University of Lyon, Centre Hospitalier Le Vinatier, Bron and Institut Fédératif des Neurosciences de Lyon, Bron, France
University of Marrakech, Morocco
University of Lyon, Centre Hospitalier Le Vinatier and Institut Fédératif des Neurosciences de Lyon, Bron, France
Institut National de la Santé et de la Recherche (INSERM) and Centre Hospitalier Le Vinatier, Bron
University of Lyon, Centre Hospitalier Le Vinatier and Institut Fédératif des Neurosciences de Lyon, Bron, France
University of Marrakech, Morocco
Correspondence: Dr Benoit Bediou,CH Le Vinatier,University of Lyon, EA 3092, 95 Boulevard Pinel, 69677 Bron Cedex, France. Email: benoit.bediou{at}ch-le-vinatier.fr
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Aims To establish whether facial expression recognition is impaired in unaffected siblings of patients.
Method Emotion and gender recognition were evaluated in a three-group pre—post study design in drugnaive patients with first-episode schizophrenia (n=40) and their unaffected siblings (n=30) compared with controls (n=26).
Results Patients and their healthy siblings showed impaired emotion recognition but normal gender recognition compared with controls. Patientsperformance did not improve despite effective clinical stabilisation.
Conclusions Impaired performance in healthy siblings and time stability in patients provides evidence of impairment of facial emotion recognition as an actual phenotype of schizophrenia.
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Our hypothesis was that facial expression recognition performance, like AHC volume, would be distributed along a continuum between controls and patients with schizophrenia, with healthy relatives of patients having intermediate values, as has been shown for several cognitive (Saoud et al, 2000; Brunelin et al, 2007) and social functions (Toomey et al, 1999). Moreover, as previously reported with other cognitive deficits, patients are unlikely to perform as well as controls even when the disorder is effectively treated (Brunelin et al, 2007). To test this hypothesis we measured facial emotion recognition in patients experiencing their first schizophrenic episode before and after first antipsychotic treatment and in their healthy siblings compared with controls. The method used in the current study is original in using morphing controlling for performance in another task requiring facial feature analysis, that is gender recognition (Bediou et al, 2005). Our previous report of patients preserved performance in this task suggests that it does not rely on the same dysfunctional structures.
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To address time stability and treatment effect of facial emotion recognition deficit in schizophrenia, participants in the schizophrenia group were tested twice, at baseline and after clinical stabilisation of symptoms under haloperidol treatment (mean dose 10.0 mg, s.d.=1.6). To control for potential learning or training effects, the other two study groups were also tested twice over a similar period. The mean interval between baseline and follow-up testing was 30.2 days (s.d.=5.3) in each group. Testing sessions consisted of each participant performing the expression and gender recognition task described below, the order being counterbalanced across participants and, for each participant, between testing sessions. At the time of each testing, symptoms were assessed by a trained clinician masked to participants status. Patients schizophrenic symptoms were assessed with the Positive and Negative Syndrome Scale (PANSS; Kay et al, 1987). Since depression may affect emotion recognition performance, depressive symptoms were also controlled for in the sibling and schizophrenia groups with the Calgary Depression Scale for Schizophrenia (CDSS; Addington et al, 1990) and in the control group with the abbreviated version of the Beck Depression Inventory (BDI; Beck et al, 1961). Schizotypal personality traits were evaluated in the sibling and control groups with the Schizotypal Personality Questionnaire (SPQ; Raine, 1991). Demographic and clinical data are summarised in Table 1.
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View this table: [in a new window] | Table 1 Characteristics of the sample (values are means and standard deviation) |
Tasks
We used sensitive measures of emotional facial expression recognition and a
control measure of facial gender recognition, both using morphed faces. A
detailed description of the method can be found in our previous work
(Bediou et al,
2005).
Emotional facial expression recognition
Briefly, 132 faces from two male and two female faces morphed between a
neutral and an emotional expression (happiness, disgust, fear, anger) in 10%
steps were each randomly presented on a computer screen for 1000 ms, preceded
by a fixation cross (250 ms) and followed by the choice list (neutral,
happiness, disgust, fear or anger), which was maintained on the screen until
the participant responded. Participants were required to match each face with
the word that best described the emotional expression displayed by pressing
the corresponding key.
Facial gender recognition
In the gender recognition task 132 faces, morphed between an average face
with no gender (obtained by averaging 20 male and 20 female
faces in equal proportion) and one of six male or one of six female faces in
10% steps, were each randomly presented for 1000 ms, preceded by a fixation
cross (250 ms) and followed by the choice list (male or female). Participants
were instructed to match each face with the word that best described its
gender by pressing the corresponding key.
Statistical analyses
Dependant variables were the percentage of correct responses in the emotion
and gender recognition tasks. Independent variables were the group
(schizophrenia, siblings or control); the phase (baseline or follow-up); the
task (expression or gender); the intensity of emotional expression or gender
(0– 100% in 10% increments); the expression (happiness, disgust, fear or
anger); and the gender (male or female). The first variable is an
intra-individual factor whereas the others are inter-individual factors.
Performance of the schizophrenia group, time stability/treatment effect and performance of the siblings group were tested by means of repeated-measures analyses of variance; a main 3 group x 2 phase x 2 task x 10 intensities multivariate analysis of variance (MANOVA) was followed by a 3 group x 2 phase x 4 expression x 10 intensities and a 3 group x 2 phase x 2 gender x 10 intensities MANOVA. To correct for multiple comparisons, Bonferroni correction led to a level of significance retained at 0.01 for analyses of variance (ANOVAs). Significant effects and interactions were then tested by means of planned comparisons using single-factor ANOVAs for interindividual factors and contrast analyses using the full data-set for intra-individual factors (e.g. phase effects: baseline v. follow-up). Students t-tests were used to probe group differences in socio-demographic characteristics. Given the significant group differences in age and education, and since performance did not correlate with either of these variables, repeated-measures analyses of covariance (MANCOVA) were also performed to control for a potential influence of these variables on significant effects. Since the same significant effects and interactions were found with MANOVAs and corresponding MANCOVAs, only results from the MAN-OVAs are reported.
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Emotion and gender recognition performance
The main MANOVA revealed a main effect of group
(F(2,186)=28.97, P<0.001), task
(F(1,186)=777.86, P<0.001) and intensity
(F(9,1674)=917.27, P<0.001) and significant
group x task (F(2,186)=31.75, P<0.001),
group x intensity (F(18,1674)=10.51,
P<0.001), task x intensity
(F(9,1674=105.14, P<0.001) and group x
task x intensity interactions (F(18,1674)=6.86,
P<0.001). There was no main effect of phase nor any interaction of
phase with group, task or intensity. Consistent with our hypothesis,
performance did not differ significantly between baseline and follow-up in any
of the groups. There was a difference between overall emotion and gender
recognition performance in all groups, suggesting that the emotion task was
more difficult: emotion and gender recognition in the control group were 61%
(s.d.=9) and 78% (s.d.=7) respectively. However, this effect was modulated by
group. The schizophrenia group performed significantly worse than the control
group on emotion recognition at baseline (F(1,186)=38.76,
P<0.001) and the difference remained significant at follow-up
(F(1,186)=38.16, P<0.001) despite clinical
stabilisation of symptoms. Moreover, the siblings group performed
significantly worse than the control group but significantly better than the
schizophrenia group in overall facial expression recognition
(Fig. 1; scores averaged across
all emotions and phases). No difference was found between groups on overall
gender recognition (F(1,186)=1.06, NS). This absence of
group difference was confirmed at all intensities of the gender morphs.
![]() View larger version (10K): [in a new window] [as a PowerPoint slide] |
Fig. 1 Recognition of facial expressions as a function of emotion intensity. In
the absence of phase effect or group x phase interaction, reported
values are the mean between baseline and follow-up averaged among all
emotions. Between-group differences according to single factor analysis of
variance for the schizophrenia v. control and sibling v.
control group comparisons are all significant (P<0.001) at
intensities of 30% and above.
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Analysis by emotion category revealed significant group x expression (F(6,558)=3.69, P<0.001) and group x expression x intensity interactions (F(54,5022)=1.50, P=0.01). Performance differed between the schizophrenia and control groups in the recognition of all basic emotions and between the sibling and control groups in the recognition of disgust and fearful faces (Fig. 2). Correlations between facial expression recognition and age (r = –0.029, P=0.88) and between facial expression recognition and educational level (r=0.075, P=0.71) were not significant. Likewise, emotion recognition performance did not correlate with mood (assessed with the BDI) in the control group, nor with depression scores (assessed with the CDSS) in the sibling and schizophrenia groups.
![]() View larger version (12K): [in a new window] [as a PowerPoint slide] |
Fig. 2 Recognition of facial expressions of basic emotions by the control, sibling
and schizophrenia groups. In the absence of phase effect or group x phase
interaction, reported values are the mean between baseline and follow-up
averaged among all emotions. P<0.001, for all between-group
differences according to single factor analysis of variance except for the
difference between the sibling and control groups in recognition of fear
(P<0.01) and anger which was not significant
(F(1.186)=3.48, P=0.063).
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Our results also revealed that facial expression recognition was already impaired at the onset of the illness, before treatment, and that performance remained impaired after 1 month of low-dose haloperidol treatment despite symptom stabilisation, suggesting trait-like features. Although age and educational level differed significantly between groups, it is unlikely that these factors account for the group differences observed here. Indeed, neither correlation nor comparison between MANOVA and MANCOVA results suggested an effect of age or education on emotion recognition. In addition, most previous studies failed to find any influence of these variables on performance.
Abnormal visual scanning of emotional faces may indirectly underlie some aspects of face recognition impairments in schizophrenia (Loughland et al, 2002). It has been related to amygdala function (Taylor et al, 2002; Adolphs et al, 2005). Evidence for the stability of these disturbances over time and illness progression points to their traitlike nature (Streit et al, 1997). Moreover, abnormal visual scanning of facial expressions has also been documented in unaffected siblings of people with schizophrenia (Loughland et al, 2004), reinforcing the assumption that emotion recognition deficit can be considered as a potential marker of familial vulnerability to schizophrenia. Unlike expression recognition, gender recognition may rely on a more holistic face analysis that is achieved in posterior occipito-temporal areas despite abnormal visual scanning of face parts.
In contrast to the overall deficit in facial expression recognition in the schizophrenia group, unaffected siblings were only impaired in fear and disgust recognition, suggesting that the emotion-specific deficit of healthy relatives may generalise with illness onset. However, a comparable deficit in fear and disgust recognition had already been observed in participants whose schizophrenia was in remission in our previous study (Bediou et al, 2005). Atypical antipsychotic treatment, used in the latter study, may therefore be more efficient than haloperidol, used in the current study, in improving emotion recognition in schizophrenia (Kee et al, 1998). However, performance was not completely restored by either of these treatments and the deficit remains significant at all phases of the illness, supporting the trait-like hypothesis. Interestingly, relatives performance appeared similar to that of patients when emotional expression was more subtle (30%), whereas it was more like that of the control group at higher intensities of emotional expression (80–90%). This may relate to the efficiency of social cognition processes (Toomey et al, 1999), facial scanning (Loughland et al, 2002), or both. However, this original result must be interpreted with caution. Indeed, even if the sibling and control groups did not differ significantly in anger recognition, lack of power might account for this result.
In social situations inaccurate decoding of emotional expression could be a source of stress and a barrier to deep social interactions and communication. It has been shown that metabolic and probably physical and social stress could cause an acute exacerbation of schizophrenia symptoms (van Os & McGuffin, 2003). Thus, one can easily conceive that abnormal interpretation of emotional expressions could increase the level of emotional stress and thus participate in precipitating vulnerable people into schizophrenia. However, the neurochemistry of emotion recognition remains to be specified. There is growing evidence that dopamine has a central role in emotion recognition (Salgado-Pineda et al, 2005). Direct evidence comes from studies using pro-dopamine (Delaveau et al, 2005) or anti-dopamine (Lawrence et al, 2002) agents in healthy volunteers. Also, indirect evidence is provided by studies of neuropsychiatric patients, specifically in schizophrenia (Bediou et al, 2005) and Parkinsons disease (Lachenal-Chevallet et al, 2006). Increasing serotonin transmission, on the other hand, can improve emotion recognition performance in healthy individuals (Harmer et al, 2003) and in people with major depressive disorder (Harmer et al, 2004). Abnormalities in the genes involved in dopamine transmission have been associated with poor cognitive functioning, reduced frontal lobe activation and high risk of schizophrenia (Goldberg et al, 2003), and variation in the serotonin transporter gene has been associated with variation in amygdala response to facial expressions (Hariri et al, 2002). This first report of emotion recognition as a marker of familial vulnerability to schizophrenia may encourage studies addressing its association with genetic polymorphism affecting dopamine or serotonin neurotransmission as well as the correlation between emotion recognition performance and amygdala volume. Moreover, given the strong relationship between emotion processing ability and functional outcome, specific remediation programmes should be encouraged (Wolwer et al, 2005). Likewise, the efficacy of emotion processing remediation programmes in vulnerable individuals as a putative preventive strategy should be evaluated.
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