Negative Schizotypy and Altered Functional Connectivity During Facial Emotion Processing

Abstract

Background

Impairment in facial emotion perception is an important domain of social cognition deficits in schizophrenia. Although impaired facial emotion perception has been found in individuals with negative schizotypy (NS), little is known about the corresponding change in brain functional connectivity.

Methods

Sixty-four participants were classified into a high NS group (n = 34) and a low NS group (n = 30) based on their total scores on the Chapman scales for physical and social anhedonia. All participants undertook a facial emotion discrimination functional imaging task that consisted of four emotional valences (angry, fear, happy, and neutral). For univariate analysis, the signal change at the bilateral amygdala was compared for each emotional contrast using SPSS (P < .05). For the functional connectivity analysis, we calculated the beta-series functional connectivity of the bilateral amygdala with the medial prefrontal cortex (mPFC) and compared the group differences in SPM12 (P < .05, small volume family-wise error correction).

Results

No significant differences were found between the high and low NS groups in accuracy and reaction time in the facial emotion discrimination task. The high NS group showed reduced brain activations at the amygdala under fearful and neutral conditions. Reduced functional connectivity between the amygdala and the mPFC/dorsal anterior cingulate cortex under the happy and fearful conditions in the high NS group was also found.

Conclusions

Our findings suggest that the individuals with high NS showed altered brain activity and functional connectivity at the amygdala during facial emotion processing and provide new evidence for understanding social cognition deficits in at-risk individuals.

Introduction

Impairments in emotion perception have been considered one of the core social cognition deficits in patients with schizophrenia, and it has been included as a social cognition measure within the Research Domain of Criteria (RDoC) framework.^1,2 Previous meta-analyses have found moderate to severe deficits (d = 0.8–1.0) in facial emotion perception in patients with schizophrenia.^3,4 Deficits in facial emotion processing have also been reported in first-episode psychosis patients⁵ and clinical and genetic high-risk individuals.^6–8 Moreover, facial emotion recognition performance has been found to correlate with the severity of negative symptoms in schizophrenia patients^9–11 and subclinical negative symptoms in nonpsychotic siblings of patients with schizophrenia.¹⁰

In terms of neural correlates, imaging studies have shown that brain activity changes across widespread brain regions, including the amygdala, the superior and medial frontal cortices, the fusiform gyrus, and various subcortical regions are associated with facial emotion perception deficits in schizophrenia patients.^12,13 Although a number of functional magnetic resonance imaging (fMRI) studies have examined brain activation during facial emotion perception (see meta-analyses mentioned above), few studies have examined the functional connectivity correlate of this process. Functional connectivity measures statistical correlations among time series of spatially separated brain regions.¹⁴ Instead of looking at isolated regions, the connectivity between brain regions may be more informative in delineating the pattern of disconnection underlying facial emotion perception deficits.^15,16 Altered functional connectivity has been observed in patients with schizophrenia during facial emotion processing, including reduced connectivity between the amygdala and frontal and parietal regions^17,18 and between the visual and limbic subnetworks in first-degree relatives of schizophrenia patients.¹⁹ Specifically, the anterior cingulate cortex (ACC) and the medial prefrontal cortex (mPFC) have been found to be involved in emotion appraisal and regulation,²⁰ and their decoupling with the amygdala has also been found to be related to the genetic risk of schizophrenia.^21,22

Schizotypy, defined as a set of personality traits resembling the symptoms of schizophrenia in the general population, has been considered a phenotype of schizophrenia²³ and has been proposed as an approach to understand the development of psychosis.^24,25 The multidimensional structure of schizotypy with a positive or psychotic-like dimension (eg, magical ideation, perceptual aberration), a negative dimension (eg, anhedonia), and a disorganized dimension has been commonly suggested.^26,27 The findings on facial emotion perception in schizotypy are mixed because of the different tasks and measures used.^28,29 For example, some studies have reported deficits in facial emotion perception in individuals with high levels of schizotypy,^30,31 but further analysis also indicated that the group differences were mainly on neutral faces and not on other emotional faces.^31,32 For the associations with dimensions of schizotypy, some studies found significant negative correlations between performance on facial emotion perception and negative schizotypy (NS),^30,33 while others reported no such significant correlation.³²

Using task-based fMRI, a few studies have reported altered brain activations in regions involved in emotion processing in individuals with high schizotypy, especially in midline regions including the mPFC/ACC.^34,35 A recent study found hyperactivation at the caudate and the ACC/mPFC regions while viewing emotion-laden pictures in individuals with high positive schizotypy compared with those with low positive schizotypy.³⁵ Using dynamic facial expression stimuli, Huang et al found less deactivation at the ACC in the “happiness appearing minus disappearing” contrast, while more deactivation at the posterior cingulate cortex and the superior temporal gyrus in the “praise minus blame” contrast in individuals with high schizotypy compared with controls.³⁴ Premkumar et al found deactivation in individuals with high positive schizotypy at the dorsal ACC (dACC), the superior frontal and ventral prefrontal cortex while viewing negative social interaction scenes.³⁶ In terms of amygdala activation during emotion processing, one study has reported increased activation of the amygdala in individuals with high positive schizotypy during an emotional Stroop task.³⁷ Another study examining both negative and positive schizotypy individuals found that only individuals with NS showed reduced activation at the amygdala during a reward task.³⁸ Taken together, these findings suggest that changes in brain activations in the mPFC/ACC and the amygdala in individuals with NS may be associated with different patterns of brain activations compared with positive schizotypy. Since the negative dimension of schizotypy such as anhedonia is closely related to dysfunction in affective and social processing,^30,39 and longitudinal studies have indicated the unique role of NS in predicting the development of psychosis,^40,41 it is important to examine the relationship between NS and altered functional connectivity during social cognition processing.

In the present study, we examined the changes in (1) brain activity during a facial emotion valence discrimination fMRI task in individuals with high levels of negative schizotypal traits and (2) functional connectivity during the task, especially the amygdala-mPFC/ACC connectivity. Based on results from previous studies, we hypothesized that activation of the amygdala and some regions in the frontal lobe (mPFC/ACC in particular) may show reduced activity, and the functional connectivity of the amygdala would be altered in individuals with NS.

Methods

Participants

Sixty-four first-year college students (31 males and 33 females) between 17 and 21 years old (mean age = 19.3, SD = 0.9) were selected from a large sample pool from a Medical University in China. All participants completed the whole set of measurements and had good image quality with no significant head motion during the scan (the original sample size was 76, 12 participants were excluded because of excessive head motion). All participants were right handed as assessed by the Annett Handedness Scale.⁴² None of them had a history of psychiatric disorder, substance abuse disorder, or neurological disorder. The IQ of the participants was estimated using the common sense, arithmetic, similarity, and digit span subtests of the Chinese Version Wechsler Adult Intelligence Scale-Revised (WAIS-R).⁴³ The estimated IQ ranged from 98 to 139 (mean = 117.02, SD = 9.97).

We classified the participants into 2 groups based on the level of NS. The level of NS was measured as the sum of scores on the Physical Anhedonia Scale and the Social Anhedonia Scale of the Chapman Psychosis Proneness Scales,^44,45 with a mean score of 23 in a large sample from our previous study in mainland China.⁴⁶ The 2 groups in the present study, namely individuals with high level of NS (high NS, n = 34, NS scores > 23) and individuals with low level of NS (low NS, n = 30, NS scores < 23), were matched on age, gender, and IQ estimates. This study was approved by the Ethics Committee of the Institute of Psychology, the Chinese Academy of Sciences. Written informed consents were obtained from all participants.

Measures

All participants completed self-report scales capturing schizotypy. The Chinese version of the Chapman Psychosis-Proneness Scales,^47–49 including the Revised Social Anhedonia Scale, the Physical Anhedonia scale, the Magical Ideation Scale, and the Perceptual Aberration Scale (166 items in total) were used to measure the positive and negative dimensions of schizotypy. The former 2 scales were used to capture negative schizotypal traits, whereas the latter 2 scales were used to capture positive schizotypal traits. The Schizotypal Personality Questionnaire (SPQ)^50,51 was also administered to all the participants to validate the 2 groups. It is a 74-item checklist that was developed based on the Diagnostic and Statistical Manual of Mental Disorders (third edition revised, DSM-III-R)⁵² criteria for schizotypal personality disorder. The Chinese version of the SPQ has been shown to possess satisfactory psychometric properties.⁵¹ There were no significant differences in levels of positive schizotypy between the 2 groups (see table 1 for more details).

Table 1.

Demographic Description and Self-reported Scores

	High NS (n = 34)		Low NS (n = 30)		t/χ2	P
	Mean	SD	Mean	SD
Age	19.21	0.95	19.23	0.86	−0.12	.904
Gender (male:female)	17:17		13:17		−0.53	.601
Years of education	13.09	0.52	13.00	0.37	0.79	.433
IQ estimates	117.41	9.99	116.43	10.29	0.39	.701
Schizotypy, Chapman
Social anhedonia	11.24	5.89	4.23	2.62	6.00	<.001
Physical anhedonia	18.74	10.11	6.00	3.83	6.50	<.001
Perceptual aberration	5.65	5.43	6.83	7.87	−0.71	.481
Magical ideation	9.62	4.76	10.97	6.21	−0.98	.330
Schizotypy, SPQ
Cognitive-perceptual	9.03	5.241	9.1	5.774	−0.05	.960
Interpersonal	9.53	6.273	6.03	4.937	2.45	<.05
Disorganized	5.32	3.624	4.87	3.683	0.50	.619
Total	22.47	11.631	19.17	11.783	1.13	.264
fMRI task performance
Mean accuracy	0.83	0.08	0.82	0.07	0.42	.678
Mean reaction time	1130.58	123.35	1129.00	121.62	0.05	.960
Head motion
Mean SP	0.14	0.07	0.13	0.07	0.70	.485
Mean FD	0.12	0.05	0.14	0.05	−1.72	.091

Note: FD, framewise displacement; SP, spike percentage; NS, negative schizotypy; SPQ, Schizotypal Personality Questionnaire; fMRI, functional magnetic resonance imaging.

Functional Imaging Task

All participants took part in two 8-minute event-related fMRI scanning sessions of the Facial Emotional Valence Discrimination Task. In brief, Asian female faces with angry, fearful, happy, or neutral expressions were presented, and participants were required to judge the presented facial expressions and press a button of a response box (left button for angry or happy faces; right button for fearful or neutral faces). This task has been used in a previous study and more details could be found in Li et al.⁵³ (also see supplementary material).

Imaging Acquisition and Preprocessing

All MRI scans were acquired on a Siemens Verio 3T MR scanner. Functional imaging data were acquired using a T2-weighted echo planar imaging sequence: 264 whole-brain volumes were collected with slice thickness = 4 mm, echo time (TE) = 28 ms, repetition time (TR) = 2000 ms, flip angle = 90°, matrix size = 64 × 64, 32 slices in coronal plane, field of view (FOV) = 210mm, voxel size = 3 × 3 × 4 mm, bandwidth = 2232 Hz/Px. The T1-weighted images were scanned using a 3D magnetization-prepared rapid gradient-echo (MPRAGE) sequence, with slice thickness = 1 mm, TE = 2.34 ms, TR = 2530 ms, flip angle = 7°, 176 slices in sagittal plane, FOV = 256 mm, voxel size = 1 × 1 × 1 mm.³ The images were screened by a radiologist to exclude any incidental clinical abnormalities before further analysis.

The functional images were preprocessed using DPABI toolbox.⁵⁴ The toolbox integrates the neuroimaging analysis packages (eg, SPM [http://www.fil.ion.ucl.ac.uk/spm/], AFNI [https://afni.nimh.nih.gov/], and FSL [https://fsl.fmrib.ox.ac.uk/fsl/fslwiki]) and was developed to be a user-friendly toolbox for preprocessing of both resting-state and task-based fMRI images. First, the time differences for slices were corrected with the middle slice as the reference slice. Then head motion parameters were estimated using 6-parameter rigid body transformation. The structural images were segmented into gray matter and white matter images using the Segmentation toolbox.⁵⁵ The DARTEL toolbox was used to create a study-specific template.⁵⁶ Then, resting-state functional images were coregistered to the structural images and normalized into Montreal Neurological Institute (MNI) space. The normalized functional images were then smoothed with a full width at half maximum Gaussian kernel of 8 mm. Last, wavelet despike was then applied to the preprocessed functional images to remove the effect caused by head motion.⁵⁷ We controlled for head motion by excluding participants with head motion exceeding 2 mm of translation, 2 degrees of rotation, or a mean framewise displacement (FD)⁵⁸ of >0.3 mm during any of the 2 scanning sessions. The 6 parameters were also entered as covariates in the subsequent first-level analysis, while the mean FD of each participant was entered as a covariate in the second-level analysis.

Statistical Analysis

We first described the demographic information, estimated IQ, and scores on the self-reported scales of the participants. Performance in the facial emotion discrimination fMRI task (accuracy and reaction time) was also reported. Group comparisons were made using independent sample t tests or chi-square tests for all the variables mentioned above in SPSS v19. Significance was set at P < .05.

Univariate Analysis

In the first-level analyses, a general linear model was used to estimate the blood oxygenation level-dependent (BOLD) response for each condition containing regressors depicting onset times of stimulus and 6 regressors for head movement parameters in the 2 sessions. We then calculated the BOLD signal change contrasts for each emotion condition relative to the neutral facial emotion condition using the general linear model, taking the 6 head motion parameters as covariates. Three contrast images were generated for each participant, including “Angry > Neutral,” “Fearful > Neutral,” and “Happy > Neutral,” which were then used for the subsequent second-level analysis in SPM12. First, one-sample t tests were conducted to examine the brain activation under each facial emotion contrasts in the whole sample, and significant clusters with family-wise error (FWE) corrected P < .05 were reported (see supplementary material for the results). Second, independent sample t tests were conducted to examine the group differences between the high and low NS groups for each emotion contrast and neutral condition, with the participants’ mean FD as covariate. The significance threshold was set as FWE-corrected P < .05 at cluster level (P < .001 for cluster-defining threshold).

Furthermore, the amygdala was chosen as a region of interest (ROI), 4-mm-radius spheres of the bilateral amygdala seeds were defined according to a previous meta-analysis⁵⁹ (left amygdala [ROI1]: x = −20, y = −6, z = −12; right amygdala [ROI2]: x = 18, y = −6, z = −14 in MNI space), and the average percentages of signal changes of ROIs were extracted for each emotion condition for each participant using MarsBar v0.44.⁶⁰ Group comparisons in brain activity of the bilateral amygdala were tested by repeated-measures analysis of variance (ANOVA), with hemisphere (left and right) and conditions (angry, fear, happy, neutral) as within-subject factors, and high/low NS groups as the between-subject factor. The main effect of group, condition and hemisphere, as well as their interactions were examined in SPSS v19, with P < .05 as the significance threshold. Post hoc t tests were further conducted for each condition if a significant group effect was found.

Functional Connectivity Analysis

Beta-series correlations were calculated to measure functional connectivity using the BASCO (BetA Series COrrelation) software.⁶¹ First, beta series of the bilateral amygdala were averaged across voxels within each sphere ROI we defined and correlated with the beta series of every other voxel in the whole brain. All correlation maps were normalized using an arc-hyperbolic tangent transform for further statistical inference. Flexible factor analyses were conducted with group as the between-subject factor and condition as the within-subject factor to examine the main effect of group and group by condition interaction using SPM12 with a mask of the mPFC/ACC generated using the WFU pickatlas software (http://www.nitrc.org/projects/wfu_pickatlas/), with small volume correction P < .05, FWE correction as threshold.

To examine other potential changes in functional connectivity, we took all the significantly activated brain regions as ROIs (4-mm sphere) from the univariate analysis together with the bilateral amygdala and calculated the beta-series correlations between each pair of seeds (22 ROIs in total, see supplementary table S1). Brain networks were constructed using the beta-series correlations between each pair of seeds in each emotion condition as edges, which represented the strength of the connectivity. The network edges were then compared using permutation tests (10 000 times) in the BASCO software. The results were reported based on a significance threshold of P < .05.

Results

Behavioral Performance and Self-reported Scores

The high NS group scored significantly higher on the SPQ interpersonal factor than the low NS group. In the fMRI task, the 2 groups did not show any difference in accuracy and reaction time. The mean FD of head motion for the 2 groups was comparable.

Univariate Analysis

Group Differences in Brain Activity.

Independent sample t tests revealed significantly higher activity under the (angry > neutral) contrast in the high NS group, including the right insula (k = 42, x = 54, y = −21, z = 24), the left middle frontal gyrus (k = 44, x = −24, y = 0, z = 63), the right putamen (k = 36, x = 27, y = 3, z = 12), and the cingulate gyrus (k = 22, x = −18, y = −15, z = 39). However, these did not survive multiple comparison correction. No significant differences were found in the other contrasts (fearful > neutral or happy > neutral). In addition, under the neutral condition, we found significantly reduced brain activity in the mPFC (k = 145, cluster-level FWE P < .05, x = −6, y = 39, z = −15) in the high NS group (see figure 1A).

Figure 1.

Group differences on brain activity and functional connectivity of amygdala during the facial emotion task (A). Compared with the low NS group, high NS group showed reduced activity at the ACC/mPFC (blue) and increased activation at the posterior insula, the putamen, the posterior cingulate, and the middle frontal gyrus (red) (B). The group effects of amygdala activation under each condition. The mean percent signal change was extracted using MarsBar, and group comparison was conducted by SPSS (C). Group effect on the functional connectivity of the right amygdala was found at the dorsal ACC. NS, negative schizotypy; ACC, anterior cingulate cortex; mPFC, medial prefrontal cortex.

Activation of the Amygdala.

Repeated-measures ANOVA showed a significant decrease in brain activity at the amygdala in the high NS group (F = 5.34, P = .024, partial eta square = 0.079) (see figure 1B). We also found a significant main effect for hemisphere (F = 8.29, P = .005, partial eta square = 0.118), indicating significantly stronger activation at the right amygdala than at the left amygdala. The following post hoc tests showed that the reduced amygdala activation corresponded to conditions with fearful faces (left amygdala: P = .049; right amygdala: P = .059) and neutral faces (left amygdala: P = .039; right amygdala: P = .049) in the high NS group.

Functional Connectivity

Group Comparison of Functional Connectivity of the Amygdala.

Flexible factor analyses showed that for the functional connectivity of the right amygdala, there was a significant main effect for group at the dACC (k = 20, x = 6, y = 36, z = 21, small volume correction, P < .05 FWE corrected) (see figure 1C). Further analyses showed a significant reduction of right amygdala-dACC functional connectivity in the high NS group under the happy faces condition (k = 34, x = 6, y = 33, z = 24, small volume correction, P < .05 FWE corrected). We also found reduced functional connectivity between the right amygdala and the medial frontal gyrus (BA6; k = 23, x = 3, y = 0, z = 60) under the fearful faces condition.

Group Comparisons of Network Edges.

Taking 22 seeds as nodes and beta-series correlations as edges, we created networks and compared the edges. The results showed increased functional connectivity between the insula and the superior frontal gyrus, and reduced functional connectivity between the amygdala and the mPFC, the middle temporal gyrus, and the fusiform gyrus in the high NS group under the different conditions (permutation tests, P < .05) (see figure 2). We also compared the network edges for each condition and the results can be found in supplementary material.

Figure 2.

Illustration of group difference on the network edges. Increased functional connectivity between the insula and the superior frontal gyrus (SFG), and reduced functional connectivity of the amygdala with the medial frontal gyrus (MedFG), the middle temporal gyrus (MTG), and the fusiform gyrus in the high NS group under different emotional conditions were found. NS, negative schizotypy.

Discussion

In this study, we examined changes in brain activity and functional connectivity during a facial emotion perception task in individuals with high and low levels of NS. At the behavioral level, we did not observe any significant group differences in accuracy or reaction time in the facial perception fMRI task. In the univariate analysis, reduced activity at the mPFC was found during neutral facial conditions in the high NS group. We also found significantly reduced activation of the amygdala in the high NS group, especially under the fearful faces condition. Second, functional connectivity analysis revealed hypoconnectivity between the amygdala and the dACC in the high NS group across different conditions. We also found reduced functional connectivity of the amygdala with the medial frontal gyrus (BA6) under the fearful faces condition. For the comparison of network edges, we found significantly reduced functional connectivity between the left amygdala and the middle temporal gyrus and the medial frontal gyrus; reduced functional connectivity between the right amygdala and the fusiform gyrus; and increased connectivity between the superior frontal gyrus and the insula.

In terms of behavioral performances, we did not find any group difference between high and low NS groups on the accuracy and reaction time in this study. Considering that positive and negative dimensions of schizotypy may have different effects on facial emotion perception,⁶² a possible reason that we did not find any significant difference may be due to the fact that we only focused on the negative dimension of schizotypy in the present study.

Brunet-Gouet and Decety⁶³ reviewed the results of functional imaging studies in schizophrenia patients and concluded that the mPFC and the amygdala are key regions involved in social cognition dysfunction in schizophrenia patients. In our univariate analysis, the high NS group showed reduced activity in the bilateral amygdala, especially in response to the fearful condition. This is consistent with another functional imaging study, which reported reduced amygdala activity during emotion processing in individuals with negative schizotypal traits.³⁸ However, previous studies have also reported increased amygdala activity while performing facial emotion perception tasks in individuals with high level of positive schizotypy.³⁵ The discrepancy reported in schizotypal studies may suggest that the positive and negative dimensions of schizotypy might be related to opposite changes in amygdala activation during emotion processing. Hyperactivation of the amygdala in individual with positive schizotypy and hypoactivity in individuals with NS may contribute to the different emotional experiences in individuals with positive and negative dimensions of schizotypy.⁴⁷ On the other hand, behavioral studies in schizophrenia patients have shown that different from other facial emotions, deficits in fear recognition could be identified as early as in the first episode of schizophrenia.⁹ The ability to identify facial expressions associated with negative emotions, such as fear, may be a potential access point for the investigation of early brain changes in individuals with schizotypy in future research.

At the same time, we found hyperactivity of the insula, the middle frontal gyrus, the putamen, and the cingulate cortex in individuals with high NS under the angry > neutral contrast. Hyper-responsiveness of the insula, the striatum, and the ACC during facial emotion perception has also been found in individuals with high positive schizotypy in a recent study.³⁵ Another study found stronger activation in prefrontal regions during the reappraisal of negative pictures in high schizotypy individuals compared with controls.⁶⁴ Individuals with NS experience less pleasure in social interaction, and the insula has been shown to play an important role in the processing of distressing stimuli.⁶⁵ Hyperactivation of the insula observed in the present study may therefore suggest that there may be a stronger negative emotional response to angry faces in social interaction in individuals with high NS. Since the prefrontal cortex modulates activity of the amygdala to accomplish top-down regulation of emotion, altered brain activity in prefrontal regions may result in the dysregulation of negative emotions. In our study, there was no significant difference in behavioral performance in the imaging task. The increased activation at the paralimbic and cognitive control regions might be related to possible protective or compensatory mechanisms in schizotypy as suggested by previous studies.⁶⁶ Together with previous studies, our findings suggest that both positive and negative schizotypy might be correlated with brain activity changes in prefrontal regions and the insula. Moreover, brain structural changes associated with schizotypy have been observed in the posterior cingulate, the medial prefrontal, the orbitofrontal, and the temporal cortical regions as well as the precuneus.^67–69 Whether these functional changes are related to any underlying structural changes should be further examined in future studies.

Using the beta-series correlations to investigate functional connectivity, we observed decoupling of the amygdala with the dACC/mPFC in the high NS group during facial emotion processing in this study. Abnormal functional connectivity between the amygdala and the PFC/ACC has been reported in patients with schizophrenia and has been associated with the deficit syndrome.²² The amygdala is involved in processing the salience of emotional stimuli, and the mPFC/ACC are involved in emotion appraisal and regulation.²⁰ Hence, the weaker connectivity of the amygdala with the mPFC/ACC may be suggestive of abnormal top-down regulation of the prefrontal cortex. Recently, a study examining the age-related changes in connectivity during an emotional face-matching task in healthy subjects (7–25 years old) found a shift from positive functional connectivity between the amygdala and the ACC/mPFC in children to negative connectivity in adults.⁷⁰ This was taken as evidence suggesting the development of top-down inhibitory control of the ACC/mPFC to the amygdala with age. This reduced functional connectivity between the amygdala and the mPFC/ACC has been found in patients with schizophrenia and clinically defined high-risk individuals as well.^15,71 This may suggest an abnormality in the development of prefrontal inhibition in individuals along the schizophrenia spectrum and might be related to changes in emotion processing or trait-like anhedonia, underlie by activity and connectivity of the amygdala. The vigilance-avoidance hypothesis⁷² proposes that when aversive threat is present, attention would be first allocated toward it but then diverted away from it to reduce the distress caused by this threat. This theory may also be helpful in interpreting our findings of reduced functional connectivity under fearful conditions.

This study has several limitations. First, our facial perception task took the neutral faces as the control condition, but we observed a significant group difference in activation at the mPFC during the neutral faces condition. As such, the validity of the emotion-laden vs neutral contrasts may therefore be affected. In fact, abnormal brain activity at the amygdala and the medial PFC/ACC when processing neutral faces has been found in patients with schizophrenia, major depression, and bipolar disorder.⁷³ Future studies may include participants with other conditions as controls to further investigate this issue. Second, our participants were college students and they were median-split into 2 groups based on their scores on self-reported scales. A more rigorous approach adopting an interview-based measure together with self-reported scale to capture NS should be incorporated in future studies. In addition, using extreme groups to examine the group difference in behavioral performance would be an alternative way to clarify this issue. Third, we did not record the daily use of alcohol or tobacco of our participants, which might affect brain activation and connectivity. This issue should be taken into consideration in future study. Fourth, social anxiety might be related to NS and may have a potential effect on brain activation when performing social cognition tasks. Future studies should consider including measures of social anxiety to control for this effect.

It should be noted that participants with schizotypy may not necessarily be associated with negative outcomes or go on to develop schizophrenia.²⁷ Our findings only suggest that individuals with social anhedonia (NS) may be associated with altered brain activity and functional connectivity at the amygdala during facial emotion processing. Future study should also examine the positive aspect of schizotypy such as creativity.

In conclusion, the present findings suggest that individuals with schizotypy showed altered brain activity and functional connectivity at the amygdala during facial emotion processing. These findings highlight the underlying neural mechanism of social cognition in these individuals.

Supplementary Material

Supplementary material is available at Schizophrenia Bulletin online.

Funding

The Beijing Municipal Science & Technology Commission Grant (Z161100000216138 to R.C.K.); National Key Research and Development Programme (2016YFC0906402 to R.C.K.); the Beijing Training Project for Leading Talents in S&T (Z151100000315020 to R.C.K.); the CAS Key Laboratory of Mental Health, Institute of Psychology, and the CAS/SAFEA International Partnership Programme for Creative Research Teams (Y2CX131003 to R.C.K.); National Science Fund China (31400884 to Y.W.); China Scholarship Council (Y.W.).