Do you see what I feel? – Electrophysiological correlates of emotional face and body perception in schizophrenia

Clinical Neurophysiology 125 (2014) 1152–1163

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Clinical Neurophysiology journal homepage: www.elsevier.com/locate/clinph

Do you see what I feel? – Electrophysiological correlates of emotional face and body perception in schizophrenia Patrizia Thoma a,⇑, Denise Soria Bauser a, Christine Norra b, Martin Brüne b, Georg Juckel b, Boris Suchan a a b

Institute of Cognitive Neuroscience, Department of Neuropsychology, Faculty of Psychology, Ruhr University Bochum, Universitätsstraße 150, 44780 Bochum, Germany Department of Psychiatry, Ruhr-University of Bochum, LWL University Hospital, Alexandrinenstraße 1, 44791 Bochum, Germany

a r t i c l e

i n f o

Article history: Accepted 21 October 2013 Available online 12 November 2013 Keywords: Psychosis P100 N170 Bodies Faces Context processing

h i g h l i g h t s  Patients with schizophrenia show overall impaired recognition of emotional faces and bodies.  In contrast to healthy controls, the P100 and N170 components are not modulated by the emotional and personal identity of faces and bodies in the patient group.  The absence of a modulation of the electrophysiological correlates of emotional face and body processing in schizophrenia might relate to deficient context processing.

a b s t r a c t Objective: We aimed to elucidate whether impaired affective face processing – behaviourally and with regard to P100 and N170 components – is paralleled by similar deficits in body processing in schizophrenia. Furthermore, we aimed to assess modulations by the processing of emotional or personal identity of the stimuli. Methods: Fourteen patients with schizophrenia and 15 healthy controls were assessed with a Delayed Matching-to-Sample Task involving variations of the emotional (same vs. different valence) and personal identity (same vs. different person) of bodies and faces. Results: Patients showed overall poorer behavioural performance. In controls, P100 amplitudes were enhanced in the ‘‘same identity/different emotions’’ vs. ‘‘same identity/same emotion’’ condition and N170 amplitudes were larger for different vs. same emotions. In the patients, P100 amplitudes were enhanced in the right relative to the left hemisphere for faces, but not for bodies. Conclusions: Patients with schizophrenia show deficient modulation of the P100 and N170 components by emotional and personal identity of faces and bodies, which may relate to deficient context processing. Significance: Our findings suggest for the first time alterations of the electrophysiological correlates of body processing in schizophrenia. Ó 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

1. Introduction Schizophrenia has been associated with impaired social behaviour (Brüne et al., 2009), which may be related to an inability to infer information about the interaction partner’s emotional state based on his or her facial and body expressions. There is a certain degree of overlap regarding the cognitive mechanisms and underlying neuronal networks mediating face and body perception (see Minnebusch and Daum, 2009). While objects are processed in a feature-based manner, additional configural processing mechanisms are activated by faces and bodies (Tanaka and Farah, 1991;

⇑ Corresponding author. Tel.: +49 234 32 22674; fax: +49 234 14622.

Leder and Bruce, 2000; Collishaw and Hole, 2000; Maurer et al., 2002). Configural processing is based on the spatial relations amongst the features constituting a stimulus: first-order relational information refers to the position of the face-defining features in space (eyes above nose above mouth); holistic processing denotes the perception of a face in terms of an integrated representation, and second-order relational information refers to the spatial distance amongst internal features (Maurer et al., 2002). The idea that configural mechanisms are involved in the processing of faces and bodies is supported by several lines of evidence. For instance, the inversion effect (Yin, 1969, 1970; Reed et al., 2003, 2006) describes the fact that faces or bodies which are presented upside down are disproportionally more difficult to process (reflected in delayed response times and higher error rates) than other inverted objects.

E-mail address: [email protected] (P. Thoma). 1388-2457/$36.00 Ó 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.clinph.2013.10.046

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Also, in prosopagnosia patients, deficient face perception goes in concert with impaired body processing (Righart and de Gelder, 2007). In comparison with other stimulus categories, neutral faces and bodies elicit enhanced event-related potentials (ERPs) peaking about 100 and 170 ms after stimulus onset (termed P100 and N170) above occipito-temporal areas (see Minnebusch and Daum, 2009 for a review). While the P100 has been associated with the processing of low-level stimulus features and the classification of a stimulus as a face/body (Herrmann et al., 2005), the N170 has been linked to the structural encoding of a stimulus and to the generation of a global stimulus configuration (Rossion et al., 2000; Eimer, 2000a,b). The P100 appears to be affected by the emotional expression of faces/bodies (Eimer, 2000a,b; Meeren et al., 2005; Righart and de Gelder, 2007; van Heijnsbergen et al., 2007), while it is not clear as yet whether emotional valence affects the N170 or not (see Batty and Taylor, 2003; Vuilleumier and Pourtois, 2007). Patients with schizophrenia show disrupted discrimination (Archer et al., 1992), encoding and recognition of neutral faces (Walther et al., 2009) as well as deficient facial emotion processing, which seems to be affected by various illness-related and demographic factors such as age at illness onset, inpatient treatment, antipsychotic medication, age (see Köhler et al., 2010) and the severity of positive and negative symptoms (Chambon et al., 2006; Köhler et al., 2010). In part, maladaptive visual face scanning patterns, involving reduced scanpaths and fewer fixations on salient facial features (eyes, nose, etc.), explain the face processing impairments in these patients (Williams et al., 1999), although this might primarily apply to passive viewing conditions (Delerue et al., 2010). Disrupted configural processing and an overreliance on the feature-based processing of faces have also been discussed (Fakra et al., 2008; Joshua and Rossell, 2009), but altered configural processing, as reflected by a reduced face inversion effect, seems to be partly eliminated by increasing stimulus presentation times (Butler et al., 2008). There is an ongoing debate about whether the facial (affect) processing deficits might stand in the broader context of a more generalised impairment of visuoperceptual processing (Norton et al., 2009; Strauss et al., 2010). In any case, it seems that greater signal strength is necessary for reliable discrimination of facial information in schizophrenia (Chen et al., 2009). Bediou et al. (2005) reported that patients with schizophrenia are particularly impaired in matching facial affect in different faces in comparison with the same face. The authors discussed this pattern of impairments in terms of a context processing deficit, a global–local processing deficit or a selective attention deficit. In electrophysiological studies, compared to healthy controls, patients with schizophrenia show attenuated N170 amplitudes, and partly increased N170 latencies, in response to both neutral and emotional faces (Herrmann et al., 2004; Caharel et al., 2005; Bediou et al., 2007; Lee et al., 2010; Kirihara et al., 2012; Wynn et al., 2013) and no or attenuated modulation of N170 amplitudes by face inversion (Tsunoda et al., 2012) or emotional valence (Campanella et al., 2006; Lynn and Salisbury, 2008; Kirihara et al., 2012; Ibanez et al., 2012). Interestingly, impaired social functioning has been related to reduced N170 amplitudes for upright faces (Tsunoda et al., 2012). P100 amplitudes appear to be attenuated in some studies involving patients with schizophrenia (Caharel et al., 2005; Campanella et al., 2006), although this has not been observed as consistently as for the N170 (Johnston et al., 2005; Wynn et al., 2008). While impaired (emotional) face processing has been extensively studied in different clinical groups (e.g. schizophrenia: Morris et al., 2009; autism spectrum disorders: Harms et al., 2010; depression: Bourke et al., 2010; social anxiety: Machado-de-Sousa et al., 2010), it is currently unknown whether face perception problems go hand in hand with impaired body processing, e.g. in

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schizophrenia. Thus, studying patients with schizophrenia could help to elucidate further whether face and body perception rely on similar mechanisms and whether impaired body processing might also contribute to the clinical symptoms and social problems these patients face. As milder degrees of face processing impairments have also been observed in individuals with an increased risk to develop schizophrenia, e.g. in non-affected first-degree relatives of patients with schizophrenia and in participants with an ‘‘at-risk’’ mental state (Li et al., 2010; Kim et al., 2010; Amminger et al., 2011; van Rijn et al., 2011; Wolwer et al., 2012), face processing deficits have been discussed as an endophenotypic marker of the disease, while it is unknown whether disrupted body processing could serve a similar function. Current literature shows a dearth of studies on body perception in patients with schizophrenia. There is preliminary behavioural evidence of disrupted configural processing of both faces and bodies (but also of cars) (Soria Bauser et al., 2012) and of poorer recognition of emotional body expressions, which is exacerbated further by conflicting emotional vocalizations (Takahashi et al., 2010; Van den Stock et al., 2011) in patients with schizophrenia relative to healthy controls. One functional magnetic resonance imaging study focused on the perception of sports-related, but not necessarily emotional, body movements, yielding evidence of diminished activation of the extrastriate body area in schizophrenia patients relative to healthy controls (Takahashi et al., 2010). To our knowledge, no ERP studies dealing with the processing of emotional body forms have been published. The aim of the current study was thus to elucidate whether patients with schizophrenia show impaired processing of emotions expressed by the body comparable to those for faces. Furthermore, we wanted to investigate whether the identification of emotions across bodies is particularly disrupted in schizophrenia patients by different identities of these bodies, as has been described for faces (Bediou et al., 2005). Also, we aimed to elucidate whether these potential body processing impairments on the behavioural level are mirrored by altered P100 and N170 amplitudes and latencies.

2. Methods 2.1. Participants Fourteen patients with schizophrenia (SZ; age range: 21– 59 years), treated in the LWL University Hospital (Department of Psychiatry, Ruhr University, Bochum) (two as outpatients), and 15 healthy controls (age range: 20–53 years) were recruited as participants for the current study. Patients were diagnosed according to the current version of the International Classification of Disease (Dilling et al., 2000) by senior psychiatrists who were blind to the cognitive data and were well trained in the application of diagnostic criteria in a research context. This was meant to ensure good diagnostic reliability in spite of the fact that a standard clinical interview could not be carried out on top of the already quite comprehensive assessment. Twelve patients were diagnosed with paranoid (ICD10, F20.0) and two with undifferentiated (ICD10, F20.3) schizophrenia. An estimated IQ below 80 represented an exclusion criterion for all participants, and patients were also excluded if they presented with a past or present comorbid psychiatric or neurological diagnosis, with the exception of mild to moderate depression (three patients) due to the high rates of comorbidity. Any current or past psychiatric or neurological disorder and/or a positive family history for schizophrenia represented additional exclusion criteria for healthy controls. Inclusion and exclusion criteria were screened on the basis of a semi-structured interview which assessed the participants’ demographic data (age, sex, handedness, family status, years and degree

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of formal education, current employment situation) as well as medical history and current health status (previous or current general somatic diseases – e.g. diabetes, cardiovascular diseases, neurological diagnoses such as traumatic brain injury with or without loss of consciousness, any current medication, family history of psychiatric disorders, previous electroconvulsive treatments and previous psychiatric or psychotherapeutic treatments). Also, the interview assessed self-reported information about the number of cigarettes smoked and the number of cups of coffee consumed per day, average daily intake of alcohol, maximum alcohol consumption per drinking occasion, age at first alcohol consumption, age at first intoxication, date of last alcohol consumption, consumption of any other legal or illegal substance of abuse currently or during the lifetime, including information about the type of the substances consumed, the duration, quantity and the frequency of consumption as well as the approximate date of the last consumption of these substances. In the patient group, additional clinical data (e.g. the number of previous hospitalisations, age at first psychiatric admission and the diagnosis related to it) were gathered during this screening, completing and confirming the information available from chart review and clinical caregivers. The severity of depressive symptoms was estimated with the German version of the Beck Depression Inventory, revised version (Hautzinger et al., 2009). An estimate of premorbid verbal intellectual ability was assessed with a 32-item multiple choice vocabulary test (Lehrl, 1995), requiring the identification of correct German words in a series of words and non-words. Table 1 presents demographic and clinical background data of both study groups. All patients were medicated at the time of testing: Eight patients were treated with atypical neuroleptic medication, two with a combination of an atypical antipsychotic and a selective serotonin reuptake inhibitor, one with an atypical antipsychotic plus a selective serotonin reuptake inhibitor plus a tricyclic antidepressant, one with an atypical antipsychotic plus valproic acid, one patient with a combination of a typical antipsychotic and lorazepam and one further patient with a typical antipsychotic only. The average chlorpromazine equivalent dose in the patient group, as calculated according to Bandelow et al. (2000) and Woods (2003) (for the newer antipsychotic drugs), amounted to 619.4 mg (SD: 500.5). BDI scores were missing for one patient. Due to hospital policy, patients were not reimbursed financially for participation, but healthy controls received a payment of 15 EUR to cover for travel expenses. The local Ethics board of the Faculty of Psychology, Ruhr University of Bochum, approved the study.

2.2. Stimuli All stimuli were presented in a grey-scaled version against a white background, saved as 300  300 pixels bitmap files.

Table 1 Clinical and demographic characteristics of the patients with schizophrenia (SZ) and healthy controls (HC).

N Age (years) IQ Years of education Sex (female:male) Beck Depression Inventory score No of weeks of hospitalisation at time of testing Age at first hospitalisation No. of psychiatric hospitalisations

SZ

HC

14 34.1 (11.2) 102.4 (10.1) 11.6 (2.8) 6:8 12.2 (11.4 3.5 (2.8) 27.1 (12.2) 2.9 (4.3)

15 34.7 (9.6) 118.9 (17.0) 12.7 (2.3) 7:8 1.3 (2.3)

Facial stimuli were taken from the NimStim face stimulus set (Tottenham et al., 2009) and presented without external features (e.g. hair and beard). We selected faces depicting neutral, positive (happy) or negative (sad or fearful) expressions, with 32 faces for each emotional valence (16 male). Only closed mouth versions of the facial stimuli were included. The body stimuli were created in our lab and presented with masked faces in order to minimise the activation of face processing mechanisms. The models (aged 20–45 years) were lab members or undergraduate students from the Faculty of Psychology, Ruhr University Bochum, participating as non-professional actors for course credit. On the day of the photo shoot, participants were asked to wear trousers, preferably in a darkish colour, and uniform black t-shirts provided by the investigators to minimise the effect of external features possibly aiding body recognition. A standard procedure was applied to instruct participants – first in written form and then orally immediately before the photograph was taken – to display a negative (sadness or fear) emotion, a positive emotion (happiness) and to adopt an emotionally neutral body posture. The order was counterbalanced across participants. No specific instructions were given as to how to display the emotion in question, but most models incorporated similar elements into their body postures, e.g. ‘‘arms up’’ for positive bodily expressions. Photographs were taken from a frontal view (0° camera, mounted on a static tripod), with the photographer directly facing the model. A pilot study involving 26 students from the Faculty of Psychology, Ruhr University Bochum, where each emotional body was presented for 1000 ms (to ensure reliable recognition during short presentation times usually used in experimental settings) and participants had to choose the emotional valence of the presented bodily expressions, confirmed the intended categorizations. Each body stimulus category (negative, positive, neutral) comprised 32 stimuli (16 male). 2.3. Delayed Matching-to-Sample Task The Delayed Matching-to-Sample Task followed a procedure described by Bediou et al. (2005) designed to assess whether stimulus identity can confound emotion recognition abilities. Two stimuli of the same category (face vs. body, half female) and same gender were presented consecutively and participants were required to indicate whether the faces or bodies depicted the same or a different emotional valence (negative, neutral or positive). In addition to the emotional valence, stimulus identity could also differ between the two stimulus presentations, so that four conditions emerged (same identity/same valence; same identity/different valence; different identity/same valence; different identity/different valence). Per stimulus category (body vs. face), each condition involved 48 trials, so that 24 correct responses would be expected for chance level response behaviour. In the ‘‘same emotion’’ conditions, 16 trials each involved positive/positive, negative/negative and neutral/neutral valence pairings of bodies/faces, half of them showing female models, either involving the same (same identity/same emotion) or different (different identity/same emotion) model identities for the first and second picture. Two task versions were created which differed regarding the order in which the pictures with different emotional valences were presented within each trial of the ‘‘different emotions’’ conditions. In version one, 16 trials each involved neutral/positive, negative/neutral and positive/negative emotion pairings, half of them showing female models, either involving the same or different model identities for the first and second picture. In version two, the order of presentation of the different emotional valences was reversed (positive/neutral, neutral/negative and negative/positive). The administration of task versions one and two was counterbalanced across participants within each group. Each trial started with the presentation of a

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fixation dot with variable display times (100, 200, 300 or 400 ms), followed by the first stimulus (body or face) (1000 ms), a scrambled mask (200 ms) introduced to delete the visual short term storage and thus to prevent direct perceptual comparison with the consecutive picture, a blank screen (400 ms) and the presentation of the second stimulus (1000 ms). The trial ended with the presentation of the response screen asking the participants to indicate whether the two stimuli depicted the same or different emotional valences by pressing one of two buttons using the index finger (‘‘same’’) or the middle finger (‘‘different’’) of the right hand within 3000 ms. Every button press within this time window ended the trial. Fig. 1 illustrates the trial structure. The experiment involved 384 trials in total [2 categories (faces vs. bodies)  3 valences (negative, neutral, positive)  32 pictures  2 (presented once in the same and once in the different condition)]. The order of trials was randomized and there was a short break after half of the trials lasting between 15 s and 10 min, with the exact duration being determined by the participants. The mean duration of the break amounted to 119.4 (SD = 82.4) s in the SZ and to 36.4 (SD = 29.0) s in the HC group (t(13) = 3.330; P = .005). Practise trials preceded the main task and could be repeated as often as necessary to ensure that each participant understood the task instructions properly before the experiment was started. By this, we aimed to minimise chances that patients might perform at a lower level due to poor understanding of task instructions. 2.4. Analyses of the behavioural data Response accuracies (number of correct responses, number of incorrect responses and misses) and reaction times (RTs) for correct responses were submitted to separate repeated-measures analyses of variance (ANOVA) with Category (bodies versus faces), Emotion (same emotion versus different emotion) and Identity

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(same identity versus different identity) as within-subject factors and Group (HC group versus SZ group) as between-subject factor. In the case of significant interactions, only higher-order interactions involving these factors were analysed further. To control for speed-accuracy trade-offs, inverse efficiency scores (see Jacques and Rossion, 2007) were also computed for each participant and condition. These are expressed in ms and defined as mean response times (correct responses only) divided by the proportion of correct responses. They allow for a combined measure of both accuracy and speed. Inverse efficiency scores were analysed in the same manner as all other behavioural parameters. Lower values stand for higher efficiency. 2.5. EEG recording and analysis During the experiment, electroencephalography (EEG) was recorded using silver-silver chloride 30 electrodes (F7, F3, Fz, F4, F8, FT7, FC3, FCz, FC4, FT8, T7, C3, Cz, C4, T8, TP7, CP3, CPz, CP4, TP8, P7, P3, Pz, P4, P8, PO7, PO3, POz, PO4, PO8) which were mounted to an electrode cap according to the 10–20 standard setup (Jasper, 1958). We used a Brain Products BrainAmp Standard Amplifier (Brain Products, Munich, Germany). Fpz was used as the ground electrode and all active electrodes were referenced to linked mastoid electrodes. Electrode impedance was kept below 5 kO. EEG signals were digitized at a sampling rate of 500 Hz and band-pass filtered with cutoffs of 0.5 and 50 Hz. Analysis was conducted offline using Brain Vision Analyzer 2.0. Trials with EEG voltage steps exceeding 50 lV were excluded by means of an automatic artifact detection algorithm. EEG artifacts resulting from eye movements and blinks were corrected by using independent component analysis (ICA) as implemented in Brain Vision Analyzer 2. EEG analysis focused on the ERP response to the second picture as behavioural response patterns are associated with the second

Fig. 1. Trial structure and stimuli in the different conditions of the Delayed-Matching to Sample Task.

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picture. Only correct trials were included in the analysis. Data were re-referenced to a common average reference. For each stimulus category, raw data were segmented in epochs starting 200 ms before stimulus onset and ending 450 ms after stimulus presentation with the first 200 ms serving as baseline. Trials with eye movements or EEG artifacts exceeding 50 lV were omitted from further analyses. Data were averaged separately for each of the eight stimulus categories. Each category contained 48 trials. Inspection of the grand mean waveforms showed that maximal or minimal amplitudes were seen at electrode positions P7 and P8. The statistical analysis focussed on the P100 and N170. P100 and N170 maxima were determined as peak amplitudes between 80 and 130 ms (P100) and 130 and 210 ms (N170). Amplitude maxima were used to determine P100 and N170 latencies. P100 and N170 amplitudes and latencies were submitted to repeated measure ANOVAS with the within-subject factors Category (bodies versus faces), Emotion (same emotion versus different emotion), Identity (same identity versus different identity) and Hemisphere (left versus right), as well as the between-subject factor Group (HC group versus SZ group). In the case of significant interactions, only higher-order interactions involving these factors will be analysed further. 2.6. Procedure First, written informed consent was obtained from all participants. Afterwards, they were all administered the semistructured clinical interview described above, followed by the Beck Depression Inventory and the IQ screening. This was followed by administration of the Delayed Matching-to-Sample Task while EEG was recorded. In total, the assessment took approximately one and a half hours. Roughly half of the EEG recordings in each group were carried out by a study nurse who was blind to the hypotheses of the study, but not to the participants’ identity (patient vs. control participant). The other half of the recordings was performed by a member of the Neuropsychology lab who was aware of both the theoretical expectations and the participants’ identity. Given the complexity of our expectations (a modulation of ERPs by the identity and/or valence of the depicted body and face stimuli), and given the fact that all instructions regarding the experimental task were provided in written form, we think that it is highly unlikely that the experimenter’s knowledge of the theoretical foundations of the study has systematically affected the participant’s performance. 3. Results 3.1. Background data SZ and HC groups did not differ on mean age or years of education (both P P 0.266), but HC subjects showed a higher verbal IQ (t(22) = 3.16; P = 0.004) and lower BDI scores (t(13) = 3.39; P = 0.005) than SZ patients (see Table 1). Supplementary 1 contains additional exploratory analyses involving BDI scores and IQ estimates as covariates in all analyses presented in the subsequent paragraphs. 3.2. Behavioural data Descriptive statistics for the behavioural data are presented in Table 2a and b. Repeated-measures ANOVAs for correct responses yielded significant main effects of Group (F(1, 27) = 16.21, P < 0.001), reflecting an overall reduced number of correct responses in the SZ relative to the HC group, and Identity (F(1, 27) = 33.23, P < 0.001),

with more correct emotion equality responses in the ‘‘same identity’’ relative to the ‘‘different identity’’ condition. Furthermore, significant Category  Emotion (F(1, 27) = 12.69, P = 0.001), Identity  Emotion (F(1, 27) = 24.44, P = 0.001) and Category  Identity  Emotion (F(1, 27) = 5.51, P = 0.026) interactions emerged. No other significant main effects or interactions were revealed (all P P 0.065). To resolve the three-way interaction, separate repeated-measures analyses were conducted for the body and face categories, involving Group, Identity and Emotion as factors. For faces, a significant Identity  Emotion (F(1, 27) = 10.03, P = 0.004) interaction emerged, which was resolved further by computing separate analyses for the ‘‘same identity’’ and ‘‘different identity’’ conditions, yielding a significant effect of Emotion on trials involving the ‘‘same identity’’ (F(1, 27) = 8.95, P = .006; more correct responses in the ‘‘same emotion’’ relative to the ‘‘different emotion’’ conditions) but not on trials involving ‘‘different identities’’ (P = 0.418). For bodies, the significant Identity  Emotion interaction (F(1, 27) = 36.39, P < 0.001) denoted a higher number of correct responses in the ‘‘same emotion’’ relative to the "different emotion’’ condition for both same identities" (F(1, 27) = 15.51, P = 0.048) and for "different identities" F(1, 27) = 4.28, P = 0.001). Repeated-measures ANOVAs of the number of incorrect responses yielded significant main effects of Category (F(1, 27) = 11.83, P = 0.002), with more incorrect responses for faces than for bodies, and Identity (F(1, 27) = 58.73, P < 0.001), with more incorrect responses in the ‘‘different identity’’ relative to the ‘‘same identity’’ condition. Furthermore, there were significant Category  Identity (F(1, 27) = 5.08, P = 0.033), Category  Emotion (F(1, 27) = 13.88, P = 0.001) and Emotion  Identity (F(1, 27) = 54.26, P < 0.001) interactions (all other P > 0.062). To resolve the Category  Identity and the Emotion  Identity interactions, two separate ANOVAs were computed for the ‘‘same’’ vs. ‘‘different’’ identity conditions involving Category, Emotion and Group as factors. In the analysis of the ‘‘same identity’’ conditions, significant main effects of Category (F(1, 27) = 15.87, P < 0.001; more incorrect responses for faces than for bodies) and Emotion (F(1, 27) = 23.07, P < 0.001; more incorrect responses in the ‘‘different emotions’’ relative to the ‘‘same emotions’’ condition) emerged. For the ‘‘different identity’’ condition, there was no significant effect of Category (P = .378), but a significant effect of Emotion (F(1, 27) = 8.59, P = 0.002; more incorrect responses for ‘‘same emotions’’ relative to ‘‘different emotions’’). To resolve the significant Category  Emotion interaction, separate ANOVAs were computed for the ‘‘same emotion’’ and ‘‘different emotion’’ conditions involving Category, Identity and Group as factors. For ‘‘same emotions’’, more incorrect responses were revealed for bodies than for faces (F(1, 27) = 4.79, P = 0.037) while for trials involving ‘‘different emotions’’, more incorrect responses were registered for faces than for bodies (F(1, 27) = 17.42, P < 0.001). The analysis of misses yielded a significant main effect of Group (F(1, 27) = 10.92, P = 0.003), reflecting more misses in the SZ relative to the HC group and a significant Group  Category interaction (F(1, 27) = 6.20, P = 0.019), but no other significant effects or interactions (all P > 0.064). To resolve the interaction, separate analyses were carried out for patients and controls involving Category, Identity and Emotion as factors. In the patients, a main effect of Category (F(1, 13) = 4.73, P = 0.049) emerged, denoting more misses for bodies than for faces, while there was no significant effect of Category in the HC group (P = 0.167). Repeated-measures ANOVAs for response times (correct responses only) yielded main effects of Group (F(1, 27) = 4.98, P = 0.034), reflecting longer response times in the SZ relative to the HC group, and Identity (F(1, 27) = 10.97, P = 0.003), reflecting

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Table 2a Correct responses and response times for the two stimulus categories (faces vs. bodies) in the four conditions (same vs. different/identity vs. emotion) of the Delayed Matching-toSample Task in the patients with schizophrenia (SZ) and in healthy controls (HC): Means (Standard Deviations). SZ (N = 14)

HC (N = 15)

Faces

Bodies

Faces

Bodies

Number of correct responses (max.: 48) Same identity/Same emotion Same identity/different emotion Different identity/same emotion Different identity/different emotion

30.6 25.4 22.5 25.1

30.3 27.3 19.4 26.7

45.5 40.1 36.8 38.1

45.7(3.8) 42.7 (7.4) 32.9 (6.3) 42.5 (5.5)

Response times (ms) for correct responses Same identity/same emotion Same identity/different emotion Different identity/same emotion Different identity/different emotion

406.5 566.1 689.0 587.0

(17.8) (14.9) (12.5) (14.9) (178.0) (171.3) (284.5) (230.6)

(18.1) (16.6) (10.4) (16.7)

496.4 571.5 584.3 517.5

(306.3) (290.5) (204.3) (201.5)

(4.3) (5.0) (6.9) (5.1)

350.3 420.4 466.0 460.9

(100.8) (127.7) (166.0) (169.6)

351.6 449.8 465.4 462.1

(97.7) (158.8) (131.7) (150.7)

Table 2b Number of incorrect responses and misses for the two stimulus categories (faces vs. bodies) in the four conditions (same vs. different/identity vs. emotion) of the Delayed Matching-to-Sample Task in the patients with schizophrenia (SZ) and in healthy controls (HC): Means (Standard Deviations). SZ (N = 14)

HC (N = 15)

Faces

Bodies

Faces

Bodies

Number of incorrect responses Same identity/same emotion Same identity/different emotion Different identity/same emotion Different identity/different emotion

1.9 (3.2) 9.8 (8.9) 13.1 (11.1) 8.5 (7.8)

1.2 (1.9) 6.07 (6.8) 14.3 (9.4) 6.4 (8.2)

1.1 (1.2) 7.3 (4.2) 10.7 (6.09) 9.6 (4.9)

1.2 (1.2) 4.9 (6.9) 14.7 (6.1) 5.2 (5.2)

Number of misses Same identity/same emotion Same identity/different emotion Different identity/same emotion Different identity/different emotion

10.9 (13.0) 7.9 (8.8) 7.5 (10.4) 8.9 (11.0)

11.4 (13.2) 10.1 (11.5) 8.6 (9.1) 9.7 (10.2)

1.5 0.6 0.5 0.3

1.1 0.5 0.4 0.3

shorter response times in the ‘‘same’’ relative to the ‘‘different identity’’ condition, as well as a significant Identity  Emotion (F(1, 27) = 21.36, P < .001) interaction. To resolve the two-way interaction, separate analyses were calculated for the ‘‘same identity’’ and ‘‘different identity’’ conditions, involving Group, Category and Emotion as factors. The interaction denoted shorter response times on trials involving the same relative to different emotions in the ‘‘same identity’’ conditions (F(1, 27) = 14.15, P = 0.001), but not in the ‘‘different identity’’ conditions (P = 0.093). No other significant effects or interactions emerged (all P P 0.056). Repeated-measures ANOVAs of inverse efficiency scores (see Table 3 for the descriptive data) yielded a significant main effect of Group (F(1, 27) = 8.86, P = 0.006), denoting overall lower response efficiency in the SZ relative to the HC group, and a significant Identity  Emotion (F(1, 27) = 7.78, P = 0.010) interaction. There were no other significant effects or interactions (all P > 0.069). To resolve the interaction, two separate ANOVAs were computed for the ‘‘same emotion’’ and ‘‘different emotion’’ conditions. For the ‘‘same emotion’’ but not for the ‘‘different emotion’’ conditions (P = 0.789), a significant effect of Identity (F(1, 27) = 5.94,

(4.4) (1.5) (1.4) (0.7)

(3.9) (0.1) (1.6) (1.0)

P = 0.022) was revealed, reflecting overall lower efficiency in the ‘‘different’’ relative to the ‘‘same’’ identity condition. 3.3. EEG data Grand mean ERPs for bodies and faces are illustrated separately for the SZ (Fig. 2a) and HC (Fig. 2b) group. To provide a clear illustration of the significant interactions involving the Group factor, the descriptive data for these interactions are also provided separately in Table 4. Full descriptive data of ERP amplitudes and latencies for all conditions and electrode sites, presented separately for SZ and HC groups are provided in Supplementary 1, Supplementary Table S1. Separate analyses were performed for the P100 and N170 amplitudes and latencies. For P100 amplitudes, ANOVA yielded main effects of Category (F(1, 27) = 12.92, P = 0.001) and Hemisphere (F(1, 27) = 16.61, P < 0.001) with enhanced P100 amplitudes for faces compared to bodies and in the right compared to the left hemisphere. In addition, there were significant interactions between the factors Category and Hemisphere (F(1, 27) = 5.38, P = 0.028), Group, Category and Hemisphere (F(1, 27) = 4.26, P = 0.049) as well as between

Table 3 Inverse efficiency scores (expressed in ms), defined as the mean response time for correct responses divided by the proportion of correct responses for the two stimulus categories (faces vs. bodies) in the four conditions (same vs. different/identity vs. emotion) of the Delayed Matching-to-Sample Task in the patients with schizophrenia (SZ) and in healthy controls (HC): Means (Standard Deviations). SZ (N = 14)

Same identity/same emotion Same identity/different emotion Different identity/same emotion Different identity/different emotion

HC (N = 15)

Faces

Bodies

992.6 (762.6) 2687.3 (4003.0) 2357.1 (2321.1) 2454.3 (3387.3)

1795.7 2369.6 2639.4 1920.5

Faces (2359.8) (3537.1) (3174.9) (2794.8)

377.6 520.7 653.6 603.4

Bodies (130.4) (202.7) (341.7) (263.2)

376.3 548.2 727.7 542.3

(127.5) (295.4) (358.6) (236.7)

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Fig. 2. Grand mean event-related potentials for bodies and faces in the different conditions for the patients with schizophrenia (a: SZ) and healthy controls (b: HC).

Group, Emotion and Identity (F(1, 27) = 5.42, P = 0.028). To resolve the three-way interactions, separate analyses were performed for the SZ and HC group (see Table 4). For the SZ group, analysis revealed a significant Category  Hemisphere interaction (F(1, 13) = 7.19, P = 0.019), reflecting a significant effect of Hemisphere (F(1, 13) = 17.19, P = 0.001; right > left) for faces, but not for bodies (P = .059), while there was no Identity  Emotion interaction (P = .357). For the HC group, analysis revealed a significant Emotion  Identity interaction (F(1, 14) = 17.16, P = 0.001), but no Category  Hemisphere interaction (P = .831). Separate ANOVAs for the ‘‘same’’ vs. ‘‘different’’ identity conditions were carried out to resolve the significant interaction which reflected larger P100 amplitudes on trials involving ‘‘different emotions’’ as compared to trials involving the ‘‘same emotions’’ only for ‘‘same identity’’ trials (F(1, 14) = 5.75, P = 0.031), but not for ‘‘different identity’’ trials (P = 0.099). Analysis of P100 latencies showed a main effect of Category with delayed P100 amplitudes for faces compared to bodies (F(1, 27) = 23.41, P < 0.001; see Fig. 2). None of the other comparisons reached significance (all P > 0.128). Analysis of N170 amplitudes revealed main effects of Category (F(1, 27) = 28.71, P < 0.001), Identity (F(1, 27) = 7.07, P = 0.013) and Hemisphere (F(1, 27) = 4.85, P = 0.036) with enhanced N170 amplitudes for faces compared to bodies, for trials involving ‘‘different identities’’ relative to ‘‘same identities’’ and in the right compared to the left hemisphere (see Fig. 2). In addition, there were significant interactions between the factors Group and Emotion

(F(1, 27) = 4.32, P = 0.047) and Category, Identity and Hemisphere (F(1, 27) = 6.70, P = 0.015). There were no other significant effects or interactions (all P > 0.139). To resolve the two-way interaction, separate analyses were performed for the SZ and HC groups (see Table 4). While there was no significant main effect of Emotion in the SZ group (P = 0.657), in the HC group, N170 amplitudes were enhanced on trials involving ‘‘different emotions’’ relative to trials involving the ‘‘same emotions’’ (F(1, 14) = 14.52, P = 0.002). Post-hoc analyses of the three-way interaction did not yield any significant results, neither when controlling for Category nor for Identity nor for Hemisphere (all P > 0.287). Analyses of N170 latencies revealed a significant main effect of Emotion (F(1, 27) = 4.45, P = 0.044), denoting delayed N170 latencies on trials involving ‘‘different emotions’’ relative to the ‘‘same emotions’’, and significant Group  Category (F(1, 27) = 4.47, P = 0.044) and Category  Emotion (F(1, 27) = 5.45, P = 0.027) interactions. There were no further significant effects or interactions (all P > 0.091). Resolving the Group  Category interaction by computing two separate ANOVAs for the SZ and HC groups (see Table 4) did not yield any significant effects, neither for the SZ (all P > 0.056), nor for the HC group (all P > .094). Resolving the Category  Emotion interaction by computing two separate ANOVAs for faces and bodies yielded a significant main effect of Emotion for bodies (F(1, 27) = 5.55, P = 0.027), reflecting delayed N170 amplitudes on trials involving "different"

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Fig. 2 (continued)

relative to the "same emotions", but not for faces (P = 0.497). There were no other significant effects or interactions (all P > 0.107). The number of correct responses was overall low in the SZ group, hardly exceeding chance level in some conditions. This also led to a reduced number of valid epochs for EEG analysis. On average, out of 384 possible valid epochs per task, 215.8 (SD: 97.6; Min.: 57/Max.: 333) entered analysis in the SZ group and 316.8 (SD: 23.9; Min.: 244/Max.: 349) entered analysis in the HC group. To address this, we performed some additional analyses involving a reduced subset of participants with at least 16 valid EEG epochs per condition. These analyses are described in the Supplementary material (see Supplementary 2).

4. Discussion The main aim of the current study was to investigate whether impaired processing of emotional faces in schizophrenia is paralleled by altered processing of emotions expressed by human bodies, both on the behavioural level and in terms of ERPs. We also aimed to elucidate whether these potential alterations are affected by the emotional or personal identity of the faces/bodies. To summarise the most relevant findings involving the group factor, patients with schizophrenia showed overall poorer behavioural performance (fewer correct responses and longer response times) than healthy controls, irrespective of stimulus category

(bodies vs. faces) and personal or emotional identity (same vs. different). Relating response times to the proportion of correct responses using inverse efficiency scores in order to control for speed-accuracy trade-offs, revealed overall lower response efficiency in the patients relative to controls, across all conditions. Patients also showed overall more misses than controls (but not more commission errors), particularly for bodies. In the control group, but not in the patient group, P100 amplitudes were larger in the ‘‘same identity/different emotions’’ relative to the ‘‘same identity/same emotion’’ condition, while in the SZ group, but not in the controls, P100 amplitudes were larger in the right compared to the left hemisphere for faces, but not for bodies. Similarly as for the P100, only the controls showed a modulation by emotion equality with regard to the N170: Amplitudes were enhanced for trials involving different relative to same emotions, independent of personal identity. Our finding of a generalised behavioural impairment of both face and body processing in schizophrenia is in line with a previous study from our lab, where we demonstrated poorer performance of patients with schizophrenia for bodies, faces and even cars (Soria Bauser et al., 2012). This speaks in favour of the assumption that similar neurocognitive mechanisms might underlie affect perception in faces and bodies so that a combined disruption of the processing of both stimulus categories can usually be observed in various clinical disorders (e.g. Anorexia Nervosa: Pollatos et al., 2008a,b; Castellini et al., 2012; Suchan et al., 2013). Also, our body

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Table 4 Descriptive data for the significant interactions involving the GROUP factor in the analyses of the ERPs for the Delayed Matching-to-Sample Task in the patients with schizophrenia (SZ) and in healthy controls (HC): Means (Standard Deviations). P100 amplitudes (lV) Group  Category  hemisphere (F(1, 27) = 4.26, P = 0.049) Faces P7 SZ 4.7 (2.3) HC 6.2 (2.3) Group  Emotion  Identity (F(1, 27) = 5.42, P = 0.028)

Bodies P8

P7

P8

7.2 (3.4) 8.2 (3.8)

4.4 (2.7) 5.6 (2.1)

5.6 (3.2) 7.5 (3.5)

Same identity

SZ HC

Different identity

Same emotion

Different emotion

Same emotion

Different emotion

5.4 (2.8) 6.7 (2.6)

5.5 (2.7) 7.1 (2.6)

5.2 (2.9) 7.0 (2.8)

5.8 (2.9) 6.6 (2.6)

N170 amplitudes (lV) Group  Emotion (F(1, 27) = 4.32, P = 0.047) Same emotion SZ HC

4.8 (2.5) 5.0 (2.3)

Different emotion 4.7 (1.9) 5.5 (2.4)

N170 latencies (ms) Group  Category (F(1, 27) = 4.47, P = 0.044)

SZ HC

Faces

Bodies

172.4 (11.2) 168.1 (9.11)

165.7 (17.1) 169.5 (10.6)

stimuli involved masked faces, and Brandman and Yovel (2012) showed that, in spite of the lack of facial features, such face percepts presented in a body context, can nevertheless induce face processing mechanisms. However, it cannot be excluded that a generalised deficit in visuoperceptual processing or – due to the specific nature of our paradigm (Matching-to-Sample Task) – well-known working memory impairments might have contributed to the overall poorer performance in the patient group. However, this is usually only the case with high working memory load, especially involving distractors not related to the task at hand, which does not apply to our (Mano and Brown, 2012). In contrast to Bediou et al. (2005), we did not observe a group  personal identity  emotional identity interaction, in that patients performed more poorly than controls when they had to match facial affect in faces depicting ‘‘different’’ relative to the ‘‘same’’ persons. This might be due to lower sample sizes and thus a lack of power in our study, but it might also be related to the fact that our sample was more acutely affected than the patients investigated by Bediou et al. (2005), who were described as remitted and clinically stable at least for a month. This might have led to a more generalised behavioural impairment in our patient group, which was not yet modulated by personal and/or emotional identity. P100 latencies were not affected by the group factor and we also did not find evidence of generally reduced P100 amplitudes in the patient group. Instead, we observed a complex interaction of the emotional and personal identity of the presented stimuli with the group factor: The P100 was enhanced in healthy controls only when they watched the same person display two different emotions relative to trials where the same person showed the same emotion twice. In healthy controls, the P100 has been associated with the processing of low-level stimulus features and the classification of a stimulus as a face/body (Herrmann et al., 2005), and it also appears to be affected by the emotional expression of faces/bodies (Eimer, 2000a,b; Meeren et al., 2005; Righart and de Gelder, 2007; van Heijnsbergen et al., 2007). It is thus plausible that the P100 was differentially affected by emotional identity (same vs. different) only in the ‘‘same identity’’ condition,

because the difference in the low-level stimulus features denoting emotional valence – which was the only relevant factor for the same/different decision the participants had to make – might be more clear-cut when the same person is depicted twice showing different emotions. In contrast to that, in the ‘‘different identity’’ condition, the P100 might respond to both low-level features representing emotional valence and to other stimulus features representing personal identity at the same time, thus leading to less pronounced differences between trials involving same and different emotions. The absence of such interactions in the patients with schizophrenia may suggest deficient modulation of the early visual processing stages by such context information. This is in line with many other reports of a context processing failure as a core deficit in schizophrenia (Servan-Schreiber et al., 1996; Bediou et al., 2005). Previous studies yielded inconsistent findings with regard to the question whether patients with schizophrenia show reduced P100 amplitudes in response to facial stimuli or not (Caharel et al., 2005; Johnston et al., 2005; Campanella et al., 2006; Wynn et al., 2008). It is striking that in the two studies (Johnston et al., 2005; Wynn et al., 2008) that did not observe any group differences with regard to the P100 amplitudes, participants had to identify the gender or the emotions in a given face. In contrast to that, the authors of the two other studies used tasks that involved some sort of comparison with other faces: Caharel et al. (2005) instructed their participants to decide whether a face was familiar or not, while Campanella et al. (2006) had their subjects detect deviant faces (either with regard to their personal identity or the emotions shown) in a stream of ‘‘standard’’ faces. A possible explanation why we did not detect a simple group effect for the P100, but rather the absence of a complex an interaction in the patient group, might be constituted by the fact that, in contrast to the paradigm used by Campanella et al. (2006), the factors personal identity and emotional identity also interacted in our task. For the N170 component a similar picture emerged as for the P100: In contrast to previous findings of reduced N170 amplitudes and delayed N170 latencies (Herrmann et al., 2004; Caharel et al., 2005; Bediou et al., 2007; Lee et al., 2010; Kirihara et al., 2012;

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Wynn et al., 2013) in patients with schizophrenia, there were no overall significant group differences between patients and controls with regard to the N170 amplitudes in our study, but again, an interaction involving the group factor emerged. Only the control group showed a modulation of the N170 amplitude by emotional identity, albeit in a less complex manner than for the P100: N170 amplitudes were overall enhanced for different vs. same emotions. Given that the N170 has been linked to the structural encoding of a stimulus and to the generation of a more global stimulus configuration (Rossion et al., 2000; Eimer, 2000a,b), it is plausible that, compared with the P100, the N170 might be more globally affected by emotional identity and be less amenable to interactions between emotional and personal identity. Similar task-related explanations as discussed in the previous paragraph for the P100 results might account for the fact that N170 amplitudes were not generally reduced in our patient group. Finally, the lateralization of the P100 differed between patients and controls. In patients, P100 amplitudes were more pronounced over the right relative to the left hemisphere for faces but not for bodies. Unusual lateralization effects have been considered as a potential biomarker of schizophrenia (Alary et al., 2013) so that the deviating lateralization pattern in the patient group relative to the control group in our study is not surprising. Scores for depressive symptoms were elevated in our patient group with three patients being medicated with antidepressant agents. In this respect, our sample can be regarded as rather representative considering the high rates of comorbidity between these two disorders: Up to 59% of patients with schizophrenia appear to also fulfil the diagnostic criteria for minor or major depressive episodes (Kessler et al., 1994). Also, at the time of testing, only five patients showed BDI scores that can be considered as clinically relevant (>17, reaching a maximum score of 30 in our sample). Nevertheless, it is possible that depression can additionally detrimentally affect face and body processing as (emotional) face processing deficits have been reported in patients with major depression (Bourke et al., 2010). We tried to determine whether controlling for depression by means of explorative post hoc ANOVAs involving BDI scores as a covariate (see Supplementary 1 for the analyses) would significantly alter the pattern of results with a focus on the effects involving the Group factor. Behaviourally, while response accuracy was still impaired in the patient group, the overall group effects with regard to response times and misses were abolished. Response efficiency was still lower in SZ patients, particularly for trials involving different facial emotions. In ERP analyses, introducing BDI scores as a covariate did not affect the lack of an interaction between emotion and identity for P100 amplitudes in patients, but abolished the interaction between group and emotion for N170 amplitudes. On the other hand, a new interaction emerged, indicating that N170 latencies might be modulated by stimulus category in patients, but not in controls. This suggests that, in future studies, the pattern of impairment of depressed vs. non-depressed patients with schizophrenia ought to be investigated with regard to the behavioural and electrophysiological correlates of body and face processing. Furthermore, antipsychotic medication could have affected the performance and associated ERPs in the patient group. However, in a meta-analysis, Köhler et al. (2010) concluded that chlorpromazine equivalents do not correlate with a greater degree of emotional face perception deficits and that patients who are medicated with first-generation antipsychotics are more impaired than those treated with second-generation antipsychotics – the latter was the case for 12 out 14 patients in our sample. Taken together, it is thus unlikely that our result pattern might mirror strong effects of medication. Patients and controls were matched with regard to years of education, but patients showed a lower verbal (premorbid) IQ than

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controls. Although demographic factors have not been related to face/body processing (Bryson et al., 1997) and intelligence is usually not even assessed in studies on face and body processing involving healthy controls, we performed additional IQ covariance analyses described in Supplementary 1. These analyses suggest that IQ differences between patients and controls may account for differences in the ERPs underlying face and body processing as some of the previously significant effects involving the group factor were now abolished. However, these negative results have to be interpreted with caution as due to the relatively small sample sizes in our study introducing covariates further reduces statistical power. Ratings of positive and negative symptoms were unfortunately not available for the current patient sample. Symptomatology has been discussed as a factor potentially affecting face processing, although the results are inconsistent and may depend on the specific scale used to assess positive and negative symptoms (see Köhler et al., 2010), and this should be borne in mind in future studies. Although, our sample sizes were small, this is not at all unusual for ERP studies in this field (see e.g. Tsunoda et al., 2012). Further less specific factors might have affected our result pattern. The fact that healthy controls were paid for travel expenses and patients were not is unlikely to have played a role as this should mean that the patients’ internal motivation to participate in the study without financial incentives ought to have been higher than in controls and should have probably been associated with better behavioural performance, which it was not. The poorer performance in the patients might be partly explained by greater fatigue (e.g. Waters et al., 2013) during the course of the experiment. However, we tried to minimise this effect by allowing participants to determine the length of the break during the experimental task. Patients chose to pause for approximately 1.5 min longer than controls. Nevertheless, it cannot be ruled out that factors such as deficient sustained attention as an indicator of well-known executive dysfunctions in schizophrenia patients (see Orellana and Slachevsky, 2013) might have contributed to the poorer behavioural performance in the patient group. Finally, a higher spontaneous eye blink rate (e.g. Chan et al., 2010) might have caused patients with schizophrenia to miss more stimuli suggesting that it would be useful to control for this in future research. As a final limitation, roughly half of the patients showed a low number of correct responses and thus a reduced number of valid epochs available for EEG analyses. The performance of some of these patients partly fell below chance level in some conditions. Supplementary 2 presents exploratory analyses involving a subset of seven patients with at least 16 valid segments per condition. This subset of patients did not significantly deviate from the HC group regarding behavioural performance apart from overall lower response efficiency. Although due to the small number of patients, these supplementary analyses have to be interpreted with great caution, they appear to suggest that higher performing SZ patients might indeed show a modulation of their behavioural performance and of P100 amplitudes by context factors such as category and emotional and personal identity, with the exact pattern sometimes deviating from the one observed in healthy controls. However, these patients might still show a complete lack of a modulation of the N170, reflecting more global processing of bodies and faces, by personal identity. It might be worthwhile to investigate this idea further in subgroups of patients showing different behavioural performance levels and to relate these differences to clinical characteristics. 4.1. Conclusions Taken together, the current study demonstrates for the first time that, compared with healthy controls, patients with

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schizophrenia are behaviourally impaired in the processing of both faces and bodies. Also, in contrast to healthy controls, they do not show a modulation of the P100 and N170 ERPs by context factors, such as the personal or emotional identity of faces and bodies. This may relate to a global deficit in context processing, potentially particularly affecting a subset of more poorly performing patients with schizophrenia. Acknowledgements We would like to thank Cäcilia Werschmann, Maren Hollmann and Sabine Bierstedt for assistance with data collection. This study was funded by grants awarded by the German Research Foundation (Deutsche Forschungsgemeinschaft – DFG) to Boris Suchan (grant number: SU 246/8-1) and to Patrizia Thoma and Boris Suchan (grant number: TH 1535/2-1). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.clinph.2013.10.046. References Alary M, Razafimandimby A, Delcroix N, Leroux E, Delamillieure P, Brazo P, et al. Reduced functional cerebral lateralization: a biomarker of schizophrenia? Bipolar Disord 2013. http://dx.doi.org/10.1111/bdi.12070 (E-Pub ahead of print). Amminger GP, Schafer MR, Papageorgiou K, Klier CM, Schlogelhofer M, Mossaheb N, et al. Emotion recognition in individuals at clinical high-risk for schizophrenia. Schizophr Bull 2011;6:450–4. Archer J, Hay DC, Young AW. Face processing in psychiatric conditions. Br J Clin Psychol 1992;31:45–61. Bandelow B, Bleich S, Kropp S. Handbuch Psychopharmaka. Göttingen, Bern, Toronto, Seattle: Hogrefe; 2000. Batty M, Taylor MJ. Early processing of the six basic facial emotional expressions. Brain Res Cogn Brain Res 2003;17:613–20. Bediou B, Krolak-Salmon P, Saoud M, Henaff MA, Burt M, Dalery J, et al. Facial expression and sex recognition in schizophrenia and depression. Can J Psychiatry 2005;50:525–33. Bediou B, Henaff MA, Bertrand O, Brunelin J, d’Amato T, Saoud M, et al. Impaired fronto-temporal processing of emotion in schizophrenia. Neurophysiol Clin 2007;37:77–87. Bourke C, Douglas K, Porter R. Processing of facial emotion expression in major depression: a review. Aust NZ J Psychiatry 2010;44:681–96. Brandman T, Yovel G. A face inversion effect without a face. Cognition 2012;125:365–72. Brüne M, Abdel-Hamid M, Sonntag C, Lehmkamper C, Langdon R. Linking social cognition with social interaction: non-verbal expressivity, social competence and ‘‘mentalising’’ in patients with schizophrenia spectrum disorders. Behav Brain Funct 2009;5:6. Bryson G, Bell M, Lysaker P. Affect recognition in schizophrenia: a function of global impairment or a specific cognitive deficit. Psychiatry Res 1997;71:105–13. Butler PD, Tambini A, Yovel G, Jalbrzikowski M, Ziwich R, Silipo G, et al. What’s in a face? Effects of stimulus duration and inversion on face processing in schizophrenia. Schizophr Res 2008;103:283–92. Caharel S, Courtay N, Bernard C, Lalonde R, Rebai M. Familiarity and emotional expression influence an early stage of face processing: an electrophysiological study. Brain Cogn 2005;59:96–100. Campanella S, Montedoro C, Streel E, Verbanck P, Rosier V. Early visual components (P100, N170) are disrupted in chronic schizophrenic patients: an event-related potentials study. Neurophysiol Clin 2006;36:71–8. Castellini G, Polito C, Bolognesi E, D’Argenio A, Ginestroni A, Mascalchi M, et al. Looking at my body. Similarities and differences between anorexia nervosa patients and controls in body image visual processing. Eur Psychiatry 2012;28:427–35. Chambon V, Baudouin JY, Franck N. The role of configural information in facial emotion recognition in schizophrenia. Neuropsychologia 2006;44:2437–44. Chan KK, Hui CL, Lamm MM, Tang JY, Wong GH, Chan SK, et al. A three-year prospective study of spontaneous eye-blink rate in first-episode schizophrenia: relationship with relapse and neurocognitive function. East Asian Arch Psychiatry 2010;20:174–9. Chen Y, Norton D, McBain R, Ongur D, Heckers S. Visual and cognitive processing of face information in schizophrenia: detection, discrimination and working memory. Schizophr Res 2009;107:92–8. Collishaw SM, Hole GJ. Featural and configurational processes in the recognition of faces of different familiarity. Perception 2000;29:893–909.

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