No evidence for structural brain changes in young adolescents at ultra high risk for psychosis.

Abstract

The onset of psychosis is thought to be preceded by neurodevelopmental changes in the brain. However, the timing of these changes has not been established. We investigated structural brain changes in a sample of young adolescents (12-18 years) at ultra high-risk for psychosis (UHR).
Structural MRI data from young UHR subjects (n=54) and typically developing, matched controls (n=54) were acquired with a 1.5 Tesla scanner and compared.
None of the measures differed between UHR subjects and controls.
Our results do not support the presence of gross neuroanatomical changes in young UHR subjects. This suggests that early changes are too subtle to detect with conventional imaging techniques. Therefore, changes observed in older cohorts may only onset later developmentally or occur secondary to prodromal symptoms.

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No evidence for structural brain changes in young adolescents at ultra high
risk for psychosis
Tim B. Ziermansa,⁎, Sarah Durstona, Mirjam Spronga, Hilde Nederveena, Neeltje E.M. van Harenb,
Hugo G. Schnackb, Bertine E. Lahuisa, Patricia F. Schothorsta, Herman van Engelanda
aDepartment of Child and Adolescent Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, the Netherlands
bDepartment of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, the Netherlands
a r t i c l e i n f o a b s t r a c t
Article history:
Received 14 August 2008
Received in revised form 7 April 2009
Accepted 12 April 2009
Available online 5 May 2009
Objective: The onset of psychosis is thought to be preceded by neurodevelopmental changes in
the brain. However, the timing of these changes has not been established. We investigated
structural brain changes in a sample of young adolescents (12–18 years) at ultra high-risk for
psychosis (UHR).
Methods: Structural MRI data from young UHR subjects (n=54) and typically developing,
matched controls (n=54) were acquired with a 1.5 Tesla scanner and compared.
Results: None of the measures differed between UHR subjects and controls.
Conclusions: Our results do not support the presence of gross neuroanatomical changes in
young UHR subjects. This suggests that early changes are too subtle to detect with conventional
imaging techniques. Therefore, changes observed in older cohorts may only onset later
developmentally or occur secondary to prodromal symptoms.
© 2009 Elsevier B.V. All rights reserved.
Keywords:
Psychosis
Ultra high risk
Adolescence
Structural MRI
Voxelbased morphometry
Neurodevelopment
1. Introduction
A growing body of evidence suggests early neurodevelop-
mental brain changes preceding psychosis that are thought to
progress into adolescence and adulthood (Rapoport et al.,
2005). Recently, neuroimaging studies have focused on
genetic and clinical high-risk cohorts to define the nature of
these changes and to identify which of these may mark
vulnerabilityforpsychosis(Cannon,2005).Subjectsofclinical
high-risk cohorts are commonly referred to as being at “ultra
high-risk” (UHR), at “prodromal high-risk” or having an “at
risk mental state” (ARMS) for psychosis. Several research
groups have reported premorbid structural and functional
brain changes in these cohorts. However, the timing of these
changes is not established (for reviews see Pantelis et al.,
2005; Wood et al., 2008): It is unclear whether they are truly
premorbid or rather associated with prodromal symptoms.
A large volumetric MRI study in a UHR population aged
20 years reported smaller whole brain volume for subjects at
UHR for psychosis compared to controls (Velakoulis et al.,
2006), while several voxelbased morphometry (VBM) studies
have shown changes in both gray (GM; Borgwardt et al., 2007,
2008; Meisenzahl et al., 2008; Pantelis et al., 2003) and white
matter (WM; Walterfang et al., 2008; Witthaus et al., 2008)
clusters in young adults (20–25 years) at UHR, predominantly
in (pre-)frontal and temporal lobe areas. Interestingly, long-
itudinal reports suggest a differential development of changes
in brain structure for individuals who convert to psychosis
comparedtothosewhodonot(Pantelisetal.,2003;Walterfang
etal.,2008).ReportstodateofbrainchangesinsubjectsatUHR
have focused on the age range of 20–25 years, when psychosis
typicallyfirstoccurs (Kessleretal.,2007).However, onaverage
the earliest prodromal signs occur 4.8 years before onset
(Hafnerand Maurer, 2006). If neurobiological changes precede
psychotic breakdown, these should be present in the at-risk
period irrespective of the age at which psychotic breakdown
occurs. To test whether this is indeed the case, we investigated
brain structure volumes in a well-defined sample of young
adolescents at UHR for psychosis (aged 12–18 years). We
Schizophrenia Research 112 (2009) 1–6
⁎ Corresponding author. Department of Child and Adolescent Psychiatry,
University Medical Center, Heidelberglaan 100, HPA01.468, 3584 CX Utrecht,
the Netherlands.
E-mail address: T.Ziermans@umcutrecht.nl (T.B. Ziermans).
0920-9964/$ – see front matter © 2009 Elsevier B.V. All rights reserved.
doi:10.1016/j.schres.2009.04.013
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hypothesized that the UHR group would have smaller total
brainvolumeandlessGMandWMdensityin(pre-)frontaland
medial temporal lobe areas.
2. Methods
2.1. Subjects
Fifty-four adolescents (52 Caucasian, 2 Asian) meeting at
least 1 of 4 criteria for UHR were referred by general
practitioners or other psychiatric clinics and included in this
study. A further 54 matched typically developing adolescents
(52 Caucasian,1 Asian, 1 Hispanic) were included. There was
also a subgroup of nineteen (35%) UHR patients that met
criteria for pervasive developmental disorder – not otherwise
specified (PDD-NOS; American Psychiatric Association,1994).
While these subjects in general showed behavioral problems
at an earlier age (Sprong et al., 2008), they also met at least
one of the UHR criteria in the last year.
A complete overview of UHR inclusion criteria is displayed
in Table 1. Briefly, the following criteria were applied: 1)
attenuated positive symptoms, 2) brief, limited, or intermit-
tent psychotic symptoms, 3) a 30% reduction in overall level
of social, occupational/school-, and psychological functioning
(i.e. GAF-score) in the past year, combined with a genetic risk
of psychosis, and 4) two or more of a selection of nine basic
symptoms, i.e. subjective deficits in cognitive, perceptual, and
motor functioning. The first three inclusion criteria were
assessed with the Structured Interview for Prodromal
Syndromes (SIPS; McGlashan et al., 2001). The fourth
inclusion criterion was assessed with the Bonn Scale for the
Assessment of Basic Symptoms-Prediction List (BSABS-P;
Schultze-Lutter and Klosterkötter, 2002). The numbers of
individuals per UHR criterion are listed in Table 2. The study
design allowed for repeated measures tobe performedat 9,18
and 24 months after inclusion. At these assessments, subjects
were re-evaluated to determine possible transition to
psychosis according to SIPS criteria (McGlashan et al.,
2001). Additionally, transition was retrospectively confirmed
by clinical expert consensus (HvE, PS).
The subgroup of patients with PDD-NOS had received a
prepubertal DSM-IV diagnosis of PDD-NOS (American Psychia-
tric Association, 1994), while also meeting criteria for MCDD
(i.e. early childhood-onset impairments in affect regulation,
social behavior/sensitivity, and cognition (Cohen et al.,1994)).
Childrenwith PDD-NOS, MCDD subtypeare at riskfordevelop-
ing psychotic disorders later in life (Van Engeland and Van der
Gaag, 1994). The diagnosis was confirmed in a psychiatric
examination including the Autism Diagnostic Interview-
Revised (Lord et al.,1994), as well as a parent interview based
on the diagnostic criteria for MCDD, which was developed for
internal use at the UMC. Diagnoses were confirmed by expert
clinical opinion (HvE, PS). A more detailed description of this
UHR-subgroup is available elsewhere (Sprong et al., 2008).
Typically developing controls were recruited from sec-
ondary schools in the region of Utrecht. They were excluded if
they met one of the UHR-criteria, if they or any first degree
relative had a history of any psychiatric illness, or if there was
a second-degree relative with a psychotic disorder. Exclusion
criteria were assessed with SIPS & BSABS-P interviews and
(parent) questionnaires.
In addition to the screening instruments a modified
version of the revised self-report Schizotypal Personality
Questionnaire (SPQ-R) was used to assess schizotypal
personality traits (Raine, 1991; Vollema and Hoijtink, 2000).
All participants were aged between 12 and 18 years and
none of them were psychotic at the time of inclusion in the
study. Subjects were excluded if there was evidence for any
past or present neurological disorder (e.g., epilepsy). Drug-
and alcohol abusewere additionalexclusion criteria, although
patients were allowed to have a history of drug use if symp-
toms had also been present in the absence of drugs. Eleven
patients reported having used drugs at least five times within
the last year (all Marijuana and two patients with additional
use of psychostimulants). Three of these patients were con-
sidered to be frequent users (at least once a week within the
last month) at the time of assessment. Also, all individuals
Table 1
Inclusion criteria.
Attenuated positive symptoms (APS)
Presence of at leastone of the following SIPS symptoms with a score between
3 and 5 and an appearance of several times per week for a period of at least
one week:
• Unusual thought content/delusional ideas (P1)
• Suspiciousness/persecutory ideas (P2)
• Grandiosity (P3)
• Perceptual abnormalities/hallucinations (P4)
• Disorganized communication (P5)
• Odd behaviour or appearance (D1)
Brief limited intermittent psychotic symptoms (BLIPS)
Presence of at least one of the following PANSS symptoms that resolve
spontaneously in 7 days and an interval between episodes with these
symptoms of at least one week (two episodes of BLIPS separated by less
than one week are considered as being one episode; if the total duration
then becomes more than one week, the transition criterion is fulfilled):
• Hallucinations (PANSS P3 score ≥4)
• Delusions (PANSS P1, P5, P6 score ≥4)
• Formal thought disorder (PANSS P2 score ≥4)
Familial risk plus reduced functioning
A change in mental state or functioning leading to a reduction of 30% or
more on the Global Assessment of Functioning scale for at least one month
within the last year compared to the highest level of previous functioning,
plus at least one of the following risk indicators:
• One first- or second-degree relative with a history of any DSM-IV psychotic
disorder (not due to a medical factor or substance induced)
• A schizotypal personality disorder of the index person according to DSM-IV
Basic symptoms
Presence of at least two of the following symptoms from the cluster
“cognitive disturbances” for more than one year, with a BSABS-P score ≥3
during the last three months:
• Inability to divide attention (A.8.4)
• Thought interferences (C.1.1)
• Thought pressure (C.1.3)
• Thought blockages (C.1.4)
• Disturbances of receptive speech (C.1.6)
• Disturbances of expressive speech (C.1.7)
• Disturbances of abstract thinking (“concretism”; C.1.16)
• Unstable ideas of reference (“subject-centrism”; C.1.17)
• Captivation of attention by details of the visual field (C.2.9)
SIPS – Structured Interview for Prodromal Syndromes; PANSS – Positive and
Negative Syndrome Scale; BSABS-P – Bonn Scale for the Assessment of Basic
Symptoms – Prediction List.
2
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had a level of verbal intellectual functioning (VIQ)≥75, as
assessed with the Wechsler Intelligence Scales (Wechsler,
1997, 2002). All subjects signed an informed consent, and for
those younger than 16, parents co-signed. Sample character-
istics are summarized in Table 2.
2.2. MRI acquisition
Magnetic resonance images were acquired on a Philips
Gyroscan (Philips Medical Systems, Best, the Netherlands)
operating at 1.5 T. For volumetric measurements T1-weighted
3Dfast-fieldechoscanswith1·5-mmcontiguouscoronalslices
of the whole head (TE 4·6 ms; TR 30 ms; flip angle 30°; FOV
256 mm; in plane voxel size, 1 mm2) and T2-weighted dual-
echo turbo spin-echo scans with 3·0-mm contiguous coronal
slices (TE114 ms; TE2 80 ms; TR 6350 ms; flip angle 90°; FOV,
256mm;inplanevoxelsize1mm2)wereacquired.Inaddition,
T2-weighted dual echo turbo spin echo scans with 17 axial
5 mmslices and a 1.2 mmgap (TE19 ms, TE2100 ms, flipangle
90°,FOV250mm,inplanevoxelsize0.98mm×0.98mm)were
acquired for clinical neurodiagnostic evaluation.
2.3. MRI-processing
2.3.1. Volumetric measurements
MRI scans were coded to ensure rater blindness to subject
identity and diagnosis and half of the scans were randomly
flipped over the y-axis to ensure blindness to laterality. The
processing pipeline has been described previously and
included semi-automated assessment of intracranial volume,
total brain volume, lateral ventricles, third ventricle and
cerebellum, as well as fully automated assessment of gray
(GM) and white matter (WM) volumes and the cortical lobes
(Durston et al., 2004; Palmen et al., 2005).
2.3.2. Voxelbased morphometry
GM and WM segments were created for individual MRI-
scans in the automated pipeline described above (Schnack
et al., 2001). For the voxelbased analyses, these segments
were blurred using a 3D Gaussian kernel (FWHM=8 mm), in
order to gain statistical power. The voxel values of these
blurred GM and WM segments reflect the local presence, or
concentration, of GM and WM, respectively, and these images
are referred to as ‘density maps.’
In order to compare brain tissue at the same anatomical
location in all subjects, the GM and WM segments were
transformed into a standardized coordinate system. These
transformations were calculated in two steps. First, the T1-
weighted images were linearly transformed to the model
brain, the previously determined ‘most average’ brain (Hulsh-
off Pol et al., 2001). In this linear step a joint entropy mutual
information metric was optimized (Maes et al., 1997). In the
second step nonlinear (elastic) transformations were calcu-
lated to register the linearly transformed images to the model
brain up to a scale of 4 mm (FWHM), thus removing global
shape differences between the brains, but retaining local
differences. For this step the program ANIMAL (Collins et al.,
1995) was used. The GM and WM density maps were now
transformed tothe model space byapplying the concatenated
linear and nonlinear transformations. Finally, the maps were
resampled to voxels of size 2×2×2.4 mm3.
2.4. Statistical analysis
All statistical analyses were conducted using the SPSS
statistical package, version 15.0 (SPSS Inc., Chicago, IL, USA).
Chi-square and independent sample t-tests were used to
assess differences in clinical and socio-demographic variables
and brain volumes. Any significant differences were then
further investigated post hoc with either non-parametric tests
for 2 samples or independent-sample t tests (two-tailed).
Cohen d standardized effect sizes were calculated from the
pairwise comparisons. An effect size of 0.20 is typically
regarded as small, 0.50 as moderate, and 0.80 as large.
Separate analyses were also performed for UHR subjects that
wereincludedonthebasisofcriteria4andsubjectswithPDD-
Table 2
Demographic data and characteristics.
Ultra high risk
subjects
(n=54)
Healthy
comparison
subjects (n=54)
Statistic
p
Mean±SDMean±SD
Age at scan
(years)
Total IQ
Height (cm)
Parental
education
(years)
SIPS total
– Positive
symptoms
– Negative
symptoms
– Disorganized
symptoms
– General
symptoms
BSABS-P total
– Cognitive
disturbances
– Perceptual
disturbances
– Motor
disturbances
SPQ total
– Positive
schizotypy
– Negative
schizotypy
– Disorganization
GAF score
15.76±2.0515.75±1.49
t=.04
p=.970
100.30±13.38
173.60±10.44
13.46±2.01
107.11±14.29
172.50±9.07
14.18±2.42
t=−2.56
t=.56
t=−1.69
p=.012
p=.576
p=.095
25.3±12.3
8.4±3.7
1.8±2.8
0.6±1.0
U=13.5
U=43.0
pb.001
pb.001
6.0±4.80.4±1.1
U=183.5
pb.001
4.6±3.9 0.3±0.6
U=237.0
pb.001
6.2±4.10.5±1.2
U=216.5
pb.001
21.6±14.5
12.9±8.3
0.9±1.3
0.7±1.0
U=361.5
U=120.5
pb.001
pb.001
7.6±7.30.2±0.5
U=153.0
pb.001
1.4±2.20.0±0.0
U=729.0
pb.001
39.7±19.7
14.6±8.3
13.2±10.8
4.4±4.7
U=486.0
U=418.0
pb.001
pb.001
16.0±9.47.0±6.2
U=612.0
pb.001
9.1±5.4
57.5±14.8
1.9±2.1
93.2±8.0
U=365.5
U=76.5
pb.001
pb.001
N
%
N
%
Male
Right-handed
Any medication
Antipsychotic
Antidepressant
Others
33
49
24
12
11
10
61.1
90.7
44.4
22.2
20.4
18.5
27
48
50.0
88.9
N2=1.35
N2=.88
p=.245
p=.645
Prodromal state criteria
Attenuated positive symptoms
Brief or intermittent psychotic symptoms
Genetic risk+reduced functioning
Basic symptoms
48 80.0
3.3
1.7
46.7
2
1
28
SIPS=Structured Interview for Prodromal Symptoms; BSABS-P=Bonn Scale for
the Assessment of Basic Symptoms-Prediction list; SPQ=Schizotypal Personality
Questionnaire, GAF=Global Assessment of Functioning.
3
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NOS because subjects in these groups may potentially
represent separate subgroups. For VBM, the same statistical
analyses were carried out on regional GM and WM densities
throughout the brain, but with covariates for age, gender and
hand preference (right vs. non-right). A correction for multi-
ple comparisons was carried out according to the false
discovery rate (αb0.05, two-tailed), allowing for an overall
5% chance of false positives (Genovese et al., 2002). Finally,
relationships between brain volumes and clinical symptom
scores were examined with Spearman's rho. Here the p level
was adjusted to pb.01 to correct for multiple comparisons.
3. Results
3.1. Sociodemographic and clinical parameters
Subjectswerematchedforage,gender,handedness,height
and parental education (Table 2). Controls had significantly
higher Total IQ (TIQ) scores than the UHR group (t=−2.56,
df=106, pb.012). Clinical parameters differed between both
groups (pb.001), with the UHR-group reporting more
symptoms and lower GAF-scores (Table 2). Fifty-one of 54
UHR subjects completed the eighteen months follow-up
period at which transition to psychosis was determined. Two
subjects had dropped out, as they felt assessments were too
time-consuming and one subject had only been included less
than a year previously. In total, seven out of 51 (14%) UHR
subjects had converted to psychosis, of whom four had
transited within the first year after inclusion (8%). For clinical
parameters, converters scored higher than non-converters on
SIPS total score (n=54, U=78, pb.026) and disorganized
symptoms (n=54, U=86, pb.042).
3.2. Brain volumes
There were no differences in brain volumes between the
UHR and control group (Table 3). These results were un-
changed when TIQ was included as a covariate in a General
Linear Model analysis. Effect sizes were small to intermediate
(range [d]=−.27–.31). Subgroup analysis for patients with
PDD-NOS (19) and patients fulfilling more than one UHR
criterionyieldedsimilarresults.Giventhecurrenteffectsizefor
total brain (d=−.14), a post-hoc power analysis showed that
it would require a sample size of nN1300 to provide sufficient
statisticalpowertodetectagroupdifferenceofthismagnitude.
3.3. Voxelbased morphometry
There were no differences in gray or white matter density
between the UHR and control groups. Exploratory analyses at
more liberal statistical thresholds also showed no differences.
3.4. Correlational analyses
There were no correlations between clinical parameters
and brain volumes.
4. Discussion
The aim of the current study was to investigate whether
structural brain changes are present in young adolescents at
clinical high risk for psychosis. In our young UHR sample of
adolescents aged 12–18 years, we find no evidence for gross
or regional brain changes. Furthermore, we find no correla-
tions between brain volumes and clinical symptoms.
These results suggest that the brain changes reported in
older UHR populations (Borgwardt et al., 2007, 2008;
Meisenzahl et al., 2008; Pantelis et al., 2003; Velakoulis
et al., 2006; Walterfang et al., 2008; Witthaus et al., 2008)
may only onset later developmentally or be secondary to
prodromal symptoms that precede the onset of psychosis.
Such symptoms are already present in our sample, but are not
accompanied by structural brain changes. This interpretation
of our data is supported by evidence from two other large
studies showing no regional brain changes in relatively young
UHR samples (Velakoulis et al., 2006; Berger et al., 2007). As
such, any brain changes present in early adolescence may be
too subtle to detect with conventional scanning procedures at
this age (Wood et al., 2008). Other imaging techniques such
as diffusionweighted MRI (DeLisi et al., 2006; Hoptman et al.,
2008), or cortical pattern matching (Sun et al., 2009) may
provide more sensitive measures to detect early changes.
Intriguingly, a few MRI-studies have examined young
cohorts with established psychosis and have already shown
brain changes in early adolescence (for a review see Arango
et al., 2008). This suggests that at onset of psychosis an exacer-
bation of existing neuropathological changes may take place or
that additional mechanisms may be affected, causing a more
pronouncedchangeofbraintissuethanintheprodromalphase.
Table 3
Brain volumes (cc) for individuals at ultra high risk and healthy comparison
subjects.
Ultra high risk
subjects
Healthy
comparison
subjects
t
(df=112)
p
Effect
size (d)
Intracranium
Total brain
Cerebral gray
matter
Cerebral white
matter
Frontal lobe
Frontal gray
matter
Frontal white
matter
Parietal lobe
Parietal gray
matter
Parietal white
matter
Temporal lobe
Temporal gray
matter
Temporal
white matter
Occipital lobe
Occipital gray
matter
Occipital white
matter
Cerebellum
Lateral
ventricles
Third ventricle
1482.75±117.27
1356.13±110.96 1372.17±120.43
744.87±64.95
1500.11±140.12
−.70
−.72
−.84
.49 −.13
.47 −.14
.40 −.20758.51±70.78
446.51±54.30450.26±56.11
−.35 .73 −.07
298.06±27.24
189.89±16.81
298.61±29.00
191.06±17.95
−.10
−.35
.92 −.02
.73 −.07
108.17±12.69 107.55±13.78.24 .81.05
214.29±18.25
135.18±10.89
219.00±20.64
138.15±12.49
−1.26
−.1.32
.21 −.24
.19 −.25
79.11±10.70 80.85±11.00
−.61.54 −.16
217.87±19.60
157.50±14.20
221.74±19.03
160.34±13.64
−1.04
−1.06
.30 −.20
.29 −.20
60.37±9.1061.40±8.50
−.61 .54 −.12
112.44±11.64
68.29±7.71
115.53±15.13
70.57±9.17
−1.19
−1.40
.24 −.23
.17 −.27
44.14±6.44 44.96±7.45
−.61 .54 −.12
153.09±13.38
12.77±6.65
152.37±13.25
11.61±9.73
.28
.72
.78
.47
.05
.14
0.66±0.34 0.57±0.241.48 .14.31
4
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In this light it would be relevant to compare individuals
where transition to psychosis takes place to those where it
does not. However, the number of transitions (n=7; 14%)
was too low in this study, to allow for such comparisons. A
possible explanation for our low conversion rate may be that
our subjects are relatively unexposed to environmental risk
factors associated with psychosis, such as unemployment,
social isolation andcannabis (Reininghauset al., 2008; Van Os
et al., 2005). All subjects were still receiving some type of
formal education at the time of assessment and/or were
living with at least one parent/caretaker.
Although this study includes a relatively large cohort of
young adolescents at risk for psychosis, there are several
limitationsthatshouldbeconsideredininterpretingtheresults.
First, our cohort includes a different type of high-risk subject
than those typically included in other studies: Our group
consisted of young adolescents of whom most had already
sought help (Sprong et al., 2008), while most UHR cohorts do
not have a history of contact with the mental health services.
Accordingly, a relatively high percentage of our subjects was
already using some form of psychotropic medication (44.4%),
half of whom were using antipsychotic drugs. Antipsychotic
medication was primarily prescribed for impulse-regulation
problems. It is important to note that our medicated subjects
still met UHR inclusion criteria. However, it is possible that
some of their symptoms were ameliorated as a result of their
medication. Other studies have reported on largely unmedi-
cated samples. Nonetheless, our inclusion criteria conform to
those used by others (Simon et al., 2006) and therefore the
phenotype is more or less comparable to other UHR studies in
the literature. If medication has a protective effect in (pre-)
psychosis, this may be reflected in our findings, although
analyses including medication as a covariate did not confirm
this: they yielded similar results to the overall analyses.
Second, our UHR sample included a subgroup of subjects
with a diagnosis of PDD-NOS, MCDD subtype (35% of our UHR
sample). The inclusion of these subjects could theoretically
have introduced a sample bias. However, these individuals
met full criteria for UHR and separate analysis of their data
yielded results similar to the overall findings. As such, it
seems unlikely that the inclusion of this group explains our
results.
Finally, our groups were not matched for IQ, although both
groups scored well within the normal range (85–115).
Interestingly, IQ has been found to decline premorbidly in
schizophrenia (Caspi et al., 2003). On average, converters in
our study did show lower IQ scores at baseline (8 points), but
this did not reach significance due to limited power.
In sum, our results do not support the presence of
structural brain changes in young adolescents at clinical
high risk for psychosis. They suggest that brain changes
preceding psychosis may only onset later developmentally or
be secondary to prodromal symptoms. Alternatively, changes
may be too subtle to detect in adolescents, due to limitations
of morphometric imaging techniques. Longitudinal imaging
studies are needed to provide further insight into the
developmental aspects of prepsychotic symptoms and their
relationship to structural brain changes. Furthermore, they
will permit the investigation of possible differential neuro-
developmental trajectories (Shaw et al., 2008) in high risk
adolescents.
Role of funding source
This work was funded by a grant from ZonMw – the Netherlands
organisation for health research and development. ZonMW had no further
role in study design; in the collection, analysis and interpretation of data; in
the writing of the report; and in the decision to submit the paper for
publication.
Contributors
Drs. Durston, van Engeland, Schothorst, and Mr. Ziermans conceived the
idea and methodology of this study. Drs. Durston, Lahuis, Schothorst, Sprong
and Mr. Ziermans were involved in subject recruitment. Drs. Lahuis,
Schothorst, Sprong, van Engeland and Mr. Ziermans were involved in clinical
and diagnostic assessments. Mr. Ziermans processed MRI images and wrote
the manuscript. Dr. Durston and Mr. Ziermans conducted the statistical
analyses. Drs. van Haren and Schnack and Ms. Nederveen provided technical
support (processing). Dr. Durston contributed in the writing of the manu-
script. All authors contributed to and have approved the final manuscript.
Conflict of interest
The authors have no competing financial interests to declare in relation
to the current work.
Acknowledgements
The authors would like to thank Anneke J. Schouten and Petra W.
Klaassen who assisted with collecting the data for our analysis.
References
American Psychiatric Association, 1994. Diagnostic and Statistical Manual of
Mental Disorders IV (DSM-IV). American Psychiatric Association,
Washington DC.
Arango, C., Moreno, C., Martinez, S., Parellada, M., Desco, M., Moreno, D.,
Fraguas, D., Gogtay, N., James, A., Rapoport, J., 2008. Longitudinal brain
changes in early-onset psychosis. Schizophr. Bull. 34, 341–353.
Berger, G.E., Wood, S.J., Velakoulis, D., Ang, A., Brewer, W.J., Phillips, L.J., Yung,
A.R., Proffitt, T.M., Pantelis, C., McGorry, P.D., 2007. Ventricle volumes in
emerging psychosis. A cross-sectional and longitudinal MRI study. Eur.
Psychiatr. 22, S30–S31.
Borgwardt, S.J., McGuire, P.K., Aston, J., Berger, G., Dazzan, P., Gschwandtner,
U., Pfluger, M., D'Souza, M., Radue, E.W., Riecher-Rossler, A., 2007.
Structural brain abnormalities in individuals with an at-risk mental state
who later develop psychosis. Br. J. Psychiatr., Suppl. 51, s69–75.
Borgwardt, S.J., McGuire, P., Fusar-Poli, P., Radue, E.W., Riecher-Rossler, A.,
2008. Anterior cingulate pathology in the prodromal stage of schizo-
phrenia. Neuroimage 39, 553–554.
Cannon, T.D., 2005. Clinical and genetic high-risk strategies in understanding
vulnerability to psychosis. Schizophr. Res. 79, 35–44.
Caspi, A., Reichenberg, A., Weiser, M., Rabinowitz, J., Kaplan, Z., Knobler, H.,
Davidson-Sagi, N., Davidson, M., 2003. Cognitive performance in
schizophrenia patients assessed before and following the first psychotic
episode. Schizophr. Res. 65, 87–94.
Cohen, D.J., Towbin, K.E., Mayes, L., Volkmar, F., 1994. Developmental
psychopathology of Multiplex Developmental Disorder. In: Friedman, S.L.,
Haywood, H.C. (Eds.), Developmental Follow-up: Concepts, Genres,
Domains, and Methods. Academic Press Inc., New York, pp.155–179.
Collins, D.L.,Holmes, C.J.,Peters,T.M., Evans,A.C.,1995. Automatic 3-Dmodel-
based neuroanatomical segmentation. Hum. Brain Mapp. 3,190–208.
DeLisi, L.E., Szulc, K.U., Bertisch, H.C., Majcher, M., Brown, K., Bappal, A.,
Branch, C.A., Ardekani, B.A., 2006. Early detection of schizophrenia by
diffusion weighted imaging. Psychiatry Res. 148, 61–66.
Durston, S., Hulshoff Pol, H.E., Schnack, H.G., Buitelaar, J.K., Steenhuis, M.P.,
Minderaa, R.B., Kahn, R.S., van Engeland, H., 2004. Magnetic resonance
imaging of boys with attention-deficit/hyperactivity disorder and their
unaffected siblings. J. Am. Acad. Child Adolesc. Psych. 43, 332–340.
Genovese, C.R., Lazar, N.A., Nichols, T., 2002. Thresholding of statistical maps
in functional neuroimaging using the false discovery rate. Neuroimage
15, 870–878.
Hafner, H., Maurer, K., 2006. Early detection of schizophrenia: current
evidence and future perspectives. World Psychiatry 5, 130–138.
Hoptman, M.J., Nierenberg, J., Bertisch, H.C., Catalano, D., Ardekani, B.A.,
Branch, C.A., DeLisi, L.E., 2008. A DTI study of white matter micro-
structure in individuals at high genetic risk for schizophrenia. Schizophr.
Res. 106, 115–124.
HulshoffPol,H.E.,Schnack,H.G.,Mandl,R.C.,vanHaren,N.E.,Koning,H.,Collins,
D.L., Evans, A.C., Kahn, R.S., 2001. Focal gray matter density changes in
schizophrenia. Arch. Gen. Psychiatry 58,1118–1125.
5
T.B. Ziermans et al. / Schizophrenia Research 112 (2009) 1–6

Page 6

Kessler, R.C., Amminger, G.P., Aguilar-Gaxiola, S., Alonso, J., Lee, S., Ustun, T.B.,
2007. Age of onset of mental disorders: a review of recent literature. Curr.
Opin. Psychiatry 20, 359–364.
Lord, C., Rutter, M., Le Couteur, A., 1994. Autism Diagnostic Interview-
Revised: a revised version of a diagnostic interview for caregivers of
individuals with possible pervasive developmental disorders. J. Autism
Dev. Disord. 24, 659–685.
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P., 1997.
Multimodality image registration by maximization of mutual informa-
tion. IEEE Trans. Med. Imag. 16, 187–198.
McGlashan, T.H., Miller, T.J., Woods, S.W., 2001. Structured Interview for
Prodromal Syndromes (version 3.0). PRIME Research Clinic, Yale School
of Medicine, New Haven.
Meisenzahl, E.M., Koutsouleris, N., Gaser, C., Bottlender, R., Schmitt, G.J.,
McGuire, P., Decker, P., Burgermeister, B., Born, C., Reiser, M., Moller, H.J.,
2008. Structural brain alterations in subjects at high-risk of psychosis: a
voxel-based morphometric study. Schizophr. Res. 102, 150–162.
Palmen, S.J., Hulshoff Pol, H.E., Kemner, C., Schnack, H.G., Durston, S., Lahuis,
B.E., Kahn, R.S., Van Engeland, H., 2005. Increased gray-matter volume in
medication-naive high-functioning children with autism spectrum
disorder. Psychol. Med. 35, 561–570.
Pantelis, C., Velakoulis, D., McGorry, P.D., Wood, S.J., Suckling, J., Phillips, L.J.,
Yung, A.R., Bullmore, E.T., Brewer, W., Soulsby, B., Desmond, P., McGuire,
P.K., 2003. Neuroanatomical abnormalities before and after onset of
psychosis: a cross-sectional and longitudinal MRI comparison. Lancet
361, 281–288.
Pantelis, C., Yucel, M., Wood, S.J., Velakoulis, D., Sun, D., Berger, G., Stuart, G.
W., Yung, A., Phillips, L., McGorry, P.D., 2005. Structural brain imaging
evidence for multiple pathological processes at different stages of brain
development in schizophrenia. Schizophr. Bull. 31, 672–696.
Raine, A.,1991. The SPQ: a scale for the assessment of schizotypal personality
based on DSM-III-R criteria. Schizophr. Bull. 17, 555–564.
Rapoport, J.L., Addington, A.M., Frangou, S., Psych, M.R., 2005. The neurode-
velopmental model of schizophrenia: update 2005. Mol. Psychiatry 10,
434–449.
Reininghaus, U.A., Morgan, C., Simpson, J., Dazzan, P., Morgan, K., Doody, G.A.,
Bhugra, D., Leff, J., Jones, P., Murray, R., Fearon, P., Craig, T.K., 2008.
Unemployment, social isolation, achievement–expectation mismatch
and psychosis: findings from the AESOP Study. Soc. Psychiatry Psychiatr.
Epidemiol. 43, 743–754.
Schnack, H.G., Hulshoff Pol, H.E., Baare, W.F., Staal, W.G., Viergever, M.A.,
Kahn, R.S., 2001. Automated separation of gray and white matter from
MR images of the human brain. Neuroimage 13, 230–237.
Schultze-Lutter,F.,Klosterkötter,J.,2002.BonnScalefortheAssessmentofBasic
Symptoms-Prediction list (BSABS-P). University of Cologne, Cologne.
Shaw, P., Kabani, N.J., Lerch, J.P., Eckstrand, K., Lenroot, R., Gogtay, N.,
Greenstein, D., Clasen, L., Evans, A., Rapoport, J.L., Giedd, J.N., Wise, S.P.,
2008. Neurodevelopmental trajectories of the human cerebral cortex.
J. Neurosci. 28, 3586–3594.
Simon, A.E., Dvorsky, D.N., Boesch, J., Roth, B., Isler, E., Schueler, P., Petralli, C.,
Umbricht, D., 2006. Defining subjects at risk for psychosis: a comparison
of two approaches. Schizophr. Res. 81, 83–90.
Sprong,M.,Becker,H.E.,Schothorst,P.F.,Swaab,H.,Ziermans,T.B.,Dingemans,
P.M., Linszen, D., van Engeland, H., 2008. Pathways to psychosis: a
comparison of the pervasive developmental disorder subtype Multiple
Complex Developmental Disorder and the “At Risk Mental State”.
Schizophr. Res. 99, 38–47.
Sun,D.,Philips,L.,Velakoulis,D.,Yung,A.,McGorry,P.D.,Wood,S.J.,vanErp,T.G.,
Thompson, P.M., Toga, A.W., Cannon, T.D., Pantelis, C., 2009. Progressive
brain structural changes mapped as psychoses develops in ‘at risk’
individuals. Schizophr. Res.108, 85–92.
Van Engeland, H., Van der Gaag, R.J.,1994. MCDD in childhood: a precursor of
schizophrenic spectrum disorders. Schizophr. Res. 11, 197.
Van Os, J., Krabbendam, L., Myin-Germeys, I., Delespaul, P., 2005. The
schizophrenia environment. Curr. Opin. Psychiatry 18, 141–145.
Velakoulis, D., Wood, S.J., Wong, M.T., McGorry, P.D., Yung, A., Phillips, L., Smith,
D., Brewer, W., Proffitt, T., Desmond, P., Pantelis, C., 2006. Hippocampal and
amygdala volumes according to psychosis stage and diagnosis: a magnetic
resonance imaging study of chronic schizophrenia, first-episode psychosis,
and ultra-high-risk individuals. Arch. Gen. Psychiatry 63,139–149.
Vollema, M.G., Hoijtink, H., 2000. The multidimensionality of self-report
schizotypy in a psychiatric population: an analysis using multidimen-
sional Rasch models. Schizophr. Bull. 26, 565–575.
Walterfang, M., McGuire, P.K., Yung, A.R., Phillips, L.J., Velakoulis, D., Wood, S.J.,
Suckling,J.,Bullmore,E.T.,Brewer,W.,Soulsby,B.,Desmond,P.,McGorry,P.D.,
Pantelis, C., 2008. White matter volume changes in people who develop
psychosis. Br. J. Psychiatry 193, 210–215.
Wechsler, D., 1997. Wechsler Adult Intelligence Scale-III NL: Afname en
scoringshandleiding[Manual].ThePsychologicalCorporationLtd.Harcourt
Publishers.
Wechsler, D., 2002. Wechsler Intelligence Scale for Children-III NL:
Handleiding en verantwoording [Manual]. The Psychological Corpora-
tion Ltd. Harcourt Assessment.
Witthaus, H., Brune, M., Kaufmann, C., Bohner, G., Ozgurdal, S., Gudlowski, Y.,
Heinz, A., Klingebiel, R., Juckel, G., 2008. White matter abnormalities in
subjects at ultra high-risk for schizophrenia and first-episode schizo-
phrenic patients. Schizophr. Res. 102, 141–149.
Wood, S.J., Pantelis, C., Velakoulis, D., Yucel, M., Fornito, A., McGorry, P.D., 2008.
Progressive changes in the development toward schizophrenia: studies in
subjects at increased symptomatic risk. Schizophr. Bull. 34, 322–329.
6
T.B. Ziermans et al. / Schizophrenia Research 112 (2009) 1–6