Early Visual Processing Deficits in
Dysbindin-Associated Schizophrenia
Gary Donohoe, Derek W. Morris, Pierfilippo De Sanctis, Elena Magno, Jennifer L. Montesi,
Hugh P. Garavan, Ian H. Robertson, Daniel C. Javitt, Michael Gill, Aiden P. Corvin, and John J. Foxe
Background: Variation at the dysbindin gene (DTNBP1) has been associated with increased risk for schizophrenia in numerous independent samples and recently with deficits in general and domain-specific cognitive processing. The relationship between dysbindin risk
variants and sensory-level deficits in schizophrenia remains to be explored. We investigated P1 performance, a component of early visual
processing on which both patients and their relatives show deficits, in carriers and noncarriers of a known dysbindin risk haplotype.
Methods: Event-related potential responses to simple visual isolated-check stimuli were measured using high-density electrical scalp
recordings in 26 individuals meeting DSM-IV criteria for schizophrenia, comprising 14 patients who were carriers of the dysbindin risk
haplotype and 12 patients who were nonrisk haplotype carriers.
Results: Carriers of the dysbindin risk haplotype demonstrated significantly reduced P1 amplitudes compared with noncarriers. A large
effect size of d .89 was calculated for the difference in P1 amplitude over scalp sites where the deficit was maximal.
Conclusions: The P1 deficits associated with a dysbindin risk haplotype previously identified in our sample presents functional confirmation ofi ts deleterious effect on brain activity. Building on evidence of dysbindin’s role in higher cognitive function, these early visual
processing deficits suggest a generalized role for dysbindin in brain function and is likely to be part of the mechanism by which illness
susceptibility is mediated.
Key Words: Dysbindin, EEG, ERP, P1, schizophrenia, sensory
processing
S
chizophrenia is associated with a generalized pattern of
cognitive dysfunction of unknown etiology, involving sensory and cognitive processes. Susceptibility genes may increase risk for the disorder through deleterious effects on these
processes. Genetic association and functional studies support involvement of dysbindin (DTNBP1; OMIM 607145) in schizophrenia
susceptibility ( 1). Although functional mutations have yet to be
identified, dysbindin risk haplotypes influence cortical expression
of the gene (2– 4). Understanding of the neurobiological role of
dysbindin is limited, but functional studies indicate involvement in
presynaptic glutamate release (3–5). This suggests that risk variants
may have a synaptic consequence in terms of glutamate availability.
Recently, dysbindin risk variants have been associated with poorer
performance in higher cognitive domains in controls ( 6 –7) and with
both general and domain-specific deficits (e.g., spatial working
memory dysfunction) in schizophrenia ( 6,8).
The relationship between dysbindin risk variation and sensorylevel deficits in schizophrenia has yet to be explored. This is
warranted because dysbindin is expressed throughout the brain,
including sensory regions such as the occipital cortex. Early visual
From the Neuropsychiatric Genetics Group (GD, DWM, IHR, MG, APC), Institute of Molecular Medicine, Trinity College Dublin, St. James Hospital,
Dublin; The Cognitive Neurophysiology Laboratory (GD, PDS, EM, JLM,
HPG, JJF), St. Vincent’s Hospital, Fairview; and Trinity College Institute of
Neuroscience (GD, EM, HPG, IHR, MG, JJF), School of Psychology, Trinity
College, Dublin, Ireland; The Cognitive Neurophysiology Laboratory
(PDS, JLM, DCJ, JJF), Nathan S. Kline Institute for Psychiatric Research
Program in Cognitive Neuroscience and Schizophrenia, Orangeburg,
and Program in Cognitive Neuroscience (DCJ, JJF), Department of Psychology, City College of the City University of New York, New York.
Address reprint requests to Gary Donohoe, D. Clin. Psych., Neuropsychiatric
Genetics Group, Institute of Molecular Medicine, Trinity College Dublin,
St. James Hospital, Dublin 8, Ireland; E-mail: donoghug@tcd.ie.
Received March 30, 2007; revised July 19, 2007; accepted July 19, 2007.
processing deficits have repeatedly been demonstrated in schizophrenia, particularly in the magnocellular system, which is glutamate/NMDA-dependent ( 9). Because dysbindin is implicated in
glutamate/NMDA transmission, early visual processing may be a
useful brain function in which to investigate dysbindin’s impact.
The P1 visual evoked potential (VEP), which occurs between
75 and 110 msec poststimulus and is recorded over both midline
and lateral occipital scalp electrodes, is an especially promising
component for investigation in this regard. In a series of studies
(10 –15), deficits in the P1 component have been repeatedly
observed in patients with schizophrenia. This effect is strong, with
P1 amplitude in patients less than half the strength of that seen in
control subjects. Furthermore, a recent study by Yeap et al. (16)
found evidence that non–clinically affected first-degree relatives of
patients with schizophrenia also show evidence of reduced P1
amplitudes, establishing a genetic contribution to this deficit as a
potential endophenotype for the disorder. Additional strengths of
the P1 component as an endophenotype is that it is straightforward
to measure and, as a largely automatic response, is not susceptible
to the same motivational issues that later cognitive components
such as those to which the P300 are prone (17–18).
We investigated the relationship between P1 performance
and a three-marker dysbindin haplotype previously associated
with increased risk for schizophrenia in two independent samples, including the sample reported here ( 19). Variants at dysbindin associated with schizophrenia risk have been inconsistent
across studies (20); the C-A-T risk haplotype in our sample is
associated with reduced cortical expression of the gene, poorer
educational attainment, and poorer spatial working memory ( 2,8).
Given these previous associations, we hypothesized that carriers of
the risk haplotype would have a significantly attenuated P1 response compared with patients who were nonrisk carriers.
Methods and Materials
Subjects
Informed consent was obtained from 26 individuals who met
DSM-IV criteria for schizophrenia (confirmed using the Struc-
tured Clinical Interview for DSM [SCID]; [ 21]). These patients
formed part of our larger genetics of psychosis sample, all of
whom were of Irish origin (Irish parents and grandparents) and
comprised 14 risk haplotype carriers and 12 nonrisk haplotype
carriers. Criteria for inclusion in the study were that patients were
in the view of their treatment team were clinically stable and 1)
aged between 18 and 60 years, 2) had no history of substance
abuse in the preceding 6 months, 3) had no other psychiatric
diagnosis, 4) had no history of head injury with loss of consciousness, and 5) had no history of epilepsy or seizures. Because
patients had already been genotyped for dysbindin, patients
were re-recruited from the overall genetics sample on the basis of
their risk haplotype status to ensure comparable numbers of risk
and nonrisk carriers in the study.
44.92, SD
Eleven of the risk haplotype carriers (mean age
9.7), and 9 of the 12 nonrisk haplotype carriers (mean age 44.43,
10.0) were right-handed as assessed by the Edinburgh
SD
Handedness Inventory ( 22). All subjects reported normal or
corrected-to-normal vision. No sex differences were apparent
between groups; 11 (81%) risk haplotype carriers were men
.56; p
.45).
versus 9 (75%) nonrisk haplotype carriers; 2
Duration ofi llness did not differ between groups (mean years for
carriers was 25.1 [SD 10.8], and mean years for noncarriers was
22.8 [SD 10.0]). When compared on the Positive and Negative
Symptoms Severity Scale, no difference in symptom severity was
observed between groups (mean positive symptoms scale score
9.5] versus 24.8 [SD
9 5] for
for carriers was 23.9 [SD
.67; p .5; mean negative symptoms scale
noncarriers, t(24)
8.5] versus 17.8 [SD 8.0] for
scores for carriers was 18.4 [SD
.56, p .50). All patients were on psychononcarriers, t(24)
tropic medication at the time of testing. Although dosage information was not available, there were no differences between
groups in either type of antipsychotic prescribed (eight carriers
[57%] were prescribed atypical antipsychotics vs. six [52%] non.31; p
.58), number prescribed clozapine
carriers; 2
specifically (two carriers [14 3%] vs. two [6.6%] noncarriers; 2
.31; p .69), or number of patients prescribed anticholinergics
(two carriers [15%] vs. 1 [10%] noncarrier; 2 .70; p .40). The
lack of difference between carriers and noncarriers on each of
variable (age, sex, duration ofi llness, symptom severity scores,
and medication prescribed) accorded with the lack of differences
observed in our total genetic sample for these variables ( n 372;
data available on request).
Stimuli and Presentation
In each experimental block, subjects were presented with
approximately 100 isolated-check images. Each image subtended
4° 4° of visual angle in total and consisted of an 8 8 array of
.32
isolated checks with each individual check subtending .32
degrees of visual angle, colored gray on a white background
(i.e., the luminance of the check was below that of the background, producing negative contrast) at 64% contrast. These
were randomly interspersed with 40 line drawings of two kinds
1.8° high) on a white background. The
of animal (2.4° wide
64% contrast condition was chosen to stimulate both the magnocellular (M) and parvocellular (P) systems. A different animal
pair was chosen for each block, from a possible 22 total pairs.
The isolated-check stimuli and one pair of animal stimuli are
shown in Figure 1. On average, subjects completed 14.1 (range
12–15) blocks, each lasting 3 min. Stimuli were presented
centrally on a cathode ray tube computer monitor in random
order, with the monitor located 160 cm directly in front of the
seated subject.
Figure 1. The centrally presented visual stimuli used in the task. Evenrelated potential waveforms were derived for the isolated check nontarget
stimulus (a), whereas target discrimination was performed on the basis of
infrequently presented animal line drawings (b and c).
The timing of the presentations was such that each image
appeared for 60 msec with a variable interstimulus interval (ISI)
between 740 and 1540 msec (randomly in steps of 200 msec)
during which there was a blank white screen. The target animal
was displayed at the start of the task, and subjects were asked to
respond each time this animal was presented by pressing a
button with their right thumb. They were told only to respond to
target animals and to try to withhold responses to any other
animal presented. The target and nontarget animals were presented with equal probability, ensuring that a subject could not
rely on the exogenous alerting nature of any noncheckerboard
stimulus to respond. Furthermore, the task of discrimination was
made difficult by pairing similar-looking animals (e.g., dolphin
and whale). The use of this task ensured that subjects were
actively observing the stimuli. Only ERPs to the standard checkerboard stimuli were analyzed here.
Electrophysiological Data Acquisition and Statistical Analysis
Continuous electroencephalogram (EEG) was acquired
through the ActiveTwo BioSemi electrode system (BioSemi,
Amsterdam, the Netherlands) from 72 scalp electrodes, digitized at 512 Hz with an open passband from DC to 150 Hz.
(For a detailed description of the referencing and grounding
conventions used by the BioSemi active electrode system,
refer to http://www.biosemi.com/faq/cms&drl.htm .) For analysis and display purposes, data were subsequently filtered
with a 0 phase-shift 45-Hz low-pass filter (24 dB/octave) after
acquisition. No high-pass filter was applied. All data were
re-referenced to a nasion electrode after acquisition, for
analysis.
Data were analyzed using BESA Version 5.08 (Brain Electrical
Source Analysis) software ( http://www.besa.de ; MEGIS Software
GmbH, Grafelfing, Germany). All electrode channels were subjected to an artifact criterion of 120 V from 200 to 400 msec
to reject trials with excessive electromyogram or other noise
transients. The vertical and horizontal electrooculograms were
also visually inspected for blinks and large eye movements, and
affected epochs were then manually excluded. Epochs were
calculated for a time-window from 200-msec prestimulus to
400-msec poststimulus, and baseline-corrected relative to the
interval 200 to 0 msec. Accepted trials were then averaged only
for the isolated-check stimuli. Only one subject had less than 700
sweeps, but inspection of his data showed near zero noise levels,
and thus it was deemed reasonable to include his data. The
average epoch acceptance rate for the risk group was 62.7
12.2% (mean 1055 7; SD 200.6; range 802–1457), and for the
16.2% (mean 922; SD 24.1; range 418 –
nonrisk group 61.6
1347).
Our primary analysis was motivated by a specific hypothesis
on the basis of previous research by our group regarding
reduction in early visual sensory processing (i.e., the P1 processing period) in healthy first-degree relatives, relative to healthy
control subjects. A measure of P1 amplitude was defined as the
area under the curve (vs. the 0- V baseline) spanning the P1
component, found in the 80- to 95-msec interval, with a peak
between 91 and 92 msec (Figure 2A). These area measures were
then submitted to a repeated-measures analysis of variance
(ANOVA) using SPSS software (SPSS Inc., Chicago, Illinois) with
between-subjects factor of group (dysbindin risk carriers versus
noncarriers) and within-subjects factor of scalp region (left
parietooccipital/right parietooccipital). P1 responses were averaged from six symmetrical pairs of scalp sites over parietooccipital scalp sites (P1/P3/P5/P7/PO7/PO3 for the left and P2/P4/P6/
P8/PO8/PO4 for the right). These electrodes were selected on
the basis of previous evidence that they are positioned at the
centre of the topographic distributions of the P1 component
(11,16). All tests were two-tailed with a preset alpha level of p
.05. In all statistical tests, Greenhouse-Geisser Correction was
used.
Genotyping
The dysbindin “risk” haplotype identified by Williams et al.
(19) is derived from alleles C-A-T at single nucleotide polymorphisms (SNPs) rs2619539, rs3213207, and rs2619538. All SNPs
were genotyped using Custom Taqman SNP Genotyping Assays
from Applied Biosystems (Nattick, Massachusetts) on an ABI
Prism 7900HT Sequence Detection System (Applied Biosystems).
Duplicate genotyping in a subset of this sample indicated a
genotyping error level of 1%. To compare dysbindin at-risk
haplotype carriers (estimated frequency of 0.21 in cases in the
overall sample) with noncarriers, assignment of haplotype carrier
status to individual cases was performed using the Bayesian
approach to reconstructing haplotypes implemented in the programme PHASE (v2. 0.2) ( 23). A case was only included in the
study if the identity ofi ts two haplotypes could be determined at
p .95. Only 5% of patients in our sample were found to carry
two copies of the C-A-T risk haplotype, and these individuals did
not differ demographically or clinically from those carrying one
copy of the haplotype. Therefore all analysis was carried out as
a direct comparison between carriers and noncarriers of the risk
haplotype. Individual SNPs (or alternative haplotypes) were not
analyzed separately in relation to ERP data to minimize the issue
of multiple testing.
Results
The mean target hit rate (to target animals) for nonrisk carriers
56.9), and for risk
was .854 (reaction time: 526.3 msec; SD
81.9). The mean
carriers was .882 (reaction time: 543.9; SD
.488, p
rates did not differ significantly between groups [t(24)
.63]. The mean correct rejection rate (to nontarget animals) for
nonrisk subjects was .953 and .965 for risk subjects. The mean
.810, p
rates did not differ significantly between groups [t(24)
.426]. These rates indicate that subjects were actively engaged
in this task and must have been centrally fixating to accurately
identify target stimuli.
Figure 2. (A) Even-related potential morphology across the scalp for both
groups illustrating responses from six representative electrodes spanning
frontal, parietal, and occipital scalp regions. (B) P1 morphology, over a
shorter timebase, for two additional parietooccipital scalp sites where the
P1 difference was observed. Non-risk: non-carriers of the dysbindin risk
haplotype; Risk: carriers of the dysbindin risk haplotype.
Figure 2A provides an overall illustration of ERP morphology
across the scalp for both groups, showing responses from six
representative electrodes spanning frontal, parietal, and occipital
scalp regions. Group differences are clearly evident, particularly
Figure 3. Scatterplot of P1 amplitudes (measured at its maximal point) for
individual patient on the basis of multielectrode mean values from each
electrode used in statistical analyses.
in posterior regions. Figure 2B shows enlarged versions, on a
shorter timebase, for two additional parietooccipital scalp sites
where the P1 difference was observed. The distribution of P1
amplitudes for both groups at electrode site P6, the site at which
the P1 difference was found to be largest, is shown in the form
of a scatter plot in Figure 3. The mean P1 amplitudes were 3.54
.966) and 5.85 (SD
2.13) for risk haplotype carriers and
(SD
noncarriers, respectively.
2 ANOVA (2 groups
2 scalp regions) was used to
A2
compare P1 amplitudes between groups over the left and right
lateral occipital visual areas. We observed a significant main
group effect, the P1 being of significantly smaller amplitude in
the risk group [F (1,24) 7.22, p .013]. A significant main effect
5.40, p .029], indicating
of region was also present [F (1,24)
that the P1 was generally larger over the right than the left
hemiscalp. There was no interaction of the factors Group and
256, p .618]. An effect size of d .89 was
Region [F (1,24)
calculated for the difference in P1 amplitude measured between
groups over the interval 80 –95 msec, which constitutes a large
effect size according to the criterion of Cohen. Figure 4 shows
mapping of the difference topography associated with dysbindin.
Following the observation of robust P1 differences between
dysbindin carriers and noncarriers, differences between groups
during the N1 component were also analyzed. The Visual N1
component was seen to peak at 120 msec for both groups, and
it had a distinct midline inferior occipital topography. It was
clearly maximal over electrodes O1, OZ, O2, and IZ. To test for
significant group differences, an integrated amplitude measure
was derived for the 120 –130 msec time window spanning the
peak at these four electrode sites. These values were then
averaged and submitted to a simple two-tailed t test. No group
.18, p .85).
difference in N1 amplitude was found [ t(2,24)
that patients with schizophrenia who carried the dysbindin risk
haplotype showed significantly greater P1 deficits than patients
with schizophrenia who were noncarriers. The observed decrement in P1 generation was found in the absence of differences
between groups on age, gender, duration ofi llness, symptoms
severity, or medication effects. The observed difference between
risk carriers and noncarriers is not subtle. In scalp sites where the
effect was largest, risk haplotype carriers showed maximal P1
amplitudes that were little more than half those seen in patients
who were noncarriers, despite both groups having similar illness
duration. The parietooccipital location of this deficit accords
closely with our previous studies in chronic patients ( 10,11,14).
How risk variants at dysbindin affect gene function is uncertain, but the risk haplotype investigated in this study is of
particular interest because it is associated with reduced cortical
expression of dysbindin ( 2). Variants in dysbindin that are
associated with schizophrenia risk have been identified as associated with poorer general cognitive performance in controls ( 6)
and with both general and specific cognitive deficits in patients
with schizophrenia ( 6,8), although the variants identified have
been inconsistent across groups ( 20). Our study extends these
previous reports by indicating that variation at the gene may play
a significant role in early visual processing as well as higher
cognitive processing. The influence of dysbindin on multiple
cognitive and sensory processes both in patients and control
subjects suggests a more general role for dysbindin in brain
function, rather than a regionally or cognitively specific role. As
with most brain-expressed genes, dysbindin is widely expressed
and lacks regional specificity ( http://www.brain-map.org ) ( 2– 4),
and we view this as consistent with a global involvement in
presynaptic glutamate availability. This view of dysbindin’s more
general impact on brain behavior is further supported by recent
evidence ofi ts association with increased negative symptom
severity ( 24 –25), symptoms that co-occur with, but are only
partly explained by, cognitive deficits.
As an endophenotype measure, the P1 has the major advantage of being relatively easy to measure quickly and accurately.
As a largely automatic response, it is not as susceptible to the
same motivational factors or fluctuations in clinical state as later
cognitive components such as the P300. However, this is not to
say that the P1 is not cognitively penetrable. Although early
stages of perceptual processing (from as early as 50 –100 msec
poststimulus) serve an important role in “spotlighting” of relevant
Discussion
Impaired P1 generation has repeatedly been demonstrated in
patients with schizophrenia ( 10 –15) and recently in their firstdegree relatives, supporting a genetic contribution to this deficit
(16). We investigated the relationship between early visual
processing deficits as indexed by the P1 and a dysbindin
haplotype associated with increased schizophrenia risk and
poorer higher cognitive functioning in our sample. We observed
Figure 4. Mapping of the difference topography associated with dysbindin.
Subtraction of the grand-averaged waveforms of the risk group from those
of the nonrisk group enables the difference effect to be isolated and
mapped. The P1 decrement has a bilateral lateral occipital distribution, not
dissimilar to that of the base maps of the P1.
information for later processing, these early processing stages
(from 70 msec onward) appear to be reciprocally modulated by
higher processing areas ( 26). It is therefore interesting that in our
sample, a twin association between a dysbindin risk haplotype
and both sensory and higher cognitive processes is evidenced
(previously) by its association with poorer visuospatial working
memory ( 8) and in this study by its association with early visual
processing deficits. These associations may reflect the reciprocal
relationship between early sensory and higher cognitive function, particularly for visual information. On one hand, deficits in
“capturing” visual information are likely to increase difficulties in
efficiently maintaining and updating that information “online”
during spatial working memory tasks. Conversely, an inability to
maintain context during later stages of context processing leads
to difficulties focusing on relevant information during earlier
stages of visual processing. Although the purpose of our study
was not to tease apart the relationship between P1 deficits and
attentional control, the notion that the P1 deficits we observed
are a result of spatial attention deficits is highly unlikely. The
equivalent performance of both groups in target detection rates
suggests that the groups did not differ in fixation on and attention
to the centrally presented stimuli (although it cannot be inferred
that the groups were of equivalent cognitive ability, given the
simplicity of the task used). Furthermore, the eliciting of the P1
by centrally presenting stimuli is not modulated by directing
attention to or from that location (unlike peripheral stimuli),
again making this effect unlikely to result from deployment of
visual spatial attention (27). In either case, this study evidences a
shared genetic component in both processes wherein the deleterious effects of dysbindin are seen in the same sample at both
sensory and higher cognitive levels of processing.
The effects of dysbindin on early sensory processing in this
relatively small sample will require replication in independent
case samples to establish its true effect size, particularly for the
other identified dysbindin risk variants ( 20). Such replication is
important to establish the predictive value of dysbindin in
delineating a schizophrenia subgroup with pronounced sensorylevel visual processing deficits. In turn, this predictive ability
would contribute to establishing the utility of P1 deficits as a
potential biomarker for investigating dysbindin and related
schizophrenia susceptibility genes. Follow-up study of these
effects in both relatives and unaffected control subjects genotyped
for this risk haplotype will determine whether this effect interacts
with disease risk (genotype information was not available to investigate this in relatives and control subjects who participated in our
previous studies). Given the difference in associated alleles and
haplotypes associated schizophrenia across studies, whether other
dysbindin variants associated with schizophrenia are associated
with sensory processing also merits investigation. Finally, using a
larger sample of patients and control subjects the investigation of
dysbindin’s association with early visual deficits could be extended
to investigate whether these deficits correlate with the behavioral
deficits already described in this group but that we did not have
power to investigate in our EEG sample.
Conclusions
Early visual processing occurs via a glutamate dependent
brain system (28,29). Both visual processing deficits and glutamate system abnormalities are increasingly implicated in schizophrenia. A role for presynaptic dysbindin in this transmitter
system has already been highlighted (3,5). The reduced P1 VEP
associated with the dysbindin risk haplotype previously identi-
fied in our sample presents functional confirmation ofi ts deleterious effect on brain activity, making it likely to be part of the
neurobiology of schizophrenia.
Financial support for this study was provided by grants from
the National Institute of Mental Health (NIMH) in the United
States (Grant Nos. MH65350 to JJF and MH49334 to DCJ), the
Science Foundation Ireland (MG and AC), and Higher Education Authority Ireland (MG and AC).
None of the authors have conflicts ofi nterests to declare in
relation to this study.
We thank Dr. Sherlyn Yeap for help facilitating patient
recordings at the Cognitive Neurophysiology Laboratory at St.
Vincent’s Hospital in Fairview, Dublin.
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