Context The AX version of the visual continuous performance task (AX-CPT) is widely used for investigating visual working memory dysfunction in schizophrenia. Event-related potentials (ERP) provide an objective index of brain function and can be used to evaluate brain substrates underlying impaired cognition in schizophrenia.
Objective To assess the mechanisms that underlie visual working memory dysfunction in schizophrenia relative to impairment of early visual processing.
Design Case-control study.
Setting Inpatient and outpatient facilities associated with the Nathan Kline Institute for Psychiatric Research.
Participants A total of 30 individuals with schizophrenia and 17 healthy comparison subjects.
Interventions Three versions of the AX-CPT, with parametric variations in the proportions of trial types, were used to test performance and underlying neural activity during differential challenge situations. Contrast sensitivity measures were obtained from most subjects.
Main Outcome Measures Behavioral performance was assessed using d' context scores. Integrity of stimulus- and task-related cortical activation to both cue and probe stimuli was assessed using sensory (C1, P1, N1) and cognitive (N2, contingent negative variation [CNV]) ERP components. Early magnocellular/parvocellular function was assessed using contrast sensitivity. Linear regression and path analyses were used to assess relations between physiological and behavioral parameters.
Results Patients showed reduced amplitude of both early sensory (P1, N1) and later cognitive (N2, CNV) ERP components. Deficits in sensory (N1) and cognitive (N2) component activation to cue stimuli contributed independently to impaired behavioral performance. In addition, sensory deficits predicted impaired cognitive ERP generation. Finally, deficits in performance correlated with impairments in contrast sensitivity to low, but not high, spatial frequency stimuli.
Conclusions Working memory deficits in schizophrenia have increasingly been attributed to impairments in stimulus encoding rather than to failures in memory retention. This study provides objective physiological support for encoding hypotheses. Further, deficits in sensory processing contribute significantly to impaired working memory performance, consistent with generalized neurochemical models of schizophrenia.
Deficits in cognitive processing have been extensively documented in schizophrenia and shown to correlate with poor long-term outcome.1,2 Recently, deficits in early sensory processing have been demonstrated as well, using methods such as contrast sensitivity,3 functional magnetic resonance imaging (fMRI),4-6 and steady-state7,8 or transient9 event-related potentials (ERP). However, the relationships between sensory dysfunction and higher-order cognitive impairments have been investigated only to a limited degree.10-13 The present study uses variations of the AX-type continuous performance test (AX-CPT) combined with high-density ERPs to investigate contributions of early visual dysfunction to impaired cognitive processing in schizophrenia.
The AX-CPT is a well-established behavioral paradigm that elicits a consistent pattern of impairments in performance in both patients with schizophrenia14-20 and unaffected first-degree relatives21 compared with controls. In AX-CPT, letters are presented sequentially on a computer screen and subjects must respond to a cue-target sequence consisting of the letter A followed by the letter X (AX sequences), while ignoring all other cue-probe sequences. Invalid sequences include those with invalid cues (collectively referred to as B) followed by the letter X (BX sequences), invalid probes (collectively referred to as Y) following the letter A (AY sequences), or sequences with both invalid cues and invalid probes (BY sequences).
In the most widely used version of the task (termed AX-70),15,16,18-20 70% of the trials consist of AX sequences, creating a strong prepotency to respond whenever the probe X is presented. Following an invalid cue (B), therefore, subjects need to use local (within-trial) information to overcome this global prepotency. Thus, correct AX-CPT performance requires that cue information be correctly encoded and then retained across the delay to guide behavior.15,22,23 In the AX-70, patients with schizophrenia show not only significant reductions in hit rate to AX trials but also an increase in false alarm to BX, suggesting a specific deficit in the ability to use cue information to guide subsequent target response.
The initial use of the AX-70 version of the task in schizophrenia was based largely on dopaminergic theories,24-26 which postulated that patients would be particularly impaired when retaining the local information online owing to specific dysfunction of prefrontal dopamine-recipient prefrontal regions. In addition, interpretation of these models is derived from cognitive models that postulate discrete input, association, context, and response stages.15,22 Recent developments in schizophrenia and cognitive neuroscience research have challenged both sets of hypotheses.
First, deficits in working memory in schizophrenia have increasingly been attributed to failures of encoding rather than memory retention.19,27,28 Second, schizophrenialike deficits in AX-CPT performance may be reproduced by administration of the N-methyl-D-aspartate (NMDA) receptor antagonist ketamine to healthy volunteers,20 consistent with glutamatergic29,30 and other distributed neural theories31 of schizophrenia.
Third, recent studies of visual processing suggest that sensory input to prefrontal regions arises via 2 distinct pathways: a rapid, low-resolution“perception for action” system that receives input primarily from the magnocellular visual system and projects primarily via the dorsal stream visual pathway and a slower, higher-resolution“perception for identification” system that projects primarily via the ventral visual system.32,33 Deficits in magnocellular/dorsal stream processing have been demonstrated consistently in schizophrenia3,4,6-9 and contribute significantly to impairments in processes such as perceptual closure33 and face recognition.12 However, potential involvement of sensory systems in deficits on cognitive tasks such as AX-CPT has been understudied. This is the first study to obtain neurophysiological measures to evaluate the integrity of initial sensory responses during AX-CPT.
We used an ERP paradigm that has been validated based on both dipole mapping and direct intracranial recording in monkeys that were awake and performing a version of AX-CPT.17,34 In this paradigm, early components including P1 and N1 index function at the level of dorsal- and ventral-stream visual cortex located in middle occipital gyrus,13,35 while later components including N2 and the contingent negative variation (CNV) index function of frontal brain regions such as the dorsolateral prefrontal and medial prefrontal cortex, including the anterior cingulate cortex.36
Our study also used unique parametric variations of the AX-CPT in which visual stimuli and task instructions remain the same but stimulus probabilities change, leading to a different global response set (Table 1).17 These variations permit subfractionation of underlying cognitive processes. Based on prior research, we hypothesized that sensory processes, as indexed by sensory P1 and N1 components, would be impaired across task versions in schizophrenia and would contribute significantly to overall impairments in task performance. We hypothesized that amplitude of frontal N2 and CNV potentials would be reduced as well, consistent with impaired encoding of cue-related information.17
Finally, we evaluated basic visual processing using contrast sensitivity to low- vs mid/high–spatial frequency (SF) sine-wave stimuli along with AX-CPT performance in a subgroup of subjects. Low-SF stimuli are processed preferentially by the magnocellular visual system, while high-SF stimuli are processed preferentially by the parvocellular system. We have previously observed that patients with schizophrenia show preferential deficits in magnocellular function.4,7 For this study, we hypothesized that preferential deficits in detection of low-SF stimuli would again be observed and would correlate with impaired AX-CPT performance, consistent with early visual contributions to overall cognitive impairment in schizophrenia.
Thirty patients who met DSM-IV criteria for schizophrenia and 17 healthy volunteers participated. Patients were recruited from inpatient and outpatient facilities associated with the Nathan Kline Institute for Psychiatric Research. Diagnoses were obtained using the Structured Clinical Interview for DSM-IV (SCID)37 and available clinical information. Controls were recruited through the Volunteer Recruitment Pool at the Nathan Kline Institute. All subjects provided informed consent and received cash compensation for their time. This study was approved by the Nathan Kline Institute internal review board. Healthy volunteers with a history of SCID-defined Axis I psychiatric disorder were excluded. Patients and controls were excluded if they had any neurological or ophthalmological disorders that might affect performance or if they met criteria for alcohol or substance dependence within the last 6 months or abuse within the last month. All patients were receiving antipsychotic medication at the time of testing. Chlorpromazine equivalents were calculated as described previously.38 All participants had normal or corrected-to-normal visual acuity. Clinical and demographic information are included in Table 2. Data from 3 patients and 2 controls were discarded owing to excessive movement artifacts. Contrast sensitivity was assessed in a subgroup of 20 patients and 9 controls who had no significantly different characteristics, using previously described methods.44
During recordings, subjects sat in a comfortable chair inside a darkened, electrically shielded recording chamber, facing a computer monitor. Subjects were instructed to respond quickly and accurately by pressing a button on a response pad. They had a practice block of trials before the recordings were initiated.
Cue-probe sequences were presented sequentially on a computer screen located 137 cm from subject's eyes using Presentation software (Neurobehavioral Systems, Inc, Albany, California). Letters subtended approximately 2° of visual arc and were presented for 100 milliseconds, white on black, using Helvetica font.
The interval between onset of cue and onset of probe letters was 1240 milliseconds, and the interval between successive cue-probe sequences was 1390 milliseconds. Subjects were instructed to respond by pressing a button following an AX sequence while ignoring all other cue-probe sequences. Invalid (B) cues and invalid (Y) probes consisted of letters other than A and X. The response window following target presentation was 1 second.
In each task variant, 1 trial type was presented with 70% probability, while all other sequences were presented with 10% probability, in pseudorandom order (Table 2). Performance was assessed using the parameter d' context, which reflects the ability to use the cue stimulus to determine the correct response to a subsequently presented valid target letter (AX vs BX sequence).18,19,45,46
Each subject performed 6 blocks of 93 trials for each of the 3 tasks, totaling 1674 trials. In most cases, task AX-70 was presented first because this was the condition of primary interest. Task order did not significantly affect performance either across or within group (main effect of order: F3,41 = 0.25, P = .86; group× order interaction: F3,41 = 0.76, P = .52). Subjects took breaks between blocks and did not report abnormal fatigue during the tasks.
Continuous electroencephalography was acquired using an ActiveTwo system (Biosemi, Amsterdam, the Netherlands) from 168 scalp electrodes and digitized at 512 Hz. The continuous data were separated into epochs (−100 to 750 milliseconds) surrounding the digital event tags, baseline corrected from−100 milliseconds to stimulus onset, and an artifact rejection criterion of±80μV was applied. Epochs for the correct trials were averaged separately for each trial type, for cue and probe periods, and for each subject, and a nasal reference was applied to the averaged data. Subjects with fewer than 25 accepted trials in any condition were removed from the analysis. For the remaining subjects, the mean (SD) percentage of accepted sweeps was 77.9% (12.5%) for controls and 72.0% (15.1%) for patients. Grand mean averages for each group were obtained by averaging the data from all subjects. Electrode placement was highly consistent across subjects owing to the use of an electrode cap that constrained interelectrode spacing and placement. An averaged version of the electrode locations on the head was used for group topographic data.
Spatiotemporal windows for peak detection were determined based on previous results.17,35 The averaged ERP were filtered (low pass, cutoff value, 45 Hz; zero phase shift, 48 dB), and for each component, peak amplitude was obtained from the median of 6 electrodes that covered the area of scalp where this component was best represented (Figure 1). Time windows were C1, 60 to 120 milliseconds; P1, 75 to 130 milliseconds; N1, 100 to 200 milliseconds; N2, 220 to 350 milliseconds; and CNV, 1200 to 1250 milliseconds. Peaks amplitudes were chosen as the dependent measure because of the large latency difference between patients and controls.
Horizontal sine-wave gratings were presented for 32 milliseconds at spatial frequencies of 0.5, 7, or 21 cycles per degree.3 A spatial 2-alternative forced-choice procedure was used. On each trial, a sine-wave grating was presented on one side of a monitor and a uniform field of average luminance was presented simultaneously on the opposite side. Sides were randomly varied across trials. The viewing distance was 160 cm, and the grating and uniform field together subtended 5.7°× 5.7° of visual angle. Participants stated which side of the display contained the grating. An up-down transform rule was used to determine contrast sensitivity associated with 79.4% correct responses for each SF in steps of 1.5 dB. The mean of 8 reversals was used to estimate a threshold.
Between-group analyses were conducted using repeated-measures multivariate analysis of variance with diagnostic group as a between-subject factor and cue type (A vs B), probe type (X vs Y), preceding cue type (A vs B preceding probe stimuli), and task variant (AX-70, AY-70, BX-70) as within-subject factors, as appropriate. The relationship between variables was determined using stepwise multivariate regression analysis, with statistical significance determined using r change parameter. Further relationship among variables was evaluated by path analysis, as implemented in AMOS 18.0 (SPSS Inc, Chicago, Illinois) (eAppendix). Effect sizes were calculated using the Cohen's d statistic.47 All tests were 2-tailed with a preset level of significance of P < .05. Unless otherwise noted, data in text represent mean (standard deviation).
As expected, patients showed increased AX errors (F1,35 = 30.3; P < .001) and greater BX, relative to AY, errors (F1,35 = 5.78; P = .02) across all task versions and in the AX-70 alone (Figure 2A). As a result, both d' (F1,35 = 28.9; P < .001) and d' context (F2,34 = 18.5; P < .001) scores were significantly reduced across task variants, with large effect size differences between groups (Figure 2B, eTable 2). The task× group interaction was also significant (F2,34 = 4.4; P = .02), reflecting greater deficits in AY-70 (F1,35 = 4.5; P = .04) and BX-70 (F1,35 = 6.7; P = .01) relative to AX-70 in patients vs controls.
Patients also had longer reaction times to the correct target than controls (controls: 296 [84] milliseconds; patients: 435 [105] milliseconds; F1,35 = 34.9; P < .001; d = 1.46), and reaction times varied by task (F2,34 = 54.9; P < .001) but there was no significant group× task interaction.
Waveforms elicited in the AX-70 condition are illustrated in Figure 3 and Figure 4. Similar components were obtained in the other task variants (eFigure 1, eFigure 2, eFigure 3, and eFigure 4). Peak amplitudes for sensory (C1, P1, N1) and cognitive (N2, CNV) components were calculated for each subject in response to cue and target stimuli separately and analyzed across subjects. Probe trial types were grouped according to type of preceding cue (A or B) and type of probe (X or Y). Effect sizes are summarized in eTable 2.
Separate repeated-measures multivariate analyses of variance were conducted for the 2 sensory (P1, N1) and 2 cognitive (N2, CNV) potentials and for cue and probe stimuli.
Amplitudes of both P1 (cue: F1,35 = 5.94, P = .02; probe: F1,35 = 5.77, P = .02) and N1 (cue: F1,35 = 7.83; P = .008; probe: F1,35 = 10.9, P = .002) were significantly reduced in schizophrenia across all conditions, reflecting significant impairment in early sensory processing (Figure 5 and Figure 6). Larger N1 responses were observed to A vs B cues (F1,35 = 13.1; P = .001) (Figure 5) and X vs Y probes (F1,35 = 23.2; P < .001) (Figure 6) across tasks. However, the cue type× group (F1,35 = 0.04; P = .80) and probe type× group (F1,35 = 1.06; P = .30) interactions were not significant, suggesting similar differentiation across groups.
For cues, larger sensory responses were observed to A vs B cues in the BX-70 condition only (Figures 5A), as reflected in significant cue type× task interactions for both the P1 (F2,34 = 8.10, P = .001) and N1 (F2,34 = 4.69, P = .02) components. Nevertheless, the degree of modulation was similar across groups, as reflected in nonsignificant cue× task× group interactions (P1: F2,34 = 0.6, P = .50; N1: F2,34 = 0.03, P = .97).
For probes, greater P1 response amplitudes were observed to X vs Y cues in the AY-70 condition vs greater Y vs X amplitudes in other conditions, resulting in a significant probe type× task interaction (F2,34 = 10.2; P < .001). Although the probe type× task× group interaction was marginally significant (F2,34 = 2.70; P = .08), this reflected a tendency for greater (rather than reduced) differential P1 modulation in patients vs controls (Figure 6B).
Larger N1 responses were observed to probe stimuli following A than B cues across both groups and all tasks, resulting in a significant main effect of preceding cue type (F1,35 = 36.9; P < .001) (Figure 6A). The degree of N1 modulation was also significantly greater in the BX-70 vs other task variants (Figure 6A) and to X than Y probes in the AY-70 task only, resulting in a significant preceding cue× task (F2,34 = 13.2; P < .001) and preceding cue× probe type× task (F2,34 = 2.95; P = .001) interactions. Nevertheless, all interactions involving group were not significant, suggesting similar modulation in patients vs controls.
In addition to being reduced in amplitude, both cue and probe potentials showed longer latencies in patients (cue: P1 F1,35 = 11.51, P = .002 and N1 F1,35 = 12.94, P = .001; probe: P1 F3,33 = 15.69, P < .001 and N1 F3,33 = 17.93, P < .001) (Figures 3 and 4).
A weak C1 was also present over the central occipital region in both groups (controls,−2.50 [2.19]μV; patients,−1.82 [0.97]μV). As opposed to P1 and N1, peak C1 amplitudes were not statistically different between groups (F1,40 = 1.84; P = .18).
N2 was significantly reduced in amplitude for both cue (F1,35 = 8.34; P = .007) and probe (F1,35 = 4.88; P = .03) stimuli in patients vs controls across all conditions. Across groups, N2 was larger (more negative) to B cues in the AX-70 condition, larger to A cues in BX-70, and approximately equal to A and B cues in the AY-70 task (Figure 5C), leading to a highly significant cue type× task interaction (F2,34 = 14.1; P < .001). Patients showed significantly less difference in response to A vs B cues and significantly less modulation by task than controls, leading to a highly significant cue type× task× group interaction (F2,34 = 14.1; P < .001).
In response to probes, N2 was elicited primarily by AX sequences in the AY-70 condition, and AY trials in the AX-70 condition (Figure 6C), leading to a highly significant cue type× probe type effect (F1,35 = 24.1; P < .001). Patients showed highly significant reductions in both conditions, leading to a highly significant probe type× task× group interaction (F2,34 = 25.7; P < .001).
CNV was significantly reduced in patients across tasks and stimulus types (F1,35 = 8.56; P = .006). Across groups, CNV was observed primarily for A vs B cues across task variants (F1,35 = 115.5; P < .001). For A cues, CNV was larger in BX-70 vs other conditions, leading to a significant effect of task (F2,34 = 25.8; P < .001) (Figure 7). The task× group interaction was significant (F2,34 = 5.34; P = .01), suggesting less modulation of CNV by task in patients vs controls. Despite being reduced in amplitude in patients, the slope of the CNV showed a parallel time course across groups over the course of the cue-probe interval. The period 550 to 1200 milliseconds from cue onset showed no group effect of slope (F1,35 = 1.74; P = .20) (eAppendix; eTable 1).
Intercorrelation among measures
Primary analyses were performed using regression analysis vs performance, as indexed by d' context (Figure 8). A supplemental path analysis was performed as well to assess directional relations (eFigure 5).
Across task versions, N1 amplitude to B cues correlated significantly with performance (r = 0.28; n = 116; P = .002) (Figure 8A), as did N1 amplitude to probe Y in sequence AY (r = 0.40; n = 116; P < .001) (Figure 8B), with larger N1 amplitudes correlating with better performance. The correlation for both B cues (r = 0.20; P = .03) and AY probes (r = 0.25; P = .007) remained significant even when controlling for task type and group membership.
In the AX-70 condition, N2 to B cues correlated significantly with performance (r = 0.37; P = .02), while in the BX-70 condition, N2 to A cues correlated with performance (r = 0.47; P = .004) (Figure 8C). However, neither correlation remained significant following consideration of group status (AX-70: r = 0.23, P = .11; BX-70: r = 0.19, P = .17). Furthermore, when variables were entered simultaneously vs performance in the AX-70 condition, the contribution of N1 remained highly significant (r = 0.37; P = .004), whereas the contribution of N2 became marginal (r = 0.24; P = .05).
Path analysis showed a similar relationship, with both N1 and N2 contributing significantly to performance (eFigure 5). In addition, path analysis revealed a significant contribution of P1 to N2 amplitude and group effects at the level of both P1 and N1. Because of the small sample size, however, these findings require replication in an independent sample.
Finally, across conditions there was a significant correlation between CNV amplitude to A cues and response latency (r = 0.23; P = .01), although the correlation was no longer significant once group status was considered (r = 0.08; P = .33).
Patients showed reduced contrast sensitivity at low (0.5 cycles per degree) spatial frequency vs controls (F1,27 = 10.7; P = .003) but not at middle (F1,27 = 2.28; P = .14) or high (F1,27 = 2.08; P = .16), leading to a significant main effect of group (F1,27 = 11.3; P = .002) and a significant group× SF interaction (F2,26 = 5.49; P = .01). There was a significant correlation between contrast sensitivity and performance both across conditions (r = 0.62; P = .01) and in the AX-70 condition individually (r = 0.61; P = .001) (Figure 8D). Both the overall (r = 0.53; P = .02) and AX-70 correlations (r = 0.42; P = .01) remained significant, even when controlling for group status. Finally, the correlation was significant, even in patients alone (r = 0.52; P = .02), and remained significant even when Wechsler Adult Intelligence Scale (WAIS) performance intelligence quotient (IQ) was included in the regression to control for general cognitive dysfunction (r = 0.55; P = .02).
AX-CPT is a widely used task for evaluating the neural basis of working memory and executive processing deficits in schizophrenia.14,18,19,48 Although reduced visual cortical activation has been observed across working memory/executive processing tasks in schizophrenia,36 contributions of sensory dysfunction to impairment in such tasks has not been studied systematically. The present study used ERP to investigate neural bases of impaired AX-CPT performance, focusing on both sensory and higher cognitive measures.
The primary findings are that patients showed reduced sensory responses along with reduced higher cortical activation. Furthermore, performance deficits correlated specifically with reduced ability to process low visual spatial frequency information, consistent with impaired magnocellular input.7,33 Finally, significant task- and sequence-related modulation of sensory responses were observed in this study, reflecting potential top-down modulatory control of sensory responses. Notably, however, such modulations were not significantly reduced in patients despite the reduced amplitudes of the sensory responses. Thus, these findings suggest that sensory deficits may be observed during cognitive tasks in schizophrenia even in the face of normal top-down cognitive control.
In the present study, patients showed significantly increased rates of AX errors and greater deficits in BX vs AY errors relative to controls, suggesting a specific response pattern.11,15,46 Furthermore, deficits were not limited to the most commonly used version of this task (ie, AX-70) but were observed across task versions. These findings suggest that cognitive deficits in schizophrenia are not confined to those related to frontal response inhibition.49
Sensory processing was assessed using 2 approaches. First, ERP were collected over visual regions in response to cue and probe stimuli, and amplitudes of P1 and N1 visual components were analyzed. Second, patients were assessed using contrast sensitivity, which measures the ability of individuals to detect low-contrast stimuli across a range of spatial frequencies.
Consistent with recent studies using other sensory and cognitive paradigms,12,33,50 patients showed highly significant deficits in P1 and N1 generation. Generators of these components have been localized to the middle occipital gyrus.13,35 Notably, reduced middle occipital gyrus activation was also observed in a recent meta-analysis of fMRI studies of executive processing in schizophrenia (including some which used the AX-CPT),36 supporting our finding of reduced sensory ERP response in this task. Selective reductions in middle occipital gyrus and medial prefrontal fMRI activation have also been reported following ketamine administration in healthy subjects, suggesting a potential link to underlying NMDA dysfunction.51
In prior studies involving psychophysically precise stimuli (sine-wave gratings), we have found preferential P1 reduction to low vs high SF stimuli, consistent with preferential magnocellular vs parvocellular visual system impairment.7 In the present study, psychophysically complex stimuli (letters) were used so exact SFs could not be determined. However, readers in general use a frequency band of approximately 3 cycles per letter,52,53 suggesting that the main frequency band for letter evaluation in this task was likely centered around 1.5 cycles per degree. This frequency band is within the range of prominent magnocellular sensitivity,54 suggesting that the key stimulus-related information may have been contained within a preferential magnocellular SF band. Furthermore, performance deficits in patients correlated significantly with both reduced N1 sensory response (Figure 8A,B) and impaired contrast sensitivity to low- but not high-SF stimuli (Figure 8D), suggesting a significant contribution of impaired sensory processing to impaired behavioral performance in schizophrenia.
This study also analyzed generation of 2 cognitive potentials, N2 and CNV, that have been shown to reflect activity in the prefrontal cortex (medial prefrontal and dorsolateral prefrontal cortex) based on human dipole mapping studies17,55 and direct intracranial recordings in primates.34 These regions show consistently reduced activation in fMRI studies of AX-CPT in schizophrenia,18,56 consistent with our present findings.
No significant correlation was observed with response accuracy (d' context) for either N2 or CNV, suggesting that impaired function of these regions may not be directly linked to the most prominent behavioral impairment observed on this task. Nevertheless, these findings suggest that prefrontal regions may contribute to 2 alternative AX-CPT–related processes indexed by the distinct N2 and CNV ERP components.
First, N2 was larger to B cues in the AX-70 task variant and to A cues in the BX-70 variant, and thus may index conflict between the local and global prepotency.17 In a recent fMRI study of AX-CPT using the AX-70 task version, reduced frontal activation was not related to overall accuracy but only to type of error (BX vs AX).56 Given the larger N2 response to the less frequent B vs A cues in the AX-70 task variant but to the less frequent A vs B cues in the BX-70 task variant, reduced frontal activation during AX-CPT in schizophrenia may thus reflect, at least in part, reduced conflict processing, as has been reported in other response inhibition tasks.57,58 The conflict monitoring role of N2 was underscored in this study by the fact that large N2 responses were also observed to AY sequences in the AX-70 paradigm. Notably, however, patients had no increase in AY false alarms, underscoring the separability of frontal activation vs performance.
Second, CNV amplitude was maintained throughout the cue-probe interval and varied across tasks with the conditional probability of a response, consistent with a role in maintenance of response bias across delay.59,60 The fact that CNV was significantly reduced in amplitude to cue stimuli in patients, but nevertheless showed a parallel time course in patients vs controls during the cue-probe interval, is consistent with the increasing suggestion over recent years that working memory deficits are associated with impairments in stimulus encoding11,19,27,48,61 rather than memory retention.46 Furthermore, the present study suggests that ERP, because of their high temporal resolution, may be more sensitive than fMRI to differential evaluation of encoding vs retention subcomponents during working memory tasks, and thus may offer complementary information.
Although the present study was not designed a priori to evaluate top-down modulations of sensory response, 3 apparent examples of top-down modulation were observed. First, in the BX-70 condition (vs other conditions), subjects showed larger sensory ERP responses to A vs B cues (Figure 5). Second, in the AY-70 task variant (vs other variants), subjects showed larger amplitude responses to X vs Y probes (Figure 6). Third, N1 was larger to all probe stimuli following A vs B cues across tasks. These context-related modulations permitted post hoc assessment of integrity of top-down control of sensory processing during this task in schizophrenia.
In the first 2 cases, the stimuli enhanced by these modulations were relatively infrequent and therefore highly informative regarding response selection. Modulation of P1 and N1 by expectation has been described previously.62 In the present study, no significant differences were observed in the degree of task-related modulation of either cue or probe responses between patients and controls, suggesting similar top-down effects across group.
In the case of probe N1 modulation by prior cue type, subjects must attend to the subsequent probe following valid cues to determine the correct response, whereas this is not true following invalid cues (ie, AX and AY sequences require different behavioral responses, whereas BX and BY sequences both require the same response). Thus, a greater degree of attention may be required following valid vs invalid cues and may result in increased N1 amplitude. The similar degree of N1 modulation as a function of prior cue in patients vs controls despite the overall reduction in N1 amplitude (Figure 6) therefore also suggests relatively intact top-down attentional control mechanisms despite impaired bottom-up sensory activation.
Overall models of cognitive dysfunction
Working memory deficits in schizophrenia were, at one point, considered to reflect impairments in retention of information across delay, consistent with local dysfunction within prefrontal brain regions. In recent years, however, behavioral studies have increasingly shown that performance deficits in AX-CPT and other working memory tasks reflect primarily a failure of encoding, with limited, if any, deficit in retention.11,27 This study supports and adds to this finding and highlights the role of sensory dysfunction as a basis for encoding dysfunction. Moreover, although top-down modulations of sensory processing were observed in this paradigm, they were not significantly different between patients and controls, supporting bottom-up models.
Within the auditory system, deficits in low-level auditory processing have increasingly been shown to contribute to deficits in higher-order processing such as emotion recognition63 and P3 generation.64 This study suggests that a similar relationship may also exist in the visual system in schizophrenia. Thus, while impairments in frontal function and top-down processing undoubtedly occur, sensory deficits and bottom-up influences on cognition in schizophrenia must also be considered.
Correspondence: Daniel C. Javitt, PhD, Center for Translational Schizophrenia Research, The Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Bldg 35, Orangeburg, NY 10962 (javitt@nki.rfmh.org).
Submitted for Publication: May 17, 2010; final revision received November 18, 2010; accepted January 3, 2011.
Published Online: March 7, 2011. doi:10.1001/archgenpsychiatry.2011.17
Financial Disclosure: None reported.
Funding/Support: This study was supported by grants R37MH49334 and P50MH086385 (Dr Javitt) and MH84848 (Dr Butler) from the National Institutes of Health.
Previous Presentations: This study was presented at the American College of Neuropsychopharmacology Annual Meeting; December 7, 2009; Hollywood, Florida; and 15th International Congress on Event-Related Potentials of the Brain (XV EPIC); April 25, 2009; Bloomington, Indiana.
Additional Contributions: The authors would like to thank Stephan Bickel, MD, PhD, for comments on the article and Jeanette Piesco for technical assistance.
1.Carter CS, Barch DM, Buchanan RW, Bullmore E, Krystal JH, Cohen J, Geyer M, Green M, Nuechterlein KH, Robbins T, Silverstein S, Smith EE, Strauss M, Wykes T, Heinssen R. Identifying cognitive mechanisms targeted for treatment development in schizophrenia: an overview of the first meeting of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia Initiative.
Biol Psychiatry. 2008;64(1):4-10
PubMedGoogle ScholarCrossref 2.Green MF. Cognitive impairment and functional outcome in schizophrenia and bipolar disorder.
J Clin Psychiatry. 2006;67(10):e12
PubMedGoogle ScholarCrossref 3.Butler PD, Zemon V, Schechter I, Saperstein AM, Hoptman MJ, Lim KO, Revheim N, Silipo G, Javitt DC. Early-stage visual processing and cortical amplification deficits in schizophrenia.
Arch Gen Psychiatry. 2005;62(5):495-504
PubMedGoogle ScholarCrossref 4.Martínez A, Hillyard SA, Dias EC, Hagler DJ Jr, Butler PD, Guilfoyle DN, Jalbrzikowski M, Silipo G, Javitt DC. Magnocellular pathway impairment in schizophrenia: evidence from functional magnetic resonance imaging.
J Neurosci. 2008;28(30):7492-7500
PubMedGoogle ScholarCrossref 5.Green MF, Lee J, Cohen MS, Engel SA, Korb AS, Nuechterlein KH, Wynn JK, Glahn DC. Functional neuroanatomy of visual masking deficits in schizophrenia.
Arch Gen Psychiatry. 2009;66(12):1295-1303
PubMedGoogle ScholarCrossref 6.Wible CG, Kubicki M, Yoo SS, Kacher DF, Salisbury DF, Anderson MC, Shenton ME, Hirayasu Y, Kikinis R, Jolesz FA, McCarley RW. A functional magnetic resonance imaging study of auditory mismatch in schizophrenia.
Am J Psychiatry. 2001;158(6):938-943
PubMedGoogle ScholarCrossref 7.Butler PD, Schechter I, Zemon V, Schwartz SG, Greenstein VC, Gordon J, Schroeder CE, Javitt DC. Dysfunction of early-stage visual processing in schizophrenia.
Am J Psychiatry. 2001;158(7):1126-1133
PubMedGoogle ScholarCrossref 8.Schechter I, Butler PD, Silipo G, Zemon V, Javitt DC. Magnocellular and parvocellular contributions to backward masking dysfunction in schizophrenia.
Schizophr Res. 2003;64(2-3):91-101
PubMedGoogle ScholarCrossref 9.Butler PD, Martinez A, Foxe JJ, Kim D, Zemon V, Silipo G, Mahoney J, Shpaner M, Jalbrzikowski M, Javitt DC. Subcortical visual dysfunction in schizophrenia drives secondary cortical impairments.
Brain. 2007;130(pt 2):417-430
PubMedGoogle ScholarCrossref 10.Javitt DC, Liederman E, Cienfuegos A, Shelley AM. Panmodal processing imprecision as a basis for dysfunction of transient memory storage systems in schizophrenia.
Schizophr Bull. 1999;25(4):763-775
PubMedGoogle ScholarCrossref 11.Javitt DC, Shelley AM, Silipo G, Lieberman JA. Deficits in auditory and visual context-dependent processing in schizophrenia: defining the pattern.
Arch Gen Psychiatry. 2000;57(12):1131-1137
PubMedGoogle ScholarCrossref 12.Butler PD, Abeles IY, Weiskopf NG, Tambini A, Jalbrzikowski M, Legatt ME, Zemon V, Loughead J, Gur RC, Javitt DC. Sensory contributions to impaired emotion processing in schizophrenia.
Schizophr Bull. 2009;35(6):1095-1107
PubMedGoogle ScholarCrossref 13.Haenschel C, Bittner RA, Haertling F, Rotarska-Jagiela A, Maurer K, Singer W, Linden DE. Contribution of impaired early-stage visual processing to working memory dysfunction in adolescents with schizophrenia: a study with event-related potentials and functional magnetic resonance imaging.
Arch Gen Psychiatry. 2007;64(11):1229-1240
PubMedGoogle ScholarCrossref 14.Beck LH, Bransome ED Jr, Mirsky AF, Rosvold HE, Sarason I. A continuous performance test of brain damage.
J Consult Psychol. 1956;20(5):343-350
PubMedGoogle ScholarCrossref 15.Servan-Schreiber D, Cohen JD, Steingard S. Schizophrenic deficits in the processing of context: a test of a theoretical model.
Arch Gen Psychiatry. 1996;53(12):1105-1112
PubMedGoogle ScholarCrossref 16.Cohen JD, Braver TS, O’Reilly RC. A computational approach to prefrontal cortex, cognitive control and schizophrenia: recent developments and current challenges.
Philos Trans R Soc Lond B Biol Sci. 1996;351(1346):1515-1527
PubMedGoogle ScholarCrossref 17.Dias EC, Foxe JJ, Javitt DC. Changing plans: a high density electrical mapping study of cortical control.
Cereb Cortex. 2003;13(7):701-715
PubMedGoogle ScholarCrossref 18.Barch DM, Carter CS, Braver TS, Sabb FW, MacDonald A III, Noll DC, Cohen JD. Selective deficits in prefrontal cortex function in medication-naive patients with schizophrenia.
Arch Gen Psychiatry. 2001;58(3):280-288
PubMedGoogle ScholarCrossref 19.Javitt DC, Rabinowicz E, Silipo G, Dias EC. Encoding vs retention: differential effects of cue manipulation on working memory performance in schizophrenia.
Schizophr Res. 2007;91(1-3):159-168
PubMedGoogle ScholarCrossref 20.Umbricht D, Schmid L, Koller R, Vollenweider FX, Hell D, Javitt DC. Ketamine-induced deficits in auditory and visual context-dependent processing in healthy volunteers: implications for models of cognitive deficits in schizophrenia.
Arch Gen Psychiatry. 2000;57(12):1139-1147
PubMedGoogle ScholarCrossref 21.Snitz BE, Macdonald AW III, Carter CS. Cognitive deficits in unaffected first-degree relatives of schizophrenia patients: a meta-analytic review of putative endophenotypes.
Schizophr Bull. 2006;32(1):179-194
PubMedGoogle ScholarCrossref 22.MacDonald AW III. Building a clinically relevant cognitive task: case study of the AX paradigm.
Schizophr Bull. 2008;34(4):619-628
PubMedGoogle ScholarCrossref 23.MacDonald AW III, Thermenos HW, Barch DM, Seidman LJ. Imaging genetic liability to schizophrenia: systematic review of FMRI studies of patients' nonpsychotic relatives.
Schizophr Bull. 2009;35(6):1142-1162
PubMedGoogle ScholarCrossref 24.Cohen JD, Servan-Schreiber D. Context, cortex, and dopamine: a connectionist approach to behavior and biology in schizophrenia.
Psychol Rev. 1992;99(1):45-77
PubMedGoogle ScholarCrossref 25.Braver TS, Barch DM, Cohen JD. Cognition and control in schizophrenia: a computational model of dopamine and prefrontal function.
Biol Psychiatry. 1999;46(3):312-328
PubMedGoogle ScholarCrossref 26.MacDonald AW III, Carter CS. Event-related FMRI study of context processing in dorsolateral prefrontal cortex of patients with schizophrenia.
J Abnorm Psychol. 2003;112(4):689-697
PubMedGoogle ScholarCrossref 28.Gold JM, Hahn B, Zhang WW, Robinson BM, Kappenman ES, Beck VM, Luck SJ. Reduced capacity but spared precision and maintenance of working memory representations in schizophrenia.
Arch Gen Psychiatry. 2010;67(6):570-577
PubMedGoogle ScholarCrossref 30.Javitt DC. Glutamate and schizophrenia: phencyclidine,
N-methyl-D-aspartate receptors, and dopamine-glutamate interactions.
Int Rev Neurobiol. 2007;78:69-108
PubMedGoogle Scholar 31.Lewis DA, Moghaddam B. Cognitive dysfunction in schizophrenia: convergence of gamma-aminobutyric acid and glutamate alterations.
Arch Neurol. 2006;63(10):1372-1376
PubMedGoogle ScholarCrossref 32.Bar M, Kassam KS, Ghuman AS, Boshyan J, Schmid AM, Dale AM, Hämäläinen MS, Marinkovic K, Schacter DL, Rosen BR, Halgren E. Top-down facilitation of visual recognition [printed correction appears in
Proc Natl Acad Sci U S A. 2006;103(8):3007].
Proc Natl Acad Sci U S A. 2006;103(2):449-454
PubMedGoogle ScholarCrossref 33.Sehatpour P, Dias EC, Butler PD, Revheim N, Guilfoyle DN, Foxe JJ, Javitt DC. Impaired visual object processing across an occipital-frontal-hippocampal brain network in schizophrenia: an integrated neuroimaging study.
Arch Gen Psychiatry. 2010;67(8):772-782
PubMedGoogle ScholarCrossref 34.Dias EC, McGinnis T, Smiley JF, Foxe JJ, Schroeder CE, Javitt DC. Changing plans: neural correlates of executive control in monkey and human frontal cortex.
Exp Brain Res. 2006;174(2):279-291
PubMedGoogle ScholarCrossref 35.Di Russo F, Martínez A, Sereno MI, Pitzalis S, Hillyard SA. Cortical sources of the early components of the visual evoked potential.
Hum Brain Mapp. 2002;15(2):95-111
PubMedGoogle ScholarCrossref 36.Minzenberg MJ, Laird AR, Thelen S, Carter CS, Glahn DC. Meta-analysis of 41 functional neuroimaging studies of executive function in schizophrenia.
Arch Gen Psychiatry. 2009;66(8):811-822
PubMedGoogle ScholarCrossref 37.Gibbon M, Spitzer RL, Williams JBW, Benjamin LS, First MB. Structured Clinical Interview for DSM-IV Axis II Personality Disorders (SCID-II). Washington, DC: American Psychiatric Press, Inc; 1997
40.Loeb P. ILS: Independent Living Scales Manual. San Antonio, TX: The Psychological Corporation, Harcourt Race Jonanovich, Inc; 1996
41.Ammons R, Ammons C. The Quick Test (QT): provisional manual.
Psychol Rep. 1962;11:111-162
Google Scholar 42.Andreasen NC. The Scale for the Assessment of Negative Symptoms (SANS). Iowa City, IA: University of Iowa; 1984
43.Hollingshead AB. Four Factor Index of Social Status. New Haven, CT: Yale University Department of Sociology; 1975
44.Butler PD, Hoptman MJ, Nierenberg J, Foxe JJ, Javitt DC, Lim KO. Visual white matter integrity in schizophrenia.
Am J Psychiatry. 2006;163(11):2011-2013
PubMedGoogle ScholarCrossref 45.Swets JA, Sewall ST. Invariance of signal detectability over stages of practice and levels of motivation.
J Exp Psychol. 1963;66(2):120-126
ArticleCrossref Google Scholar 46.Barch DM, Carter CS, MacDonald AW III, Braver TS, Cohen JD. Context-processing deficits in schizophrenia: diagnostic specificity, 4-week course, and relationships to clinical symptoms.
J Abnorm Psychol. 2003;112(1):132-143
PubMedGoogle ScholarCrossref 47.Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates; 1988
48.Perlstein WM, Dixit NK, Carter CS, Noll DC, Cohen JD. Prefrontal cortex dysfunction mediates deficits in working memory and prepotent responding in schizophrenia.
Biol Psychiatry. 2003;53(1):25-38
PubMedGoogle ScholarCrossref 49.Kiehl KA, Smith AM, Hare RD, Liddle PF. An event-related potential investigation of response inhibition in schizophrenia and psychopathy.
Biol Psychiatry. 2000;48(3):210-221
PubMedGoogle ScholarCrossref 50.Yeap S, Kelly SP, Sehatpour P, Magno E, Javitt DC, Garavan H, Thakore JH, Foxe JJ. Early visual sensory deficits as endophenotypes for schizophrenia: high-density electrical mapping in clinically unaffected first-degree relatives.
Arch Gen Psychiatry. 2006;63(11):1180-1188
PubMedGoogle ScholarCrossref 51.Abel KM, Allin MP, Kucharska-Pietura K, Andrew C, Williams S, David AS, Phillips ML. Ketamine and fMRI BOLD signal: distinguishing between effects mediated by change in blood flow versus change in cognitive state.
Hum Brain Mapp. 2003;18(2):135-145
PubMedGoogle ScholarCrossref 53.Majaj NJ, Pelli DG, Kurshan P, Palomares M. The role of spatial frequency channels in letter identification.
Vision Res. 2002;42(9):1165-1184
PubMedGoogle ScholarCrossref 55.Bekker EM, Kenemans JL, Verbaten MN. Source analysis of the N2 in a cued Go/NoGo task.
Brain Res Cogn Brain Res. 2005;22(2):221-231
PubMedGoogle ScholarCrossref 56.Yoon JH, Minzenberg MJ, Ursu S, Ryan Walter BS, Wendelken C, Ragland JD, Carter CS. Association of dorsolateral prefrontal cortex dysfunction with disrupted coordinated brain activity in schizophrenia: relationship with impaired cognition, behavioral disorganization, and global function [printed correction appears in
Am J Psychiatry. 2008;165(10):1359].
Am J Psychiatry. 2008;165(8):1006-1014
PubMedGoogle ScholarCrossref 57.Laurens KR, Ngan ET, Bates AT, Kiehl KA, Liddle PF. Rostral anterior cingulate cortex dysfunction during error processing in schizophrenia.
Brain. 2003;126(pt 3):610-622
PubMedGoogle ScholarCrossref 58.Mathalon DH, Jorgensen KW, Roach BJ, Ford JM. Error detection failures in schizophrenia: ERPs and FMRI.
Int J Psychophysiol. 2009;73(2):109-117
PubMedGoogle ScholarCrossref 59.Walter WG, Cooper R, Aldridge VJ, McCallum WC, Winter AL. Contingent negative variation: an electrical sign of sensorimotor association and expectancy in the human brain.
Nature. 1964;203:380-384
PubMedGoogle ScholarCrossref 60.Praamstra P. Electrophysiological markers of foreperiod effects. In: Nobre AC, Coull JT, eds. Attention and Time. Oxford, England: Oxford University Press; 2010:331-344
61.Cohen JD, Barch DM, Carter C, Servan-Schreiber D. Context-processing deficits in schizophrenia: converging evidence from three theoretically motivated cognitive tasks.
J Abnorm Psychol. 1999;108(1):120-133
PubMedGoogle ScholarCrossref 62.Scerif G, Worden MS, Davidson M, Seiger L, Casey BJ. Context modulates early stimulus processing when resolving stimulus-response conflict.
J Cogn Neurosci. 2006;18(5):781-792
PubMedGoogle ScholarCrossref 63.Leitman DI, Hoptman MJ, Foxe JJ, Saccente E, Wylie GR, Nierenberg J, Jalbrzikowski M, Lim KO, Javitt DC. The neural substrates of impaired prosodic detection in schizophrenia and its sensorial antecedents.
Am J Psychiatry. 2007;164(3):474-482
PubMedGoogle ScholarCrossref 64.Leitman DI, Sehatpour P, Higgins BA, Foxe JJ, Silipo G, Javitt DC. Sensory deficits and distributed hierarchical dysfunction in schizophrenia.
Am J Psychiatry. 2010;167(7):818-827
PubMedGoogle ScholarCrossref