎ a
School of Psychology, University of Sydney, NSW, Australia The Brain Dynamics Centre, Sydney Medical School and Westmead Millennium Institute, University of Sydney, NSW, Australia ARC Centre of Excellence in Cognition and Its Disorders, Sydney, NSW, Australia d Walker Unit, Concord Centre for Mental Health, Discipline of Psychiatry, Sydney Medical School, University of Sydney, Sydney, Australia e Department of Psychological Medicine, The Children's Hospital, Westmead, NSW, Australia f Discipline of Psychiatry, Sydney Medical School, University of Sydney, Sydney, Australia g Psychiatry and Behavioral Sciences, Stanford University, CA, USA b c
a r t i c l e
i n f o
Article history: Received 29 July 2014 Received in revised form 21 October 2014 Accepted 23 October 2014 Available online 13 November 2014 Keywords: Early onset psychosis Facial emotion Social cognition Schizophrenia
a b s t r a c t Facial emotion identification (FEI) deficits are common in patients with chronic schizophrenia and are strongly related to impaired functioning. The objectives of this study were to determine whether FEI deficits are present and emotion specific in people experiencing early-onset psychosis (EOP), and related to current clinical symptoms and functioning. Patients with EOP (n = 34, mean age = 14.11, 53% female) and healthy controls (HC, n = 42, mean age 13.80, 51% female) completed a task of FEI that measured accuracy, error pattern and response time. Relative to HC, patients with EOP (i) had lower accuracy for identifying facial expressions of emotions, especially fear, anger and disgust, (ii) were more likely to misattribute other emotional expressions as fear or disgust, and (iii) were slower at accurately identifying all facial expressions. FEI accuracy was not related to clinical symptoms or current functioning. Deficits in FEI (especially for fear, anger and disgust) are evident in EOP. Our findings suggest that while emotion identification deficits may reflect a trait susceptibility marker, functional deficits may represent a sequelae of illness. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Cognitive impairment is core to schizophrenia (Heinrichs and Zakzanis, 1998; Dickinson et al., 2008; Fioravanti et al., 2012). This impairment has also been demonstrated in first episode schizophrenia (Mesholam-Gately et al., 2009) and early-onset psychosis (Holmén et al., 2010; Bombin et al., 2013). A key component of this impairment is social cognition, which refers to the capacity to perceive, interpret and regulate responses to emotional information (Green et al., 2008). The ability to accurately identify facial expressions, a fundamental building block of social cognition, plays a pivotal role in facilitating emotional connection and effective social communication. Reduced accuracy of facial emotion identification (FEI) has been found in established schizophrenia (for meta-analyses, see Chan et al., 2010; Kohler et al., 2010; Salva et al., 2013) and first-episode psychosis (Edwards et al., 2001; Addington et al., 2006; Bediou et al., 2007; Pinkham et al., 2007; Reske et al., 2009; Williams et al., 2009; Comparelli et al., 2011; Leung et al., 2011; Thompson et al., 2012; Comparelli et al., 2013). While some studies showed impaired FEI across ⁎ Corresponding author at: Psychiatry and Behavioral Sciences, 401 Quarry Road, Mail Code 971, Stanford University, CA 94305, USA. E-mail address: [email protected] (L.M. Williams).
http://dx.doi.org/10.1016/j.schres.2014.10.035 0920-9964/© 2014 Elsevier B.V. All rights reserved.
a range of expressions of emotion (Kucharska-Pietura et al., 2005; Silver et al., 2009) others provided evidence of a selective impairment, most commonly involving FEI of fear, disgust and sadness (Marwich and Hall, 2008; Kohler et al., 2010; Thompson et al., 2012). The presence and nature of FEI deficits in children and adolescents with early-onset psychosis (b18 years, EOP) remain largely unexplored. Of the two papers that have examined FEI in children and adolescents with early-onset schizophrenia, one found that patients demonstrate lower overall performance compared to healthy controls (Seiferth et al., 2009), and another found specific deficits in the recognition of fear and sadness, but not anger, happiness, disgust, or surprise facial expressions, compared to healthy controls (Amminger et al., 2012). No study has yet examined FEI deficits in a young group experiencing psychosis across a range of diagnostic groups. If FEI deficits are evident, and of a similar magnitude to those experienced by people with established schizophrenia, this would underline the centrality of FEI deficits to the cognitive deficits in psychosis and have significant implications for the treatment and recovery of even very young people with psychosis. It is unclear which disease-related factors contribute to FEI deficits in schizophrenia. Meta-analyses have identified relationships between impaired FEI and negative symptoms (Chan et al., 2010), inpatient status and longer duration of illness (Salva et al., 2013), age of onset,
S.J. Barkl et al. / Schizophrenia Research 160 (2014) 150–156
number of hospitalisations and positive and negative symptoms (Kohler et al., 2010). The most consistent finding is between FEI deficits and negative symptoms (Chan et al., 2010; Kohler et al., 2010). Alternatively, the conceptualisation of cognitive deficits as an independent domain of psychopathology may better explain the place of FEI and other neurocognitive deficits in psychosis. In neuropsychological literature, recognition of certain basic emotions has been linked to specific structures: amygdala with fear (Allman and Brothers, 1994; Adolphs et al., 1995), the insula with disgust (Sprengelmeyer et al., 1996; Phillips et al., 1997; Calder et al., 2000) and the ventral striatum with anger (Blair et al., 1999; Calder et al., 2004), allowing for the possibility that the identification of some emotions is more impaired than others. A large number of neuroimaging investigations have found evidence of disruptions to specialised networks of structures and circuits that subserve facial emotion processing in patients with schizophrenia, including under-recruitment of the amygdala accompanied by a lack of activation throughout the ventral temporal–basal ganglia–prefrontal cortex system (Gur et al., 2002; Takahashi et al., 2004; Das et al., 2007; Williams et al., 2007; Li et al., 2010). Research with adults with schizophrenia have found relationships between these functional abnormalities and impaired task performance (Quintana et al., 2003; Williams et al., 2007; Habel et al., 2010), however little has been done to link these measures in people at the early stages of illness (Seiferth et al., 2009). Deficits in FEI in people with schizophrenia have important clinical implications because they are associated with impairments in social functioning, interpersonal skills, work functioning and independent living (Kee et al., 2003; Couture et al., 2006; Pinkham and Penn, 2006; Williams et al., 2008; Irani et al., 2012). This connection is thought to explain the nexus between neurocognitive impairment in schizophrenia and burden of illness (Brekke et al., 2005; Schmidt et al., 2011). It is yet to be established whether this relationship exists for people experiencing EOP. Therefore, the aim of the present study was to determine whether emotion recognition deficits are present and emotion specific in EOP, and to assess whether performance on a FEI task was related to clinical symptoms and impairments in functional outcome. We addressed this question by giving a test of facial emotion identification to a sample experiencing EOP while concurrently gathering clinical and functional data. We hypothesised that (i) participants with EOP would demonstrate impaired performance on a task of FEI compared to healthy matched controls, as measured by reduced accuracy and longer reaction time of responses (ii) that patients would have greater difficulties in identifying negative emotions than healthy matched controls, and that (iii) these cognitive deficits would be associated with impairments in functional outcome. We were also interested in examining the relationship between clinical variables and task performance.
151
volunteers (Koslow et al., 2013; Gordon et al., 2008) matched individually on gender, age (± 1 year) and level of education (± 1 year) (see Table 1). The diagnosis of EOP was made using the psychosis and mood disorder modules of the Structured Clinical Interview for DSM-IV-TR Axis 1 Disorders (SCID-1) (First et al., 2002), with modified probe questions used if needed (Matzner et al., 1997). The diagnoses made were schizophrenia spectrum disorders (n = 11; schizophrenia, schizophreniform and schizoaffective disorder), affective psychoses (n = 17; bipolar affective disorder, psychotic depression), and psychotic disorders not otherwise specified (PD NOS; n = 6) as diagnosed by SCID-1 criteria. Interviews were conducted by two of the authors (JS, CH) and confirmed by consensus with two independent child and adolescent psychiatrists. Exclusion criteria for entry into the study were epilepsy, previous head injury causing a loss of consciousness ≥ 10 min, intelligence quotient (IQ) b 70, an organic cause for the psychotic symptoms, treatment with electroconvulsive therapy in the past six months or an inability to speak sufficient English to understand the study questions. Testing was carried out once acute state had been stabilized, though participants were frequently still symptomatic. 2.2. Clinical status Current psychotic symptoms were measured using the raw scores from the Positive and Negative Syndrome Scale (PANSS) (Kay et al., 1987). Mood symptoms were assessed using the t scores from the Children's Depression Inventory (CDI; Sitarenios and Kovacs, 1999), which rates the severity of symptoms related to depression and/or dysthymic disorder in children and adolescents, the Depression Anxiety Stress Scale (DASS; Lovibond & Lovibond, 1995), and raw scores from the Young Mania Rating Scale (YMRS; Young et al., 1978), which assesses manic symptoms over the past 48 h, including elevated mood, increased motor activity and irritability, and decreased need for sleep. Antipsychotic drug dose was converted into chlorpromazine equivalent dose (Andreasen et al., 2010). Information about clinical variables was obtained at interview, from the SCID and by reviewing medical records. 2.3. Functional outcome Current functional outcome was measured using raw scores from the Role Functioning Scale (RFS; Goodman et al., 1993), which measures an individual's functioning in his or her natural environment, and is the recommended scale by the Department of Health in Australia (Stedman et al., 1997) to measure outcomes in mental health. This scale assesses the domains of personal self-care, cognitive/affective functioning, social/familial relationships, and vocational/educational functioning. Current functional outcome was also measured using raw scores from the Global Assessment of Functioning (GAF; American Psychiatric Association, 2000).
2. Methods 2.4. Explicit facial emotion identification task This study was a part of a larger EOP project evaluating clinical, neuropsychological and neurophysiological factors in patients (see Starling et al., 2013). All participants and their parents or carers gave written informed consent to participate, in accordance with the Australian National Health and Medical Research Council guidelines. The Human Research Ethics Committees of the Children's Hospital at Westmead, the Western Sydney Area Health Service and the University of Sydney approved the study. 2.1. Participants Participants were 34 children and adolescents diagnosed with EOP recruited from paediatric mental health services in Western Sydney, Australia, and 41 healthy participants selected from a pool of healthy
The Explicit Facial Emotion Identification test is part of the emotional cognition domain of the “IntegNeuro” test battery. IntegNeuro is a touch screen computerized battery with previously established norms (Clark et al., 2006; Williams et al., 2009), reliability (Williams et al., 2005), convergent and divergent validity (Paul et al., 2005), and validation in Table 1 Baseline demographic characteristics: Means (standard deviation).
Age (years) Gender (% female) Years of education
EOS (n = 34)
Control (n = 41)
Test used
14.11 (2.08) 53 8.55 (2.27)
13.80 (2.30) 51 8.12 (2.29)
t(73) = .619, p = .538 χ2 = .780, p = .403 t(73) = .794, p = .430
152
S.J. Barkl et al. / Schizophrenia Research 160 (2014) 150–156
relation to neuroimaging measures of brain circuits (Rowe et al., 2007). IntegNeuro also has demonstrated utility for assessing general and emotional cognition in patients with schizophrenia, including first onset patients (Williams et al., 2008; Silverstein et al., 2010; Forbes et al., 2012). The tests included in the IntegNeuro battery assess domains of cognitive function comparable to those assessed by the MATRICS Consensus Cognitive Battery (MCCB), which was developed out of the MATRICS initiative (Kern et al., 2011). A previous study of schizophrenia has compared IntegNeuro directly to the MCCB (Silverstein et al., 2010). The details of the Explicit Emotion Identification task have been described previously (Williams et al., 2009). The task has been normed for children down to 6 years of age (Gordon, 2003; Clark et al., 2006) and has been used in other paediatric clinical populations, including ADHD (Williams et al., 2010; 6–18 years, M = 12.29, SD = 3.08), anorexia nervosa (Hatch et al., 2010; 11.86–18.01 years, M = 15.16, SD = 1.63) and conversion disorder (Kozlowska et al., 2014; 8.5–18 years, M = 13.56, SD = 2.2 years). In this test, participants are shown a series of facial expressions of emotion (neutral, happy, sad, fear, angry, or disgust). There are 8 presentations of each emotion, presented in pseudorandom order, and each presentation displays a different individual (4 males, 4 females). Participants were instructed to identify the emotion depicted on each face by selecting the corresponding emotion label from six options using a mouse click. From this test we quantified for each emotion, the percentage of correct identifications, the percentage of misclassifications, and reaction time.
Table 2 Clinical characteristics of the early onset psychosis group (n = 34). Mean (SD)
Range
Descriptor
Clinical measure Medication (CPZ mg) Age at first onset of psychotic symptoms (years) Duration of prodromal symptoms (months) Duration of untreated psychosis (months)
130.3 (100.1) 12.4 (2.8)
0–456 5–15
7.37 (10.0); 0 (median) 3.66 (4.1); 1.0 (median)
0–36
Psychotic symptoms PANSS Positive Total
14.4 (4.8)
8–34
14.7 (7.2) 31.1 (8.4)
7–34 11–34
Mood symptoms DASS Depression Scale DASS Anxiety Scale DASS Stress Scale CDI total
17.9 (14.6) 16.1 (12.0) 18.6 (12.5) 17.6 (12.1)
0–42 0–40 0–42 0–41
YMR score total
9.2 (10.2)
0–41
PANSS Negative total PANSS General Psychopathology
0–12
Slightly below averagea Below averagea Slightly below averagea
Moderate range Severe range Moderate range Slightly above average–average Mild rangeb
CPZ: chlorpromazine equivalent dose; PANSS: Positive and Negative Syndrome Scale; DASS: Depression Anxiety Stress Scale; CDI: Children's Depression Scale. YMR: Young Mania Rating. a Compared to a sample of 240 medicated schizophrenic inpatients (Kay et al., 1987). b Lukasiewicz et al. (2013).
2.5. Statistical analyses 3.2. Group differences in facial emotion identification
3. Results 3.1. Characteristics of participants The EOP and control groups did not differ in age, gender, or years of education (see Table 1). Clinical characteristics of the EOP group are in Table 2.
There was a significant group by emotion interaction [F(3.97, 289.75) = 2.43, p = .048], consistent with our main hypothesis that the patient group would be comparatively impaired on specific emotions. Contrasts showed that patients were less accurate than controls at recognising fear [t(73) = − 3.59, p = .001], disgust [t(73) = − 2.96, p = .004] and anger [t(73) = − 2.32, p = .023], but did not differ from controls in the recognition of happy [t(73) = − 1.047, p = .229], sad [t(73) = − 1.25, p = .215], or neutral [t(73) = −.152, p = .880] facial expressions. This interaction may account for the group main effect of generally lower performance in patients [F(1,73) = 13.67, p b .001]. The interaction was also observed in the context of a main effect for emotion [F(3.97, 289.75) = 2.43, p = .048] which confirmed the typical pattern of generally higher identification of happy than negative facial expressions, with identification of neutral in the middle (see Fig. 1). Across groups neutral and happy facial expressions were identified with significantly greater accuracy than sadness, fear, anger, and disgust (ps b .001), sad facial expressions were identified with significantly
Percentage Correct
Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS, Version 20), with alpha level set at .05. Normal distribution of quantitative variables was assessed by means of Kolmogorov–Smirnov tests and homogeneity of variances with Levene tests. When variables were not normally distributed, nonparametric statistical tests were chosen. To test for differences in demographic variables between patients and controls, t or χ2 tests were used. Explicit Identification of Emotion accuracy (% correct) and reaction time (ms) were analysed with repeated measures ANOVA with emotion as within-subject factor and group as between-subject factor. Where misclassifications were present, we compared groups using t-tests. Pearson correlations were used to assess the clinical and functional significance of group differences on emotion identification. We assessed associations between emotion identification measures, antipsychotic medication (chlorpromazine equivalents), and symptoms, measured by the PANSS, DASS, CDI and YMRS scores. Given that early-onset psychosis is a heterogeneous condition with symptoms shared by multiple diagnoses, a dimensional analysis of the PANSS was conducted. A 5-factor structure has been proposed as an effective way to analyse the PANSS (Smith et al., 1998) and has been found to be a stable representation of the structure of clinical symptoms in early-onset psychosis (Rapado-Castro et al., 2010). Accuracy on a task of facial emotion identification was correlated with measures of clinical features, including the YMRS, the CDI, and a 5-factor model of the PANSS (Negative, Positive, Excitement, Cognition, and Depression/Anxiety domains). We also assessed associations between emotion identification measures and functional outcome, measured by RFS and GAF scores.
100 90 80 70 60 50 40 30 20 10 0
Early-onset psychosis Control
Neutral
Happiness Sadness
Fear
Anger
Disgust
Emotion Accuracy (%±SD) of emotion recognition tasks *p =
Contents lists available at ScienceDirect
Schizophrenia Research journal homepage: www.elsevier.com/locate/schres
Facial emotion identification in early-onset psychosis Sophie J. Barkl a,b,c, Suncica Lah a,c, Jean Starling d, Cassandra Hainsworth e, Anthony W.F. Harris b,f, Leanne M. Williams b,g,⁎ a
School of Psychology, University of Sydney, NSW, Australia The Brain Dynamics Centre, Sydney Medical School and Westmead Millennium Institute, University of Sydney, NSW, Australia ARC Centre of Excellence in Cognition and Its Disorders, Sydney, NSW, Australia d Walker Unit, Concord Centre for Mental Health, Discipline of Psychiatry, Sydney Medical School, University of Sydney, Sydney, Australia e Department of Psychological Medicine, The Children's Hospital, Westmead, NSW, Australia f Discipline of Psychiatry, Sydney Medical School, University of Sydney, Sydney, Australia g Psychiatry and Behavioral Sciences, Stanford University, CA, USA b c
a r t i c l e
i n f o
Article history: Received 29 July 2014 Received in revised form 21 October 2014 Accepted 23 October 2014 Available online 13 November 2014 Keywords: Early onset psychosis Facial emotion Social cognition Schizophrenia
a b s t r a c t Facial emotion identification (FEI) deficits are common in patients with chronic schizophrenia and are strongly related to impaired functioning. The objectives of this study were to determine whether FEI deficits are present and emotion specific in people experiencing early-onset psychosis (EOP), and related to current clinical symptoms and functioning. Patients with EOP (n = 34, mean age = 14.11, 53% female) and healthy controls (HC, n = 42, mean age 13.80, 51% female) completed a task of FEI that measured accuracy, error pattern and response time. Relative to HC, patients with EOP (i) had lower accuracy for identifying facial expressions of emotions, especially fear, anger and disgust, (ii) were more likely to misattribute other emotional expressions as fear or disgust, and (iii) were slower at accurately identifying all facial expressions. FEI accuracy was not related to clinical symptoms or current functioning. Deficits in FEI (especially for fear, anger and disgust) are evident in EOP. Our findings suggest that while emotion identification deficits may reflect a trait susceptibility marker, functional deficits may represent a sequelae of illness. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Cognitive impairment is core to schizophrenia (Heinrichs and Zakzanis, 1998; Dickinson et al., 2008; Fioravanti et al., 2012). This impairment has also been demonstrated in first episode schizophrenia (Mesholam-Gately et al., 2009) and early-onset psychosis (Holmén et al., 2010; Bombin et al., 2013). A key component of this impairment is social cognition, which refers to the capacity to perceive, interpret and regulate responses to emotional information (Green et al., 2008). The ability to accurately identify facial expressions, a fundamental building block of social cognition, plays a pivotal role in facilitating emotional connection and effective social communication. Reduced accuracy of facial emotion identification (FEI) has been found in established schizophrenia (for meta-analyses, see Chan et al., 2010; Kohler et al., 2010; Salva et al., 2013) and first-episode psychosis (Edwards et al., 2001; Addington et al., 2006; Bediou et al., 2007; Pinkham et al., 2007; Reske et al., 2009; Williams et al., 2009; Comparelli et al., 2011; Leung et al., 2011; Thompson et al., 2012; Comparelli et al., 2013). While some studies showed impaired FEI across ⁎ Corresponding author at: Psychiatry and Behavioral Sciences, 401 Quarry Road, Mail Code 971, Stanford University, CA 94305, USA. E-mail address: [email protected] (L.M. Williams).
http://dx.doi.org/10.1016/j.schres.2014.10.035 0920-9964/© 2014 Elsevier B.V. All rights reserved.
a range of expressions of emotion (Kucharska-Pietura et al., 2005; Silver et al., 2009) others provided evidence of a selective impairment, most commonly involving FEI of fear, disgust and sadness (Marwich and Hall, 2008; Kohler et al., 2010; Thompson et al., 2012). The presence and nature of FEI deficits in children and adolescents with early-onset psychosis (b18 years, EOP) remain largely unexplored. Of the two papers that have examined FEI in children and adolescents with early-onset schizophrenia, one found that patients demonstrate lower overall performance compared to healthy controls (Seiferth et al., 2009), and another found specific deficits in the recognition of fear and sadness, but not anger, happiness, disgust, or surprise facial expressions, compared to healthy controls (Amminger et al., 2012). No study has yet examined FEI deficits in a young group experiencing psychosis across a range of diagnostic groups. If FEI deficits are evident, and of a similar magnitude to those experienced by people with established schizophrenia, this would underline the centrality of FEI deficits to the cognitive deficits in psychosis and have significant implications for the treatment and recovery of even very young people with psychosis. It is unclear which disease-related factors contribute to FEI deficits in schizophrenia. Meta-analyses have identified relationships between impaired FEI and negative symptoms (Chan et al., 2010), inpatient status and longer duration of illness (Salva et al., 2013), age of onset,
S.J. Barkl et al. / Schizophrenia Research 160 (2014) 150–156
number of hospitalisations and positive and negative symptoms (Kohler et al., 2010). The most consistent finding is between FEI deficits and negative symptoms (Chan et al., 2010; Kohler et al., 2010). Alternatively, the conceptualisation of cognitive deficits as an independent domain of psychopathology may better explain the place of FEI and other neurocognitive deficits in psychosis. In neuropsychological literature, recognition of certain basic emotions has been linked to specific structures: amygdala with fear (Allman and Brothers, 1994; Adolphs et al., 1995), the insula with disgust (Sprengelmeyer et al., 1996; Phillips et al., 1997; Calder et al., 2000) and the ventral striatum with anger (Blair et al., 1999; Calder et al., 2004), allowing for the possibility that the identification of some emotions is more impaired than others. A large number of neuroimaging investigations have found evidence of disruptions to specialised networks of structures and circuits that subserve facial emotion processing in patients with schizophrenia, including under-recruitment of the amygdala accompanied by a lack of activation throughout the ventral temporal–basal ganglia–prefrontal cortex system (Gur et al., 2002; Takahashi et al., 2004; Das et al., 2007; Williams et al., 2007; Li et al., 2010). Research with adults with schizophrenia have found relationships between these functional abnormalities and impaired task performance (Quintana et al., 2003; Williams et al., 2007; Habel et al., 2010), however little has been done to link these measures in people at the early stages of illness (Seiferth et al., 2009). Deficits in FEI in people with schizophrenia have important clinical implications because they are associated with impairments in social functioning, interpersonal skills, work functioning and independent living (Kee et al., 2003; Couture et al., 2006; Pinkham and Penn, 2006; Williams et al., 2008; Irani et al., 2012). This connection is thought to explain the nexus between neurocognitive impairment in schizophrenia and burden of illness (Brekke et al., 2005; Schmidt et al., 2011). It is yet to be established whether this relationship exists for people experiencing EOP. Therefore, the aim of the present study was to determine whether emotion recognition deficits are present and emotion specific in EOP, and to assess whether performance on a FEI task was related to clinical symptoms and impairments in functional outcome. We addressed this question by giving a test of facial emotion identification to a sample experiencing EOP while concurrently gathering clinical and functional data. We hypothesised that (i) participants with EOP would demonstrate impaired performance on a task of FEI compared to healthy matched controls, as measured by reduced accuracy and longer reaction time of responses (ii) that patients would have greater difficulties in identifying negative emotions than healthy matched controls, and that (iii) these cognitive deficits would be associated with impairments in functional outcome. We were also interested in examining the relationship between clinical variables and task performance.
151
volunteers (Koslow et al., 2013; Gordon et al., 2008) matched individually on gender, age (± 1 year) and level of education (± 1 year) (see Table 1). The diagnosis of EOP was made using the psychosis and mood disorder modules of the Structured Clinical Interview for DSM-IV-TR Axis 1 Disorders (SCID-1) (First et al., 2002), with modified probe questions used if needed (Matzner et al., 1997). The diagnoses made were schizophrenia spectrum disorders (n = 11; schizophrenia, schizophreniform and schizoaffective disorder), affective psychoses (n = 17; bipolar affective disorder, psychotic depression), and psychotic disorders not otherwise specified (PD NOS; n = 6) as diagnosed by SCID-1 criteria. Interviews were conducted by two of the authors (JS, CH) and confirmed by consensus with two independent child and adolescent psychiatrists. Exclusion criteria for entry into the study were epilepsy, previous head injury causing a loss of consciousness ≥ 10 min, intelligence quotient (IQ) b 70, an organic cause for the psychotic symptoms, treatment with electroconvulsive therapy in the past six months or an inability to speak sufficient English to understand the study questions. Testing was carried out once acute state had been stabilized, though participants were frequently still symptomatic. 2.2. Clinical status Current psychotic symptoms were measured using the raw scores from the Positive and Negative Syndrome Scale (PANSS) (Kay et al., 1987). Mood symptoms were assessed using the t scores from the Children's Depression Inventory (CDI; Sitarenios and Kovacs, 1999), which rates the severity of symptoms related to depression and/or dysthymic disorder in children and adolescents, the Depression Anxiety Stress Scale (DASS; Lovibond & Lovibond, 1995), and raw scores from the Young Mania Rating Scale (YMRS; Young et al., 1978), which assesses manic symptoms over the past 48 h, including elevated mood, increased motor activity and irritability, and decreased need for sleep. Antipsychotic drug dose was converted into chlorpromazine equivalent dose (Andreasen et al., 2010). Information about clinical variables was obtained at interview, from the SCID and by reviewing medical records. 2.3. Functional outcome Current functional outcome was measured using raw scores from the Role Functioning Scale (RFS; Goodman et al., 1993), which measures an individual's functioning in his or her natural environment, and is the recommended scale by the Department of Health in Australia (Stedman et al., 1997) to measure outcomes in mental health. This scale assesses the domains of personal self-care, cognitive/affective functioning, social/familial relationships, and vocational/educational functioning. Current functional outcome was also measured using raw scores from the Global Assessment of Functioning (GAF; American Psychiatric Association, 2000).
2. Methods 2.4. Explicit facial emotion identification task This study was a part of a larger EOP project evaluating clinical, neuropsychological and neurophysiological factors in patients (see Starling et al., 2013). All participants and their parents or carers gave written informed consent to participate, in accordance with the Australian National Health and Medical Research Council guidelines. The Human Research Ethics Committees of the Children's Hospital at Westmead, the Western Sydney Area Health Service and the University of Sydney approved the study. 2.1. Participants Participants were 34 children and adolescents diagnosed with EOP recruited from paediatric mental health services in Western Sydney, Australia, and 41 healthy participants selected from a pool of healthy
The Explicit Facial Emotion Identification test is part of the emotional cognition domain of the “IntegNeuro” test battery. IntegNeuro is a touch screen computerized battery with previously established norms (Clark et al., 2006; Williams et al., 2009), reliability (Williams et al., 2005), convergent and divergent validity (Paul et al., 2005), and validation in Table 1 Baseline demographic characteristics: Means (standard deviation).
Age (years) Gender (% female) Years of education
EOS (n = 34)
Control (n = 41)
Test used
14.11 (2.08) 53 8.55 (2.27)
13.80 (2.30) 51 8.12 (2.29)
t(73) = .619, p = .538 χ2 = .780, p = .403 t(73) = .794, p = .430
152
S.J. Barkl et al. / Schizophrenia Research 160 (2014) 150–156
relation to neuroimaging measures of brain circuits (Rowe et al., 2007). IntegNeuro also has demonstrated utility for assessing general and emotional cognition in patients with schizophrenia, including first onset patients (Williams et al., 2008; Silverstein et al., 2010; Forbes et al., 2012). The tests included in the IntegNeuro battery assess domains of cognitive function comparable to those assessed by the MATRICS Consensus Cognitive Battery (MCCB), which was developed out of the MATRICS initiative (Kern et al., 2011). A previous study of schizophrenia has compared IntegNeuro directly to the MCCB (Silverstein et al., 2010). The details of the Explicit Emotion Identification task have been described previously (Williams et al., 2009). The task has been normed for children down to 6 years of age (Gordon, 2003; Clark et al., 2006) and has been used in other paediatric clinical populations, including ADHD (Williams et al., 2010; 6–18 years, M = 12.29, SD = 3.08), anorexia nervosa (Hatch et al., 2010; 11.86–18.01 years, M = 15.16, SD = 1.63) and conversion disorder (Kozlowska et al., 2014; 8.5–18 years, M = 13.56, SD = 2.2 years). In this test, participants are shown a series of facial expressions of emotion (neutral, happy, sad, fear, angry, or disgust). There are 8 presentations of each emotion, presented in pseudorandom order, and each presentation displays a different individual (4 males, 4 females). Participants were instructed to identify the emotion depicted on each face by selecting the corresponding emotion label from six options using a mouse click. From this test we quantified for each emotion, the percentage of correct identifications, the percentage of misclassifications, and reaction time.
Table 2 Clinical characteristics of the early onset psychosis group (n = 34). Mean (SD)
Range
Descriptor
Clinical measure Medication (CPZ mg) Age at first onset of psychotic symptoms (years) Duration of prodromal symptoms (months) Duration of untreated psychosis (months)
130.3 (100.1) 12.4 (2.8)
0–456 5–15
7.37 (10.0); 0 (median) 3.66 (4.1); 1.0 (median)
0–36
Psychotic symptoms PANSS Positive Total
14.4 (4.8)
8–34
14.7 (7.2) 31.1 (8.4)
7–34 11–34
Mood symptoms DASS Depression Scale DASS Anxiety Scale DASS Stress Scale CDI total
17.9 (14.6) 16.1 (12.0) 18.6 (12.5) 17.6 (12.1)
0–42 0–40 0–42 0–41
YMR score total
9.2 (10.2)
0–41
PANSS Negative total PANSS General Psychopathology
0–12
Slightly below averagea Below averagea Slightly below averagea
Moderate range Severe range Moderate range Slightly above average–average Mild rangeb
CPZ: chlorpromazine equivalent dose; PANSS: Positive and Negative Syndrome Scale; DASS: Depression Anxiety Stress Scale; CDI: Children's Depression Scale. YMR: Young Mania Rating. a Compared to a sample of 240 medicated schizophrenic inpatients (Kay et al., 1987). b Lukasiewicz et al. (2013).
2.5. Statistical analyses 3.2. Group differences in facial emotion identification
3. Results 3.1. Characteristics of participants The EOP and control groups did not differ in age, gender, or years of education (see Table 1). Clinical characteristics of the EOP group are in Table 2.
There was a significant group by emotion interaction [F(3.97, 289.75) = 2.43, p = .048], consistent with our main hypothesis that the patient group would be comparatively impaired on specific emotions. Contrasts showed that patients were less accurate than controls at recognising fear [t(73) = − 3.59, p = .001], disgust [t(73) = − 2.96, p = .004] and anger [t(73) = − 2.32, p = .023], but did not differ from controls in the recognition of happy [t(73) = − 1.047, p = .229], sad [t(73) = − 1.25, p = .215], or neutral [t(73) = −.152, p = .880] facial expressions. This interaction may account for the group main effect of generally lower performance in patients [F(1,73) = 13.67, p b .001]. The interaction was also observed in the context of a main effect for emotion [F(3.97, 289.75) = 2.43, p = .048] which confirmed the typical pattern of generally higher identification of happy than negative facial expressions, with identification of neutral in the middle (see Fig. 1). Across groups neutral and happy facial expressions were identified with significantly greater accuracy than sadness, fear, anger, and disgust (ps b .001), sad facial expressions were identified with significantly
Percentage Correct
Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS, Version 20), with alpha level set at .05. Normal distribution of quantitative variables was assessed by means of Kolmogorov–Smirnov tests and homogeneity of variances with Levene tests. When variables were not normally distributed, nonparametric statistical tests were chosen. To test for differences in demographic variables between patients and controls, t or χ2 tests were used. Explicit Identification of Emotion accuracy (% correct) and reaction time (ms) were analysed with repeated measures ANOVA with emotion as within-subject factor and group as between-subject factor. Where misclassifications were present, we compared groups using t-tests. Pearson correlations were used to assess the clinical and functional significance of group differences on emotion identification. We assessed associations between emotion identification measures, antipsychotic medication (chlorpromazine equivalents), and symptoms, measured by the PANSS, DASS, CDI and YMRS scores. Given that early-onset psychosis is a heterogeneous condition with symptoms shared by multiple diagnoses, a dimensional analysis of the PANSS was conducted. A 5-factor structure has been proposed as an effective way to analyse the PANSS (Smith et al., 1998) and has been found to be a stable representation of the structure of clinical symptoms in early-onset psychosis (Rapado-Castro et al., 2010). Accuracy on a task of facial emotion identification was correlated with measures of clinical features, including the YMRS, the CDI, and a 5-factor model of the PANSS (Negative, Positive, Excitement, Cognition, and Depression/Anxiety domains). We also assessed associations between emotion identification measures and functional outcome, measured by RFS and GAF scores.
100 90 80 70 60 50 40 30 20 10 0
Early-onset psychosis Control
Neutral
Happiness Sadness
Fear
Anger
Disgust
Emotion Accuracy (%±SD) of emotion recognition tasks *p =
