Neural bases of different cognitive strategies for facial affect processing in schizophrenia

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Schizophrenia Research 100 (2008) 191 – 205 www.elsevier.com/locate/schres

Neural bases of different cognitive strategies for facial affect processing in schizophrenia Eric Fakra a,b,⁎, Pilar Salgado-Pineda a , Pauline Delaveau a , Ahmad R. Hariri c , Olivier Blin a a

CIC-UPCET, Hôpital de la Timone, UMR CNRS 6193 INCM, Marseille, France Service Hospitalo-Universitaire de Psychiatrie, Hôpital Ste. Marguerite, Marseille, France Department of Psychiatry and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania, USA b

c

Received 24 December 2006; received in revised form 27 November 2007; accepted 30 November 2007 Available online 30 January 2008

Abstract Objective: To examine the neural basis and dynamics of facial affect processing in schizophrenic patients as compared to healthy controls. Method: Fourteen schizophrenic patients and fourteen matched controls performed a facial affect identification task during fMRI acquisition. The emotional task included an intuitive emotional condition (matching emotional faces) and a more cognitively demanding condition (labeling emotional faces). Individual analysis for each emotional condition, and second-level t-tests examining both within-, and between-group differences, were carried out using a random effects approach. Psychophysiological interactions (PPI) were tested for variations in functional connectivity between amygdala and other brain regions as a function of changes in experimental conditions (labeling versus matching). Results: During the labeling condition, both groups engaged similar networks. During the matching condition, schizophrenics failed to activate regions of the limbic system implicated in the automatic processing of emotions. PPI revealed an inverse functional connectivity between prefrontal regions and the left amygdala in healthy volunteers but there was no such change in patients. Furthermore, during the matching condition, and compared to controls, patients showed decreased activation of regions involved in holistic face processing (fusiform gyrus) and increased activation of regions associated with feature analysis (inferior parietal cortex, left middle temporal lobe, right precuneus). Conclusions: Our findings suggest that schizophrenic patients invariably adopt a cognitive approach when identifying facial affect. The distributed neocortical network observed during the intuitive condition indicates that patients may resort to feature-based, rather than configuration-based, processing and may constitute a compensatory strategy for limbic dysfunction. © 2007 Elsevier B.V. All rights reserved. Keywords: Schizophrenia; Facial emotion; Emotion regulation; fMRI; Functional connectivity; Amygdala

⁎ Corresponding author. Service Universitaire de Psychiatrie Adulte, Hôpital Sainte Marguerite, 270 BD Sainte-Marguerite, 13274 Marseille Cedex 9, France. Tel.: +33 491 744082; fax: +33 491 745578. E-mail address: [email protected] (E. Fakra). 0920-9964/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.schres.2007.11.040

1. Introduction Impairment in social functioning is one of the most pervasive characteristics of schizophrenic disorders. Data consistently show that this impairment is strongly linked

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to a deficit in correctly identifying and reacting to facial affect in others (Hooker and Park 2002; Kee et al., 2003; Mueser et al., 1996; Poole et al., 2000; Wolwer et al., 2005). However, even though deficits in perceiving facial affect have repeatedly been reported in schizophrenia (Edwards et al., 2002, Mandal et al., 1998), the nature and specificity of this deficit are unclear. Neuroimaging experiments have implicated the amygdala in processing facial expressions of emotion, particularly negative emotions, such as fear (Breiter et al., 1996, Morris et al., 1996) and anger (Suslow et al., 2006, Yang et al., 2002). However, activation of the amygdala is only apparent when subjects perform automatic or intuitive processing of the emotional stimuli (LeDoux 1996, Phillips et al., 2003a). Conversely, this activation reduces when subjects are asked to manipulate the emotional stimuli through cognitive operations such as labeling the emotion or attending to non-emotional characteristics of the stimuli (Critchley et al., 2000, Hariri et al., 2000, Liberzon et al., 2000). Aside from this amygdala inactivation, neuroimaging studies have shown regions of the neocortex, such as the prefrontal cortex (PFC), to be specifically involved in the cognitive appraisal of emotional stimuli (Gusnard et al., 2001, Lane et al., 1997, Simpson et al., 2000) or in the inhibition of emotional reactions (Beauregard et al., 2001, Levesque et al., 2003). This inverse relationship between activity in the amygdala and PFC suggest that PFC exerts top-down regulation of the amygdala. In line with this, Hariri et al. (2000) have shown that adopting a cognitive, rather than an intuitive, approach when identifying facial affect results in attenuation of amygdala activity, which correlates with simultaneous increase in PFC activity. Together, these findings suggest 1) that the extent to which sub-cortical and cortical regions are involved when identifying facial affect depends upon the evaluation approach (intuitive or cognitive) and 2) the existence of a close functional interaction between regions implicated in primary emotional processing and regions implicated in the cognitive manipulation of emotional information. Neuroimaging studies of schizophrenic patients performing emotional tasks have shown dysfunction of the limbic system, particularly in the amygdala and frontal areas, as compared to healthy volunteers. These reports usually demonstrate a failure to activate the amygdala in the patient group (Das et al., 2007, Gur et al., 2002, Hempel et al., 2003, Johnston et al., 2005, Phillips et al., 1999, Schneider et al., 1998), although this finding has been contested (Holt et al., 2006, Holt et al., 2005, Kosaka et al., 2002). Some authors have also related this lack of limbic activation to an over-activation of the PFC (Andreasen 2002, Taylor et al., 2005) and suggested that this pattern not only attests to the limbic dysfunction

in schizophrenia but also reveals a compensatory role for the PFC. Interestingly, this pattern is similar to that observed when healthy subjects modulate their emotional reactions through cognitive processing (Hariri et al., 2000, Pessoa et al., 2005, Phillips et al., 2003a). Therefore, this pattern seems to indicate that patients with schizophrenia resort to cognitive rather than intuitive processes when identifying emotional stimuli. To our knowledge, only one study so far has compared the neural correlates of distinct approaches for evaluating emotional stimuli in schizophrenic patients versus healthy volunteers. In this study, Hempel et al. (2003) were interested in performance-related changes in brain activity. Their findings suggest that compensatory strategies linked to increasing task difficulty, led to increased activity in medial prefrontal cortex. Our own study aimed to examine disease-related activity and functionally-specific neural changes during identification of facial affect. The aim of this present study was to use functional magnetic resonance imaging (fMRI) to 1) determine, in schizophrenic patients compared to healthy volunteers, how different ways of evaluating facial affect (intuitive versus cognitive) influences cerebral activity, 2) examine, using psychophysiological interaction (PPI), changes in the neural interaction between the amygdala and other brain regions as a function of changes in the evaluation method and 3) infer, on the basis of these differential neural responses, the strategy used by schizophrenic patients when identifying facial affect. For this, we used the paradigm proposed by Hariri et al. (2000), which included two facial affect recognition conditions. The first condition (matching) requires automatic and intuitive processing. This has been confirmed by previous findings of significant limbic activation (especially amygdala) and relatively modest PFC activation, in healthy subjects performing this task (Altshuler et al., 2005, Hariri et al., 2003, Meyer-Lindenberg et al., 2006, Tessitore et al., 2005, Wang et al., 2004, Wright et al., 2006). The second condition (labeling) engages higher cognitive processes. Theses cognitive processes appear to modulate limbic regions, as attested by previous findings of decreased Blood Oxygenation Level Dependent (BOLD) signal in the amygdala and increased BOLD signal in PFC during task performance (Hariri et al., 2003, Tessitore et al., 2005, Wang et al., 2004). In line with most of the neuroimaging studies on emotional processing in schizophrenic patients (Das et al., 2007, Gur et al., 2002, Hempel et al., 2003, Johnston et al., 2005, Phillips et al., 1999, Schneider et al., 1998), we predicted that in all conditions patients would fail to engage the amygdala but would preferentially recruit neocortical regions. Consequently, we expected to observe

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similar patterns of activation in patients versus healthy subjects for the cognitive evaluation (labeling) but not for the intuitive evaluation (matching) of facial affect. We also predicted that the between-group comparison for the matching condition would reveal activation of additional cortical areas in the patient group, confirming the existence of a compensatory strategy. PPI analysis allows examination of changes in the influence that one brain region exerts on another, in response to variations in experimental conditions (Friston et al., 1997). Given the role of the amygdala in emotional processing and its consistent dysfunction reported in neuroimaging studies with schizophrenic patients, our study focused on the interaction of this structure with other brain regions. In healthy volunteers, we hypothesized that the PPI findings would yield changes in the interaction between the amygdala and PFC regions, which are known to be involved in the cognitive manipulation of emotional stimuli. By contrast, for the patient group, we hypothesized no change in the interaction between the amygdala and other brain regions as a function of changes in experimental condition. Finally, in order to determine the functional relevance of these activation patterns, we examined whether they were related to experimental task performance and, in schizophrenic patients, the extent to which clinical factors contributed to the abnormal pattern of neural response. 2. Method and materials 2.1. Participants Fourteen right-handed patients with a DSM-IV diagnosis of schizophrenia were included in this study. Two psychiatrists established diagnoses after independent assessment. The assessments were performed at least 6 months prior to study entry and again at study entry to confirm diagnoses. Patients were recruited from the Psychiatry department of Sainte Marguerite Hospital in Marseille. They were either outpatients or inpatients and all had stabilized antipsychotic treatment at the time of the study (i.e. receiving the same antipsychotic at the same dosage for at least 6 weeks). Psychopathological assessment of each patient was based on the PANSS (Positive and Negative Schizophrenia Scale; Kay et al., 1987). The same senior psychiatrist (author E.F.) administered the scale to all patients. In order to examine the influence of specific clinical factors, we used the five dimensions model (negative, positive, excitation, cognitive and depression) of the PANSS (Lancon et al., 2000, Lindenmayer et al., 1994). Clinical characteristics of patients are presented in Table 1.

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Table 1 Clinical and demographic characteristics of patients and controls

Age (years, SD) Gender (male/female) Education level (years, SD) Parent's education level (years, SD) Duration of illness (years, SD) Total PANSS score (mean, SD) Negative factor (mean, SD) Positive factor (mean, SD) Excitation factor (mean, SD) Cognitive factor (mean, SD) Depression factor (mean, SD)

Healthy controls

Schizophrenic patients

n = 14

n = 14

34.64 (5.96) 9/5 14.23 (2.05) 12.21(3.31)

37.29 (8.87) 9/5 12.15 (4.22) 13.08 (4.42)

13.14 (5.46) 24.71 (8.42) 8.43 (2.47) 7.93 (3.32) 6.5 (2.74)

Patients were matched to 14 healthy subjects on the following variables: handedness, age and parents' socioeconomic status (expressed as the highest level of education completed by either parent). Table 1 resumes the different demographic characteristics of the two subject groups. Control subjects were recruited by advertisement at the C.P.C.E.T. (Centre Pharmacologique Clinique et d'Evaluations Thérapeutiques). Prior to study entry, they were interviewed by both a psychologist and a general practitioner to confirm the absence of psychiatric or neurological disease. They were in good health and had no history of alcoholism or drug abuse; nor had they used central nervous system drugs. This study was conducted in accordance with the principles of the declaration of Helsinki. Approval was obtained from the local Ethics Committee of La Timone Hospital (C.C.P.P.R.B. Marseille). Each participant was registered on the French National File and gave informed written consent before entering the study. 2.2. Experimental paradigm Both experimental tasks presented human faces depicting angry or fearful expressions, but differed in the way in which subjects evaluated these faces. In the matching condition, subjects viewed a target face and had to select which one of two faces presented on the same screen expressed the same emotion. In the labeling condition, subjects viewed the same target face but had to judge which of two linguistic labels, angry or afraid, best described the emotion of the target face (see Fig. 1). As a control task, subjects viewed a target oval shape, and chose which of two ovals matched the target exactly. Subjects indicated their response on a two-choice response pad. A complete description of the task is described in the

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Fig. 1. Task paradigm. The task included two experimental conditions, matching and labeling, and one control condition. Each experimental condition presented photographs of faces expressing either fear or anger, but differed in how the subjects had to evaluate the stimuli. In the first condition (matching), subjects were instructed to select the face (bottom) whose affect matched that of the target face (top). In the second condition, subjects were instructed to select the label (bottom) which best described the affect of the target face (top). As a sensorimotor control, subjects were instructed to select the geometric form (bottom) that matched the target geometric form (top). The paradigm was counterbalanced across subjects. Each session began with a control block followed by either the matching or labeling task and then followed by an alternating pattern of control– activation 1–control–activation 2.

paper by Hariri et al. (2000). This task has consistently produced robust measures of performance and patterns of brain activation across several different studies (Altshuler et al., 2005, Hariri et al., 2003, Meyer-Lindenberg et al., 2006, Tessitore et al., 2005, Wang et al., 2004, Wright et al., 2006). Since we wanted subjects to produce a natural response, no specific instruction was given regarding the time to complete each trial. However, subjects were expressly asked to concentrate on task requirements. Also, to minimize subject motion, we instructed subjects to remain still and placed foam padding around their head. 2.3. MRI acquisition and processing All data acquisition was performed on a 3-T MEDSPEC 30/80 AVANCE imager (Bruker, Ettlingen, Germany). All stimuli were generated on a computer and back-projected onto a screen that the subjects viewed though a mirror positioned above their eyes. After an initial localizing scan to place image slices, fMRI scans were acquired using a T2⁎-weighted gradient-echoplanar sequence (TR/TE = 3000/35 ms; FOV = 19.2 × 19.2 cm, 64 × 64 matrix; flip angle = 90°). Thirty-six interleaved axial slices were obtained with a contiguous slice thickness of 3 mm. The fMRI paradigm consisted of two sessions of twelve experimental blocks. Three blocks each of matching or

labeling facial emotion (each of 44.5 s duration), were interleaved with six control blocks (of 22 s duration). The inter-block interval was 2 s, giving a total scan length of 14:03 min. For each emotional condition, sixty different images were used, ten per block, five of each gender, all derived from the Karolinska database (Lundqvist et al., 1998). For the control condition, four different sets of geometric forms were used. Each block contained five stimuli. Images were presented for a period of 4 s with an inter-stimulus interval of 0.5 s, and in a randomized manner. The paradigm was counterbalanced across subjects. Each session began with a control block followed by either the matching or labeling task and then followed an alternating pattern of control–activation 1–control– activation 2. Following the fMRI scans, a set of high-resolution T1weighted images were acquired for the purpose of anatomical identification (sagittal MPRAGE Sequence, TE/TR = 5/25 ms, TI = 800 ms, Flip Angle = 15°, Matrix = 256 × 256 × 128). 2.4. Data analysis 2.4.1. Behavioral data Differences in task performance (accuracy score and reaction time) between control and patient groups were compared using a non-parametric Mann–Whitney test

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for independent groups from the SPSS (v11.0) statistical package. A non-parametric test was used since the variables were not normally distributed (distribution analyzed by the Shapiro–Wilk test). The threshold for statistical significance was fixed at p b 0.05 (two-tailed). Since responses made after the 4 s window (delayed responses) could not be recorded by the software, and no specific instruction was given regarding time to respond, only trials for which the subject answered within the 4 s window were considered in the analysis of accuracy scores. For the analysis of reaction time however, these delayed responses represented crucial information that could not be disregarded. Therefore, a minimum reaction time of 4000 ms was attributed to all delayed responses. 2.4.2. fMRI data Data were processed using SPM2 software (Wellcome Department of Cognitive Neurology, University College London) implemented in Matlab 6.5 (Mathworks Sherborn, MA). The first 12 s of each session, corresponding to a period of signal stabilization, was discarded. The remaining scans were corrected for differences in slice acquisition time. After this step, the last two volumes were discarded to prevent invalid temporal interpolation. To remove the effects of head movement during scanning, the 141 scans of each session were realigned to the first scan of the session. Head movement was corrected when it exceeded 1.5 mm and/or 1.5°. Criteria for rejecting data due to motion were movement in any axis superior to 3.5 mm and/or 3.5°. All images were thus transformed into a standardized coordinate system corresponding to the MNI (Montreal Neurological Institute) space. For spatial normalization, we used SPM2's EPI-template, and a trilinear interpolation method. The normalized images were then spatially smoothed with an isotropic Gaussian kernel (full width at half maximum of 6 mm). Individual statistical maps were calculated for each subject to evaluate differences between the emotional versus control conditions. Each condition of each session was modeled by a box-car convolved with a canonical hemodynamic response function. The within-subject contrast images were then entered into a second-level ttest to examine both within- and between-group effects. To this end, we carried out random effects analysis, which accounted for both scan-to-scan and subject-to-subject variability. Results were thresholded at p b 0.05 (corrected using the False Discovery Rate (FDR)). Only clusters with an extent greater than 40 mm3 were considered in the analysis. Between-group comparisons were constrained to task-specific regions, using a Small Volume Correction (SVC) approach (p FDR corrected b 0.05) with a mask

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derived from the within-group results, thresholded at p ≤ 0.001. In order to ensure sampling of all clusters involved in the task, the mask was generated by taking a conjunction of all voxels from the within-group analysis of each group. Tests of significant psychophysiological interactions (PPI) with the amygdalar nuclei (separately for right and left) were conducted for each group of subjects. PPI analysis reveals changes in the interaction between brain regions in relation to the experimental paradigm (Friston et al., 1997). Our analyses aimed to detect regions that showed changes in their interaction with the amygdala, in response to labeling versus matching conditions. To perform PPI analyses, the first eigenvariate time series was extracted from a sphere of 6 mm radius centered on the right and left amygdala, respective x, y, z Talairach coordinates: 22, −5, −14 and −21, −7, −16. These coordinates were selected due to their existence in the initial effects of interest analysis across all subjects (allowing an “acceptable” distance from the individual ROI centers of ±1.5 mm on each axis; thresholded at a uncorrected p value inferior to 0.05 to ensure its presence in all subjects). The PPI function in SPM2 was used to construct an interaction variable representing the interaction between the time series and the psychological variable (labeling versus matching). The effect of this interaction term was evaluated individually for each subject in each group. The individual contrast images of each group were then taken to the second level in order to perform random effects analysis of the PPI pattern in each group (using a onesample t-test). For these tests, a SVC was applied using the same mask as that used in the brain activity analysis, in Table 2 Task performance Task

Mean percentage of correct answers (SD) Controls

a) Accuracy Matching 94.26 (0.04) Labeling 95.43 (0.04) Control 96.02 (0.01)

a

90.63 (0.05) 92.18 (0.05) 95.82 (0.01)

Controls

Patients

3433.9 (561.7) 3041.3 (529.8) 822.8 (77.9)

3640.9 (579.6) 3239.6 (811.4) 1326.8 (929.5)

Statistically significant.

p

38.5 54.5 64

0.064 0.371 0.689

U (Mann– Whitney)

p

27 40 37

0.018 a 0.121 0.082

Patients

b) Reaction time Task Mean reaction time in ms (SD)

Matching Labeling Control

U (Mann– Whitney)

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order to evaluate all clusters involved in task performance. The significance threshold was fixed at a p (FDR corrected) value inferior to 0.05 and a minimal extension of 5 voxels. 2.4.3. Correlation analysis To determine the overall influence of performance on brain activation, correlation analyses were performed between individual statistical maps and 1) the percentage of correct answers and 2) reaction time (in seconds). These correlations were conducted separately for each group and for each task. A statistical threshold of p b 0.05 (SVC correction) was used. SVC was conducted using the same mask that was generated for the activation

analysis. Only clusters with an extent greater than 40 mm3 were considered in the analysis. All correlation analyses were conducted using SPM2. As amygdala activation in schizophrenia has been previously related to clinical symptoms, an additional analysis was carried out in patients. Mean percentage of BOLD signal-change values were extracted from the functionally defined amygdala clusters (i.e. identical to the cluster used in the PPI analysis) for each patient. Multiple correlation analyses were employed to examine the relationship between amygdala reactivity and scores on each dimension of the PANSS (negative, positive, excitation, depression and cognitive).

Fig. 2. Statistical parametric maps illustrating differential patterns of activation in each task for each group (healthy subjects and patients) separately. In the matching task, healthy subjects showed activation of bilateral amygdala. Patients did not demonstrate this activation but, instead, recruited inferior parietal and medial frontal cortex during task performance. In the labeling task, the patterns of activation were similar in healthy subjects and schizophrenic patients. This pattern included activation of bilateral dorsolateral and medial prefrontal cortex. According to anatomical convention, left is on the left side of the image in these sagittal, coronal and axial views. Voxels reaching significance at the FDR-corrected p b 0.05 level are rendered onto a normal T1-weighted image.

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3. Results 3.1. Behavioral performance The two groups were statistically comparable in terms of accuracy for both emotional (matching and labeling) and control conditions (see Table 2a). In terms of reaction time however, schizophrenic patients took significantly longer to respond in the matching condition. There was no difference between the two groups in the labeling or control conditions (see Table 2b). 3.2. fRMI results 3.2.1. Within-group analysis The exact Talairach coordinates and Student-t values of the cerebral structures significantly activated during the within-group analysis can be consulted in the additional data. 3.2.1.1. Control group. Comparison of the matching to control task led to bilateral activation of the amygdalar nuclei. Other activations were found bilaterally in fusiform, precuneus, dorsolateral prefrontal and anterior and

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middle cingulate cortices, as well as parahippocampal gyri, putamen and thalamic pulvinar. In the right hemisphere, activations extended to middle temporal gyri. Comparison of the labeling to control task showed bilateral activation in hippocampal nuclei, and in fusiform, anterior cingulate, middle temporal and dorsolateral prefrontal cortices. In the left hemisphere, there was additional activation in the thalamic pulvinar nucleus as well as in medial frontal gyrus. In the right hemisphere, there was additional activation in the caudate nucleus and hippocampal gyrus. 3.2.1.2. Patient group. Comparison of the matching to control task showed bilateral activation in fusiform gyrus, precuneus, inferior parietal cortex (left supramarginal and angular gyrus, right supramarginal gyrus), anterior cingulate, medial frontal and dorsolateral prefrontal cortices, as well as hippocampus. In addition, left-lateralized activations were found in somatosensory cortex and thalamic pulvinar. Comparison of the labeling to control task revealed bilateral activation in fusiform, dorsolateral prefrontal and frontal medial cortices. In addition, left-lateralized activations were noted in supramarginal and middle

Table 3 Differences in brain activation for the matching task Structure

Talairach coordinates

Student-t⁎

p

Size (mm3)

a) Healthy N patient group Right amygdala Left amygdala Left putamen Right putamen Right superior temporal gyrus (BA 22) Left inferior frontal gyrus (BA 44) (BA 45) Right inferior frontal gyrus (BA 45) (BA 47) Left hypothalamus Left fusiform gyrus (BA 37)

20, − 4, − 16 −16, − 2, − 16 −26, 6, 26 25, − 2, 14 52, − 42, 10 −30, 2, 30 −40, 2, 32 48, 20, 18 38, 40, −12 −4, − 3, − 3 −42, − 40, − 16

5.40 4.76 3.86 3.73 4.32 5.02 3.49 3.74 3.36 3.67 3.39

0.004 0.004 0.006 0.006 0.005 0.004 0.006 0.006 0.006 0.006 0.006

392 344 440 64 432 168 136 96 40 80 40

−36, − 44, 56 −54, − 31, 46 −26, − 44, 61 42, − 36, 52 25, − 440, 56 −25, 28, 50 −22, 34, 36 −26, 50, 22 −4, 60, 20 −12, − 10, 56 −48, 0, − 14 2, − 42, 62

4.40 4.34 4.16 4.05 3.57 4.99 4.30 4.45 4.11 4.02 4.90 3.56

0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003

568 88 104 56 40 280 128 64 104 48 328 144

b) Patients N healthy group Left inferior parietal cortex (BA 40) Right inferior parietal cortex (BA 40) Left superior frontal gyrus

(BA 8) (BA 8) (BA 10) Left medial frontal gyrus (BA 9) (BA 6) Left middle temporal gyrus (BA21) Right precuneus ⁎All p values FDR (False Discovery Rate) corrected.

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Fig. 3. Statistical parametric maps illustrating the direct comparison of the two groups during the matching task. These data show a greater BOLD response in bilateral amygdala in healthy subjects compared to schizophrenic patients. By contrast, schizophrenic patients showed an increased response in an extended neocortical network, including left inferior parietal, left medial temporal, left medial and superior frontal gyri. According to anatomical convention, left is on the left side of the image on these axial views. Voxels reaching significance at the FDR-corrected p b 0.05 level were rendered onto a normal T1-weighted image. The chromatic scale represents the Student-t value of the between-group differences in activation. The number below each slice indicates the Brodmann coordinate of the z axis. The exact coordinates of these areas are listed in Table 3.

orbital gyri; and a right-lateralized activation in middle temporal gyrus and hippocampus (Fig. 2). 3.2.2. Between-group comparisons The between-group comparison indexes the difference between emotional and control conditions in volunteers,

compared to the difference between emotional and control conditions in patients. For the matching task, activation was significantly lower in schizophrenic patients compared to controls in bilateral amygdala and putamen, as well as inferior frontal gyrus, hypothalamus and right superior temporal cortex (see Table 3 and Fig. 3). Acti-

Table 4 Summary of psychophysiological interaction analyses in the healthy control group, for the contrast of labeling versus matching Structure

Talairach coordinates

Student-t⁎

Modulation

Psychophysiological interaction with right amygdala Right anterior cingulate (BA 24/32) Right hippocampus Right visual cortex (BA 19)

20, 36, 6 24, −24, −8 42, −84, 10

5.79 5.40 5.36

Positive Positive Positive

Psychophysiological interaction with left amygdala Left visual cortex (BA 19) Right superior frontal gyrus (BA 6) Right middle temporal gyrus (BA 21) (BA 21/37) Anterior cingulate gyrus (BA 24) Left visual cortex (BA 19) Right medial frontal gyrus (BA 6) Right visual cortex (BA 19)

− 36, − 62, 34 28, −2, 64 50, −46, 2 46, −56, 6 − 16, 22, 22 − 28, − 90, 26 8, 10, 60 16, −72, 36

6.62 6.04 5.42 4.96 4.91 4.85 4.40 4.38

Negative Negative Negative Negative Negative Negative Negative Negative

⁎All p FDR (False Discovery Rate) corrected values inferior to 0.05.

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Fig. 4. Statistical parametric maps illustrating the psychophysiological interaction analyses in healthy subjects for both, left and right amygdala. The right superior and medial frontal cortex, the middle temporal cortex and the bilateral visual cortex showed negative covariation with the left amygdala (top). The right anterior cingulate cortex, hippocampus and visual cortex showed positive covariation with the right amygdala (bottom). The exact coordinates of these areas are listed in Table 4. No regions covaried with either the right or left amygdala in schizophrenic patients. The chromatic bright color-scale (red-yellow) represents the Student-t value for negative covariation, the chromatic dull color-scale (blue-green) represents the Student-t value for positive covariation. According to anatomical convention, left is on the left side of the image on these axial views. Voxels reaching significance at the FDR-corrected p b 0.05 level were rendered onto a normal T1-weighted image. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

vation was significantly higher in patients compared to controls mainly in the left inferior parietal (supramarginal) cortex. There were also significantly greater activation in the left medial and superior frontal gyri, the left middle temporal cortex and the right precuneus (see Table 3 and Fig. 3). For the labeling task, there was no statisticallysignificant difference in the pattern of activation between the two groups.

The right anterior cingulate cortex, hippocampus and visual cortex showed positive covariation with the right amygdala. In schizophrenic patients, there were no regions whose activity covaried with either the right or left amygdala (Table 4, Fig. 4).

3.2.3. Psychophysiological interaction analysis In the control group, the right superior and medial frontal cortex, the middle temporal cortex and the bilateral visual cortex showed negative covariation with the left amygdala in the labeling compared to matching condition.

Reaction time and accuracy measures did not correlate significantly with neural activity for either group. There was no correlation between the amygdalar reactivity (mean percentage of BOLD signal-changes) and PANSS factors (for both analyses, all p N 0.1).

3.3. Correlational analyses

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4. Discussion Our paradigm allowed investigation of neural activity and changes in functional connectivity during two different types of facial affect evaluation: a matching condition, which is based on automatic and intuitive processing, and a labeling condition, which is dependent on higher cognitive processes, such as judgment and interpretation. The central findings of the present study were 1) similar patterns of brain activity in healthy and schizophrenic subjects during the labeling condition but not during the matching condition, in which schizophrenic patients invariably failed to engage amygdala; 2) PPI findings which paralleled the first set of results i.e. in healthy volunteers, manipulation of the experimental condition revealed differences in the interaction between the amygdala and several brain regions known to be implicated in amygdala modulation, whereas patients showed no such difference; 3) a two-fold pattern of brain activation in schizophrenic patients during the matching condition, such that patients not only failed to engage the limbic structures usually solicited in automatic emotional processing, but instead additionally recruited neocortical areas, such as inferior parietal cortex (IPC), left middle temporal cortex and right precuneus, which are not normally engaged by emotional assessment. In the control group, the patterns of activation observed during the two emotional conditions are in line with previous studies using the same paradigm (Hariri et al., 2003, Tessitore et al., 2005, Wang et al., 2004): during the matching condition, we found activation of both, cortical and sub-cortical structures (amygdala, anterior and middle cingulate as well as parahippocampal gyri, putamen, thalamic pulvinar nuclei and right middle temporal gyrus) all of which have previously been implicated in the automatic processing of emotional information. Conversely, during the labeling condition, we observed activation of cortical and sub-cortical regions (medial prefrontal regions and hippocampus nuclei), which are instead implicated in the cognitive processing of emotional stimuli. In addition, both labeling and matching conditions recruited inferior occipital and fusiform gyri, typically involved in face processing (Phan et al., 2002), as well as bilateral dorsolateral prefrontal cortex, which presumably indexes working memory operations such as representation and manipulation of nonemotive visuospatial components of face processing (Goldman-Rakic 1987, Hariri et al., 2000, Phillips et al., 2003a). In the patient group, subjects showed an activation pattern for both matching and labeling conditions that was comparable to that of healthy subjects during the labeling condition. Thus, during the matching condition patients, in comparison to healthy volunteers, displayed signifi-

cantly lower activation in several limbic structures, particularly in the amygdala. This finding is consistent with most neuroimaging studies of facial affect processing in schizophrenia (Das et al., 2007, Gur et al., 2002, Hempel et al., 2003, Johnston et al., 2005, Phillips et al., 1999, Schneider et al., 1998). However three studies have found patients to show increased amygdala activation (Holt et al., 2006, Holt et al., 2005, Kosaka et al., 2002). Differences in task procedure may account for this discrepancy amongst studies. In the study of Kosaka et al. (2002), subjects were asked to evaluate which faces expressed more intense emotion. In Holt et al.'s studies (2005, 2006), subjects were asked to passively view emotional faces. In these three studies, the tasks had no specific instruction or, at the most, required subjects to judge emotional intensity, whereas in other studies, subjects were asked to actively identify the exact nature of the emotion expressed and to discriminate facial affect. This may, even for tasks with low cognitive requirements such as our matching condition, trigger cognitive processing of emotional stimuli and thus allow patients to resort to alternative strategies. Our PPI analyses add further support for this hypothesis. In healthy subjects, PPI analyses revealed negative covariation between PFC regions (right superior and medial frontal gyrus, anterior cingulate gyrus) and the left amygdala in the labeling compared to matching condition. This enhanced negative interaction during the most cognitively demanding condition (labeling) is consistent with the view that when cognitive processing of the emotional stimuli is required, the PFC provides ‘top-down’ modulation of amygdala function. By contrast, patients with schizophrenia did not show any covariation between activity in amygdala and other brain regions. Taken with the finding of a failure to activate this structure in the matching condition, this result suggests that patients may employ a cognitive strategy even when faced with an emotional task, essentially relying on automatic or intuitive processing. Our finding of limbic dysfunction, aside the loss of flexibility in the functional interaction between cortical and sub-cortical regions, is consistent with the model proposed by Phillips et al. (2003b) in which the patient's dysfunction in emotional processing is explained by both structural and functional abnormalities within the cerebral regions involved in the automatic and the effortful regulation of emotions. Surprisingly, in healthy subjects, the left amygdala showed negative covariation with other brain regions in the labeling relative to matching condition, while the right amygdala showed positive covariation. Several explanations could account for this functional lateralization in connectivity. First, reviews and meta-analysis of

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functional neuroimaging studies have observed a stronger preponderance of left amygdala activation over right amygdala in emotional studies (Baas et al., 2004, Wager et al., 2003). This greater activation of the left amygdala could lead to a predominant regulation of this structure through top-down processes and justify the negative covariation found here. Second, it has also been suggested that the right amygdala is engaged in rapid processing of emotional information, while the left amygdala is involved with more sustained emotional reactions (Glascher and Adolphs 2003). The extent of the fMRI acquisition timecourse could explain why only the enduring modulation of the left amygdala is discernible in our study. Finally, a differential link between visual cortex and the left, rather than right, amygdala has been previously reported in PPI analysis using similar stimuli (Das et al., 2005). In total, our results, in terms of both activation patterns and PPI analyses, support the hypothesis that schizophrenic patients preferentially rely on cognitive processing of facial affect in order to identify these stimuli. Furthermore, our activation results may convey additional information as to the cognitive strategy employed by schizophrenic patients during the intuitive condition. It should first be emphasized that matching of facial affect can be achieved by different strategies: one such strategy is to consider the facial expression holistically. This configuration-based (configural) strategy enables a rapid and intuitive processing of the emotion depicted on the face, and leads to automatic production of the corresponding affective state (e.g., finding the other face that provokes the same emotional feeling). Another strategy is to match salient features of faces individually and independently by a feature-based strategy (e.g., finding the other frowning brow). In the latter case, the emotional significance of the facial expression may be missed. Our findings in the between-group comparison strongly favor the possibility that schizophrenic patients exclusively use the second of these two strategies. First, compared to healthy controls, patients failed to activate regions implicated in the rapid aspects of face processing that are necessary for direct emotional experience, such as bilateral amygdala, putamen and inferior frontal cortex (Critchley et al., 2000, Glascher and Adolphs 2003, Whalen et al., 1998, Williams et al., 2004). Secondly, the between-group comparison also showed that patients had greater activation of bilateral IPC, left middle temporal cortex, left medial and superior frontal gyri, and right precuneus, but reduced activation of left fusiform gyrus. The IPC has been strongly associated with the processing of spatial information and the perception of spatial relationships in both animal (Latto 1986) and human (Fink et al., 1997) studies. Moreover, neuroimaging

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studies have confirmed that IPC plays a predominant role in the ability to attend to spatial aspects of visual stimuli (Corbetta et al., 2000, Epstein et al., 2006, Kastner et al., 1999, Lane et al., 1997, Posner and Dehaene 1994, Posner and Petersen 1990). Taken together, these data suggest that preferential activation of IPC indexes schizophrenic patients' tendency to focus their attention on different parts of the stimulus, consistent with the hypothesis that they rely on a feature-based strategy to match facial affect. Activation of precuneus also supports this hypothesis as this structure has been implicated in visual attentional shifts (Le et al., 1998) and, more precisely, in attentional shifts between object features (Nagahama et al., 1999). Similarly, neuropsychological findings suggest that left temporal lobe is recruited when difficult feature distinctions need to be made, whereas right temporal lobe (which was more activated in controls) is involved in global shape processing (Graham et al., 2006, Lamb et al., 1990, Mevorach et al., 2006). By contrast, fusiform gyrus, which showed decreased activation in schizophrenic patients, is a region highly associated with face processing (Haxby et al., 2000), and, more specifically, with holistic or configuration-based face processing (Gauthier et al., 2003). Unfortunately, we did not ask participants about the strategy they had used while performing the task, which could have added anecdotal evidence to support our hypothesis. However, our hypothesis is in agreement with previous behavioral studies showing that schizophrenic patients are impaired in using configural information when recognizing faces (Frith et al., 1983, Grusser et al., 1990) and, more specifically, when identifying facial emotions (Mandal and Palchoudhury 1989). Finally, our results showed no correlation between PANSS factors and brain activity. These negative findings suggest that the compensatory mechanism used by schizophrenic patients is unlikely to be the consequence of clinical symptoms and may instead be linked to the schizophrenic illness per se. In other words, this compensatory strategy may reflect a trait marker for schizophrenia. A few caveats should be kept in mind. Because increases in cognitive demand have been associated with changes in activation pattern (Callicott et al., 1999), one possible criticism would be that our findings only account for the more general cognitive impairments that characterize schizophrenia, such as working memory (Manoach 2003). We believe this to be improbable for at least three reasons. First, several studies using the same, or a similar, experimental paradigm showed that while task difficulty could affect activation of structures normally engaged by the task (such as prefrontal cortex) it left other cerebral regions unaffected (Bokde et al., 2005, Hempel et al., 2003). Therefore, activation of IPC

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or precuneus in the patient group suggests the utilization of an alternative strategy. Second, our results did not show any correlation, positive or negative, between accuracy of performance and cerebral activation. Finally, patients and healthy volunteers showed similar accuracy scores across all tasks. The only between-group difference in task performance was for reaction time in the matching condition. The increased reaction time observed in schizophrenic patients most likely represents the use of a more time-consuming cortical pathway needed to perform the task. This is consistent with the fact that although feature-based processing allows accurate identification of facial affect, this strategy may require a longer response time than configural processing (De Sonneville et al., 2002). Another criticism concerns the behavioral performance data. Both groups showed high accuracy scores on matching and labeling tasks, which could seem problematic when interpreting group differences in activation. However, as previously mentioned, this task has been used in numerous neuroimaging studies of healthy volunteers, with similar behavioral performance and activation patterns across all studies. This implies a robust paradigm, with activation patterns strongly related to task performance. Therefore, the presence of comparable behavioral performance, but dissimilar activation patterns, across groups, is more suggestive of disease-related changes than differences due to task difficulty. Also, it should be remembered that activation in the amygdala is sensitive to motion. Thus, it could be argued that the lack of amygdala activation could be due to increased movement in the schizophrenic group (provoked by akathesia or simply increased anxiety). Finally, all patients were regularly taking antipsychotic medication at the time of the study (in fact, this was even part of the selection criteria). Since limbic regions and possibly even some regions of neocortex, are the site of action of antipsychotic medication, it is conceivable that treatment may have influenced our results. However, existing literature suggests that antipsychotics have little influence on the activation pattern we observed: for example, a PET study (Paradiso et al., 2003), performed with medication-free patients, also reported a failure to activate the amygdala during assessment of unpleasant pictures. Consistent with this finding, imaging studies assessing medicated patients found no correlation between medication dose and amygdala activity (Kosaka et al., 2002, Phillips et al., 1999). Even if these findings suggest a limited influence of treatment on patterns of brain activation, this issue certainly requires thorough examination in the future. Additional investigations into the effects of antipsychotics, and particularly atypical agents, on activation of

limbic (Joyce et al., 1997) and other neocortical cerebral regions (Stip et al., 2005) are warranted. In conclusion, our findings provide important neurofunctional data on the cognitive processes that schizophrenic patients engage when identifying facial affect. Our results showed that, when labeling fearful or angry emotions, patients and healthy subjects displayed similar patterns of brain activation. However, when matching facial affect, schizophrenic patients, although showing similar accuracy scores on behavioral performance, failed to activate regions normally solicited in the automatic processing of emotional information. Complementing our findings on activation patterns, the PPI analyses showed that, unlike healthy controls, schizophrenic patients did not display any change in the amygdala's interaction with other brain regions as a function of change in experimental condition. Altogether, these findings suggest schizophrenic patients invariably adopt a cognitive strategy when identifying facial affect. Furthermore, patients showed, during the matching condition, a pattern of activation in favor of a feature-analysis based strategy. We hypothesize that patients, when asked to actively perform an intuitive emotional task, may use a more cognitive strategy based on the analysis of individual features, rather than treating the stimuli holistically and intuitively. This strategy may embody a compensatory mechanism for limbic dysfunction. Role of funding source This research was supported by grants from the French National Center for Scientific Research (CNRS). Eric Fakra obtained a grant from the Lilly Foundation for this study and Pilar Salgado-Pineda was financed by a postdoctoral grant (EX2003-1132) of the Spanish Government (MECD). These funding bodies had no influence on study design, collection, analysis and interpretation of data, writing of the report or on decision to submit the study for publication. This funding support had no role in study design, in collection, analysis and interpretation of data, in the writing of the report, or in the decision to submit the study for publication.

Contributors Authors E. Fakra, P. Salgado-Pineda, P. Delaveau, A. R. Hariri and O. Blin designed the study, interpreted results and contributed in the writing of the manuscript. Authors E. Fakra and P. Salgado-Pineda wrote the protocol. Authors E. Fakra, P. Salgado-Pineda and P. Delaveau conducted the study. Authors P. Salgado-Pineda and P. Delaveau undertook the data analysis. All authors contributed to and approved the final manuscript.

Conflict of interest There are no conflicts of interest for any authors to declare.

Acknowledgments The authors wish to thank Jennifer Coull and Christine Gard for their correction of the English text.

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