Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2019 May 15.
Published in final edited form as: Brain Res. 2018 Mar 3;1687:144–154. doi: 10.1016/j.brainres.2018.03.002

The P1 Visual-Evoked Potential, Red Light, and Transdiagnostic Psychiatric Symptoms

Jeffrey S Bedwell 1,*, Christopher C Spencer 1, Chi C Chan 1, Pamela D Butler 2,3, Pejman Sehatpour 2,4, Joseph Schmidt 1
PMCID: PMC5882500  NIHMSID: NIHMS950565  PMID: 29510142

Abstract

A reduced P1 visual-evoked potential amplitude has been reported across several psychiatric disorders, including schizophrenia-spectrum, bipolar, and depressive disorders. In addition, a difference in P1 amplitude change to a red background compared to its opponent color, green, has been found in schizophrenia-spectrum samples. The current study examined whether specific psychiatric symptoms that related to these P1 abnormalities in earlier studies would be replicated when using a broad transdiagnostic sample. The final sample consisted of 135 participants: 26 with bipolar disorders, 25 with schizophrenia-spectrum disorders, 19 with unipolar depression, 62 with no current psychiatric disorder, and 3 with disorders in other categories. Low (8%) and high (64%) contrast check arrays were presented on gray, green, and red background conditions during electroencephalogram, while an eye tracker monitored visual fixation on the stimuli. Linear regressions across the entire sample (N = 135) found that greater severity of both clinician-rated and self-reported delusions/magical thinking correlated with a reduced P1 amplitude on the low contrast gray (neutral) background condition. In addition, across the entire sample, higher self-reported constricted affect was associated with a larger decrease in P1 amplitude (averaged across contrast conditions) to the red, compared to green, background. All relationships remained statistically significant after covarying for diagnostic class, suggesting that they are relatively transdiagnostic in nature. These findings indicate that early visual processing abnormalities may be more directly related to specific transdiagnostic symptoms such as delusions and constricted affect rather than specific psychiatric diagnoses or broad symptom factor scales.

Keywords: visual-evoked potentials, schizophrenia, bipolar, depression, EEG, event-related potentials

1. Introduction

A reduction in the amplitude of the P1 transient visual-evoked potential (VEP) component is a particular visual processing abnormality reported across several psychiatric disorders. The P1 potential is a positive voltage deflection that peaks approximately 90 – 160 ms following stimulus onset, and is thought to reflect activity from both dorsal (i.e., magnocelluar [M] pathway) and ventral (i.e., parvocelluar [P] pathway) processing streams (Di Russo et al., 2002). A reduction in P1 mean amplitude has been reported primarily in schizophrenia (Butler et al., 2013; Friedman et al., 2012; Jahshan et al., 2015; Schechter et al., 2005) and psychometrically defined schizotypy (Bedwell et al., 2013; Koychev et al., 2010); but has also been reported in bipolar I disorder (Verleger et al., 2013; Yeap et al., 2009) and subtypes of depression (Normann et al., 2007; Pierson et al., 1996).

Based on these findings, our group recently reported a transdiagnostic pilot study that examined P1 amplitude and psychiatric symptom relationships across a broad sample consisting primarily of mood and schizophrenia-spectrum disorders, as well as individuals with no disorder (Bedwell et al., 2015). Across the entire sample, a smaller P1 amplitude from a low contrast check pattern was found in participants with higher scores on the item of Emotional Withdrawal (a negative symptom) from the clinician-administered Positive and Negative Syndromes of Schizophrenia (PANSS; Kay et al., 1992) and the subscale of Eccentric Behavior (a disorganized symptom) from the self-report Schizotypal Personality Questionnaire – Brief Revised (SPQ-BR; Cohen et al., 2010). These relationships did not interact with diagnostic class, suggesting that they are relatively transdiagnostic. While there do not appear to be additional published studies examining P1 amplitude across a transdiagnostic sample, studies examining schizophrenia samples found that a reduction in P1 amplitude predicted increased scores on the PANSS Conceptual Disorganization (Brodeur et al., 2016) and categorical P1 component dysfunction related to increased scores on the PANSS items of Delusions and Suspiciousness/Persecution (Gonzalez-Hernandez et al., 2014). This association with delusions is similar to a finding from our group showing that a reduced P1 amplitude in a nonpsychiatric sample was associated with increased scores on the SPQ Magical Ideation subscale, which measures severity of delusional thoughts (Bedwell et al., 2013). In addition, two of these studies are consistent in terms of P1 amplitude associations with disorganized symptoms (Bedwell et al., 2015; Brodeur et al., 2016).

A separate line of research has reported that individuals with schizophrenia (Bedwell et al., 2011) and their first-degree relatives (Bedwell et al., 2003) do not show the typical modulation of performance on a visual backward masking task by the introduction of a red background, as compared to nonpsychiatric controls. This is thought to be related to the known ability of diffuse red light to partially suppress M-pathway activity in healthy primates (de Monasterio, 1978; Livingstone and Hubel, 1984). Our group has also examined the change in P1 amplitude to high contrast stimuli on a red vs. green (the opponent color of red) background in a psychometrically defined schizotypy sample. Participants who scored higher on the SPQ subscale of Ideas of Reference, a type of delusional thought, showed a larger increase in P1 amplitude to the red background (Bedwell et al., 2013). Our transdiagnostic pilot study also examined the effect on P1 amplitude of a red (vs. green) background, but used low, rather than high, contrast stimuli (Bedwell et al., 2015). Across the schizophrenia-spectrum and nonpsychiatric control participants, individuals with a larger decrease in P1 amplitude to the red background scored higher on the SPQ-BR Interpersonal factor score and reported more social anhedonia (i.e., lower total score) on the Anticipatory and Consummatory Interpersonal Pleasure Scale (ACIPS). The different direction of change in P1 amplitude to the red background in relation to symptom severity between the two studies may be secondary to the differences in the contrast of stimuli. As stimulus contrast influences the relative contribution of M pathway activity to the P1 component (Di Russo et al., 2002), the influence of red light on P1 amplitude may differ depending on stimulus contrast.

While our transdiagnostic pilot study (Bedwell et al., 2015) did not separate the SPQ-BR Interpersonal factor score into the constituent subscales (No Close Friends/Constricted Affect and Social Anxiety), secondary analysis of that dataset revealed that both Social Anxiety and the sum of the three Constricted Affect items showed a significant relationship with the change in P1 amplitude to red light. This is consistent with an earlier report that individuals with schizophrenia showed an abnormal visual backward masking response to a red background as compared to nonpsychiatric controls, which was associated with greater PANSS negative, but not positive or disorganized, symptom severity (Bedwell et al., 2011). Secondary analysis of that dataset revealed that PANSS Blunted Affect (analogous to SPQ-BR Constricted Affect items) was the only negative symptom that related to change in backward masking to the red background. The relationship of change in P1 amplitude to a red background with severity of negative symptoms appears to be relatively independent of a general reduction in P1 amplitude, as relationships remained significant after covarying for P1 amplitude with the green background in the previous two studies (Bedwell et al., 2011, 2015).

These symptom relationships with P1 amplitude may result from different theoretical mechanisms. For example, abnormalities in neural networks contributing to the P1 component may initiate a cascade of perceptual and cognitive distortions that eventually contribute to withdrawal from the environment (e.g., negative symptoms) and/or misinterpretation of visual stimuli (e.g., ideas of reference; Green et al., 2012; Sehatpour et al., 2010). Similarly, it is possible that a change in the expected suppression of the M pathway to diffuse red light may relate to disruptions in typically seamless M to P (parvocellular) pathway interactions. Such a disruption could lead to problems with integrating objects through difficulties with framing by the M pathway and filling by the P pathway (Butler et al., 2013; Sehatpour et al., 2010). The resulting visual disturbance could contribute to unique but overlapping symptoms with those found to be related to a reduced P1 amplitude with a neutral background. Alternatively, it is plausible that an underlying brain abnormality that leads more directly to specific psychiatric symptoms may also simultaneously cause dysfunction in early visual processing and explain the same set of relationships.

A wide range of abnormalities in visual processing have been reported in psychiatric disorders, including schizophrenia (Butler et al., 2008; Tan et al., 2013), bipolar disorder (Trillenberg et al., 2017; Yeap et al., 2009), and depression (Furey et al., 2013; Normann et al., 2007; Norton et al., 2016). However, findings and methodology vary within and across disorders regarding the specific type of visual processing abnormality. In addition, while usually not directly addressed, it is evident that only a subset of individuals with these disorders show a given abnormality. This suggests, consistent with the National Institute of Mental Health Research Domain Criteria (RDoC) framework (Cuthbert and Insel, 2013), that at least some types of visual processing dysfunction may cross traditional diagnostic boundaries. From the range of visual processing abnormalities reported, any specific abnormality may be present in subsets of individuals with different disorders who may share a common neurocircuitry dysfunction associated with one or more particular psychiatric symptoms (Clementz et al., 2016).

The aim of the current study is to better understand how P1 amplitude differences, both on a gray background and the change from green to red, each relate to specific symptoms across a large transdiagnostic sample. This knowledge will inform RDoC-based efforts to better understand causes and correlates of psychopathology in order to create more effective and tailored treatments, as well as potential prevention strategies. In order to address this aim, the current study contains numerous improvements on the methodology of our transdiagnostic pilot study (Bedwell et al., 2015), including the use of a larger sample, both high and low contrast stimuli, and an eye tracker.

We hypothesized that when examining PANSS item scores and SPQ-BR subscales across the entire sample: 1) a reduced P1 amplitude from the gray background condition will be associated with an increase of severity in the symptoms that have been reported in more than a single study using that background: delusions (PANSS Delusions item; SPQ-BR Magical Thinking subscale) and disorganization (PANSS Conceptual Disorganization; SPQ-BR Eccentric Behavior and Odd Speech subscales); and 2) as there was not a symptom replication across the two P1 studies involving the red background, the change in P1 amplitude from the red (minus green) background will negatively relate to symptoms found in the similar transdiagnostic pilot study: constricted affect (PANSS Blunted Affect item; sum of SPQ-BR Constricted Affect items), social anxiety (SPQ-BR Social Anxiety subscale), and social anhedonia (PANSS Passive/Apathetic Social Withdrawal item; ACIPS total score [note: the relationship to P1 amplitude is hypothesized in the opposite direction; as lower ACIPS scores represent more social anhedonia]).

Symptom relationships with either P1 effect that were previously reported in the literature but not mentioned in the hypotheses were included in the analyses in an exploratory manner. We also examined potential differences in P1 amplitude between specific diagnoses and nonpsychiatric controls as exploratory secondary analyses to compare to the existing literature.

2. Results

Across all participants, an average of at least 184 segments remained after artifact rejection for averaging the P1 component within each hemisphere (left hemisphere: mean = 248.43, SD = 7.47; right hemisphere: mean = 248.45, SD = 7.26). See Table 2 for descriptive statistics of the latency and mean amplitude of P1 by color, contrast, diagnostic class, and specific diagnoses of interest. We explored potential confounding influences on the bilateral average P1 amplitude averaged across all conditions in zero-order Pearson correlations with: age, visual acuity, eye tracking timeouts between stimuli (missing for five participants), level of education, and average number of segments used in averaging. Results revealed that only age showed a statistically significant relationship, as older participants had a smaller P1 amplitude, r(135) = -.28, p = .001. In addition, an independent samples t-test revealed that females had a larger P1 amplitude across conditions than males, t(133) = 2.27, p = .03, d = 0.39. Therefore, we included age and sex as covariates in all analyses of P1 amplitude.

Table 2.

Diagnostic Class Gray Background Green Background Red Background
8% Contrast 64% Contrast 8% Contrast 64% Contrast 8% Contrast 64% Contrast
Latency Amplitude Latency Amplitude Latency Amplitude Latency Amplitude Latency Amplitude Latency Amplitude
Entire Sample (N = 135)* 124.36 (20.73) 5.28 (4.01) 107.94 (20.68) 6.63 (5.29) 113.04 (17.85) 7.26 (5.93) 108.48 (17.58) 7.78 (6.41) 108.30 (15.93) 8.11 (6.47) 106.15 (18.98) 8.18 (6.96)
Schizophrenia-Spectrum Disorders (N = 25) 121.45 (17.08) 3.78 (2.19) 104.18 (20.01) 4.57 (3.26) 112.27 (20.43) 5.28 (3.51) 102.30 (17.89) 5.37 (3.50) 109.38 (17.02) 5.50 (3.73) 101.91 (18.64) 5.34 (3.12)
Schizophrenia (N = 15) 122.01 (18.40) 3.63 (1.86) 102.73 (19.87) 4.35 (2.81) 107.75 (21.54) 4.39 (2.61) 105.73 (19.44) 4.92 (3.14) 107.75 (15.41) 4.26 (2.95) 98.05 (18.55) 4.95 (2.84)
Bipolar Disorders (N = 26) 125.90 (20.48) 5.61 (4.26) 105.17 (19.24) 6.12 (4.92) 109.56 (18.05) 6.87 (5.00) 108.32 (17.43) 7.03 (5.81) 102.58 (10.24) 7.65 (5.44) 104.57 (19.02) 7.84 (6.13)
Bipolar I Disorder (N = 24) 125.29 (21.09) 5.48 (4.25) 104.33 (19.82) 5.90 (5.00) 109.42 (17.82) 6.77 (4.96) 106.24 (16.08) 6.58 (5.82) 103.19 (10.14) 7.53 (5.61) 103.31 (18.17) 7.59 (6.32)
Unipolar Depression (N = 19) 127.83 (22.42) 6.25 (4.37) 111.28 (15.95) 7.72 (4.98) 115.13 (15.64) 8.28 (6.61) 112.31 (9.94) 9.19 (6.84) 110.35 (14.03) 9.22 (7.43) 109.16 (17.64) 9.41 (7.34)
Recurrent MDD -nonpsychotic (N = 12) 128.91 (21.68) 6.19 (4.80) 114.58 (17.48) 7.29 (4.68) 114.10 (17.47) 6.96 (5.81) 113.53 (10.74) 7.83 (6.02) 111.82 (15.76) 7.28 (5.96) 111.25 (21.07) 8.06 (6.97)
No Current Psychiatric Disorder (N = 62) 123.57 (22.24) 5.25 (4.03) 110.32 (22.92) 7.17 (5.90) 114.27 (17.86) 7.52 (6.25) 110.07 (19.11) 8.34 (6.92) 109.74 (17.98) 8.67 (6.85) 108.08 (19.84) 8.75 (7.76)
No History of Psychiatric Disorder (N = 36) 120.44 (22.47) 5.76 (4.63) 112.98 (26.41) 8.22 (6.61) 111.57 (19.84) 8.45 (7.00) 112.41 (22.73) 9.38 (7.77) 109.43 (20.29) 9.34 (7.18) 107.58 (22.96) 9.70 (8.79)
Open in a new tab

Latency in ms; Amplitude = mean bilateral amplitude (μV) from a 20 ms window centered on the P1 component averaged across six electrodes from each hemisphere: TP7/8, CP5/6, CP3/4, P7/8, P5/6, and P3/4; values are in format: mean (standard deviation); MDD = Major Depressive Disorder

*

Includes three participants with anxiety disorders that are not included in the other diagnostic classes

2.1. Gray Background Symptom Relationships

A 2 (contrast) × 2 (hemisphere) repeated-measures ANCOVA, covarying for age and sex, revealed a significant main effect of contrast, F(1,132) = 4.29, p = .04, η2 = .03, as the P1 amplitude was larger in the higher contrast (64%) condition. The main effect of hemisphere and the interaction of contrast and hemisphere were not significant (both ps > .70). To reduce the overall number of comparisons in the study, we therefore averaged P1 amplitude across hemispheres for the remainder of analyses with the gray background.

The regression for averaged bilateral P1 amplitude from the 8% contrast on the gray background, covarying for age and sex, and simultaneously entering the four PANSS symptom predictors (Delusions, Conceptual Disorganization, Suspiciousness, and Emotional Withdrawal), revealed one statistical outlier who was then removed from the regression. The resulting analysis showed a significant relationship for only Delusions (β = -.26, p = .01) in the model, F(6,127) = 4.60, p < .001, adjusted R2 = .14 (see Figure 1), such that a higher Delusions score was associated with a smaller P1 amplitude. The same regression on the 64% contrast revealed the same outlier who was again removed. The regression similarly showed that only Delusions (β = -.26, p = .01) entered the model, F(6,127) = 4.20, p = .001, adjusted R2 = .13. For both contrast conditions, when diagnostic class was added to the first block of the regression, the relationship with Delusions remained statistically significant (8%: β = -.26, p = .02; 64%: β = -.24, p = .02), and the other symptoms remained non-significant.

Figure 1.

Figure 1

Open in a new tab

Scatterplot of the relationship between clinician-rated delusion severity and P1 amplitude from the 8% contrast gray background condition.

PANSS = Positive and Negative Syndrome Scale. Notes: P1 amplitude residual scores are adjusted for age and sex. The three participants with anxiety disorders are depicted along with the nonpsychiatric controls for clarity of presentation. The one statistical outlier excluded from the regression is not depicted in the figure.

The regression for the 8% contrast, covarying for age and sex, and simultaneously entering the four self-report symptom scales (SPQ-BR Magical Thinking, Eccentric Behavior, Odd Speech, and Suspiciousness), revealed two outliers who were then removed. The resulting analysis showed a significant relationship for only Magical Thinking (β = -.21, p = .03) in the model, F(6,126) = 4.50, p < .001, adjusted R2 = .14 (see Figure 2). The addition of diagnostic class in the first block of the regression did not change the statistical significance of the Magical Thinking (β = -.20, p = .04), suggesting that this relationship was relatively transdiagnostic. The same regression for the 64% contrast revealed one outlier who was then removed. None of the self-report scales were statistically significant in this analysis (all ps > .10).

Figure 2.

Figure 2

Open in a new tab

Scatterplot of the relationship between magical thinking severity and P1 amplitude from the 8% contrast gray background condition.

SPQ-BR: Schizotypal Personality Questionnaire Brief-Revised. Notes: P1 amplitude residual scores are adjusted for age and sex. The three participants with anxiety disorders are depicted along with the nonpsychiatric controls for clarity of presentation. The two statistical outliers excluded from the regression are not depicted in the figure.

2.2. Symptom Relationships with the Change in P1 Amplitude from Red Background Condition

Our planned analyses regarding the influence of a red background on the P1 amplitude used the green background (the opponent color of red) as the comparison condition, in order to control for the general effect of color. A 2 (color) × 2 (contrast) × 2 (hemisphere) repeated-measures ANCOVA, covarying for age and sex, revealed no significant main effects or interactions across the entire sample (all ps > .07). To reduce to overall number of comparisons, we therefore averaged P1 amplitude across contrast and hemisphere for symptom analyses. Based on recommendations from Meyer and colleagues (2017), we created a residual score to examine individual differences in P1 amplitude due to color. This was created by saving the unstandardized residuals from a regression in which the averaged green background P1 amplitude was entered predicting the averaged red background P1 amplitude. This single score was then used as the dependent variable in the regressions that explored symptom relationships.

The regression for the red background P1 amplitude residual scores, covarying for sex and age, and simultaneously entering the three PANSS symptom predictors (Blunted Affect, Passive/Apathetic Social Withdrawal, and Delusions), revealed one outlier who was then removed from the analysis. None of the symptoms entered the resulting model (all ps > .06). When the same regression was run entering the four self-report symptom scales (SPQ-BR Constricted Affect, Social Anxiety, Ideas of Reference; ACIPS total score), results revealed the same outlier who was again removed from the analysis. The analysis then revealed that only Constricted Affect (β = -.31, p = .007) was significant, F(6,127) = 1.69, p = .12, adjusted R2 = .03. Thus, participants who showed a larger reduction in P1 amplitude to the red, compared to green, background, self-reported more constricted affect (see Figure 3). The addition of diagnostic class in the first block of the regression did not change the statistical significance of Constricted Affect (β = -.30, p = .008), suggesting that this relationship was also relatively transdiagnostic.

Figure 3.

Figure 3

Open in a new tab

Scatterplot of the relationship between constricted affect severity and P1 amplitude change to the red (vs. green) background condition across both contrasts.

SPQ-BR = Schizotypal Personality Questionnaire Brief-Revised. Constricted Affect Severity is the sum of the three constricted affect items on the No Close Friends/Constricted Affect subscale. Notes: P1 amplitude residual scores are depicted from the red background after adjusting for age, sex, and P1 amplitude from the green background; negative scores represent a reduced P1 amplitude to the red as compared to green background, averaged across both contrast backgrounds. The three participants with anxiety disorders are depicted along with the nonpsychiatric controls for clarity of presentation. The statistical outlier excluded from the regression is not depicted in the figure.

2.3. Exploratory Analyses of P1 Amplitude Symptom Relationships with Scale Factor Scores

As the use of symptom-level severity reported above have less established psychometric properties, we followed these with exploratory analyses of the overarching factor scores from the PANSS and SPQ-BR which have established reliability and validity. Linear regressions, covarying for age and sex and removing outliers, followed by simultaneous entry of the two PANSS scales (Positive and Negative) on the average P1 amplitude from the gray background within each contrast revealed no significant relationships (all ps > .07). However, the regression on the red background P1 amplitude residual score revealed a statistically significant relationship with the PANSS Negative scale (β = -.20, p = .03). The same linear regressions on the four factors from the SPQ-BR (Callaway et al., 2014) – Cognitive Perceptual, Disorganized, No Close Friends/Constricted Affect, and Social Anxiety, revealed no statistically significant relationships across the three P1 amplitude measures (all ps > .17).

2.4. Exploratory Analyses of Diagnosis-Related Differences in P1 Amplitude

To explore findings in publications from other laboratories involving a reduction of P1 amplitude in specific disorders, we examined how our participants with schizophrenia (N = 15), bipolar I disorder (N = 24), and recurrent major depressive disorder with no history of psychosis (N = 12), each compared to a control group with no history of psychiatric illness (N = 36) on bilateral P1 amplitude. See Table 2 for descriptive statistics of P1 amplitude of these specific diagnoses by color and contrast condition. These groups did not show an overall significant difference in age, sex, eye tracking timeouts, or number of segments used for averaging P1. However, they did differ in visual acuity (p = .002), which we included as a covariate along with age and sex used in previous analyses. For the gray background, two (diagnosis) by two (contrast) mixed ANCOVAs, covarying for age, sex, and visual acuity, revealed a main effect for diagnosis as individuals with schizophrenia showed a reduced averaged bilateral P1 amplitude as compared to individuals with no history of a psychiatric disorder, F(1,46) = 4.80, p = .03, η2 = .10 (see Figure 4), which did not interact with contrast (p = .19). The bipolar I and depression groups did not show statistically significant main effects of diagnosis or diagnosis by contrast interactions when each were compared to the control group (all ps > .11). When examining the red background P1 amplitude unstandardized residual scores (created by regressing P1 amplitude from the green background onto P1 amplitude from the red background) with one factor (diagnosis) ANCOVAs, covarying for age, sex, and visual acuity, there were no group differences compared to the controls (all ps > .23).

Figure 4.

Figure 4

Open in a new tab

Grand average waveforms depicting the averaged bilateral P1 component from both contrast conditions on the gray background for the participants with schizophrenia as compared to nonpsychiatric controls with no history of a psychiatric disorder.

3.0 Discussion

Our specific aim was to better understand how P1 amplitude differences, both on a neutral (gray) background and the change from green to red, each relate to previously-reported symptom relationships across a large transdiagnostic sample. The overarching goal was to inform RDoC-based efforts to better understand causes and correlates of psychopathology in order to create more effective and tailored treatments, as well as potential prevention strategies.

Our first hypothesis was partially supported as we found that, across the entire sample, individuals with increased scores on clinician-rated PANSS Delusions (across both contrasts; see Figure 1) and the related self-report scale of SPQ-BR Magical Thinking (in the 8% contrast; see Figure 2), showed a reduced averaged bilateral P1 amplitude on the gray background. These relationships involving delusions/magical thinking are consistent with a study examining a schizophrenia sample (Gonzalez-Hernandez et al., 2014) and in a nonpsychiatric sample (Bedwell et al., 2013), but is inconsistent with one study that examined the PANSS Delusions item relationship to P1 amplitude in a schizophrenia sample and did not find a relationship (Brodeur et al., 2016). While earlier work included samples with a discrete diagnosis, our findings in this transdiagnostic sample suggest that higher severity of delusions/magical thinking is associated with reduced P1 amplitude, regardless of diagnosis. We did not find a P1 amplitude relationship with either clinician-rated or self-reported disorganized symptom measures, as found in two previous studies (Bedwell et al., 2015; Brodeur et al., 2016).

Our second hypothesis was also partially supported as we found that the change in bilateral P1 amplitude from the red minus green background condition was related to self-reported constricted affect (see Figure 3). In both the current study and the previous pilot study, individuals showing a greater decrease in P1 amplitude to the red, compared to green, background, self-reported greater severity of constricted affect (i.e., the sum of the three Constricted Affect items) on the SPQ-BR (Bedwell et al., 2015). These findings are also consistent with our secondary analysis of an earlier report (Bedwell et al., 2011) that a change in visual backward masking response to a red background in a schizophrenia sample related specifically to the Blunted Affect item severity score from the PANSS. As red light is known to suppress the M pathway (de Monasterio, 1978; Livingstone and Hubel, 1984), this replication across three studies using different methodology may indicate an abnormal response to M pathway modulation in individuals with higher levels of constricted/blunted affect. Future studies that examine the P1 amplitude change to a red background with objective markers of range of affect (e.g., facial electromyography, vocal prosody analysis, pupilometry) can add additional information about this relationship, including whether it is specific to particular features of expressed affect.

Dysfunction in glutamate NMDA receptors is a leading theoretical candidate for explaining the mechanism that may account for the relationship between these VEP abnormalities with delusions and constricted affect. Research has demonstrated a plausible link between glutamate NMDA receptor dysfunction and M pathway abnormalities in schizophrenia (Butler et al., 2005; Javitt, 2015). Changes in the NMDA system have also been shown to affect clinical symptoms. For example, administration of the NMDA antagonist ketamine to nonpsychiatric participants acutely increased delusional thought (Stone et al., 2015) and persistent delusional symptoms corresponded with higher frequency of use in chronic ketamine users (Morgan et al., 2010). Furthermore, glutamate/GABA+ ratio was found to be associated with increased SPQ Constricted Affect scores in nonpsychiatric adults (Ford et al., 2017) and administration of ketamine during surgery induced blunted affect more than tramadol - an opioid agonist (Hidayah et al., 2014). Given that both M pathway abnormalities and clinical symptoms have been linked to the NMDA system, dysfunction within this system may be a common underlying mechanism. It is possible that the different P1 amplitude-to-symptom relationships found in the current study correspond with different subtypes of glutamate dysfunction, including possible abnormalities in the related inhibitory functions of GABA.

In addition, path analysis using both ERP and fMRI has shown that a reduced P1 amplitude links with upstream impairment in prefrontal cortex activation and behavioral deficits on perceptual organization tasks in schizophrenia (Sehatpour et al., 2010). Another study showed that early visual perception deficits in a schizophrenia sample were associated with impairment in social cognition, which, through alteration in beliefs and motivation, predicted deficits in daily functioning (Green et al., 2012). As the P1 component represents the very early stages of visual processing, it is possible that disruptions in underlying networks can lead to higher order disturbances in beliefs (e.g., delusions) and control of affect expression through impaired upstream input to regions such as the prefrontal cortex.

Our exploratory diagnosis-related analyses revealed that schizophrenia participants had a smaller averaged bilateral P1 amplitude when compared to participants with no history of a psychiatric disorder, across both contrasts conditions on the gray background (see Figure 4). This is consistent with a large number of studies from other labs which report reduced P1 amplitude in schizophrenia samples when compared to nonpsychiatric controls (e.g., Butler et al., 2013; Friedman et al., 2012; Jahshan et al., 2015; Schechter et al., 2005). The primary difference between the current study and these previous studies is that we included eye tracking to ensure that the participants had their eyes open and fixated near the center of the screen immediately prior to each stimulus presentation. Research has shown that reduced visual attention can reduce the amplitude of P1 in nonpsychiatric participants (Bayer et al., 2017; Villena-Gonzalez et al., 2017). As we were able to partially control for attention with the eye tracker, it appears that the reduction in P1 amplitude reported across schizophrenia samples is unlikely a simple byproduct of reduced attention to the visual stimuli. The bipolar I and recurrent depression groups did not differ from the nonpsychiatric controls on P1 amplitude, which is inconsistent with findings from a smaller number of studies reporting a reduction in P1 amplitude in bipolar disorder (Verleger et al., 2013; Yeap et al., 2009) and subtypes of depression (Normann et al., 2007; Pierson et al., 1996). This difference may be explained by Type II error from our relatively small sample sizes of these disorders or potentially due to our novel use of eye tracking to partially control for the influence of reduced attention.

We were surprised that individuals with schizophrenia did not differ from nonpsychiatric controls on the change in P1 amplitude to a red background, as our earlier pilot study found that a broader schizophrenia-spectrum group showed this difference (Bedwell et al., 2015). This discrepancy may be due to the added use of eye tracking and/or the difference in the composition of disorders across the studies. We also did not find a difference in the change of P1 amplitude to red light in the bipolar or recurrent depression groups, as compared with nonpsychiatric controls. This is the first time, to our knowledge, that the red light effect has been examined in those diagnoses. As we did not find any diagnostic-level differences in the P1 amplitude change to red light, and our regression findings did not change when covarying for diagnostic group, this suggests that the relationship with constricted affect is transdiagnostic and relatively specific to the symptom rather than constellations of symptoms related to these particular disorders.

The current study was limited by relatively small sample sizes of participants with specific diagnoses such as schizophrenia. However, as the study was designed to examine transdiagnostic symptom relationships, it contributes unique information to existing studies on P1 amplitude within diagnostic categories. The negative correlation that we found between P1 amplitude and age has been reported by others (Ceponiene et al., 2008; Stothart et al., 2014) – suggesting that our measurement of P1 was relatively valid and reliable. This is further supported by the expected increase of P1 amplitude to the higher contrast on the gray background that we observed across the entire sample. The study is also limited by examination of specific items of scales rather than the higher order factors which have more established psychometric support. This approach was based on our interest in distinct symptoms rather than symptom clusters, and to allow direct comparisons of our results with those reported by other groups who also analyzed specific PANSS item severity relationships to P1 amplitude (Brodeur et al., 2016; Gonzalez-Hernandez et al., 2014). To address this limitation, we included an exploratory analysis of the scale factors which revealed that a larger decrease in P1 to the red background was related to a higher score on the PANSS Negative Scale. There were no additional significant relationships between the factor scales from the PANSS and SPQ-BR with the three P1 amplitude measures. Therefore, we encourage future studies to include a more comprehensive assessment of particular symptoms of interest, which can be accomplished with multimethod techniques (e.g., adding behavioral and physiological assessment of constricted affect). Finally, while our transdiagnostic symptom relationships remained significant after covarying for broad diagnostic class, it is possible that inclusion of specific diagnoses such as schizophrenia (see Figure 4) may have accounted for a meaningful portion of that variance. Further research with larger numbers of participants with particular disorders can clarify this issue.

In summary, across a broad transdiagnostic sample, a reduced P1 amplitude was associated with increased delusion severity and a larger reduction in P1 amplitude to a red background was related to increased constricted affect. Future research assessing mediating/moderating factors influencing the observed links between these early low-level visual processing abnormalities with delusions and constricted affect could provide novel treatment targets for these symptoms across disorders, and may aid new classification efforts.

4. Methods

4.1. Participants

This study was approved by the Institutional Review Board of the University of Central Florida and follows the ethical guidelines described in the Declaration of Helsinki. We recruited participants using two different types of advertisements. One mentioned that we were looking for individuals with a diagnosis of schizophrenia, schizoaffective disorder, or bipolar disorder, while the other mentioned that we were looking for adults between the ages of 21 and 55. These advertisements were placed in a variety of locations including local psychiatric facilities, local newspapers, and the Craigslist website. For the psychiatric group, inclusion criteria included having any psychiatric diagnosis, even though advertisements were targeting particular ones. For the nonpsychiatric group, the presence of any current psychiatric disorder was an exclusion criteria, as was having a biological relative with a psychotic disorder or bipolar disorder. For both groups, inclusion criteria included being between the ages of 21 and 55, and further exclusion criteria included: history of a neurological disorder, seizures, or head injury resulting in loss of consciousness for more than 15 minutes, substance abuse or dependence in the last six months (per diagnostic interview), heavy alcohol use or any drug use in the 72 hours prior to each session (per self-report), estimated full scale IQ < 70, visual acuity worse than 20/50 (allowing for corrective lenses), and indication of color blindness. Following a phone screening, eligible participants were invited to the first of two sessions. Participants were provided a monetary stipend at the end of each session.

Following recruitment attempts, we assessed 178 participants in the first of the two sessions. Of these, 43 individuals were not eligible for use in the final analyses for the following reasons: 22 not able or willing to return to the second session, 5 had substance dependence or abuse during past six months, 6 EEG problems, 3 problems calibrating the eye tracker, 3 visual acuity worse than 20/50, 2 excessive drowsiness during EEG recording, 1 color blindness, and 1 estimated IQ < 70.

This resulted in 135 participants included in the final analyses. While sample diagnoses are similar to our previously published study on visual-evoked potentials (Bedwell et al., 2015), only five of those individuals participated in the current study. See Table 1 for demographic and clinical characteristics. Diagnoses by broad classes included: 1) no current psychiatric disorder (N = 62 [46%]); 2) bipolar disorders (N = 26 [19%]; 24 bipolar I disorder [7 with psychotic features] and 2 bipolar disorder not otherwise specified); 3) schizophrenia spectrum disorder (N = 25 [19%]; 15 schizophrenia, 4 schizoaffective disorder, 2 delusional disorder, and 3 schizotypal and 1 paranoid personality disorder); and 4) recurrent or current unipolar depressive disorders (N = 19 [14%]; 14 major depressive disorder, recurrent [2 with psychotic features], 2 major depressive disorder, single episode [current], 2 dysthymic disorder, and 1 depressive disorder not otherwise specified). Transdiagnostic analyses also included three participants with other current psychiatric disorders: 1 adjustment disorder with anxiety, 1 social phobia, and 1 obsessive compulsive disorder.

Table 1. Demographic and Clinical Characteristics by Diagnostic Class.

All participants* Schizophrenia-Spectrum Disorders Bipolar Disorders Unipolar Depression# No current Psychiatric disorder
N 135 25 26 19 62
Age 37.79 (10.35) 38.36 (9.97) 38.23 (10.50) 39.11 (10.59) 37.10 (10.73)
Sex (Male) 47.40% 52.00% 34.60% 52.60% 50.00%
Race: Caucasian 66.70% 48.00% 88.50% 68.40% 64.50%
African American 18.50% 36.00% 7.70% 21.10% 16.10%
Asian American 3.70% 4.00% 0% 5.30% 4.80%
More than One Race 11.10% 12.00% 3.80% 5.30% 14.50%
Ethnicity (Hispanic) 19.30% 12.00% 15.40% 21.10% 22.60%
Years of Education 14.70 (2.19) 13.60 (1.76) 15.77 (2.60) 13.63 (1.86) 15.00 (2.01)
Estimated Full Scale IQ** 101.57 (10.46) 99.96 (8.07) 102.35 (9.38) 97.89 (14.15) 102.45 (9.65)
PANSS Item s and Scales Included in Analyses
Delusions (P1) 1.33 (0.86) 2.60 (1.32) 1.04 (0.20) 1.16 (0.50) 1.02 (0.13)
Conceptual Disorganization (P2) 1.26 (0.66) 1.60 (0.96) 1.35 (0.75) 1.21 (0.54) 1.10 (0.43)
Suspiciousness/Persecution (P6) 1.85 (1.12) 2.92 (1.29) 1.96 (1.15) 1.89 (0.94) 1.35 (0.73)
Blunted Affect (N1) 1.48 (1.06) 2.20 (1.47) 1.58 (1.17) 1.53 (1.07) 1.15 (0.60)
Emotional Withdrawal (N2) 1.45 (0.83) 1.84 (1.07) 1.50 (0.91) 1.74 (1.05) 1.19 (0.47)
Passive/Apathetic Social Withdrawal (N4) 1.95 (1.30) 2.72 (1.60) 2.15 (1.46) 2.32 (1.11) 1.47 (0.94)
Positive Scale 9.69 (3.26) 14.48 (3.72) 9.69 (1.95) 9.53 (2.37) 7.81 (1.23)
Negative Scale 10.16 (3.73) 12.68 (4.01) 10.62 (4.65) 10.58 (3.40) 8.76 (2.49)
Psychometric Self-Report Scales Included in Analyses
SPQ-BR Constricted Affect Subscale^ 4.43 (3.01) 6.72 (2.42) 4.42 (3.38) 5.89 (2.79) 3.15 (2.45)
SPQ-BR Ideas of Reference Subscale^ 4.90 (3.69) 8.32 (3.42) 5.81 (3.56) 5.21 (3.38) 3.02 (2.70)
SPQ-BR Suspiciousness Subscale^ 5.01 (3.61) 8.08 (3.43) 5.69 (3.75) 6.58 (2.71) 2.95 (2.58)
SPQ-BR Magical Thinking Subscale 5.11 (4.25) 8.68 (4.66) 4.92 (3.92) 5.42 (4.10) 3.47 (3.22)
SPQ-BR Eccentric Behavior Subscale 8.16 (4.25) 10.04 (3.21) 9.88 (4.20) 9.42 (3.58) 6.32 (4.23)
SPQ-BR Odd Speech Subscale 9.04 (4.19) 10.20 (4.00) 11.77 (3.40) 10.53 (3.39) 6.94 (3.86)
SPQ-BR Social Anxiety Factor 7.57 (4.95) 11.52 (3.87) 10.31 (4.90) 8.58 (4.18) 4.63 (3.65)
SPQ-BR No Close Friends/Constricted Affect Factor 9.90 (6.37) 14.88 (5.02) 11.23 (6.82) 12.79 (5.63) 6.45 (5.00)
SPQ-BR Cognitive Perceptual Factor 19.39 (12.69) 33.16 (10.63) 21.50 (10.75) 22.95 (10.60) 11.48 (8.31)
SPQ-BR Disorganized Factor 17.20 (7.37) 20.24 (6.05) 21.65 (5.73) 19.95 (6.25) 13.26 (7.00)
ACIPS Total Score 80.40 (13.39) 71.36 (15.60) 81.38 (13.13) 77.58 (9.27) 84.65 (12.06)
Current Psychotropic Medications
Antipsychotic Medications 24.40% 76.00% 42.30% 10.50% 1.60%
SSRI/SNRI Medications 21.50% 32.00% 38.50% 42.10% 4.80%
Other Antidepressant Medications 14.80% 44.00% 23.10% 10.50% 1.60%
Sedative/Benzodiazapine Medications 21.50% 36.00% 61.50% 15.80% 1.60%
Lithium 5.20% 0% 26.90% 0% 0%
Open in a new tab
#

Unipolar Depression group includes recurrent depressive disorders or a single current episode of major depressive disorder

*

Includes three participants with anxiety disorders that are not included in the other diagnostic classes

**

IQ estimated from standard score on Reading subtest from the Wide Range Achievement Test – 4th Edition

^

Created by summing the three respective items from the constituent scales of No Close Friends/Constricted Affect and Ideas of Reference/Suspiciousness

SPQ-BR: Schizotypal Personality Questionnaire Brief-Revised; SSRI: selective serotonin reuptake inhibitor; SNRI: serotonin and norepinephrine reuptake inhibitor; ACIPS: Anticipatory and Consummatory Interpersonal Pleasure Scale

The four diagnostic class groups did not differ on age, F(3,131) = 0.26, p = .85, η2 = .006, sex, χ2 (3, N = 135) = 2.21, p = .53, race, χ2 (9, N = 135) = 13.54, p = .14, or Hispanic/Latino(a) ethnicity χ2 (3, N = 135) = 1.75, p = .63. However, level of education differed between the four groups (see Table 1), F(3,131) = 6.95, p < .001, η2 = .14. The no current psychiatric disorder group had a significantly higher level of education as compared to the depressive, F(1,82) = 7.41, p = .01, η2 = .08, and schizophrenia spectrum, F(1,88) = 9.86, p = .002, η2 = .10, disorder groups, who did not differ from each other, F(1,42) = 0.01, p = .95, η2 < .001. The bipolar disorders group had a significantly higher level of education than the depressive, F(1,43) = 9.34, p = .004, η2 = .18, and schizophrenia spectrum, F(1,49) = 12.11, p = .001, η2 = .20, disorder groups, but did not differ from the no current disorder group, F(1,89) = 2.25, p = .14, η2 = .03.

4.2. Procedures

Due to the length of the procedures, the study was administered over two meetings of approximately two to three hours, which were 19.76 days apart on average, with six participants completing both sessions on the same day. In cases in which more than a month elapsed between sessions (N = 26), the clinician-administered diagnostic interviews and symptom measures were re-administered to reflect any changes.

4.2.1. Session #1 Procedures

After an informed consent procedure, participants completed the Colenbrander Mixed Contrast Card Set (Precision Vision, La Salle, IL) to estimate visual acuity. Participants with a corrected visual acuity worse than 20/50 at an intermediate distance were excluded, as were participants exhibiting color blindness using Ishihara's Tests for Colour Deficiency (Kanehara Trading Inc., Tokyo, Japan). Participants completed the Wide Range Achievement Test – 4th edition (WRAT-4; Wilkinson and Robertson, 2006) Reading subtest to estimate full scale IQ, and were excluded if the standardized score was less than 70. A trained clinical psychology doctoral student administered the Structured Clinical Interview for DSM-IV Axis I Disorders (First et al., 1997b), the schizotypal, paranoid, and avoidant personality disorder (i.e., schizophrenia-spectrum) sections of the Structured Clinical Interview for DSM-IV Axis II Disorders (First et al., 1997a), and the Structured Interview for Positive and Negative Syndromes of Schizophrenia (PANSS; Kay et al., 1992). Final diagnoses were determined through case presentations with a licensed psychologist (author JSB). Participants also completed the Schizotypal Personality Questionnaire – Brief Revised (SPQ-BR; Cohen et al., 2010), and the Anticipatory and Consummatory Interpersonal Pleasure Scale (ACIPS; Gooding and Pflum, 2014). Participants were invited to Session #2 if they passed the exclusion criteria listed above, and did not meet diagnostic criteria for a substance use disorder in the past six months.

4.2.2. Session #2 Procedures

After an informed consent procedure, participants completed the visual evoked potential task. Stimuli consisted of 8 × 8 arrays of checks, as used in several other transient VEP publications (Bedwell et al., 2015; Butler et al., 2007; Yeap et al., 2009). E-Prime 2.0 Professional was used for stimuli presentation. The array of gray checks appeared in the center of the monitor screen (see Figure 5), subtending a visual angle of 6.67 × 6.67 degrees. The checks were presented on three different backgrounds of gray, green, and red, which surrounded the checks and the rest of the screen, subtending a visual angle of 32.48 × 18.53 degrees. The gray background was chosen as the primary baseline color to allow for comparison to VEP studies from other labs. We also had specific interest in the effect of the red background, so green was included for that comparison as it is the opponent color of red. The background colors were approximately matched for a physical luminance of 30 cd/m2, using a spot photometer placed at the center of the screen. The gray checks were brighter than the background to create low (8%) and high (64%) contrast stimuli for each of the three color backgrounds. The ambient light in the room was dim (approximately 0.23 cd/m2).

Figure 5.

Figure 5

Open in a new tab

Example stimulus: high contrast (64%) checks on the gray background.

The three color backgrounds cycled in blocks, with participants randomly assigned to the order of green–gray–red or red–gray–green. Each of these sequences contained a 20 second countdown rest period on a black background following each color block. Each color block contained 50 trials of each of the two contrasts presented in a random order. The color blocks cycled five times, resulting in 250 trials of each contrast by color condition. In addition, line drawings of four different animals were presented during 26 random trials within each color block, with 13 of those trials representing a target animal for which the participant was asked to press a button on a game controller. These animal stimuli were included to help participants engage and attend to the passive task – consistent with past studies using check arrays (Bedwell et al., 2015; Butler et al., 2007; Yeap et al., 2009). Each stimulus was presented for 60 ms followed by a blank screen of the same color background for an interstimulus interval (ISI) that varied randomly between 540 – 1,340 ms for the majority of the trials, although it could remain up to 5,840 ms if the eye tracker did not detect a fixation near center of the screen (details provided below). The duration of the entire task was approximately 50 minutes. A graphic representation of task flow is provided in Figure 6.

Figure 6.

Figure 6

Open in a new tab

Depiction of visual-evoked potential task conditions, timing, and flow.

Note: Participants were randomly assigned to receive either the order of colors depicted or the order of red – gray – green. An eye tracker monitored fixation near middle of screen during the interstimulus interval (ISI) and would delay the presentation of the next stimulus for up to 5,840 ms if there was not a sufficient central fixation (500 ms). However, in the majority of trials, the actual ISI ranged between 540 and 1,350 ms as depicted above.

EEG was recorded using a 64 channel Neuroscan Synamps2 system, with DC recording digitized at 1,000 Hz, with a 200 Hz low pass filter, no high-pass filter, and impedance of less than 10 kOhms at each electrode. The average from unlinked bilateral mastoid electrodes were used as the active reference, and vertical and horizontal ocular electrodes were used to estimate blinks and large eye movements.

Eye movements were recorded monocularly from the right eye using an EyeLink 1000 remote eye tracker (SR Research Ltd., Mississsauga, Ontario, Canada), sampling at 500 Hz. The task began with a five-point calibration routine to set the average and maximum calibration error at less than 0.5° and 1.0° of visual angle respectively. Following each stimulus and an initial randomly selected ISI of 40 to 840 ms, the eye tracker monitored for a fixation of at least 500 ms in the center of the screen (7° × 7° visual angle), which would then trigger the presentation of the next stimulus. This helped ensure that participants had their eyes open and fixated near the center of the screen immediately prior to the presentation of each stimulus. If a sufficiently long central fixation was not detected within 5,000 ms, the eye tracker recorded that the ISI had timed out and proceeded to the next trial. This procedure resulted in a total possible ISI duration of 540 -5,840 ms.

4.2.3. VEP Measurement Procedures

The VEP data was analyzed using Brain Vision Analyzer 2.1.2 software and was filtered offline using a high pass filter of 0.10 Hz and low pass filter of 45 Hz, both using a roll off of 48 db/oct. The data was segmented from -100 to 500 ms based on the onset of the check stimuli (stimulus-locked), and was then corrected for blinks and large eye movements using independent component analysis on the ocular electrodes. Data were down-sampled to 500 Hz, re-referenced to the common average, and baseline corrected from -100 to 0 ms. Artifact rejection removed segments from particular electrodes containing more than a +/- 100 μV deflection. We then segmented the data based on the contrast and color condition and averaged the segments within each condition using individual channel mode.

Brain electric source analysis (BESA)

The basic premise of source modeling is that a component can be defined as the part of the scalp waveform that results from the compound local activity of a circumscribed brain region (Scherg and Von Cramon, 1985). BESA employs a least squares fitting algorithm, over which the user has interactive control. Source localization proceeds by a search within the head model for a location where the sources can explain a maximal amount of variance (Scherg and Picton, 1991). Here, we assumed bilateral symmetrical sources. An assumption of spatial symmetry considerably reduces the number of independent parameters to be determined (Scherg and Picton, 1991) and is consistent with the known generators of P1 (Di Russo et al, 2002). The inverse leadfield matrix (i.e., the matrix of coefficients that maps current sources to surface potentials) was then used to project the modeled dipolar activity back onto the scalp surface in order to select the optimal channels used for the ERP analysis of the P1 component. Source modeling resulted in dipoles localized to generators in dorsal extrastriate cortex of the middle occipital gyrus consistent with previous studies (Di Russo et al., 2002). The model produced a goodness-of-fit value of 96.4% (residual variance = 3.6%) across the modeled epoch of 80 – 116 ms (see Figure 7 a,b). Based on the distribution of the dipolar surface projection, we chose to measure P1 from the average of six electrodes from each hemisphere: TP7/8, CP5/6, CP3/4, P7/8, P5/6, and P3/4, generating one analysis region of interest (ROI) on each hemisphere (see Figure 7c). We measured P1 separately for each ROI, using the largest positive peak appearing in the time window of 60 to 195 ms. The average amplitude of this peak was measured using a 20 ms window centered on the peak from the averaged waveform for each hemisphere and condition within each participant. See Figure 8 for a depiction of the grand average waveforms by color and contrast across all participants.

Figure 7.

Figure 7

Open in a new tab

a) Occipital view of scalp ERP showing the distribution of the P1 topography; b) Dipole inverse solution of the ERP for the time-window of 80 – 116 ms post stimulus onset. Goodness of fit (GOF) = 96.4%. Talairach areas: X = ±30, Y = -82, Z = 17. c) Scalp distribution of the forward model derived from the dipole solution indicating the choice of the electrodes (region of interest) in right hemisphere that would be appropriate for the P1 analysis. Black circle represents the six electrodes chosen to average for right hemisphere P1 mean amplitude measurement based on the topography: P4, P6, P8, CP4, CP6, and TP8. As the distribution was approximately equivalent for left hemisphere (see Figure 7a), the analogous six electrodes were chosen for that hemisphere (P3, P5, P7, CP3, CP5, and TP7).

Figure 8.

Figure 8

Open in a new tab

Grand average waveforms depicting the averaged bilateral P1 component across the entire sample by contrast and color background.

Note: Thin lines = 8% contrast condition; Thick lines = 64% contrast condition; color of line indicates the color of the background condition

4.3. Statistical Analyses

Prior to examining symptom relationships, we examined P1 amplitude across the entire sample using repeated-measures ANCOVAs to assess the general effects of contrast and hemisphere. For examination of symptom relationships with the P1 amplitude, we conducted linear regressions, with hypothesized symptoms entered simultaneously. As there was substantial shared variance between clinician-rated and self-reported symptoms, we ran separate regressions for each type of assessment modality.

Analyses with the gray background included the two hypothesized clinician-rated PANSS symptoms of Delusions (P1) and Conceptual Disorganization (P2), along with two other symptoms found in a single past study - Suspiciousness/Persecution (P6), and Emotional Withdrawal (N2), and corresponding self-reported symptoms from the SPQ-BR subscales of: Magical Thinking, Eccentric Behavior, Odd Speech, and Suspiciousness (sum of the three items; excluding Ideas of Reference items).

Analyses examining the change from the green to red background included the two hypothesized PANSS symptoms of Blunted Affect (N1) and Passive/Apathetic Social Withdrawal (N4), and related self-report scales of SPQ-BR Constricted Affect (sum of the three items; excluding No Close Friends items), SPQ-BR Social Anxiety, and the ACIPS total score. We also added SPQ-BR Ideas of Reference (sum of the three items; excluding Suspiciousness items) and the corresponding PANSS Delusions item to analyses as one past study using the full original SPQ found this association (Bedwell et al., 2013). Any statistically significant regressions were then repeated with the addition of covarying for diagnostic class in order to examine the relative influence of that variable. The three individuals with disorders outside of the classes of interest were combined with the nonpsychiatric controls for these post hoc analyses.

All regressions were examined for multicollinearity (Variance Inflation Factor > 4 or Condition Index > 30) and statistical outliers (defined by combination of Studentized residual > 3.0 and Cook's d > .03). Any deviations from these parameters are reported in the results.

Highlights.

  • Delusion severity negatively related to amplitude of P1 visual-evoked potential

  • Constricted affect negatively related to change in P1 amplitude to red background

  • Participants with schizophrenia showed reduced P1 amplitude compared to controls

Acknowledgments

The authors would like to thank the following research assistants for help with various aspects of this study: Julian Montaquila, Benjamin Trachik, Dana Braden, Stanley Desire, Carina Viegas, Travis Hatfield, Nicholas Joseph, Luke Van de Krol, Adam Benzekri, Cierra Godwin, Alyssa Finner, Soraya Allen, Maya Rose, Breanna Davis, and Thomas Giallella. The authors would also like to thank Drs. Vance Zemon and James Gordon for assistance with the design of the stimulus and paradigm, and Dr. Nathan T. Carter for assistance with planning statistical analyses.

Funding: This study was funded by the National Institute of Mental Health (1R15MH097222-01A1; PI: J.S. Bedwell) and matching funds from University of Central Florida College of Sciences and Office of Research and Commercialization.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

References

  1. Bayer M, Rossi V, Vanlessen N, Grass A, Schacht A, Pourtois G. Independent effects of motivation and spatial attention in the human visual cortex. Soc Cogn Affect Neurosci. 2017;12:146–156. doi: 10.1093/scan/nsw162. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bedwell JS, Brown JM, Miller LS. The magnocellular visual system and schizophrenia: what can the color red tell us? Schizophrenia Research. 2003;63:273–284. doi: 10.1016/s0920-9964(02)00356-0. [DOI] [PubMed] [Google Scholar]
  3. Bedwell JS, Butler PD, Chan CC, Trachik BJ. Transdiagnostic psychiatric symptoms related to visual evoked potential abnormalities. Psychiatry Res. 2015;230:262–270. doi: 10.1016/j.psychres.2015.09.004. [DOI] [PubMed] [Google Scholar]
  4. Bedwell JS, Chan CC, Trachik BJ, Rassovsky Y. Changes in the visual-evoked P1 potential as a function of schizotypy and background color in healthy young adults. J Psychiatr Res. 2013;47:542–547. doi: 10.1016/j.jpsychires.2012.12.012. [DOI] [PubMed] [Google Scholar]
  5. Bedwell JS, Rassovsky Y, Orem DM, Kamath V. The backward masking red light effect in schizophrenia: Relationship to clinical features and neurocognitive performance. J Abnorm Psychol. 2011;120:490–496. doi: 10.1037/a0022302. [DOI] [PubMed] [Google Scholar]
  6. Brodeur MB, Kiang M, Christensen BK. The beneficial influence of inattention on visual interference in schizophrenia. Neuropsychology. 2016;30:664–672. doi: 10.1037/neu0000280. [DOI] [PubMed] [Google Scholar]
  7. Butler PD, Abeles IY, Silverstein SM, Dias EC, Weiskopf NG, Calderone DJ, et al. An event-related potential examination of contour integration deficits in schizophrenia. Frontiers in psychology. 2013;4:132. doi: 10.3389/fpsyg.2013.00132. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Butler PD, Martinez A, Foxe JJ, Kim D, Zemon V, Silipo G, et al. Subcortical visual dysfunction in schizophrenia drives secondary cortical impairments. Brain : a journal of neurology. 2007;130:417–430. doi: 10.1093/brain/awl233. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Butler PD, Silverstein SM, Dakin SC. Visual perception and its impairment in schizophrenia. Biol Psychiatry. 2008;64:40–47. doi: 10.1016/j.biopsych.2008.03.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Butler PD, Zemon V, Schechter I, Saperstein AM, Hoptman MJ, Lim KO, et al. Early-stage visual processing and cortical amplification deficits in schizophrenia. Arch Gen Psychiatry. 2005;62:495–504. doi: 10.1001/archpsyc.62.5.495. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Callaway DA, Cohen AS, Matthews RA, Dinzeo T. Schizotypal Personality Questionnaire-Brief Revised: psychometric replication and extension. Personality disorders. 2014;5:32–38. doi: 10.1037/per0000041. [DOI] [PubMed] [Google Scholar]
  12. Ceponiene R, Westerfield M, Torki M, Townsend J. Modality-specificity of sensory aging in vision and audition: evidence from event-related potentials. Brain Res. 2008;1215:53–68. doi: 10.1016/j.brainres.2008.02.010. [DOI] [PubMed] [Google Scholar]
  13. Clementz BA, Sweeney JA, Hamm JP, Ivleva EI, Ethridge LE, Pearlson GD, et al. Identification of Distinct Psychosis Biotypes Using Brain-Based Biomarkers. Am J Psychiatry. 2016;173:373–384. doi: 10.1176/appi.ajp.2015.14091200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Cohen AS, Matthews RA, Najolia GM, Brown LA. Toward a more psychometrically sound brief measure of schizotypal traits: introducing the SPQ-Brief Revised. Journal of personality disorders. 2010;24:516–537. doi: 10.1521/pedi.2010.24.4.516. [DOI] [PubMed] [Google Scholar]
  15. Cuthbert BN, Insel TR. Toward the future of psychiatric diagnosis: the seven pillars of RDoC. BMC medicine. 2013;11:126. doi: 10.1186/1741-7015-11-126. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. de Monasterio FM. Properties of concentrically organized X and Y ganglion cells of macaque retina. J Neurophysiol. 1978;41:1394–1417. doi: 10.1152/jn.1978.41.6.1394. [DOI] [PubMed] [Google Scholar]
  17. Di Russo F, Martinez A, Sereno MI, Pitzalis S, Hillyard SA. Cortical sources of the early components of the visual evoked potential. Hum Brain Mapp. 2002;15:95–111. doi: 10.1002/hbm.10010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. First MB, Gibbon M, Spitzer RL, Williams JBW, Benjamin LS. Structured Clinical Interview for DSM-IV Axis II Personality Disorders (SCID-II) American Psychiatric Press; Washington, DC: 1997a. [Google Scholar]
  19. First MB, Spitzer RL, Gibbon M, Williams JBW. Structured Clinical Interview for DSM-IV Axis I Disorders - CV. American Psychiatric Press; Washington, D.C: 1997b. [Google Scholar]
  20. Ford TC, Nibbs R, Crewther DP. Glutamate/GABA+ ratio is associated with the psychosocial domain of autistic and schizotypal traits. PloS one. 2017;12:e0181961. doi: 10.1371/journal.pone.0181961. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Friedman T, Sehatpour P, Dias E, Perrin M, Javitt DC. Differential relationships of mismatch negativity and visual p1 deficits to premorbid characteristics and functional outcome in schizophrenia. Biol Psychiatry. 2012;71:521–529. doi: 10.1016/j.biopsych.2011.10.037. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Furey ML, Drevets WC, Hoffman EM, Frankel E, Speer AM, Zarate CA., Jr Potential of pretreatment neural activity in the visual cortex during emotional processing to predict treatment response to scopolamine in major depressive disorder. JAMA psychiatry. 2013;70:280–290. doi: 10.1001/2013.jamapsychiatry.60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Gonzalez-Hernandez JA, Pita-Alcorta C, Padron A, Finale A, Galan L, Martinez E, et al. Basic visual dysfunction allows classification of patients with schizophrenia with exceptional accuracy. Schizophr Res. 2014;159:226–233. doi: 10.1016/j.schres.2014.07.052. [DOI] [PubMed] [Google Scholar]
  24. Gooding DC, Pflum MJ. The assessment of interpersonal pleasure: introduction of the Anticipatory and Consummatory Interpersonal Pleasure Scale (ACIPS) and preliminary findings. Psychiatry Res. 2014;215:237–243. doi: 10.1016/j.psychres.2013.10.012. [DOI] [PubMed] [Google Scholar]
  25. Green MF, Hellemann G, Horan WP, Lee J, Wynn JK. From perception to functional outcome in schizophrenia: modeling the role of ability and motivation. Arch Gen Psychiatry. 2012;69:1216–1224. doi: 10.1001/archgenpsychiatry.2012.652. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Hidayah MN, Liu CY, Joanna OS. Ketamine and tramadol for the prevention of shivering during spinal anaesthesia. La Clinica terapeutica. 2014;165:193–198. doi: 10.7417/CT.2014.1732. [DOI] [PubMed] [Google Scholar]
  27. Jahshan C, Wynn JK, Mathis KI, Green MF. The neurophysiology of biological motion perception in schizophrenia. Brain and behavior. 2015;5:75–84. doi: 10.1002/brb3.303. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Javitt DC. Neurophysiological models for new treatment development in schizophrenia: early sensory approaches. Annals of the New York Academy of Sciences. 2015;1344:92–104. doi: 10.1111/nyas.12689. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Kay SR, Opler LA, Fiszbein A. The structured clinical interview for the positive and negative syndromes of schizophrenia. Multi-Health Systems; New York: 1992. [Google Scholar]
  30. Koychev I, El-Deredy W, Haenschel C, Deakin JFW. Visual information processing deficits as biomarkers of vulnerability to schizophrenia: An event-related potential study in schizotypy. Neuropsychologia. 2010;48:2205–2214. doi: 10.1016/j.neuropsychologia.2010.04.014. [DOI] [PubMed] [Google Scholar]
  31. Livingstone MS, Hubel DH. Anatomy and physiology of a color system in the primate visual cortex. J Neurosci. 1984;4:309–356. doi: 10.1523/JNEUROSCI.04-01-00309.1984. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Meyer A, Lerner MD, De Los Reyes A, Laird RD, Hajcak G. Considering ERP difference scores as individual difference measures: Issues with subtraction and alternative approaches. Psychophysiology. 2017;54:114–122. doi: 10.1111/psyp.12664. [DOI] [PubMed] [Google Scholar]
  33. Morgan CJ, Muetzelfeldt L, Curran HV. Consequences of chronic ketamine self-administration upon neurocognitive function and psychological wellbeing: a 1-year longitudinal study. Addiction (Abingdon, England) 2010;105:121–133. doi: 10.1111/j.1360-0443.2009.02761.x. [DOI] [PubMed] [Google Scholar]
  34. Normann C, Schmitz D, Furmaier A, Doing C, Bach M. Long-term plasticity of visually evoked potentials in humans is altered in major depression. Biol Psychiatry. 2007;62:373–380. doi: 10.1016/j.biopsych.2006.10.006. [DOI] [PubMed] [Google Scholar]
  35. Norton DJ, McBain RK, Pizzagalli DA, Cronin-Golomb A, Chen Y. Dysregulation of visual motion inhibition in major depression. Psychiatry Res. 2016;240:214–221. doi: 10.1016/j.psychres.2016.04.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Pierson A, Ragot R, Van Hooff J, Partiot A, Renault B, Jouvent R. Heterogeneity of information-processing alterations according to dimensions of depression: an event-related potentials study. Biol Psychiatry. 1996;40:98–115. doi: 10.1016/0006-3223(95)00329-0. [DOI] [PubMed] [Google Scholar]
  37. Schechter I, Butler PD, Zemon VM, Revheim N, Saperstein AM, Jalbrzikowski M, et al. Impairments in generation of early-stage transient visual evoked potentials to magno- and parvocellular-selective stimuli in schizophrenia. Clin Neurophysiol. 2005;116:2204–2215. doi: 10.1016/j.clinph.2005.06.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Scherg M, Picton TW. Separation and identification of event-related potential components by brain electric source analysis. Electroencephalography and clinical neurophysiology. Supplement. 1991;42:24–37. [PubMed] [Google Scholar]
  39. Scherg M, Von Cramon D. Two bilateral sources of the late AEP as identified by a spatio-temporal dipole model. Electroencephalography and clinical neurophysiology. 1985;62:32–44. doi: 10.1016/0168-5597(85)90033-4. [DOI] [PubMed] [Google Scholar]
  40. Sehatpour P, Dias EC, Butler P, Revheim N, Guilfoyle D, Foxe JJ, et al. Impaired Visual Object Processing Across an Occipital-Frontal-Hippocampal Brain Network in Schizophrenia An Integrated Neuroimaging Study. Archives Of General Psychiatry. 2010;67:772–782. doi: 10.1001/archgenpsychiatry.2010.85. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Stone J, Kotoula V, Dietrich C, De Simoni S, Krystal JH, Mehta MA. Perceptual distortions and delusional thinking following ketamine administration are related to increased pharmacological MRI signal changes in the parietal lobe. Journal of psychopharmacology (Oxford, England) 2015;29:1025–1028. doi: 10.1177/0269881115592337. [DOI] [PubMed] [Google Scholar]
  42. Stothart G, Tales A, Hedge C, Kazanina N. Double peaked P1 visual evoked potentials in healthy ageing. Clin Neurophysiol. 2014;125:1471–1478. doi: 10.1016/j.clinph.2013.11.029. [DOI] [PubMed] [Google Scholar]
  43. Tan HR, Lana L, Uhlhaas PJ. High-frequency neural oscillations and visual processing deficits in schizophrenia. Frontiers in psychology. 2013;4:621. doi: 10.3389/fpsyg.2013.00621. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Trillenberg P, Sprenger A, Talamo S, Herold K, Helmchen C, Verleger R, et al. Visual and non-visual motion information processing during pursuit eye tracking in schizophrenia and bipolar disorder. Eur Arch Psychiatry Clin Neurosci. 2017;267:225–235. doi: 10.1007/s00406-016-0671-z. [DOI] [PubMed] [Google Scholar]
  45. Verleger R, Talamo S, Simmer J, Smigasiewicz K, Lencer R. Neurophysiological sensitivity to attentional overload in patients with psychotic disorders. Clin Neurophysiol. 2013;124:881–892. doi: 10.1016/j.clinph.2012.11.003. [DOI] [PubMed] [Google Scholar]
  46. Villena-Gonzalez M, Moenne-Loccoz C, Lagos RA, Alliende LM, Billeke P, Aboitiz F, et al. Attending to the heart is associated with posterior alpha band increase and a reduction in sensitivity to concurrent visual stimuli. Psychophysiology. 2017;54:1483–1497. doi: 10.1111/psyp.12894. [DOI] [PubMed] [Google Scholar]
  47. Wilkinson GS, Robertson GJ. WRAT 4: wide range achievement test. 4th. Psychological Assessment Resources, Inc; Lutz, FL: 2006. [Google Scholar]
  48. Yeap S, Kelly SP, Reilly RB, Thakore JH, Foxe JJ. Visual sensory processing deficits in patients with bipolar disorder revealed through high-density electrical mapping. J Psychiatry Neurosci. 2009;34:459–464. [PMC free article] [PubMed] [Google Scholar]

RESOURCES