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. Author manuscript; available in PMC: 2025 Apr 1.
Published in final edited form as: Eur J Neurosci. 2023 May 23;59(8):1863–1876. doi: 10.1111/ejn.16034

Neurophysiological indices of face processing in people with psychosis and their siblings: An event-related potential study

Jonathan K Wynn 1,2, Peter E Clayson 3,1, Michael F Green 2,1, Amy Jimenez 1,2, Junghee Lee 4,2, Eric A Reavis 2,1, William P Horan 5,1,2
PMCID: PMC10632544  NIHMSID: NIHMS1916107  PMID: 37160716

Abstract

People with schizophrenia experience difficulties with social interactions. One contributor to these social deficits is dysfunction in processing facial features and facial emotional expressions. However, it is not known whether face processing deficits are evident in those with other psychotic disorders or in those genetically at-risk for psychosis (i.e., first-degree relatives of those with psychosis). We assessed event-related potentials (ERPs) during a facial and emotion processing task in 100 people with a diagnosis of schizophrenia or another psychotic condition (PSY), 32 of their siblings (SIB), and 45 healthy comparison participants (CTL). In separate blocks, participants identified the sex (male or female) or emotion (happy, angry, neutral) of faces. In a comparison condition, participants indicated whether buildings had one or two floors. ERPs were examined in two stages. First, we compared ERPs across the emotion, sex, and building identification conditions. Second, we compared ERPs among the three different facial emotions. PSY exhibited significantly lower amplitudes over parietal-occipital regions between 111–151 ms when viewing faces but not buildings than CTL, consistent with a face-selective N170 ERP component deficit. The SIB group was intermediate for faces, but not significantly different than PSY or CTL. During emotion identification, all three groups showed increased N170 amplitudes to angry and happy vs. neutral expressions, with no group differences. In follow up analyses, we examined differences between PSY with or without affective psychosis, and differences between those with schizophrenia vs. other psychotic disorders; there were no significant differences in these analyses. Face processing deficits assessed with ERPs were observed in a group of diverse psychotic disorders, though deficits were not seen to be modulated by facial emotion expression. Additionally, N170 deficits are not evident in siblings of those with PSY.

Keywords: psychosis, face processing, affect processing, ERP, siblings

Introduction

People with psychotic disorders (PSY) experience high levels of social dysfunction, and, across the course of their illness, have fewer social connections, poorer quality of connections, and more stressful connections than healthy individuals (Bowie et al., 2010; Cornblatt et al., 2012; Harvey et al., 2012; McClure, Harvey, Bowie, Iacoviello, & Siever, 2013). In conjunction with these social deficits, PSY also exhibit diminished emotion perception (i.e., reduced ability to identify facial expressions) (see (Kohler, Walker, Martin, Healey, & Moberg, 2010) for a review), which may further impair social functioning. Most studies of reduced social functioning and impaired emotion perception have been conducted in people with the specific diagnosis of schizophrenia. However, less is known about these deficits in individuals with other psychotic diagnoses and in those genetically at-risk for developing psychosis (e.g., siblings of PSY). In the current study, we examined face processing with event-related potentials (ERPs) to determine the trans-diagnostic nature of deficits in emotion perception.

A large literature indicates that the amplitude of the N170 component (a negative voltage deflection peaking approximately 130–170 ms over bilateral parieto-occipital scalp regions) is selectively larger for face versus non-face stimuli (Bentin, Allison, Puce, Perez, & McCarthy, 1996; Vuilleumier & Pourtois, 2007). It is thought that the N170 reflects the structural encoding of facial features (Bentin et al., 1996; Eimer & McCarthy, 1999). However, the N170 is also sensitive to emotional valence when the task involves identification of facial expressions (e.g., Brenner, Rumak, Burns, & Kieffaber, 2014; Chai et al., 2012; Krombholz, Schaefer, & Boucsein, 2007), with larger responses for happy and angry facial expressions versus neutral faces when subjects are required to identify emotional expressions (cf. (Eimer & Holmes, 2002, 2007) for exceptions). Moreover, individual differences in N170 or the magnetoencephalography (MEG) equivalent M170 responses have been linked to attachment styles, social skills, social anxiety, and psychopathy, and negative symptoms (Escobar et al., 2013; Meaux, Roux, & Batty, 2014; Ohara et al., 2020; Rossignol et al., 2012; Rossignol, Fisch, Maurage, Joassin, & Philippot, 2013; Zhang, Wang, Luo, & Luo, 2012).

There is an extensive literature documenting reduced N170 amplitudes in schizophrenia (Campanella, Montedoro, Streel, Verbanck, & Rosier, 2006; Maher, Mashhoon, Ekstrom, Lukas, & Chen, 2016; Salisbury, Krompinger, Lynn, Onitsuka, & McCarley, 2019; Wynn, Jahshan, Altshuler, Glahn, & Green, 2013; Wynn, Nori, Tillery, & Green, 2006). A meta-analysis of 21 studies of the N170 in schizophrenia found medium effect size deficits (weighted mean effect size Hedge’s g = 0.64), strongly supporting dysfunction of this neural response in the disorder (McCleery et al., 2015). Additionally, people with schizophrenia appear to show normal modulation of the N170 with facial expression (i.e., larger N170 amplitudes to faces with emotion vs. neutral faces), though overall amplitudes remain smaller than healthy controls (Brenner, Rumak, & Burns, 2016).

Far less research has been done in those considered genetically at risk for developing a psychotic disorder (e.g., first-degree relatives) and none in people with other psychotic diagnoses. To date only two studies assessing the N170 in relatives of schizophrenia have been published. In the first (Ibanez et al., 2014), relatives were assessed in addition to schizophrenia patients and controls (n = 13 in each group). Results showed a reduction in N170 amplitudes in patients compared to controls, but there was no relative – control difference. However, when examining if the N170 was modulated by expression, both patients and relatives failed to show modulation whereas controls did (i.e., larger amplitudes to positive vs. negative faces). In the second study (Yang et al., 2017), 26 siblings were assessed in addition to 30 people with schizophrenia and 30 healthy controls. The authors reported no significant differences between the three groups, though the study used few trials per condition, so confidence in the results is limited. In short, N170 findings in first-degree relatives are limited in comparison to those with schizophrenia.

In the current study, we examined: 1) whether N170 amplitudes to faces are reduced in a broad sample of people with psychotic disorders and a sample of their unaffected siblings, 2) whether there were group differences in the sensitivity of the N170 based on type of facial expression, and 3) whether N170 amplitudes to faces and facial expression correlate with clinical symptoms in people with a psychotic disorder. We hypothesized that PSY would have significantly reduced amplitudes compared to CTL, with SIB having intermediate amplitudes. We further hypothesized that ERP amplitudes to faces with happy or angry expressions would be larger compared to neutral expressions in all three groups. Additionally, given the large PSY sample, we conducted two follow-up analyses to examine if ERP amplitudes significantly differ between those with a non-affective vs. affective psychosis diagnosis, and if amplitudes differed between those with a schizophrenia diagnosis or any other psychosis diagnosis.

Methods and Materials

Participants

Participants came from a National Institutes of Mental Health-sponsored study of social approach and avoidance in people with psychotic disorders (“Social Affiliation in Psychosis: Mechanisms and Vulnerability Factors”, MH107422, PI: William Horan). In total, 100 people with a psychotic disorder (PSY), 32 siblings of those with a psychotic disorder (SIB), and 45 healthy control participants (CTL) participated in this study.

All participants with psychosis were clinically stable outpatients with a Diagnostic and Statistical Manual of Mental Disorders – 4th Edition (DSM-IV) (DSM-IV, 2000) psychotic disorder diagnosis, including schizophrenia (n = 48), schizoaffective disorder (n = 9), bipolar disorder with psychotic features (n = 20), major depressive disorder with psychotic features (n = 3), delusional disorder (n = 2), schizophreniform (n = 1), or unspecified psychotic disorder (n = 17). Participants were recruited from the Veterans Affairs Greater Los Angeles Healthcare System (VAGLAHS), the University of California, Los Angeles (UCLA), and outpatient board and care facilities and clinics in the greater Los Angeles area. Most PSY participants were receiving clinically determined doses of medication. All were tested outside of mood episodes. Healthy control participants were recruited using ads placed on the internet (e.g., Craigslist). Sibling participants were recruited by phone or letter if their PSY sibling agreed to release their sibling’s name to our recruiters. All procedures were approved by the Institutional Review Boards of VAGLAHS and UCLA, with participants providing written informed consent. All participants were assessed for their capacity to provide informed consent using the IRB-approved Mental Illness Research, Education and Clinical Center (MIRECC) Evaluation of Capacity to Sign Consent form, which assesses a participant’s level of understanding of the nature of the study, the potential risks and benefits, and their rights as a research participant.

Inclusion criteria for all participants were: a) age 18 – 65, b) sufficient understanding of English to comprehend the procedures and interviews, c) IQ > 70 and no developmental disability based on chart review, d) no history of neurological disease (e.g., epilepsy), e) no evidence of past serious head injury or loss of consciousness > 15 minutes, neuropsychological sequelae, or cognitive rehabilitation post-head-injury, f) no sedatives or benzodiazepines 12 hours prior to testing, g) normal or corrected vision (participants had to be able to read at least line 6 on a Snellen chart, corresponding to 20/40 vision), h) no positive urine toxicology screening at the time of assessment, i) no substance or alcohol dependence three months prior to participation or substance or alcohol abuse one month prior to participation, and j) no mood episode meeting clinical criteria for depression, mania or hypomania in the past two months.

Additional inclusion criteria for PSY participants included clinical stability, i.e., no inpatient hospitalizations during the past 3 months and no psychoactive medication changes in the four weeks prior to enrollment, including changes in medication type or dosage. Additional inclusion criteria for siblings included no history of primary psychotic symptoms and no current mood episode. Additional inclusion criteria for healthy controls were: a) no history of psychotic disorder, bipolar spectrum disorder, or other major mood disorder based on SCID-I interview (First, Spitzer, Gibbons, & Williams, 1997); b) no personality disorder in the schizophrenia spectrum (including avoidant, paranoid, schizotypal, or schizoid) or borderline personality disorder based on the Structured Clinical Interview for DSM-IV Axis II Disorders (SCID-II) (First, Gibbons, Spitzer, & Williams, 1996), and c) no psychotic or bipolar disorder in first-degree relatives, based on participant report.

All clinical interviewers were trained through the Treatment Unit of the Department of Veterans Affairs Desert Pacific MIRECC and met a minimum kappa of 0.75 for psychotic and mood items (Ventura et al., 1998). To corroborate self-report information when necessary for PSY, medical records and reports from their clinicians were examined if available. For all participants, positive symptoms were assessed using the positive symptom factor (Kopelowicz, Ventura, Liberman, & Mintz, 2008) from the Brief Psychiatric Rating Scale (BPRS) (Ventura et al., 1993) and negative symptoms were assessed with the two subscales from the Clinical Assessment Interview for Negative Symptoms (CAINS) (Kring, Gur, Blanchard, Horan, & Reise, 2013): motivation and pleasure (MAP) and expressive negative symptoms (EXP).

ERP task

Visual stimuli were drawn from the NimStim database of faces and facial expressions (Tottenham et al., 2009)1; images of one- and two-story houses were selected via images.google.com. Stimuli were shown in separate, counterbalanced blocks, with each condition (emotion, sex, building identification) presented in three blocks with 24 trials in each block. In each block, participants had to identify the facial expressions (happy, angry, neutral), identify the sex of the face (male, female), or identify how many stories a house had (one or two). Three blocks of each condition were presented for each of the three conditions. There was a total of 36 actors (18 male, 18 female; 18 white, 18 black) each depicting one of the three different expressions (happy, angry, or neutral). There was a total of 36 houses (18 one-story, 18 two-story). In the emotion identification condition, each type of expression was shown a total of 24 times. There were 4 practice trials for each identification condition presented prior to the beginning of the EEG recording.

The experiment was programmed in E-Prime 2.0 (Psychology Software Tools, Inc., Sharpsburg, PA). Participants sat approximately 1 m from the screen. Stimuli were presented at fixation on a liquid crystal display monitor set at 1920 × 1080 pixels, running at 120 Hz. Stimuli were 506 × 624 pixels, corresponding to an approximate visual angle of 14.4° X 15.0°. Participants were told at the beginning of each block which aspect of the stimulus they were to identify (emotion, sex, or building height). A trial sequence began with a 400 ms fixation cross, followed by a blank screen for 400 ms, then the presentation of a face or house for 1000 ms. After the image disappeared another blank screen was presented for 500 ms. This was followed by a screen that prompted the participant to identify the image type, depending on the block (i.e., emotion identification, sex identification, or building identification) (see Figure 1 for a timeline of a trial sequence). The tester manually entered the participant’s response after which the next trial began following a 500 ms delay. Performance in all groups was near ceiling across all three groups, with PSY having significantly fewer mean correct responses compared to SIB and CTL (see Supplemental Table 1 for means and standard deviations). However, this difference was trivial with a difference of approximately 1–2 correct responses depending on the condition.

Figure 1:

Figure 1:

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Example of a trial sequence for the task. In separate blocks, a face is presented or a house is presented and participants must identify either the expression or sex of the face, or how many stories tall a house is (one or two story).

EEG acquisition and analysis

Continuous EEG was recorded using a BioSemi ActiveTwo system (BioSemi B.V., Amsterdam, Netherlands) with sintered Ag/AgCl active electrodes and 64 channel custom electrode caps (Cortech Solutions, Wilmington, NC). EEG signals were preamplified at the electrode with a gain of one, digitized at a sampling rate of 1,024 Hz with a 24-bit analog-to-digital converter, and filtered online using a low-pass, fifth-order sinc filter with a half-power cut-off of 204.8 Hz. Two electrodes, placed above and below the left eye, were used to measure vertical electrooculography (EOG), and two electrodes, placed at the outer canthus of each eye, were used to measure horizontal EOG. Additional active electrodes were placed on the left and right mastoid, and on the tip of the nose. Each electrode was measured online with respect to a common mode sense electrode, forming a monopolar channel.

Data were processed offline. The EEG data were algebraically rereferenced to averaged mastoids, segmented into 1000 ms long epochs (−200 to +800 ms; 1024 points) using EEGLab v2019.0 (Delorme & Makeig, 2004), filtered in ERPLab v6.1.4 (Lopez-Calderon & Luck, 2014) using a sixth-order IIR Butterworth filter with half-amplitude cutoffs at 0.05 and 30 Hz, and baseline adjusted using the 200 ms pre-stimulus period. Eyeblinks and saccadic eye movements were removed from the segmented waveforms using independent components analysis (ICA) using the ERP PCA Toolkit v2.83 (Dien, 2010). ICA components with a correlation of 0.8 or above with the scalp topography of a blink template and at 0.8 or above with the scalp topography of vertical and horizontal saccade templates were removed from the data. After ocular artifact correction, trials that contained more than a 100 μV step within 100-ms intervals or a voltage difference of 300 μV through the duration of the epoch for any electrode were rejected. A minimum average of 90.6% of trials were accepted for analyses across all groups, and there were no significant differences between groups or conditions in terms of number of trials accepted. The mean (standard deviation) number of trials accepted by group and condition can be found in Supplemental Table 2.

Following artifact correction and rejection, individual-subject ERPs were created for each condition (emotion, sex, or building identification; within emotion identification: happy, angry, and neutral) and exported for analysis using a mass univariate analysis approach implemented in the Mass Univariate Toolbox (Groppe, Urbach, & Kutas, 2011) and the Factorial Mass Univariate Toolbox (Fields & Kuperberg, 2020). We conducted data-driven non-parametric permutation analyses (Luck, 2014; Vogel, Luck, & Shapiro, 1998) to determine which electrodes and time windows showed significant main effects of condition (e.g., face vs. building; happy or angry vs. neutral) and group (PSY, SIB, CTL), or condition interactions. For the non-parametric permutation tests, we used an Fmax statistic (Blair & Karniski, 1993). This method is best suited when there are focal spatial and temporal effects, such as a clear P100, N170, or P250 at parieto-occipital sites. The Fmax permutation testing involves forming a null distribution by running multiple analyses of variance (ANOVAs) on each time point and electrode and then selecting the largest F-value for each test. Fmax values that exceed the 0.05 percentile are considered significant. Fmax permutation testing strongly controls for family-wise error rates. In all permutation analyses, we used 5,000 permutations per comparison. As recommended (Groppe et al., 2011; Luck, 2014) and used in prior studies (Jahshan, Wynn, Mathalon, & Green, 2017), data were down-sampled to 128 Hz, and all time points between ~41 and ~502 ms (59 time points) across 20 parieto-occipital scalp electrodes (P9, P7, P5, P3, P1, Pz, P2, P4, P6, P8, P10, PO7, PO3, POz, PO4, PO8, O1, Oz, O2, Iz) were included (i.e., 1,180 total comparisons). These constraints (limiting data samples and time windows) increase power to detect real differences in relevant time windows and electrodes. The results are depicted as a raster diagram highlighting in color the time points and electrodes showing significant effects.

We conducted two separate sets of factorial mass univariate analyses. In the first set we examined if there were group differences in processing of faces vs. buildings by conducting a 3 X 3 mixed repeated measures ANOVA with Group as the between-subjects factor and Condition (emotion identification, sex identification, building identification) as the within-subject factor. Our effect of interest was the Group X Condition interaction to determine if groups differed in the processing of faces vs. buildings. In the second set of analyses, we examined if there were group differences in emotional expression processing by examining ERPs during the emotion identification condition only. We conducted a 3 X 3 mixed repeated measures ANOVA with Group as the between-subjects factor and Emotion (happy, angry, neutral) as the within-subject factor. Our effect of interest was the Group X Emotion interaction. In each set of analyses, if a significant interaction was detected, we extracted the mean amplitude within the identified time window and the electrodes for follow-up analyses to decompose any significant interactions or main effects.

Additional analyses

In addition to our primary analyses, we conducted follow-up tests within the PSY group only: 1) comparing those with an affective (n = 22) vs. non-affective (n = 70) diagnosis (not including those with a schizoaffective diagnosis in either category), and 2) comparing those with a schizophrenia diagnosis (n = 48) vs. those with any other psychotic disorder diagnosis (n = 52). We also examined correlations between ERP measures and clinical symptom ratings.

Results

Demographics and clinical characteristics

The demographic and clinical characteristics can be seen in Table 1. There was a significant between-group difference in age, with SIB being significantly younger than PSY but not CTL. There was also a significant difference in sex distribution between the groups, with SIB having more female participants compared to the PSY and CTL groups. Finally, as expected, PSY had significantly lower levels of personal education compared to SIB and CTL; however, there were no differences in parental education between the three groups. When including age and gender as variables in the follow-up ANOVAs to the factorial mass univariate analyses the results remain the same, and thus these variables are not considered further. The PSY group had mild to moderate levels of symptoms as assessed by the BPRS and CAINS, with significantly higher ratings than SIB and HC; SIB and HC did not differ in levels of symptoms. In the PSY group, 69 were taking a second-generation medication, 4 were taking a first-generation antipsychotic, and 27 either were not taking an antipsychotic medication or did not provide medication information.

Table 1:

Demographics and symptom ratings for each of the three groups (PSY = psychosis, SIB = sibling, CTL = healthy control).

PSY (n = 100) SIB (n = 32) CTL (n = 45)
Age 48.4 (11.6) 41.8 (14.8) 47.5 (9.5) F2,173 = 3.80, p = 0.024
PSY > SIB
Sex (M:F) 73:27 14:18 31:14 χ2 (df = 2) = 9.47, p = 0.009
Personal Education (years) 13.5 (1.9) 14.5 (2.3) 14.7 (3.1) F2,173 = 7.96, p < 0.001
PSY < SIB, CTL
Parental Education (years) 14.1 (3.6) 15.7 (3.3) 14.7 (3.1) F2,161 = 2.57, p = 0.080
BPRS: Positive Symptoms 1.83 (0.76) 1.21 (0.26) 1.13 (0.21) F2,167 = 27.61, p < 0.001
PSY > SIB, CTL
CAINS MAP 1.44 (0.81) 0.70 (0.51) 0.80 (0.79) F2,164 = 16.64, p < 0.001
PSY > SIB, CTL
CAINS EXP 0.81 (0.82) 0.26 (0.47) 0.17 (0.27) F2,168 = 17.64, p < 0.001
PSY > SIB, CTL
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BPRS: Brief Psychiatric Rating Scale.

CAINS: Clinical Assessment Interview for Negative Symptoms.

Event-related potentials

Face vs. building effects

Figure 2 shows the results from the factorial mass univariate analyses, with representative waveforms averaged across electrodes identified in the analyses for each group and condition shown in Figure 3. The mass univariate analysis revealed significant Group and Condition main effects. The Group main effect was seen over one relatively early time window (~50–100 ms) and one relatively late time window (~325–450 ms). The Condition main effect was present in nearly all electrodes and time windows, reflecting large differences in ERP amplitudes between faces and buildings, as expected. As can be seen from visual inspection of the waveforms in Figure 3, the Condition main effect was due to larger amplitudes to faces vs. buildings in all three groups. Importantly, there was also a significant Group X Condition interaction from 111–151 ms, over bilateral parieto-occipital sites (P4, P6, P7, P8, P10, PO7, PO4, PO8, O1, O2). The topography and time window suggest that this interaction was specific to the typical N170 response. Inspecting the waveforms in Figure 2, the interaction reflects smaller N170 amplitudes for both face conditions in PSY compared to SIB and CTL; waveform amplitudes for buildings were similar across all three groups. The mean (SD) amplitude in the interaction time window (i.e., 111–151 ms) and across the significant electrodes can be seen in Table 2; group means and individual data points can be seen in Supplemental Figure 1. In addition, we include waveforms with bootstrapped 95% confidence interval bands as Supplemental Figure 2.

Figure 2:

Figure 2:

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Factorial mass univariate analysis results for examination of expression, sex, and building identification (Condition) by Group. The y-axis represents individual electrodes analyzed; x-axis represents time (ms). Color scales indicate significant F-values; gray squares indicate no significant effects. The figure depicts results for main effects of Group (top), Condition (middle), and the Group X Condition interaction (bottom).

Figure 3:

Figure 3:

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Event-related potential (ERP) waveforms for each condition shown separately for each group. ERPs were averaged over significant electrodes (P4, P6, P7, P8, P10, PO7, PO4, PO8, O1, O2) identified in the factorial mass univariate analysis. ERPs to building identification shown in light gray, expression identification in orange, and sex identification in blue.

Table 2:

Mean (standard deviation) amplitudes (between 111–151 ms) in μV for the analysis of face processing (i.e., Emotion vs. Sex vs. Building identification).

PSY (n = 100) SIB (n = 32) CTL (n = 45)
Emotion+ −1.52 (3.44) −2.49 (3.64) −2.83 (4.26)
Sex+ −1.53 (3.53) −2.66 (3.53) −3.02 (4.36)
Building++ 0.67 (2.74) 1.88 (3.42) 0.92 (3.16)
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+

= PSY > CTL, p < 0.05

++

= PSY < SIB, p < 0.05

To follow up on the significant interaction, we conducted a series of independent t-tests comparing effects in each condition between each pair of groups. There was a significant difference in N170 amplitude between PSY and CTL for both face conditions (emotion, t143 = 1.97, p = 0.05, Cohen’s d = 0.35, and sex identification, t143 = 2.19, p = 0.03, Cohen’s d = 0.39) but there was no group difference in the building condition. There was a significant difference in the ERP amplitude for buildings between SIB and PSY, t130 = 2.04, p = 0.044, Cohen’s d = 0.41, but not for faces. Finally, there were no significant differences between SIB and CTL for any of the three conditions.

Facial emotion effects

Figure 4 shows the results from the factorial mass univariate analyses comparing effects across different facial expression conditions, with representative waveforms shown in Figure 5. There were significant main effects for both Group and Emotion. For the Emotion main effect, there were significant differences in several parieto-occipital electrodes in one broad time window between 119–500 ms, across all electrodes analyzed except for P3 and P4. The Group main effect appears to be largely located to the time window at approximately 90 ms, particularly over the right hemisphere, which is consistent with a P100 response. However, there was no significant Group X Emotion interaction effect at any time point or electrode.

Figure 4:

Figure 4:

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Factorial mass univariate analysis results for examination of angry, happy, and neutral expression identification (Expression) by Group. The y-axis represents individual electrodes analyzed; x-axis represents time (ms). Color scales indicate significant F-values; gray squares indicate no significant effects. The figure depicts results for main effects of Group (top), Expression (middle), and the Group X Expression interaction (bottom).

Figure 5:

Figure 5:

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Event-related potential (ERP) waveforms for each condition shown separately for each group. ERPs were averaged over significant electrodes (P4, P6, P7, P8, P10, PO7, PO4, PO8, O1, O2) identified in the factorial mass univariate analysis for the face vs. building analyses. ERPs to angry expressions shown in blue, happy in red, and neutral in yellow.

To interpret the main effects of Group and Emotion, we conducted a 3 (Group) X 3 (Emotion) repeated measures ANOVA for the relevant N170 time window described above (111–151 ms). The mean (SD) amplitude in this time window and across the significant electrodes can be seen in Table 3; group means and individual data points can be seen in Supplemental Figure 3. In addition, we include waveforms with bootstrapped 95% confidence interval bands as Supplemental Figure 4. As expected, there was a significant main effect of Emotion, F2,348 = 3.38, p = 0.035. There was no significant Group main effect or Group X Emotion interaction within this time window. Post-hoc pairwise comparisons revealed significantly larger amplitudes for happy faces compared to neutral faces, p = 0.018, and a marginally significant difference for angry faces compared to neutral faces, p = 0.055. There was no significant difference between happy and angry faces.

Table 3:

Mean (SD) amplitudes in μV for the analysis of facial expression processing (i.e., happy vs. angry vs. neutral identification).

PSY (n = 100) SIB (n = 32) CTL (n = 45)
Angry −1.62 (3.59) −2.36 (3.81) −3.01 (4.52)
Happy −1.54 (3.59) −2.71 (3.74) −2.99 (4.30)
Neutral −1.41 (3.50) −2.40 (3.76) −2.48 (4.17)
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Psychosis subgroup analyses

Within the psychosis group we conducted two separate subgroup analyses for face and emotion processing: 1) comparing those with an affective vs. non-affective psychosis diagnosis; and 2) comparing those with a schizophrenia diagnosis vs. those with any other psychosis diagnosis. The descriptive statistics and full results from these statistical analyses can be found in Supplemental Tables 3-5. For face processing in those with an affective vs. non-affective psychosis diagnosis, there was a significant condition main effect (ηp2 = 0.429), indicating larger N170 amplitudes to faces vs. buildings; the group (ηp2 = 0.012) and group X condition (ηp2 = 0.003) effects were not significant, indicating comparable amplitudes for all three conditions between groups. For type of emotion, there were no significant main effects of emotion (ηp2 = 0.015) or group (ηp2 = 0.008), and the group X emotion interaction also was not significant (ηp2 = 0.006), for these subgroups.

Regarding differences between the schizophrenia vs. other psychosis diagnosis comparison, there was a significant condition main effect (ηp2 = 0.511), indicating larger N170 amplitudes to faces vs. buildings; the group main effect (ηp2 = 0.009) and the group X condition interaction (ηp2 = 0.012) were not significant. For emotion identification, neither the group (ηp2 = 0.014) or condition main effects (ηp2 = 0.010), nor the group X condition interaction (ηp2 = 0.001) were significant, for these subgroups.

Symptom Correlates

Within PSY there were no significant correlations with N170 amplitudes for any face identification type (i.e., sex or facial expression) between the BPRS total score or the MAP and EXP subscales of the CAINS.

Discussion

Using a rigorous data-driven analytical approach, we found reduced N170 amplitudes in a large, diverse group of people with psychotic disorders, relative to a healthy comparison group without a history of psychotic illness in first-degree relatives. Siblings of those with a psychotic disorder were intermediate between those with psychosis and the comparison group. Despite the overall N170 amplitude deficits, those with a psychotic disorder exhibited similar sensitivity to emotional expression (i.e., happy and angry amplitudes were larger than neutral) as the other two groups. Finally, N170 amplitudes were not related to symptom ratings in the psychosis group. The present findings relate to previous findings of N170 deficits specifically in patients with schizophrenia (McCleery et al., 2015) and extend them to those with a broader range of psychotic disorders and to siblings.

The N170 deficits in schizophrenia is a well-established finding and was the subject of a meta-analysis that revealed an overall Hedge’s g medium effect size of 0.64 (McCleery et al., 2015). The current study extends these findings by including a transdiagnostic group with a range of psychotic diagnoses, and also showed a small-medium effect size deficit compared with controls (effect size d = 0.35 – 0.39). It should be noted that N170 deficits are also present in other disorders associated with disorders in social processing, namely autism (J. McPartland, Dawson, Webb, Panagiotides, & Carver, 2004; J. C. McPartland et al., 2011; Tye et al., 2014; Wong, Fung, Chua, & McAlonan, 2008) and prosopagnosia (Dobel, Putsche, Zwitserlood, & Junghofer, 2008; Eimer & McCarthy, 1999; Kress & Daum, 2003), and are thus not exclusive to psychotic disorders.

While N170 deficits were evident in participants with a psychotic disorder, they were not seen in the first-degree siblings. Only two previous N170 studies have been conducted with siblings of those with schizophrenia, and the results were equivocal due to having a small sample or not enough trials (Ibanez et al., 2014; Yang et al., 2017). Our results, using more rigorous, data-driven analytical approaches, are in support of relatively intact N170 amplitudes in siblings of psychotic disorder participants. These results are not consistent with viewing the N170 as an endophenotype for psychosis. The N170 findings in siblings stand in contrast to face and emotional expression deficits seen in first-degree relatives assessed with behavioral tasks (de Achaval et al., 2010; Erol, Mete, Sonmez, & Unal, 2010; Kee, Horan, Mintz, & Green, 2004) and functional MRI (Barbour et al., 2010; Li et al., 2012; Quarto et al., 2018). It could be that while early processing of faces (reflected in the N170) is relatively intact in siblings, face and emotional expression deficits occur at a later stage of processing than the N170. Alternatively, we may not have been sufficiently statistically powered to detect a difference in groups.

The current study also replicated previous findings that showed the N170 was sensitive to the emotional content of a face (Brenner et al., 2014; Chai et al., 2012; Krombholz et al., 2007). Consistent with previous reports, we found larger N170 amplitudes for happy and angry vs. neutral faces, even in the psychosis group who exhibited overall reduced N170 amplitudes. These findings suggest that N170 is sensitive to emotional expression for those with a psychotic disorder (and in their siblings), and that identification of emotional expression may be dependent, in part, on structural encoding of faces. Again, this finding suggests that any neurophysiological deficits associated with emotional expression identification in those with psychosis or their first-degree relatives likely occurs after processing of structural features is complete.

The study had several limitations. First, we had a relatively smaller sample of siblings than the other two groups. As the siblings had non-statistically significant lower N170 amplitudes compared to controls, we may have been underpowered to detect any potential deficits. Second, we did not have a behavioral test of emotional expression identification, making it difficult to interpret the N170 findings in the context of any potential behavioral deficits. This design was deliberate and resulted in near-ceiling performance in all groups, as we wanted to focus on neural responses to face processing rather than behavioral responses. However, it is possible that the rather simple task and resulting high-accuracy may have influenced the N170 amplitudes with regards to the identification of specific expressions. Third, nearly all of the psychosis participants were on clinically-determined doses of antipsychotic medication which could have potentially impacted the ERP responses. Fourth, we did not include those who were clinically at-risk for psychosis (i.e., exhibiting subclinical symptoms), and thus we cannot say whether N170 deficits are also seen on that end of the psychosis spectrum. Fifth, we focused on “traditional” ERP analyses to make comparisons to previous N170 studies in psychosis, and we did not examine other aspects of the EEG signal (e.g., frequency-domain measures such as spectral power or coherence). Examining the full content of the EEG signal could potentially provide further insight into the nature of the neural deficit underlying poor face processing in psychosis. Finally, the stimuli were not equated for low-level visual characteristics (e.g., luminance, contrast, etc.), which may have potentially impacted findings given well-documented early visual processing deficits in schizophrenia (Butler et al., 2001; Cadenhead, Dobkins, McGovern, & Shafer, 2013). For example, N170 (and the earlier P100 component) sensitivity to emotional expressions might be reflective of low-level visual properties rather than the specific expression (though see Schindler, Bruchmann, Gathmann, Moeck, & Straube, 2021).

In conclusion, this study revealed that N170 amplitudes in people with a broad range of psychotic disorders are diminished in comparison to a healthy control sample, suggesting early processing deficits associated with structural feature processing of faces. These N170 deficits in psychotic disorders are consistent with many studies reporting deficits in those with a specific schizophrenia diagnosis (McCleery et al., 2015). However, N170 deficits are not seen in a smaller sample of first-degree relatives (i.e., siblings). Our findings indicate that the N170 may be a biomarker for psychosis generally but is unlikely to be an endophenotype for the disease.

Supplementary Material

Supplemental Tables 1-5
Supp Fig 1

Supplemental Figure 1: Bar chart representing means (+/− 1 standard error bar) for each Condition (building, expression, sex identification) and Group. Dots represent mean amplitudes between 111–151 ms averaged over electrodes P4, P6, P7, P8, P10, PO7, PO4, PO8, O1, O2 for each individual participant.

Supp Fig 2

Supplemental Figure 2: Event-related potential (ERP) waveforms with bootstrapped 95% confidence interval bands for each condition shown separately for each group. ERPs were averaged over significant electrodes (P4, P6, P7, P8, P10, PO7, PO4, PO8, O1, O2) identified in the factorial mass univariate analysis. ERPs to building identification shown in light gray, expression identification in orange, and sex identification in blue.

Supp Fig 3

Supplemental Figure 3: Bar chart representing means (+/− 1 standard error bar) for each facial Expression (angry, happy, neutral) and Group. Dots represent mean amplitudes between 111–151 ms averaged over electrodes P4, P6, P7, P8, P10, PO7, PO4, PO8, O1, O2 for each individual participant.

Supp Fig 4

Supplemental Figure 4: Event-related potential (ERP) waveforms with bootstrapped 95% confidence interval bands for each condition shown separately for each group. ERPs were averaged over significant electrodes (P4, P6, P7, P8, P10, PO7, PO4, PO8, O1, O2) identified in the factorial mass univariate analysis for the face vs. building analyses. ERPs to angry expressions shown in blue, happy in red, and neutral in yellow.

Acknowledgements

This work was supported by the National Institute of Mental Health (R01 MH107422). The authors would like to thank the study participants for volunteering their time, and the research staff for their aid in collecting data.

List of Abbreviations

BPRS

Brief Psychiatric Rating Scale

CAINS

Clinical Assessment Interview for Negative Symptoms

CTL

Healthy control

EEG

Electroencephalography

EOG

Electrooculography

ERP

Event-related potential

EXP

Expressive negative symptoms CAINS subscale

ICA

Independent components analysis

MAP

Motivation and pleasure negative symptoms CAINS subscale

PSY

Psychosis

SCID

Structured Clinical Interview for Diagnosis

SIB

Sibling of person with psychosis

Footnotes

1

The following NimStim bitmap images were used: 01F_AN_O, 01F_HA_O, 01F_NE_C, 02F_AN_O, 02F_HA_O, 02F_NE_C, 03F_AN_O, 03F_HA_O, 03F_NE_C, 05F_AN_O, 05F_HA_O, 05F_NE_C, 06F_AN_O, 06F_HA_O, 06F_NE_C, 07F_AN_O, 07F_HA_O, 07F_NE_C, 08F_AN_O, 08F_HA_O, 08F_NE_C, 09F_AN_O, 09F_HA_O, 09F_NE_C, 10F_AN_O, 10F_HA_O, 10F_NE_C, 12F_AN_O, 12F_HA_O, 12F_NE_C, 14F_AN_O, 14F_HA_O, 14F_NE_C, 17F_AN_O, 17F_HA_O, 17F_NE_C, 20M_AN_O, 20M_HA_O, 20M_NE_C, 21M_AN_O, 21M_HA_O, 21M_NE_C, 22M_AN_O, 22M_HA_O, 22M_NE_C, 23M_AN_O, 23M_HA_O, 23M_NE_C, 24M_AN_O, 24M_HA_O, 24M_NE_C, 25M_AN_O, 25M_HA_O, 25M_NE_C, 32M_AN_O, 32M_HA_O, 32M_NE_C, 34M_AN_O, 34M_HA_O, 34M_NE_C, 37M_AN_O, 37M_HA_O, 37M_NE_C, 40M_AN_O, 40M_HA_O, 40M_NE_C, 42M_AN_O, 42M_HA_O, 42M_NE_C, 45M_AN_O, 45M_HA_O, 45M_NE_C

Conflict of Interest Statement

MFG has been a consultant or speaker for Biogen, Otsuka, Sumitomo Pharma, and Teva. WPH is Vice President of Clinical Science at WCG VeraSci. The rest of the authors report no biomedical financial interests or potential conflicts of interest.

Data Availability Statement

The data and code that support the findings of this study are available from the corresponding author upon reasonable request. The raw EEG data have been uploaded to the NIMH Data Archive (NDA), collection title “Social affiliation in psychosis: Mechanisms and vulnerability factors,” https://nda.nih.gov/edit_collection.html?id=2317.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Tables 1-5
NIHMS1916107-supplement-Supplemental_Table_1.docx (16.7KB, docx)
Supp Fig 1

Supplemental Figure 1: Bar chart representing means (+/− 1 standard error bar) for each Condition (building, expression, sex identification) and Group. Dots represent mean amplitudes between 111–151 ms averaged over electrodes P4, P6, P7, P8, P10, PO7, PO4, PO8, O1, O2 for each individual participant.

NIHMS1916107-supplement-Supp_Fig_1.tiff (514.9KB, tiff)
Supp Fig 2

Supplemental Figure 2: Event-related potential (ERP) waveforms with bootstrapped 95% confidence interval bands for each condition shown separately for each group. ERPs were averaged over significant electrodes (P4, P6, P7, P8, P10, PO7, PO4, PO8, O1, O2) identified in the factorial mass univariate analysis. ERPs to building identification shown in light gray, expression identification in orange, and sex identification in blue.

NIHMS1916107-supplement-Supp_Fig_2.tiff (335.7KB, tiff)
Supp Fig 3

Supplemental Figure 3: Bar chart representing means (+/− 1 standard error bar) for each facial Expression (angry, happy, neutral) and Group. Dots represent mean amplitudes between 111–151 ms averaged over electrodes P4, P6, P7, P8, P10, PO7, PO4, PO8, O1, O2 for each individual participant.

NIHMS1916107-supplement-Supp_Fig_3.tiff (512.3KB, tiff)
Supp Fig 4

Supplemental Figure 4: Event-related potential (ERP) waveforms with bootstrapped 95% confidence interval bands for each condition shown separately for each group. ERPs were averaged over significant electrodes (P4, P6, P7, P8, P10, PO7, PO4, PO8, O1, O2) identified in the factorial mass univariate analysis for the face vs. building analyses. ERPs to angry expressions shown in blue, happy in red, and neutral in yellow.

NIHMS1916107-supplement-Supp_Fig_4.tiff (396.6KB, tiff)

Data Availability Statement

The data and code that support the findings of this study are available from the corresponding author upon reasonable request. The raw EEG data have been uploaded to the NIMH Data Archive (NDA), collection title “Social affiliation in psychosis: Mechanisms and vulnerability factors,” https://nda.nih.gov/edit_collection.html?id=2317.

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