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Original Article |

Limbic Activation Associated With Misidentification of Fearful Faces and Flat Affect in Schizophrenia FREE

Raquel E. Gur, MD, PhD; James Loughead, PhD; Christian G. Kohler, MD; Mark A. Elliott, PhD; Kathleen Lesko, BA; Kosha Ruparel, MSE; Daniel H. Wolf, MD,PhD; Warren B. Bilker, PhD; Ruben C. Gur, PhD
[+] Author Affiliations

Author Affiliations: Departments of Psychiatry (Drs R. E. Gur, Loughead, Kohler, Wolf, and R. C. Gur and Mss Lesko and Ruparel), Radiology (Drs R. E. Gur, Elliott, and R. C. Gur), and Biostatistics (Dr Bilker), University of Pennsylvania School of Medicine, Philadelphia.


Arch Gen Psychiatry. 2007;64(12):1356-1366. doi:10.1001/archpsyc.64.12.1356.
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Published online

Context  Deficits in emotion processing are prominent in schizophrenia, and flat affect is resistant to treatment and portends poor outcome. Investigation of the underlying neural circuitry can elucidate affective dysfunction.

Objective  To examine the brain circuitry for facial emotion processing, dissecting response to task demands from effects of the appearance of facial expressions.

Design  A facial emotion identification task was presented during high-field (4-T) magnetic resonance imaging. Blood oxygenation level–dependent changes were contrasted for task compared with a scrambled face baseline (blocked analysis) and for the appearance of each of the following 4 target expressions compared with neutral faces (event related): happy, sad, anger, and fear.

Setting  Participants from the Schizophrenia Research Center underwent a functional magnetic resonance imaging study at the University of Pennsylvania Medical Center.

Participants  Patients with DSM-IV–defined schizophrenia (n = 16) and healthy controls (n = 17) were recruited from the community.

Main Outcome Measures  The percentage of signal change for each contrast and performance and clinical symptom severity ratings.

Results  Patients showed reduced limbic activation compared with controls for the emotion identification task. However, event-related analysis revealed that whereas in controls greater amygdala activation was associated with correct identifications of threat-related (anger and fear) expressions, patients showed the opposite effect of greater limbic activation, portending misidentifications. Furthermore, greater amygdala activation to the presentation of fearful faces was highly correlated with greater severity of flat affect.

Conclusions  Abnormal amygdala activation in schizophrenia in response to presentation of fearful faces is paradoxically associated with failure to recognize the emotion and with more severe flat affect. This finding suggests that flat affect in schizophrenia relates to overstimulation of the limbic system.

Figures in this Article

Deficits in emotion processing in schizophrenia disrupt social functioning.1,2 Flat affect is a cardinal symptom that particularly diminishes the ability to communicate emotions. Like other negative symptoms, it is resistant to treatment and is associated with poor functioning and outcome.3,4 Patients have deficits in identification and expression of emotions but apparently not in reported experience.47 Notably, patients with flat affect, compared with those without flat affect, have further deficits in identifying facial emotions without being more impaired cognitively, except for verbal memory.4

Complementing findings in patients with brain lesions8,9 and animal paradigms,10,11 functional magnetic resonance imaging (fMRI) studies in healthy people have helped elucidate brain systems and processes that modulate emotion. Because the face is a major conveyor of emotion, it is used extensively and consistent findings have emerged. In healthy people, identifying facial emotions results in activation of a network that includes the limbic (amygdala and hippocampus), visual (fusiform), frontal (medial and inferior), and thalamic regions.1214 A growing literature in schizophrenia, applying block design fMRI, suggests diminished limbic activation for facial emotion processing tasks.1517 Few studies have examined cerebral activity in relation to symptom dimensions. Differences have been observed between patients with and without paranoia15,18,19 and between those with and without blunted affect.20

Event-related fMRI permits further dissection of regional activation than that feasible with block design approaches. When tasks are presented in blocks of stimuli associated with specific instructions, their comparison to a baseline stimulus establishes activation for the overall top-down (executive) control effects in response to task demands. Event-related fMRI can measure signal change time locked to the induced bottom-up effects of appearance of specific stimuli within a task. This feature is especially useful for examining deficits associated with neuropsychiatric disorders because activation can be linked to the response, separating correct from incorrect processing. Correlating blocked effects with performance can be difficult to interpret, whereas activation concomitant with performance can pinpoint aberrant processing.

The purpose of the present study was to examine brain circuitry involved in the identification of facial emotions in schizophrenia. We applied a hybrid (blocked and event-related) design that enabled characterization of both task-related and stimulus-related activation. For the latter, the design provided separation of correct from incorrect identifications. The stimuli included happy, sad, anger, fear, and neutral expressions, which are universally recognized21 and represent both social and threat-related emotions.22,23 The hybrid design was set to answer 2 consecutive questions. The blocked analysis specifies regions activated by a task that required identification of a target emotion compared with a resting fixation on a stimulus with comparable features. The event-related analysis can focus on activated regions to examine hemodynamic changes, within these regions, that are time locked to the appearance of a face showing a specific emotion and how this differs between correct and incorrect responses. We hypothesized that top-down (blocked analysis) activation would occur in a network that includes limbic, frontal, and thalamic regions, with patients showing less robust activation. We further hypothesized that bottom-up (event-related) effects would show error-related differences with more pronounced abnormalities associated with flat affect. In schizophrenia, flat affect relates to emotion expression deficits and has been linked to impaired performance on emotion identification tasks.4

PARTICIPANTS

The original sample included 20 patients and 20 healthy controls, who were consecutive right-handed volunteers at the Schizophrenia Research Center. However, 4 patients and 2 controls were excluded from further analysis because of excess motion (>4 mm), and 1 control participant was excluded for an incidental finding of abnormal structural MRI. The final sample included 16 patients with schizophrenia (12 men) and 17 healthy controls (12 men), who completed the study with high-quality data. The patients were approximately 5 years older on average (patients: mean ± SD, 30.1 ± 6.5 years; range, 21-41 years; controls: mean ± SD, 25.0 ± 3.9 years; range, 19-33 years; t31 = 2.73; P = .01) and as expected had a lower educational level (patients: mean ± SD, 12.8 ± 2.3 years; range, 9-16 years; controls: 15.8 ± 2.2 years; range, 12-20 years; t30 = 3.72; P < .001). However, they had comparable parental educational levels (patients: mean ± SD, 14.1 ± 3.6 years; range, 7-20 years; controls: mean ± SD, 16.3 ± 2.9 years; range, 9-20 years; t = 1.95; P = .06). After complete description of the study, written informed consent was obtained.

Participants underwent standardized assessment procedures, including medical, neurologic, psychiatric, and neurocognitive evaluations and laboratory tests. The psychiatric evaluation for patients included clinical assessment with the Structured Clinical Interview for DSM-IV,24 which was conducted by a trained clinical research coordinator; history obtained from family, health care professionals, and records; and scales for measuring symptoms administered by investigators trained to a criterion reliability of 0.90 (intraclass correlation). Patients had a DSM-IV diagnosis of schizophrenia established in a consensus conference based on all information available and had no history of other disorders or events that affected brain function, including no comorbid psychiatric diagnoses. The consensus conference includes a formal presentation of the research participants by research psychiatrists who conduct an intake clinical interview. The information is presented in a written summary that integrates all available data. In the consensus conference, members of the Clinical Core independently describe their diagnostic formulation of the case presented. These formulations are discussed and a consensus is reached and entered in the database. Mean ± SD age at onset of psychotic symptoms in the context of functional decline was 20.1 ± 3.8 years (range, 12-29 years), with an illness duration of 9.6 ± 7.1 years and 3.6 ± 4.1 (range, 0-15) hospitalizations. These clinically stable outpatients had mild symptoms at the time of the study. Global ratings on the Scale for Assessment of Negative Symptoms (SANS)25 averaged 1.3 ± 0.9 (range, 0-3.0), and ratings on the Scale for the Assessment of Positive Symptoms (SAPS)26 averaged 1.4 ± 0.6 (range, 0-2.3). At the time of imaging, 1 patient was untreated with antipsychotics and 15 were receiving stable doses: 2 received first-generation (chlorpromazine equivalents = 542 ± 292 per day),27,28 11 received second-generation (olanzapine equivalents = 18.2 ± 2.8 per day),29 and 2 received both (chlorpromazine equivalents = 16.7 per day, olanzapine equivalents = 11.3 per day) medications. Controls underwent the same evaluation procedures.30 They had no history of major psychiatric illness in first-degree relatives.

PROCEDURES
Imaging Tasks

The face emotion identification task included 4 conditions (separate time series), presented in a counterbalanced order, each with a specific target expression: happy, sad, anger, or fear. Stimuli were selected from a set validated in healthy people31 and patients with schizophrenia.29 The specific task conditions were further piloted to ensure comparable performance for target emotions in patients and controls, yet with sufficient number of errors to permit performance-based analysis of time series data. Each condition included four 90-second blocks of emotion identification, separated by 24 seconds of rest during which a scrambled face with a central cross-hair for fixation was displayed (Figure 1). Each block contained 8 target faces (eg, 8 fear), 12 foil faces (eg, 4 happy, 4 sad, 4 angry), and 10 neutral faces. Thus, a condition included a total of 120 faces: 32 target, 48 foil, and 40 neutral in a pseudorandom sequence. Faces appeared for 3 seconds, and participants endorsed “target” or “other” using the 2-button response pad. Within a block, target expressions (eg, fear) and foil expressions (eg, happy, sad, or anger) were separated by a variable number of neutral faces (range, 0-5 faces, which equals 0-15 seconds), allowing for event-related modeling of the hemodynamic response with neutral faces as a within-block baseline. This interblock design also permitted modeling of events based on accurate target identification and errors. Abbreviated response instructions remained visible throughout the task. The same faces were cycled through the 4 conditions serving as targets or foils, depending on the condition, and they were equally distributed for sex and balanced for ethnicity (65% white, 23% African American, and 11% other). Each condition (time series) lasted 8 minutes, with a total task duration of approximately 32 minutes.

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Figure 1.

Face emotion identification task (fearful target).

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fMRI Procedures

Participants were administered a brief practice task before placement in the scanner. Earplugs were fitted to muffle noise, and head fixation was ensured through a foam-rubber device mounted on the head coil. Stimuli presentation was triggered by the scanner and synchronized with image acquisition using PowerLaboratory32 (MacLaboratory Inc, Devon, Pennsylvania) on a Macintosh computer (Apple, Cupertino, California). Stimuli were rear-projected to the center of the visual field using a PowerLite 7300 video projector (Epson America Inc, Long Beach, California) and viewed through a head coil–mounted mirror. Participants were randomly assigned use of their right or left hand, and responses were recorded via a nonferromagnetic keypad (Current Design Inc, Philadelphia, Pennsylvania).

Image Acquisition

Data were acquired on a 4-T scanner (GE Signa Scanner; General Electric, Milwaukee, Wisconsin), using a quadrature transmit-and-receive head coil. Structural images consisted of a sagittal T1-weighted localizer, followed by a T1-weighted acquisition of the entire brain in the axial plane (24-cm field of view and 256 × 256 matrix, resulting in a voxel size of 0.9375 × 0.9375 × 4 mm). This sequence was used for spatial normalization to a standard atlas33 and for anatomic overlays of the functional data. Functional imaging was performed in the axial plane using a 16-slice, single-shot, gradient-echo, echo-planar sequence (repetition time/echo time = 1500/21 ms, field of view = 240 mm, matrix = 64 × 40, section thickness/gap = 5/0 mm). This sequence delivered a nominal voxel resolution of 3.75 × 3.75 × 5 mm. The 5-mm section thickness was a compromise to permit optimal visualization of the amygdala with minimal sacrifice in brain coverage. Because of the size of the amygdala in the z direction (approximately 10 mm), we avoided using section gaps to increase coverage. Total sections per volume were also limited by a 1.5-second repetition time, which was selected to provide 2 volume acquisitions per stimulus exposure (3 seconds per face). The sections were acquired from the superior cerebellum up through the frontal lobe. Inferiorly, this corresponded to a level just below the inferior aspect of the temporal lobes and superiorly to approximately the level of the hand-motor area in the primary motor cortex.

Because the gradient echo echoplanar images can be degraded in the presence of nonuniform magnetic fields, we paid special attention to the image quality in the anterior medial temporal lobes. An automated shimming was performed manually in a region of interest that contained the anterior medial temporal lobe.34 After the shimming, pilot echoplanar images were obtained, which were visually inspected before fMRI acquisition to ensure good image quality in the amygdala region. The images were then corrected for residual geometric distortion35 based on a magnetic field map acquired with a 1-minute reference scan.

STATISTICAL ANALYSIS
Performance Analysis

Differences in the percentage correct of all responses (true positive and true negative) and response time (in milliseconds) for correct responses were evaluated for each of the 4 target emotions. They were analyzed using separate repeated-measures diagnosis × emotion analyses of variance (ANOVAs), with 1 grouping and 1 repeated-measures factor. To satisfy the normality assumptions of ANOVA, the arcsine transformation was applied to percentages.

Image Analysis

The fMRI data were preprocessed and analyzed using FEAT (FMRI Expert Analysis Tool) version 5.1, part of Oxford Centre for Functional Magnetic Resonance Imaging of the Brain's Software Library (www.fmrib.ox.ac.uk/fsl). Images were section time corrected with the Fourier-space time series phase shifting, motion corrected to the median image using trilinear interpolation with 6 df,36 high pass filtered (120 seconds), spatially smoothed (8-mm full width at half maximum, isotropic), and scaled with mean-based intensity normalization. The median functional and anatomical volumes were coregistered then transformed into the standard anatomical space (T1 Montreal Neurological Institute template) with the trilinear interpolation, and the brain extraction tool was used to remove nonbrain areas.3739

Subject-level time series statistical analysis was performed with Oxford Centre for Functional Magnetic Resonance Imaging of the Brain's Improved Linear Model with local autocorrelation correction.39 Each time series (ie, happy, sad, anger, fear) was regressed to a canonic hemodynamic response function modeling emotion discrimination blocks relative to cross-hair. These data were submitted to group-level analyses. First, each participant's mean activation across the 4 target conditions and across all responses was calculated. To identify within-group effects, the averages (across 4 conditions) were entered into a separate single-group t test for patients and control participants. Differences between diagnostic groups were examined with 2-sample t tests, masked by the corrected and binarized single sample results (ie, controls > patients contrast masked by controls > baseline and patients > controls contrast masked by patients > baseline). To test for regions differentially activated by happy, sad, anger, or fear target conditions, the β weights for each target emotion were entered into a voxelwise repeated-measures ANOVA with 1 grouping (diagnosis) and 1 repeated-measures (target emotion) factor. All z (gaussianized T or F ratios) statistical images were corrected for spatial extent (AFNI AlphaSim; R. W. Cox, National Institutes of Health, Bethesda, Maryland) using a minimum z threshold of 2.33 or greater and a cluster P <.05 (for display, control > baseline is presented at z ≥ 4.20 because of the large number of activated voxels). The cluster's peak z score coordinates were labeled using the Talairach Daemon database,40 and region labels were then confirmed by manual examination of peak values and cluster centroid coordinates.

The event-related subject-level analysis modeled 5 performance-based regressors (correct target, incorrect target, correct foil, incorrect foil, and no response), with neutral faces serving as baseline. Mean scaled β coefficients (percentage of signal change) for correct and incorrect target identifications were extracted for offline analysis from regions identified in the block analysis using atlas-derived regions of interest (Wake Forest University pickatlas).41 We also performed voxelwise analyses of the event-related data and examined group differences in activation for correct and incorrect responses to each target emotion.

Offline analysis of the percentage of signal change was performed using SAS statistical software (SAS Institute Inc, Cary, North Carolina). The activation data were entered into a group (schizophrenia, controls) × emotion (happy, sad, anger, fear) × region × correct vs incorrect repeated-measures multivariate ANOVA. Significant interactions were decomposed by univariate analyses. Spearman correlations were calculated between the percentage of signal change (across correct and incorrect trials) and the SANS25 and SAPS26 clinical rating subscales. The average ratings for each subscale were used for these correlations, rather than global ratings, because they provide smoother and more normally distributed scores.

PERFORMANCE

Performance data are summarized in Table 1. For the percentage correct, no main effect of diagnosis was found (F1,31 = 2.33; P = .14). However, a main effect for emotion was found (F3,93 = 33.78; P < .001). Both groups performed better for happy than the other expressions (post hoc least significant difference, P < .05). For response time, likewise no between-group differences were found (F1,31 = 0.26; P = .61), but a main effect for emotion was found (F3,93 = 5.83; P = .001), again with the happy faces being recognized faster than the others (post hoc least significant difference, P < .05). A similar pattern was observed when examining correctly identified target emotions (true-positive responses) with no main effect of diagnosis (F1,31 = 3.41, P = .07) and a significant main effect for emotion (F3,93 = 15.60, P < .001) also due to the happy condition (post hoc least significant difference, P < .05). There were no group × emotion interactions.

Table Graphic Jump LocationTable 1. Performance During Emotion Identification in Patients With Schizophrenia and Healthy Controls
BLOCKED ANALYSIS

The blocked analysis showed significant activation for the emotion identification task in a distributed network of regions that included clusters in amygdala, hippocampus, thalamus, fusiform gyrus, and frontal and visual association cortex. The activation was more robust in controls than in patients. As seen in Figure 2 and Table 2, several regions showed significantly greater activation in controls, yet no region showed the reverse. No region showed differential activation among the target conditions when corrected for spatial extent. Inspection at a liberal threshold (P < .05, uncorrected) revealed that the anterior portion of the inferior frontal gyrus was less active in the happy condition. This effect seemed stronger in the control group, but no diagnosis × emotion interaction was observed at P < .05, uncorrected. Although the order of conditions (target emotion) was counterbalanced, we examined order effects in view of evidence for amygdala habituation.42 The order effect was not statistically significant, and no order × diagnosis interactions were found.

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Figure 2.

Regions activated for emotion identification task relative to baseline (block analysis) in controls (upper row), patients (middle row), and the controls−patients contrast (bottom row). No patients−controls contrast survived correction. Significance thresholds are based on spatial extent using a height of z ≥ 3.1 and a cluster probability of P ≤ .05. Images are displayed over a Talairach-normalized template in radiological convention (left hemisphere to viewer's right). The z-level coordinates are provided. AM indicates amygdala; IF (47), inferior frontal (Brodmann area 47); HI, hippocampus; IF (45), inferior frontal (Brodmann area 45); and TH, thalamus.

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Table Graphic Jump LocationTable 2. Local Maxima, Coordinates, and Brodmann Areas of Blood Oxygenation Level–Dependent Functional Magnetic Resonance Imaging Signal Change Relative to Scrambled Face Baseline (Block Analysis) for Patients With Schizophrenia, Healthy Controls, and Group Contrasts
EVENT-RELATED ANALYSIS

Contrast maps between patients and controls were generated, separating correct from incorrect responses to emotional relative to neutral faces and thresholded at an uncorrected significance level of P ≤ .001 (z ≥ 3.1). No significant voxels differentiating patients from controls were found in response to happy and sad faces, but significant differences in amygdala and other limbic regions emerged for anger and fear (Figure 3 and Table 3). As can be seen in Figure 3 (top row), controls showed greater activation for correct responses to the appearance of angry faces in inferior frontal and orbitofrontal regions and had a maximum that fell just mesially to the amygdala proper in Brodmann area 34 (10, −1, −10; z = 3.69) with a second peak at 12, −2, 18 (z = 3.66). For fear (Figure 3, bottom row), controls showed greater activation in inferior frontal cortex for correct responses, but the most pronounced finding was of greater activation in patients associated with incorrect responses. This effect is especially notable in the amygdala bilaterally (Table 3). To examine the distribution of activated voxels in this region, we applied a more liberal threshold (z = 1.96, P = .01, uncorrected; see insert in Figure 3). A visual comparison of 2 different group contrasts can be misleading, but the differential effects for anger (controls > patients) and fear (patients > controls) are in strikingly different limbic regions. As can be seen in the image, the medial activation associated with anger (controls > patients) abuts the more lateral activation associated with fear.

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Figure 3.

Activation maps showing peak amygdala response (see Table 3) for anger (A) and fear (B) conditions for the event-related analysis. Images are displayed over a Talairach-normalized template in radiological convention and thresholded at z ≥ 3.1, uncorrected (>25 continuous voxels). Outline (green) shows extent of atlas-derived amygdala regions of interest (Wake Forest University pickatlas) used for percentage of signal change extraction. Insert (C) highlights patients’ > controls’ (blue) incorrect responses superimposed on controls’ > patients’ (red) correct responses at z ≥ 1.64, uncorrected.

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Table Graphic Jump LocationTable 3. Local Maxima, Coordinates, and Brodmann Areas of Blood Oxygenation Level–Dependent Functional Magnetic Resonance Imaging Signal Change for Fear and Anger Conditions for Correct Responses and Incorrect Responses in the Event-Related Performance-Based Model

Analysis of the percentage of signal change (event-related model) extracted from the regions of interest that were identified in the blocked analysis showed that patients and controls had a nearly identical pattern and magnitude of activation time locked to the specific appearance of emotional compared with neutral faces. When performance was ignored, the diagnosis × region ANOVA on the percentage of signal change produced no main effects or interactions across emotions. Separately modeling the percentage of signal change for correct and incorrect responses, however, revealed a significant diagnosis × correct vs incorrect interactions with emotion and region. Specifically, the diagnosis × correct vs incorrect × emotion × region ANOVA showed significant effects for region (F6,186 = 9.31, P < . 001; emotion: F3,93 = 3.15, P = .03; correct vs incorrect × region: F6,186 = 2.22, P = .04; correct vs incorrect × emotion: F3,93 = 3.53, P = .02; region × emotion: F18,558 = 1.70, P = .04; and correct vs incorrect × region × emotion: F18,558 = 2.08, P = .006). The interactions that involved diagnosis were diagnosis × correct vs incorrect × emotion (F3,93 = 4.28, P = .007) and diagnosis × region × emotion (F18,558 = 2.09, P = .005). As can be seen in Figure 4, both groups showed activation of the facial affect processing network that differed for correct compared with incorrect responses. Greater activation was generally associated with incorrect identification of happy faces and correct identification of sad, anger, and fear faces. The source of the interactions with diagnosis is that patients showed less activation for correct identification of the threat-related expressions of anger and fear (2 upper right panels in Figure 4) and greater activation for incorrectly identified fear stimuli (right column, middle panel of Figure 4). Indeed, the correct-minus-incorrect subtraction (bottom panels of Figure 4) showed that in controls greater activation was associated with correct than with incorrect responses for anger and fear in most regions. By contrast, in patients the activation was greater for incorrect than for correct responses, especially for fear. This finding was confirmed by follow-up univariate analyses (available from the authors). The difference between patients and controls in the correct-minus-incorrect measure was significant for anger in fusiform gyrus and amygdala and for fear in all regions. Because the groups differed in age, the analyses were repeated covarying for age, as well as educational level and parental educational level, without diminishing the reported findings. Furthermore, an analysis of a subsample of 14 patients and 14 age- and parental educational level–matched controls did not change the results. In addition, because patients had more incorrect responses on average, we compared a subsample of 11 patients and 11 controls matched for performance on the fearful faces and determined that they had an identical pattern of activation (eFigure). Finally, medication type and dose did not relate to any of the dependent measures.

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Figure 4.

Event-related activation, in percent change units, relative to neutral faces, for correct (top row) and incorrect (middle row) identifications, and the correct-minus-incorrect subtraction (bottom row) for happy, sad, anger, and fear expressions in the activated regions: midoccipital (MO), fusiform gyrus (FG), thalamus (TH), amygdala (AM), hippocampus (HI), inferior frontal (IF), and midfrontal (MF).

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eFigure.

This figure compares the effects shown in Figure 4 in the text (left column) with the same measures obtained on a subsample of 11 patients and 11 controls matched for performance on fearful faces (right column). It displays event-related activation, in percent change units, relative to neutral faces for correct (top row) and incorrect (middle row) identifications, and the correct − incorrect subtraction (bottom row) for fear expressions in the activated regions: midoccipital (MO), fusiform gyrus (FG), thalamus (TH), amygdala (AM), hippocampus (HI), inferior frontal (IF), and midfrontal (MF).

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ASSOCIATION WITH CLINICAL MEASURES

The correlations between event-related changes and clinical severity ratings on the SANS and SAPS subscales were generally nil or low, except for very high correlations between severity of affective flattening or blunting subscale and activation of the thalamus, amygdala, and hippocampus in response to the appearance of fear expressions. This correlation was especially high for amygdala (r16 = 0.937, P < .001) (Figure 5). Examination of the distribution of scores (Figure 5) indicated that the correlation was not caused by an outlier but reflected a smooth association across the range of available scores. We also repeated the correlational analysis on the global ratings of the subscales with similar results.

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Figure 5.

Association between brain activity and clinical measures. A, Correlations between event-related activation for the 4 emotional expressions in activated regions and severity of clinical ratings for flat affect. B, Scatterplot of the association between percentage of signal change for the appearance of fear expressions and severity of flat affect. Abbreviations are defined in the legend to Figure 4.

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Patients with schizophrenia and healthy participants showed robust cerebral activation for a facial affect processing task in a network that includes limbic and thalamic components and visual association and frontal regions. As in earlier studies,1519 patients showed reduced activation in these regions compared with controls. Thus, emotion processing deficits in schizophrenia seem related to failure to recruit components of the neural system required for top-down facial affect processing tasks. This analysis, however, is not capable of differentiating brain activity related to different aspects of facial affect processing. Notably, amygdala activation was robust for all blocks, regardless of the target emotion, and no habituation effects were observed in either group. Although habituation effects to presentation of fearful stimuli have been reported,42 these are diminished when the emotion is task relevant.16,43

Examination of the event-related responses, representing bottom-up effects of the appearance of emotional stimuli compared with neutral stimuli, provided further insight into neural substrates for affect processing deficits in schizophrenia. As indicated by the lack of a main effect of diagnosis, when performance is not considered, patients generally showed hemodynamic changes similar to controls to the appearance of faces across emotions. However, they diverged from controls in activation associated with correct compared with incorrect responses. Whereas in controls greater activation was related to correct identifications of anger and fear, in patients greater activation portended failure to identify the emotion. This divergence was specific to threat-related expressions, evident in fusiform gyrus and amygdala for anger and in nearly all components of the network for fear. Notably, the anger effects (controls > patients for correct responses) are more medial than the fear finding (patients > controls for incorrect responses). We believe this post hoc finding is intriguing but should be replicated prospectively.

The paradoxic association of greater network response to the appearance of an emotional face with failure to identify the emotion suggests that patients are operating within a maladaptive range, where increased activity results in deteriorated performance. We have reported with isotopic methods that both low and high anxiety, compared with medium levels, are associated with reduced cortical blood flow and performance.44,45 Perhaps increased amygdala activation triggers reduced functioning of the cortical regions necessary for correct identification and labeling of facial expressions.46 Compensatory activation could also explain behavioral response failure associated with increased hemodynamic response.

Correlation of regional activation with symptom severity measures revealed a specific association between higher magnitude of amygdala activation to the appearance of a fearful face and more severe affective flattening. This relationship is consistent with the abnormality in activation for correct compared with incorrect responding. Meta-analyses of fMRI experiments in healthy people,13,14 as well as studies targeted to examine this issue,47,48 support a fear-sensitive response of the amygdala. In schizophrenia both the amygdala and hippocampus show activation abnormalities in response to fearful faces.18 Thus, in a blocked analysis patients had no amygdala activation habituation with repeated presentation of fearful faces.49 Similarly, fear-related abnormalities were observed in both activation and performance, assessed after scanning.18 It is unclear why flat affect is associated with increased amygdala response to fearful faces. Possibly it is an adaptation for faulty signaling from the amygdala.50 These findings can be examined in light of an extensive literature on fear conditioning in rodents,10,11 with paradigms that are applied in human fMRI studies.51,52

Our results suggest a different pattern of activation for happy and sad compared with anger and fear expressions. Perhaps, unlike the threat-related emotions of anger and fear, happy and sad expressions are more closely linked to the reward system. Abnormal activity in ventral striatum, an important limbic reward region, has been related to negative and positive symptom severity in schizophrenia.53,54 A large body of evidence relates amygdala activity to negative emotions and aversive learning10 and ventral striatal activity to positive emotions and reinforcement learning.55 Both animal and human imaging studies5661 show dissociation of amygdala and ventral striatum responses to rewarding or aversive stimuli, which is consistent with functional antagonism between the 2 regions; however, there is also evidence of coactivation of amygdala and ventral striatum.6264 A balance of excitation and inhibition, both within65 and between these structures, is likely necessary to achieve optimal response to rewarding, aversive, or threatening events. Comparing emotion identification to reward tasks in the same patients and incorporating functional connectivity methods66,67 may help elucidate both cooperative and reciprocal interactions between affective threat-related and reward-related systems.

The present study has several limitations. The sample size was powered to detect differences between patients and controls but not to examine subgroups to establish sex differences or effects of medications or chronicity. Therefore, our results should be considered cautiously with regard to whether they are similar in men and women and the extent to which they relate to medication or apply to samples with larger ranges of age or severity. Notably, our sample was predominantly male and controls were younger than patients. We have covaried for age and have analyzed a matched subsample of patients and controls, which did not affect the results. Another limitation of the study is that in an effort to cover the whole brain we failed to use smaller voxels in areas prone to susceptibility artifacts.68 Although we used special shimming procedures for visualizing the amygdala, this approach may explain our failure to see effects in orbitofrontal regions. Furthermore, the hybrid design may have compromised our ability to obtain more robust estimates of event-related activation, as would have been feasible with sparse event-related designs and perhaps more limited brain coverage.69 These improvements can be examined in future studies. Another limitation applies to the blocked analysis. Because participants only responded with button press to the faces and not to the scrambled-face baseline, task-related activation includes contributions of the motor component. However, for the event-related analysis our tight contrast included button pressing for all events.

Notwithstanding its limitations, the present study reports a novel observation related to emotion processing and flat affect in schizophrenia. The paradoxic finding in patients of greater bottom-up activation of the facial affect processing system associated with failure to recognize threat-related expressions is intriguing and merits further empirical evaluation. The high correlation between amygdala activation to fear expressions and severity of flat affect suggests that modulating this response could lead to better ways of addressing this heretofore treatment-resistant feature of schizophrenia.

Correspondence: Raquel E. Gur, MD, PhD, 10 Gates, Neuropsychiatry, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104 (raquel@upenn.edu).

Submitted for Publication: December 21, 2006; final revision received May 28, 2007; accepted July 18, 2007.

Author Contributions: Dr R. E. Gur had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Financial Disclosure: None reported.

Funding/ Support: This research is supported by National Institutes of Health grants MH-60722 and MH-19112.

Previous Presentation: Part of the data was presented at the Society for Biological Psychiatry; May 18, 2006; Toronto, Ontario, Canada.

Hooker  CPark  S Emotion processing and its relationship to social functioning in schizophrenia patients. Psychiatry Res 2002;112 (1) 41- 50
PubMed Link to Article
Kee  KSGreen  MFMintz  JBrekke  JS Is emotion processing a predictor of functional outcome in schizophrenia? Schizophr Bull 2003;29 (3) 487- 497
PubMed Link to Article
Carpenter  WT  Jr Clinical constructs and therapeutic discovery. Schizophr Res 2004;72 (1) 69- 73
PubMed Link to Article
Gur  REKohler  CGRagland  JDSiegel  SJLesko  KBilker  WBGur  RC Flat affect in schizophrenia: relation to emotion processing and neurocognitive measures. Schizophr Bull 2006;32 (2) 279- 287
PubMed Link to Article
Edwards  JJackson  HJPattison  PE Emotion recognition via facial expression and affective prosody in schizophrenia: a methodological review. Clin Psychol Rev 2002;22 (6) 789- 832
PubMed Link to Article
Kohler  CGBilker  WHagendoorn  MGur  REGur  RC Emotion recognition deficit in schizophrenia: association with symptomatology and cognition. Biol Psychiatry 2000;48 (2) 127- 136
PubMed Link to Article
Kring  AMKerr  SSmith  DANeale  JM Flat affect in schizophrenia does not reflect diminished subjective experience of emotion. J Abnorm Psychol 1993;102507- 517
Link to Article
Adolphs  RGosselin  FBuchanan  TWTranel  DSchyns  PDamasio  AR A mechanism for impaired fear recognition after amygdala damage. Nature 2005;433 (7021) 68- 72
PubMed Link to Article
Damasio  AR Towards a neuropathology of emotion and mood. Nature 1997;386 (6627) 769- 770
PubMed Link to Article
LeDoux  JE Emotion circuits in the brain. Annu Rev Neurosci 2000;23155- 184
PubMed Link to Article
LeDoux  J The emotional brain, fear, and the amygdala. Cell Mol Neurobiol 2003;23 (4-5) 727- 738
PubMed Link to Article
Baas  DAleman  AKahn  RS Lateralization of amygdala activation: a systematic review of functional neuroimaging studies. Brain Res Brain Res Rev 2004;45 (2) 96- 103
PubMed Link to Article
Murphy  FCNimmo-Smith  ILawrence  AD Functional neuroanatomy of emotions: a meta-analysis. Cogn Affect Behav Neurosci 2003;3 (3) 207- 233
PubMed Link to Article
Phan  KLWager  TTaylor  SFLiberzon  I Functional neuroanatomy of emotion: a meta-analysis of emotion activation studies in PET and fMRI. Neuroimage 2002;16 (2) 331- 348
PubMed Link to Article
Williams  LMDas  PHarris  AWLiddell  BBBrammer  MJOlivieri  GSkerrett  DPhillips  MLDavid  ASPeduto  AGordon  E Dysregulation of arousal and amygdala-prefrontal systems in paranoid schizophrenia. Am J Psychiatry 2004;161 (3) 480- 489
PubMed Link to Article
Gur  REMcGrath  CChan  RMSchroeder  LTurner  TTuretsky  BIKohler  CAlsop  DMaldjian  JRagland  JDGur  RC An fMRI study of facial emotion processing in patients with schizophrenia. Am J Psychiatry 2002;159 (12) 1992- 1999
PubMed Link to Article
Schneider  FGur  RCKoch  KBackes  VAmunts  KShah  NJBilker  WGur  REHabel  U Impairment in the specificity of emotion processing in schizophrenia. Am J Psychiatry 2006;163 (3) 442- 447
PubMed Link to Article
Russell  TAReynaud  EKucharska-Pietura  KEcker  CBenson  PJZelaya  FGiampietro  VBrammer  MDavid  APhillips  ML Neural responses to dynamic expressions of fear in schizophrenia. Neuropsychologia 2007;45 (1) 107- 123
PubMed Link to Article
Surguladze  SRussell  TKucharska-Pietura  KTravis  MJGiampietro  VDavid  ASPhillips  ML A reversal of the normal pattern of parahippocampal response to neutral and fearful faces is associated with reality distortion in schizophrenia. Biol Psychiatry 2006;60 (5) 423- 431
PubMed Link to Article
Stip  EFahim  CLiddle  PMancini-Marie  AMensour  BBentaleb  LABeauregard  M Neural correlates of sad feelings in schizophrenia with and without blunted affect. Can J Psychiatry 2005;50 (14) 909- 917
PubMed
Ekman  P Facial expressions of emotion: an old controversy and new findings. Philos Trans R Soc Lond B Biol Sci 1992;335 (1273) 63- 69
PubMed Link to Article
Davidson  RJEkman  PSaron  CDSenulis  JAFriesen  WV Approach-withdrawal and cerebral asymmetry: emotional expression and brain physiology. I. J Pers Soc Psychol 1990;58 (2) 330- 341
PubMed Link to Article
Darwin  C The Expression of Emotion in Man and in Animals.  London, England John Murray1872;
First  MBSpitzer  RLGibbon  MWilliams  JBW Structured Clinical Interview for DSM-IV Axis I Disorders, Patient Edition (SCID-P), Version 2.  New York, NY Biometrics Research1996;
Andreasen  NC Scale for the Assessment of Negative Symptoms (SANS).  Iowa City University of Iowa1984;
Andreasen  NC Scale for the Assessment of Positive Symptoms (SAPS).  Iowa City University of Iowa1984;
Davis  JM Comparative doses and costs of antipsychotic medication. Arch Gen Psychiatry 1976;33 (7) 858- 861
PubMed Link to Article
Schatzberg  AFCole  JO Manual of Clinical Psychopharmacology.  Washington, DC American Psychiatric Press1986;
Kohler  CGTurner  THBilker  WBBrensinger  CMSiegel  SJKanes  SJGur  REGur  RC Facial emotion recognition in schizophrenia: intensity effects and error pattern. Am J Psychiatry 2003;160 (10) 1768- 1774
PubMed Link to Article
First  MSpitzer  RGibbon  MWilliams  J Structured Clinical Interview for DSM-IV Axis I Disorders, Non-Patient Edition (SCID-NP).  New York, NY Biometrics Research1995;
Gur  RCSara  RHagendoorn  MMarom  OHughett  PMacy  LTurner  TBajcsy  RPosner  AGur  RE A method for obtaining 3-dimensional facial expressions and its standardization for use in neurocognitive studies. J Neurosci Methods 2002;115 (2) 137- 143
PubMed Link to Article
Chute  DLWestall  RF PowerLaboratory.  Devon, PA MacLaboratory, Inc1997;
Talairach  JTournoux  P Co-planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System: An Approach to Cerebral Imaging.  New York, NY Thieme Medical Publishers1988;
Webb  PMacovski  A Rapid, fully automatic, arbitrary-volume in vivo shimming. Magn Reson Med 1991;20 (1) 113- 122
PubMed Link to Article
Jezzard  PBalaban  RS Correction for geometric distortion in echo planar images from B0 field variations. Magn Reson Med 1995;34 (1) 65- 73
PubMed Link to Article
Jenkinson  MBannister  PBrady  MSmith  S Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 2002;17 (2) 825- 841
PubMed Link to Article
Jenkinson  MSmith  S A global optimisation method for robust affine registration of brain images. Med Image Anal 2001;5 (2) 143- 156
PubMed Link to Article
Smith  SM Fast robust automated brain extraction. Hum Brain Mapp 2002;17 (3) 143- 155
PubMed Link to Article
Woolrich  MWRipley  BDBrady  MSmith  SM Temporal autocorrelation in univariate linear modeling of FMRI data. Neuroimage 2001;14 (6) 1370- 1386
PubMed Link to Article
Lancaster  JLWoldorff  MGParsons  LMLiotti  CSFreitas  CSRainey  LKochunov  PVNickerson  DMikiten  SAFox  PT Automated Talairach atlas labels forfunctional brain mapping. Hum Brain Mapp 2000;10 (3) 120- 131
Link to Article
Maldjian  JALaurienti  PJKraft  RABurdette  JH An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage 2003;19 (3) 1233- 1239
PubMed Link to Article
Breiter  HCEtcoff  NLWhalen  PJKennedy  WARauch  SLBuckner  RLStrauss  MMHyman  SERosen  BR Response and habituation of the human amygdala during visual processing of facial expression. Neuron 1996;17 (5) 875- 887
PubMed Link to Article
Gur  RCSchroeder  LTurner  TMcGrath  CChan  RMTuretsky  BIAlsop  DMaldjian  JGur  RE Brain activation during facial emotion processing. Neuroimage 2002;16651- 662
Link to Article
Gur  RCGur  REResnick  SMSkolnick  BEAlavi  AReivich  M The effect of anxiety on cortical cerebral blood flow and metabolism. J Cereb Blood Flow Metab 1987;7 (2) 173- 177
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Gur  RCGur  RESkolnick  BEResnick  SMSilver  FLChawluk  JMuenz  LObrist  WDReivich  M Effects of task difficulty on regional cerebral blood flow: relationships with anxiety and performance. Psychophysiology 1988;25 (4) 392- 399
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Schneider  FGur  REAlavi  ASeligman  MEMozley  LHSmith  RJMozley  PDGur  RC Cerebral blood flow changes in limbic regions induced by unsolvable anagram tasks. Am J Psychiatry 1996;153 (2) 206- 212
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Knight  DCNguyen  HTBandettini  PA The role of the human amygdala in the production of conditioned fear responses. Neuroimage 2005;26 (4) 1193- 1200
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Whalen  PJKagan  JCook  RGDavis  FCKim  HPolis  SMcLaren  DGSomerville  LHMcLean  AAMaxwell  JSJohnstone  T Human amygdala responsivity to masked fearful eye whites. Science 2004;306 (5704) 2061
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Holt  DJWeiss  APRauch  SLWright  CIZalesak  MGoff  DCDitman  TWelsh  RCHeckers  S Sustained activation of the hippocampus in response to fearful faces in schizophrenia. Biol Psychiatry 2005;57 (9) 1011- 1019
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Rosenkranz  JAMoore  HGrace  AA The prefrontal cortex regulates lateral amygdala neuronal plasticity and responses to previously conditioned stimuli. J Neurosci 2003;23 (35) 11054- 11064
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Phelps  EADelgado  MRNearing  KILeDoux  JE Extinction learning in humans: role of the amygdala and vmPFC. Neuron 2004;43 (6) 897- 905
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Vuilleumier  PPourtois  G Distributed and interactive brain mechanisms during emotion face perception: evidence from functional neuroimaging. Neuropsychologia 2007;45 (1) 174- 194
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Taylor  SFPhan  KLBritton  JCLiberzon  I Neural response to emotional salience in schizophrenia. Neuropsychopharmacology 2005;30 (5) 984- 995
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Juckel  GSchlagenhauf  FKoslowski  MWustenberg  TVillringer  AKnutson  BWrase  JHeinz  A Dysfunction of ventral striatal reward prediction in schizophrenia. Neuroimage 2006;29 (2) 409- 416
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Burgdorf  JPanksepp  J The neurobiology of positive emotions. Neurosci Biobehav Rev 2006;30 (2) 173- 187
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Louilot  ASimon  HTaghzouti  KLe Moal  M Modulation of dopaminergic activity in the nucleus accumbens following facilitation or blockade of the dopaminergic transmission in the amygdala: a study by in vivo differential pulse voltammetry. Brain Res 1985;346 (1) 141- 145
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Ernst  MNelson  EEJazbec  SMcClure  EBMonk  CSLeibenluft  EBlair  JPine  DS Amygdala and nucleus accumbens in responses to receipt and omission of gains in adults and adolescents. Neuroimage 2005;25 (4) 1279- 1291
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Koelsch  SFritz  TCramon  DYMuller  KFriederici  AD Investigating emotion with music: an fMRI study. Hum Brain Mapp 2006;27 (3) 239- 250
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Morris  JSFrith  CDPerrett  DIRowland  DYoung  AWCalder  AJDolan  RJ A differential neural response in the human amygdala to fearful and happy facial expressions. Nature 1996;383 (6603) 812- 815
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Yacubian  JGlascher  JSchroeder  KSommer  TBraus  DFBuchel  C Dissociable systems for gain- and loss-related value predictions and errors of prediction in the human brain. J Neurosci 2006;26 (37) 9530- 9537
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Becerra  LBreiter  HCWise  RGonzalez  RGBorsook  D Reward circuitry activation by noxious thermal stimuli. Neuron 2001;32 (5) 927- 946
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McClure  SMYork  MKMontague  PR The neural substrates of reward processing in humans: the modern role of FMRI. Neuroscientist 2004;10 (3) 260- 268
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Gottfried  JAO'Doherty  JDolan  RJ Appetitive and aversive olfactory learning in humans studied using event-related functional magnetic resonance imaging. J Neurosci 2002;22 (24) 10829- 10837
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Huber  DVeinante  PStoop  R Vasopressin and oxytocin excite distinct neuronal populations in the central amygdala. Science 2005;308 (5719) 245- 248
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Kirsch  PEsslinger  CChen  QMier  DLis  SSiddhanti  SGruppe  HMattay  VSGallhofer  BMeyer-Lindenberg  A Oxytocin modulates neural circuitry for social cognition and fear in humans. J Neurosci 2005;25 (49) 11489- 11493
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Patel  RSBowman  FDRilling  JK A Bayesian approach to determining connectivity of the human brain. Hum Brain Mapp 2006;27 (3) 267- 276
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Robinson  SWindischberger  CRauscher  AMoser  E Optimized 3 T EPI of the amygdalae. Neuroimage 2004;22 (1) 203- 210
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Moser  EDerntl  BRobinson  SFink  BGur  RCGrammer  K Amygdala activation at 3T in response to human and avatar facial expressions of emotions. J Neurosci Methods 2007;161 (1) 126- 133
PubMed Link to Article

Figures

Place holder to copy figure label and caption
Figure 1.

Face emotion identification task (fearful target).

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Figure 2.

Regions activated for emotion identification task relative to baseline (block analysis) in controls (upper row), patients (middle row), and the controls−patients contrast (bottom row). No patients−controls contrast survived correction. Significance thresholds are based on spatial extent using a height of z ≥ 3.1 and a cluster probability of P ≤ .05. Images are displayed over a Talairach-normalized template in radiological convention (left hemisphere to viewer's right). The z-level coordinates are provided. AM indicates amygdala; IF (47), inferior frontal (Brodmann area 47); HI, hippocampus; IF (45), inferior frontal (Brodmann area 45); and TH, thalamus.

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Figure 3.

Activation maps showing peak amygdala response (see Table 3) for anger (A) and fear (B) conditions for the event-related analysis. Images are displayed over a Talairach-normalized template in radiological convention and thresholded at z ≥ 3.1, uncorrected (>25 continuous voxels). Outline (green) shows extent of atlas-derived amygdala regions of interest (Wake Forest University pickatlas) used for percentage of signal change extraction. Insert (C) highlights patients’ > controls’ (blue) incorrect responses superimposed on controls’ > patients’ (red) correct responses at z ≥ 1.64, uncorrected.

Graphic Jump Location
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Figure 4.

Event-related activation, in percent change units, relative to neutral faces, for correct (top row) and incorrect (middle row) identifications, and the correct-minus-incorrect subtraction (bottom row) for happy, sad, anger, and fear expressions in the activated regions: midoccipital (MO), fusiform gyrus (FG), thalamus (TH), amygdala (AM), hippocampus (HI), inferior frontal (IF), and midfrontal (MF).

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eFigure.

This figure compares the effects shown in Figure 4 in the text (left column) with the same measures obtained on a subsample of 11 patients and 11 controls matched for performance on fearful faces (right column). It displays event-related activation, in percent change units, relative to neutral faces for correct (top row) and incorrect (middle row) identifications, and the correct − incorrect subtraction (bottom row) for fear expressions in the activated regions: midoccipital (MO), fusiform gyrus (FG), thalamus (TH), amygdala (AM), hippocampus (HI), inferior frontal (IF), and midfrontal (MF).

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Place holder to copy figure label and caption
Figure 5.

Association between brain activity and clinical measures. A, Correlations between event-related activation for the 4 emotional expressions in activated regions and severity of clinical ratings for flat affect. B, Scatterplot of the association between percentage of signal change for the appearance of fear expressions and severity of flat affect. Abbreviations are defined in the legend to Figure 4.

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Tables

Table Graphic Jump LocationTable 2. Local Maxima, Coordinates, and Brodmann Areas of Blood Oxygenation Level–Dependent Functional Magnetic Resonance Imaging Signal Change Relative to Scrambled Face Baseline (Block Analysis) for Patients With Schizophrenia, Healthy Controls, and Group Contrasts
Table Graphic Jump LocationTable 3. Local Maxima, Coordinates, and Brodmann Areas of Blood Oxygenation Level–Dependent Functional Magnetic Resonance Imaging Signal Change for Fear and Anger Conditions for Correct Responses and Incorrect Responses in the Event-Related Performance-Based Model
Table Graphic Jump LocationTable 1. Performance During Emotion Identification in Patients With Schizophrenia and Healthy Controls

References

Hooker  CPark  S Emotion processing and its relationship to social functioning in schizophrenia patients. Psychiatry Res 2002;112 (1) 41- 50
PubMed Link to Article
Kee  KSGreen  MFMintz  JBrekke  JS Is emotion processing a predictor of functional outcome in schizophrenia? Schizophr Bull 2003;29 (3) 487- 497
PubMed Link to Article
Carpenter  WT  Jr Clinical constructs and therapeutic discovery. Schizophr Res 2004;72 (1) 69- 73
PubMed Link to Article
Gur  REKohler  CGRagland  JDSiegel  SJLesko  KBilker  WBGur  RC Flat affect in schizophrenia: relation to emotion processing and neurocognitive measures. Schizophr Bull 2006;32 (2) 279- 287
PubMed Link to Article
Edwards  JJackson  HJPattison  PE Emotion recognition via facial expression and affective prosody in schizophrenia: a methodological review. Clin Psychol Rev 2002;22 (6) 789- 832
PubMed Link to Article
Kohler  CGBilker  WHagendoorn  MGur  REGur  RC Emotion recognition deficit in schizophrenia: association with symptomatology and cognition. Biol Psychiatry 2000;48 (2) 127- 136
PubMed Link to Article
Kring  AMKerr  SSmith  DANeale  JM Flat affect in schizophrenia does not reflect diminished subjective experience of emotion. J Abnorm Psychol 1993;102507- 517
Link to Article
Adolphs  RGosselin  FBuchanan  TWTranel  DSchyns  PDamasio  AR A mechanism for impaired fear recognition after amygdala damage. Nature 2005;433 (7021) 68- 72
PubMed Link to Article
Damasio  AR Towards a neuropathology of emotion and mood. Nature 1997;386 (6627) 769- 770
PubMed Link to Article
LeDoux  JE Emotion circuits in the brain. Annu Rev Neurosci 2000;23155- 184
PubMed Link to Article
LeDoux  J The emotional brain, fear, and the amygdala. Cell Mol Neurobiol 2003;23 (4-5) 727- 738
PubMed Link to Article
Baas  DAleman  AKahn  RS Lateralization of amygdala activation: a systematic review of functional neuroimaging studies. Brain Res Brain Res Rev 2004;45 (2) 96- 103
PubMed Link to Article
Murphy  FCNimmo-Smith  ILawrence  AD Functional neuroanatomy of emotions: a meta-analysis. Cogn Affect Behav Neurosci 2003;3 (3) 207- 233
PubMed Link to Article
Phan  KLWager  TTaylor  SFLiberzon  I Functional neuroanatomy of emotion: a meta-analysis of emotion activation studies in PET and fMRI. Neuroimage 2002;16 (2) 331- 348
PubMed Link to Article
Williams  LMDas  PHarris  AWLiddell  BBBrammer  MJOlivieri  GSkerrett  DPhillips  MLDavid  ASPeduto  AGordon  E Dysregulation of arousal and amygdala-prefrontal systems in paranoid schizophrenia. Am J Psychiatry 2004;161 (3) 480- 489
PubMed Link to Article
Gur  REMcGrath  CChan  RMSchroeder  LTurner  TTuretsky  BIKohler  CAlsop  DMaldjian  JRagland  JDGur  RC An fMRI study of facial emotion processing in patients with schizophrenia. Am J Psychiatry 2002;159 (12) 1992- 1999
PubMed Link to Article
Schneider  FGur  RCKoch  KBackes  VAmunts  KShah  NJBilker  WGur  REHabel  U Impairment in the specificity of emotion processing in schizophrenia. Am J Psychiatry 2006;163 (3) 442- 447
PubMed Link to Article
Russell  TAReynaud  EKucharska-Pietura  KEcker  CBenson  PJZelaya  FGiampietro  VBrammer  MDavid  APhillips  ML Neural responses to dynamic expressions of fear in schizophrenia. Neuropsychologia 2007;45 (1) 107- 123
PubMed Link to Article
Surguladze  SRussell  TKucharska-Pietura  KTravis  MJGiampietro  VDavid  ASPhillips  ML A reversal of the normal pattern of parahippocampal response to neutral and fearful faces is associated with reality distortion in schizophrenia. Biol Psychiatry 2006;60 (5) 423- 431
PubMed Link to Article
Stip  EFahim  CLiddle  PMancini-Marie  AMensour  BBentaleb  LABeauregard  M Neural correlates of sad feelings in schizophrenia with and without blunted affect. Can J Psychiatry 2005;50 (14) 909- 917
PubMed
Ekman  P Facial expressions of emotion: an old controversy and new findings. Philos Trans R Soc Lond B Biol Sci 1992;335 (1273) 63- 69
PubMed Link to Article
Davidson  RJEkman  PSaron  CDSenulis  JAFriesen  WV Approach-withdrawal and cerebral asymmetry: emotional expression and brain physiology. I. J Pers Soc Psychol 1990;58 (2) 330- 341
PubMed Link to Article
Darwin  C The Expression of Emotion in Man and in Animals.  London, England John Murray1872;
First  MBSpitzer  RLGibbon  MWilliams  JBW Structured Clinical Interview for DSM-IV Axis I Disorders, Patient Edition (SCID-P), Version 2.  New York, NY Biometrics Research1996;
Andreasen  NC Scale for the Assessment of Negative Symptoms (SANS).  Iowa City University of Iowa1984;
Andreasen  NC Scale for the Assessment of Positive Symptoms (SAPS).  Iowa City University of Iowa1984;
Davis  JM Comparative doses and costs of antipsychotic medication. Arch Gen Psychiatry 1976;33 (7) 858- 861
PubMed Link to Article
Schatzberg  AFCole  JO Manual of Clinical Psychopharmacology.  Washington, DC American Psychiatric Press1986;
Kohler  CGTurner  THBilker  WBBrensinger  CMSiegel  SJKanes  SJGur  REGur  RC Facial emotion recognition in schizophrenia: intensity effects and error pattern. Am J Psychiatry 2003;160 (10) 1768- 1774
PubMed Link to Article
First  MSpitzer  RGibbon  MWilliams  J Structured Clinical Interview for DSM-IV Axis I Disorders, Non-Patient Edition (SCID-NP).  New York, NY Biometrics Research1995;
Gur  RCSara  RHagendoorn  MMarom  OHughett  PMacy  LTurner  TBajcsy  RPosner  AGur  RE A method for obtaining 3-dimensional facial expressions and its standardization for use in neurocognitive studies. J Neurosci Methods 2002;115 (2) 137- 143
PubMed Link to Article
Chute  DLWestall  RF PowerLaboratory.  Devon, PA MacLaboratory, Inc1997;
Talairach  JTournoux  P Co-planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System: An Approach to Cerebral Imaging.  New York, NY Thieme Medical Publishers1988;
Webb  PMacovski  A Rapid, fully automatic, arbitrary-volume in vivo shimming. Magn Reson Med 1991;20 (1) 113- 122
PubMed Link to Article
Jezzard  PBalaban  RS Correction for geometric distortion in echo planar images from B0 field variations. Magn Reson Med 1995;34 (1) 65- 73
PubMed Link to Article
Jenkinson  MBannister  PBrady  MSmith  S Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 2002;17 (2) 825- 841
PubMed Link to Article
Jenkinson  MSmith  S A global optimisation method for robust affine registration of brain images. Med Image Anal 2001;5 (2) 143- 156
PubMed Link to Article
Smith  SM Fast robust automated brain extraction. Hum Brain Mapp 2002;17 (3) 143- 155
PubMed Link to Article
Woolrich  MWRipley  BDBrady  MSmith  SM Temporal autocorrelation in univariate linear modeling of FMRI data. Neuroimage 2001;14 (6) 1370- 1386
PubMed Link to Article
Lancaster  JLWoldorff  MGParsons  LMLiotti  CSFreitas  CSRainey  LKochunov  PVNickerson  DMikiten  SAFox  PT Automated Talairach atlas labels forfunctional brain mapping. Hum Brain Mapp 2000;10 (3) 120- 131
Link to Article
Maldjian  JALaurienti  PJKraft  RABurdette  JH An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage 2003;19 (3) 1233- 1239
PubMed Link to Article
Breiter  HCEtcoff  NLWhalen  PJKennedy  WARauch  SLBuckner  RLStrauss  MMHyman  SERosen  BR Response and habituation of the human amygdala during visual processing of facial expression. Neuron 1996;17 (5) 875- 887
PubMed Link to Article
Gur  RCSchroeder  LTurner  TMcGrath  CChan  RMTuretsky  BIAlsop  DMaldjian  JGur  RE Brain activation during facial emotion processing. Neuroimage 2002;16651- 662
Link to Article
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