Download Impulsivity-related brain volume deficits in schizophrenia

doi:10.1093/brain/awq153
Brain 2010: 133; 3093–3103
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BRAIN
A JOURNAL OF NEUROLOGY
Impulsivity-related brain volume deficits in
schizophrenia-addiction comorbidity
Boris Schiffer,1 Bernhard W. Müller,2 Norbert Scherbaum,2 Michael Forsting,3 Jens Wiltfang,2
Norbert Leygraf1 and Elke R. Gizewski3,4
1
2
3
4
Department
Department
Department
Department
of
of
of
of
Forensic Psychiatry, University of Duisburg-Essen, Germany
Psychiatry and Psychotherapy, University of Duisburg-Essen, Germany
Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Germany
Neuroradiology, University Hospital Giessen, Germany
Correspondence to: Boris Schiffer,
Department of Forensic Psychiatry,
University of Duisburg-Essen,
Virchow str. 174;
45147 Essen, Germany
E-mail: [email protected]
Despite a high prevalence of schizophrenia patients with comorbid substance abuse, little is known about possible impacts on
the brain. Hence, our goal was to determine whether addicted and non-addicted schizophrenic patients suffer from different
brain deficits. We were especially interested to determine if grey matter volumes were affected by impulsivity. We hypothesized
that (comorbid) substance abuse would be associated with enhanced impulsivity and that this enhanced impulsivity would be
related to grey matter volume deficits in prefrontal areas. We employed a voxel-based morphometry approach as well as
neuropsychological assessment of executive functions and trait impulsivity in 51 participants (age range 23–55). The schizophrenia group comprised 24 patients (12 patients with paranoid schizophrenia and 12 with additional comorbid substance use
disorders). The comparison group comprised 27 non-schizophrenic individuals, matched by age and education (14 healthy
individuals and 13 patients with substance use disorders). Total grey matter volume deficits were found in all patient groups
as compared with healthy controls but were largest (8%) in both addicted groups. While grey matter volume losses in lateral
orbitofrontal and temporal regions were affected by schizophrenia, volume decreases of the medial orbitofrontal, anterior
cingulate and frontopolar cortex were associated with addiction. Compared with non-addicted schizophrenics, comorbid patients
showed significant volume decreases in anterior cingulate, frontopolar and superior parietal regions. Additionally, they showed
an increased non-planning impulsivity that was negatively related to grey matter volumes in the same regions, except for
parietal ones. The present study indicates severe grey matter volume and functional executive deficits in schizophrenia, which
were only partially exacerbated by comorbid addiction. However, the relationship between non-planning impulsivity and anterior
cingulate and frontopolar grey matter volumes points to a specific structure–function relationship that seems to be impaired in
schizophrenia-addiction comorbidity.
Keywords: schizophrenia; addiction; structural imaging; executive control; impulsivity
Abbreviations: BA = Brodmann area; BIS = Barratt Impulsiveness Scale; DSM = Diagnostic and Statistical Manual of Mental
Disorders; FDR = false discovery rate; SZ = non-addicted schizophrenic patients; SZ+A = comorbid patients (addicted schizophrenics); WCST = Wisconsin card sorting task
Received February 1, 2010. Revised April 29, 2010. Accepted May 6, 2010. Advance Access publication July 20, 2010
ß The Author (2010). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved.
For Permissions, please email: [email protected]
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Introduction
Comorbid substance abuse in patients with schizophrenia is very
common. Approximately 50% of these patients fulfil the criteria
for both schizophrenia and substance use disorder over their lifetime (Regier et al., 1990). However, persisting comorbid substance
abuse is associated with a negative outcome. This includes an
overall reduced quality of life, more frequent and longer periods
of hospitalization, higher relapse rates, less treatment compliance
and higher incidence of violent behaviour (Fazel et al., 2009).
Despite these adverse outcomes in schizophrenia-addiction
comorbidity, little is known about its actual impacts on the brain
or their functional relevance. However, structural brain abnormalities in schizophrenia were identified by using voxel-based morphometry that revealed impairments, especially in frontotemporal
cortices (Davatzikos et al., 2005; Honea et al., 2005; Williams,
2008). Most voxel-based morphometry studies to date have
focused on chronic patients and showed rather consistent
volume decreases in superior and medial temporal cortices
(Honea et al., 2005; Williams, 2008). Moreover, there is accumulating evidence for volume reductions in frontal brain regions
(Williams, 2008) resulting from meta-analyses of studies regarding
first-episode schizophrenia and other high-risk groups, such as
first-degree relatives.
Apart from alcohol misuse, substances commonly abused in
schizophrenic patients include nicotine, cocaine and cannabis
(Winklbaur et al., 2006). These substances are reinforced by an
increased dopaminergic activity, particularly in the mesolimbic
dopamine system (Gerdeman et al., 2003). As substance abuse
increases the mesolimbic dopamine activity (Gerdeman et al.,
2003) and leads to neurotoxic damage within frontosubcortical
circuits in the long term (Spanagel and Heilig, 2005), it may exacerbate the presumed pre-existing dysregulation of the dopamine
system and cause an additional disruption within frontosubcortical
circuits in patients with schizophrenia (Siever and Davis, 2004).
Furthermore, recent structural magnetic resonance imaging
(MRI) studies documented morphological changes in the frontal
lobe of patients suffering from different types of drug addiction
(Jernigan et al., 1991; Liu et al., 1998; Chanraud et al., 2007).
Unfortunately, only a few studies on brain dysmorphology in
addicted and non-addicted schizophrenic patients have been carried through and these have mainly focused on alcohol abuse.
Furthermore, their results were partly inconclusive. While a
recent study did not show significant differences between schizophrenic patients with and without comorbid alcoholism (Wobrock
et al., 2009), the largest study (Mathalon et al., 2003) at hand
documented a compounding, additive effect in schizophreniaalcoholism comorbidity in prefrontal cortex areas, important for
cognitive functioning.
Regarding cognition, both schizophrenia and substance use disorders are related to functional impairment (Bowie and Harvey,
2005; Lundqvist, 2005; Minzenberg et al., 2009). However, little
is known about the status of cognitive and executive functions in
comorbid patients. Executive functions represent supervisory cognitive coordination processes allowing for flexible, goal-directed
behaviour, particularly in novel situations (Norman and Shallice,
B. Schiffer et al.
1986). They depend upon the functional integrity of
frontosubcortical circuits that are affected both in schizophrenia
(Camchong et al., 2006) and substance use disorders (Garavan
and Stout, 2005). It has been suggested that goal-directed behaviours of addicted patients in addiction relevant situations are
mainly driven by automatic processes and that their ability to
interrupt the maladaptive automatic schemata is disturbed
(Tiffany, 1990). Continuing abstinence requires the ability to inhibit automatic response routines leading to drug consumption
and the ability to shift the cognitive focus to alternative behaviours
and goals. Accordingly, poor response inhibition, as part of the
impulsivity concept, has been found to predict problem drinking
and illegal drug use (Nigg et al., 2006).
Thus, the impulsivity construct is of central importance for substance use disorders or schizophrenia-addiction comorbidity
(Moeller et al., 2002). It can be conceptualized as a personality
trait, characterized by acting quickly and without planning in order
to satisfy a desire (Kreek et al., 2005). Hence, impulsivity is a
complex, multifaceted construct including cognitive, personality
and behavioural components (e.g. risk taking, sensation seeking
and behavioural disinhibition) (Nigg, 2000). Impulsivity has been
related to an early use of illegal substances, a high susceptibility to
developing a substance use disorder (Tarter et al., 2003) and a
decrease of local grey matter volumes, particularly in orbito-frontal
regions (Matsuo et al., 2009).
These findings indicate that comorbid patients should show
more distinct executive deficits and in particular, higher trait impulsivity than non-addicted schizophrenics. Yet, while studies regarding impulsivity are still lacking, studies regarding executive
functions revealed inconclusive results (Addington and
Addington, 1997; Pencer and Addington, 2003; Herman, 2004;
Bowie et al., 2005).
The present investigation therefore sought to determine
whether executive functions and trait impulsivity show different
impairments in patients with schizophrenia, with and without
comorbid substance use disorder, and especially, whether or
how brain volumes are associated with impulsivity. We hypothesized that (comorbid) substance abuse would be associated with
enhanced trait impulsivity and that this enhanced impulsivity
would be correlated with grey matter volume deficits in prefrontal
areas or frontosubcortical circuits, relevant for executive control.
Methods
Subjects
A total of 51 male individuals (age range 23–55 years) were recruited
for this study. The schizophrenia group comprised 24 chronic patients,
diagnosed as having Diagnostic and Statistical Manual of Mental
Disorders, fourth edition paranoid schizophrenia (DSM IV: 295.30)
and was subdivided into two subsamples. The comorbid sample
(SZ+A) comprised 12 paranoid schizophrenic patients who had a history of alcohol dependence (DSM IV: 303.90) and a misuse of substances other than alcohol: cannabis (n = 3; DSM IV: 305.20),
medicinal drugs (n = 1; DSM IV: 305.40), stimulants (n = 1; DSM IV:
305.70) or multiple substances (n = 5; DSM IV: 304.80). The ‘SZ’
sample comprised 12 patients with paranoid schizophrenia without
VBM in schizophrenia-addiction comorbidity
current or lifetime alcohol or other substance problems, except for
nicotine. Patients were recruited from the outpatient care of the
Department of Psychiatry and Psychotherapy, University of
Duisburg-Essen, and underwent medical screening and psychiatric assessment. Diagnosis was based on consensus between a research
psychologist conducting a clinical interview and a trained research assistant implementing the Structured Clinical Interview for DSMIV
(SCID-I and SCID-II) (Wittchen et al., 1996). Symptom severity was
evaluated using the Positive and Negative Syndrome Scale (Kay et al.,
1987), executed by two raters with an established inter-rater reliability
(mean Pearson’s correlation: 0.758). All schizophrenic patients were
taking antipsychotic medication [typical (n = 1), atypical (n = 12), typical and atypical (n = 11)] and were physically healthy at the time of
scanning and testing.
The comparison group comprised 27 non-schizophrenic individuals
and was also subdivided into two subsamples. The addicted patients
sample comprised 13 patients with a history of alcohol dependence
(DSM IV: 303.90) and was matched with the comorbid sample regarding other substances than alcohol abused over their lifetime: cannabis (n = 4; DSM IV: 305.20), opiates (n = 1; DSM IV: 305.50),
stimulants (n = 1; DSM IV: 305.70), multiple substances (n = 5; DSM
IV: 304.80). They were recruited from specialized aftercare facilities in
Essen. The healthy control group sample (n = 14), recruited by advertisement and local employment agencies, was screened via telephone
and then assessed by implementing structured psychiatric SCID interviews. The healthy control group was excluded for Axis I psychopathology or lifetime history of substance abuse. Both non-schizophrenic
groups were matched by age, education level and duration of substance abuse or dependence, where appropriate. They did not have a
first-degree relative with psychotic episodes. Eighty-three percent
(n = 20) of the schizophrenic patients and 78% (n = 21) of the
non-schizophrenic group smoked.
General exclusion criteria included a history of significant medical or
neurological illness, head injury resulting in loss of consciousness
(430 min), inadequate knowledge of the German language,
left-handedness and mental retardation (IQ 590), which was tested
with a multiple choice vocabulary test (Mehrfachwahl–WortschatzIntelligenztest-B, an indicator of pre-morbid abilities) (Lehrl et al.,
1995). All patients with a history of substance abuse had not
consumed any substances for at least 1 year (supported by urine analysis). Table 1 (upper half) presents demographic and clinical characteristics of the study participants. The study was approved by the local
Committee on Medical Ethics of the Medical Faculty of the University
of Duisburg-Essen, Germany and was carried out in accordance with
the Code of Ethics of the World Medical Association (Declaration of
Helsinki, amended by the 55th WMA General Assembly, Tokyo,
2004). After a detailed description of the study, written informed consent was obtained from all participants.
Assessment of addiction severity
The Michigan Alcohol Screening Test (Selzer, 1971) and the
Drug Abuse Screening Test-20 (Skinner, 1982) were used to quantify
the exposure to alcohol and illegal drugs. The Michigan Alcohol
Screening Test was developed as a quick and effective screening
method for lifetime alcohol-related problems and alcoholism, and consists of 25 items. The Drug Abuse Screening Test-20, a shortened
version of the 28-item Drug Abuse Screening Test, designed to
identify drug-use related problems, was also used as a lifetime
measure.
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Cognition and impulsivity assessments
All participants underwent neuropsychological assessments to explore
different executive domains (cognitive flexibility, response inhibition,
planning and visuospatial memory) and trait impulsivity.
The Trail Making Test (Reitan and Wolfson, 1985) and a computerized version of the modified Wisconsin Card Sorting Task (WCST)
(Nelson, 1976) were used to assess the ability to alter a behavioural
response mode in case of changing contingencies [reactive cognitive
flexibility (set-shifting)]. Furthermore, a test of verbal fluency was
applied to measure spontaneous cognitive flexibility and speed of
access to semantic information (in this study, animals with a given
time period of 60 s).
The Corsi block tapping Test (Schellig, 1997) and the visual reproduction task from the Wechsler Memory Scale, Revised (Wechsler,
1987) were applied to assess visuospatial memory.
Inhibition performance was assessed by the number of perseverative
errors in the WCST and a Go/No-go task, taken from a German
standard battery (Zimmermann and Fimm, 2009).
A computerized Tower of London task (Schall et al., 2003), an
adaptation of the Tower of Hanoi, was used to measure the planning
behaviour of our study participants. And finally, to quantify impulsivity, we used the Barratt Impulsiveness Scale (BIS-11) (Barratt, 1994),
which is regarded as the most commonly used self-report measure of
impulsivity. In its revised form, it results in a total score and three
subscale scores: attention, motor and non-planning impulsivity
(Patton et al., 1995).
Cognitive and impulsivity measures
and their relationship to local grey
matter volumes
To characterize the patients and test our hypothesis regarding impulsivity, we had to aggregate the information derived from the comprehensive test battery comprising many different dependent variables.
Based on theoretical assumptions and validated by factor analysis,
we therefore subsumed selected variables under five domains of executive functions (as described below) and computed positively connoted (i.e. the higher the value, the better the performance) mean zscores for each, which according to our prior hypothesis were partly
used as covariates in the voxel-based morphometry analyses:
(i) Reactive cognitive flexibility: WCST number of categories
achieved, WCST total number of errors [with reversed signs
(wrs)], Trail Making Test–B mean reaction time (wrs) and Trail
Making Test set shifting costs (wrs).
(ii) Spontaneous cognitive flexibility: category fluency number of
words and number of perseverations (wrs).
(iii) Visuospatial memory: Corsi block span, Wechsler Memory Scale,
revised visual reproduction (immediate recall).
(iv) Inhibition: WCST number of perseverative errors (wrs) and
number of errors in the Go/No-go task (wrs);
(v) Planning: Tower of London number of correct solutions and
mean first reaction time for each trial (wrs).
Image acquisition
Brain images were acquired on a 1.5T MRI system (Siemens Sonata,
Erlangen, Germany) using a 3D T1-weighted sequence (magnetization
prepared rapid gradient echo) with the following parameters: repetition time = 1900 ms; echo time = 3.93 ms; flip angle = 15 ; 160
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Table 1 Demographic, clinical, executive, impulsivity and brain volume measures of study participants
Healthy
controls
(n = 14)
Addicted
patients
(n = 13)
Schizophrenic
patients
(n = 12)
Co-morbid
patients
(n = 12)
Overall
statistical
test
P-value
Post hoc
comparison
(P _ 0.05,
Bonferroni-adjusted)
Age (years)
36.7 11.4
37.3 7.9
37.8 9.0
37.2 7.3
ANOVA
0.96
NA
School education (years)
9.93 0.99
9.69 1.44
9.33 1.44
9.75 1.66
ANOVA
0.91
NA
Age at schizophrenia onset (years)
NA
NA
21.0 5.3
25.5 4.9
t-test
0.04
NA
Duration of illness (years)
NA
NA
16.8 7.2
11.6 6.8
t-test
0.08
NA
Measure
Demographic
Clinical
Chlorpromazine equiv. (mg/day)
NA
NA
672.3 383
608.8 392
t-test
0.69
NA
PANSS positive
NA
NA
14.6 4.1
16.0 4.8
t-test
0.45
NA
PANSS negative
NA
NA
20.5 4.5
18.7 5.0
t-test
0.36
NA
PANSS general
NA
NA
31.9 5.9
35.8 7.5
t-test
0.18
NA
Michigan alcohol screening test
1.6 2.3
14.2 6.9
3.7 3.9
13.1 6.6
ANOVA
0.00
SZ + A = SUD 4 HC = SZ
Drug abuse screening test
0.9 0.9
12.8 6.3
3.8 3.9
9.7 6.2
ANOVA
0.00
SZ + A = SUD 4 HC = SZ
Duration of substance abuse (years)
NA
11.2 5.3
NA
13.0 5.0
t-test
0.39
NA
Abstinence duration (years)
NA
1.6 0.5
NA
3.2 2.4
t-test
0.04
NA
109.9 11.7
106.4 6.0
102.2 8.2
102.7 9.4
ANOVA
0.12
NA
Premorbid IQ (MWT-B)
Executive functions (z-scores)
Reactive cognitive flexibility
0.49 0.39
0.28 0.65
0.54 0.95
0.33 0.99
ANOVA
0.00
HC 4 SZ+A = SZ; SUD 4 SZ
Spontaneous cognitive flexibility
0.29 0.89
0.47 1.17
0.41 0.76
0.44 0.88
ANOVA
0.03
HC = SUD 4 SZ = SZ + A
Visuospatial working memory
0.52 0.30
0.07 0.31
0.77 1.34
0.09 0.82
ANOVA
0.00
HC = SUD = SZ+A4SZ
Inhibition
0.29 0.19
0.02 0.37
0.03 0.40
ANOVA
0.05
HC 4 SUD
Planning
0.47 0.37
0.20 0.52
ANOVA
0.00
HC = SUD 4 SZ+A
BIS-11 total score
63.3 6.8
69.8 7.1
65.7 8.5
72.8 11.0
ANOVA
0.05
SZ + A = SUD 4 HC
BIS-11 motor
23.6 3.6
23.4 3.0
22.5 3.6
24.8 4.3
ANOVA
0.59
NA
BIS-11 attention
15.0 2.7
18.6 3.5
18.4 3.4
18.8 3.7
ANOVA
0.04
SZ + A = SZ = SUD 4 HC
BIS-11 non-planning
24.7 4.3
27.9 3.1
24.9 4.6
29.2 4.9
ANOVA
0.03
SZ + A4HC = SZ
0.32 0.61
0.17 0.81
0.60 0.75
Impulsivity
Global volume measure (cm3)
Total brain volume
1767.4 181.4
1753.5 77.2
1782.2 184.3
1747.9 136.7
ANOVA
0.95
NA
Total grey matter volume
713.6 59.7
649.8 56.4
696.8 59.6
654.4 76.3
ANOVA
0.03
HC4SUD = SZ+A
Adjusted grey matter volumea
712.8 39.0
652.8 55.7
690.5 41.7
659.1 58.6
ANOVA
0.01
HC4SUD = SZ+A
Total white matter volume
529.4 80.6
517.1 36.9
539.0 59.7
512.9 49.4
ANOVA
0.73
NA
Total CSF volume
524.4 77.4
586.7 78.3
546.4 113.6
580.7 76.1
ANOVA
0.24
NA
Data are mean SD.
HC = healthy controls; SUD = non-schizophrenic addicted patients; NA = not applicable; PANSS = Positive and Negative Syndrome Scale; MWT-B = multiple choice
vocabulary test.
a Grey matter volume adjusted by brain-size (i.e. each individual’s total brain volume).
Significant P-values are stated in bold type.
contiguous 1 mm sagittal slices; field of view = 240 mm 240 mm,
matrix size = 240 240, voxel size = 1.0 0.9 1.0 mm.
Voxel-based grey matter volume
analysis (local approach)
Data were processed using the SPM5 software (Welcome Department
of Imaging Neuroscience Group, London, UK; http://www.fil.ion.ucl.
ac.uk/spm). We applied voxel-based morphometry standard routines
and default parameters implemented in the voxel-based morphometry
5 toolbox (http://dbm.neuro.uni-jena.de/vbm.html). Images were bias
field corrected, tissue classified and registered using linear
(12-parameter affine) and non-linear transformations (warping)
within the same generative model (Ashburner and Friston, 2005).
Analyses were performed on grey matter segments, which were multiplied by the non-linear components derived from the normalization
matrix in order to preserve actual grey matter values locally
(modulated grey matter volumes). Grey matter segments were not
multiplied by the linear components of the registration in order to
account for individual differences in brain orientation, alignment and
size globally. Finally, the modulated grey matter volumes were
smoothed with a Gaussian kernel of 14 mm full width at half maximum. These smoothed, modulated grey matter volumes are referred
to as grey matter.
Total brain and grey matter volume
analysis (global approach)
Using the tissue-classified partitions from the voxel-based morphometry analysis [i.e. grey matter, white matter and cerebrospinal fluid
(CSF)], overall volumes were determined in cm3 as the sum of
voxels representing grey matter + white matter + CSF (total brain
volume), total grey matter volume, total white matter volume and
total CSF volume.
VBM in schizophrenia-addiction comorbidity
Statistical analysis
Group differences for all demographic, clinical, executive, impulsivity
and total volume measures were analysed by means of t-tests
and ANOVAs, followed by post hoc analyses with Bonferroni
corrections.
Voxel-wise grey matter differences between single groups (healthy
controls versus all clinical groups and all clinical groups with each
other) were examined by using independent-sample t-tests. In order
to avoid possible edge effects between different tissue types, we
excluded all voxels with grey matter values of 50.1 (absolute threshold
masking). Statistical outcomes for the effects of the full-factorial
model, the independent-sample t-tests and the correlation analyses
were corrected, unless stated otherwise, using false discovery rate
(FDR) corrections for multiple comparisons. All significant outcomes
were restricted to clusters exceeding different numbers of voxels
(spatial extent threshold) in order to decrease the risk of detecting
spurious effects due to noise. The spatial extent threshold corresponds
to the expected number of voxels per cluster, calculated according to
the theory of Gaussian random fields. Although all four groups were
carefully matched by age, we conducted all voxel-based morphometry
analyses while co-varying for age to rule out according effects.
In order to test our hypothesis that impulsivity would be related to
substance abuse as well as grey matter volume decreases in prefrontal
areas, we used the mean z-scores of all behavioural impulsivity-related
executive measures (i.e. planning and inhibition) and all BIS scores as
separate covariates in full-factorial models. We computed a correlation
analysis for each, i.e. we performed post hoc Pearson correlations
between these variables and the non-adjusted grey matter estimates
of co-varying regions. Due to the numerous statistical comparisons or
correlation analyses, we considered only those with a type one error of
P50.01 in order to minimize the probability to detect false positives.
In absence of specific refutable a priori hypothesis for all other executive domains, these were used only to characterize the groups.
Results
Demographic and clinical data
One way ANOVAs of the demographic and clinical variables
(Table 1) across the four groups did not yield significant differences for age, education or any relevant clinical measure, except
illness onset. Further, as indicated by post hoc analysis, the severity of alcohol consumption (Michigan Alcohol Screening Test) and
illegal drug use (Drug Abuse Screening Test) of both addicted
groups exceeded that of non-addicted subjects but was comparable between addicted subjects.
Executive functioning and
impulsivity data
While the pre-morbid IQ did not differ between groups, significant
group differences were found for all executive measures (Table 1).
A significant schizophrenia effect was found for reactive and spontaneous cognitive flexibility. As indicated by the post hoc analysis,
both schizophrenic groups showed weaker performances on both
measures compared with healthy controls.
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We found a significant group effect between addicted
(non-schizophrenic) patients and healthy controls for the inhibition
domain. The post hoc analysis revealed that addicted patients differed significantly from healthy individuals and showed the largest
deficit in this domain. However, the most distinct deficit in comorbid patients was observed for the planning domain. Comorbid
patients, compared with non-schizophrenic subjects, showed significantly weaker performances.
Regarding BIS-11 impulsivity measures, we found significant
between-group differences in all scales, except for motor impulsivity (Table 1). As shown in the post hoc analysis, the BIS total
score showed that both addicted groups achieved comparable
values but significantly higher ones than the healthy subjects.
Corresponding to the cognitive domain planning, the BIS
non-planning scale showed the most distinct deficit in comorbid
patients. Comorbid patients achieved significantly higher nonplanning impulsivity than healthy controls and non-addicted
schizophrenics, but did not differ from addicted patients.
Global volume measures
Groups did not differ with respect to total brain volume, white
matter or CSF but in total grey matter volume (Table 1). However,
both addicted groups, compared with the healthy control group,
showed a significant, and 8% (SZ+A = 7.5%, addicted patients = 8.4%) decreased brain-size adjusted grey matter volume
(i.e. adjusted by each individuals’ total brain volume). Nonaddicted schizophrenics showed a 3.1% decrease.
Local grey matter volumes
In order to identify relevant group differences, we performed separate two sample t-tests between healthy subjects and all clinical
groups (Fig. 1, Table 2) as well as between all clinical groups
(Table 3). All three clinical groups revealed grey matter volume
decreases in several frontotemporal clusters as compared with
healthy controls. Moreover, addicted patients, compared with
healthy controls (healthy controls 4 addicted patients), showed
large additional grey matter volume decreases in the anterior cingulate cortex and the frontopolar region. The comorbid patients
showed similar but less severe deficits than the addicted patients,
whereas non-addicted schizophrenics did not show any decreases
(Table 2).
Both schizophrenia groups differed significantly in four areas
(Table 3; Fig. 2). Corresponding to the healthy controls 4 SZ+A
contrast, the SZ 4 SZ+A contrast revealed significant grey
matter volume differences in the anterior cingulate cortex, the
left frontopolar region and the left superior parietal lobule.
However, the reversed contrast (SZ+A4SZ) revealed significant
differences only in the right temporopolar cortex [Brodmann
area (BA) 38]. Due to the age matching procedure in both schizophrenic groups and their differences in illness onsets, we found
between-group differences in illness durations. In order to take
these into account we calculated an additional model with illness
duration as a covariate. The results showed that the anterior cingulate volume difference lost its significance, at least when FDR
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B. Schiffer et al.
Figure 1 Areas of decreased grey matter volume in addicted patients (red), non-addicted schizophrenic patients (blue) and comorbid
patients (green) as compared with healthy controls. Decreased grey matter volumes in all clinical groups, compared with healthy controls,
co-varied for age [corrected for multiple comparisons, FDR P50.05; t44.13; extent threshold of 200 (healthy controls 4 addicted
patients = 198; healthy controls 4 SZ = 170; healthy controls 4 SZ+A = 212) = expected voxels per cluster] as resulted from the independent sample t-tests between healthy controls and the three clinical groups. The results are visualized with the MRIcroN software by
C. Rohden, 2009 (http://www.cabiatl.com/mricro). Areas of decreased grey matter volumes in the three clinical groups compared with
healthy controls are overlaid in different colours on a standard T1-template embedded in the MRIcroN software and presented in
neurological convention (right is right).
corrected for multiple comparisons. However, the influence of illness duration was small as the trend remained (FDR P50.068).
Correlation analyses between local grey
matter volumes and impulsivity-related
measures (executive domains:
inhibition, planning and BIS scores)
Regarding the executive domain inhibition, correlation analyses
revealed a common positive relationship between inhibition performance and left frontopolar (BA 10), left anterior cingulate
cortex (BA 32), medial orbitofrontal cortex (BA 11) and superior
temporal cortex (BA 38) volumes for all groups. This result corresponds with our finding that addicted patients showed the most
distinct volume decreases in these areas and also revealed the
largest performance deficit in this domain.
The planning performance, which was largely impaired in
comorbid patients, was positively correlated with left medial orbitofrontal cortex (BA 11) and right anterior cingulate cortex (BA 24,
32) volumes in all groups. This result partly corresponds with the
finding that comorbid patients also showed volume decreases in
orbitofrontal cortex and anterior cingulate cortex volumes, compared with non-addicted schizophrenics and/or healthy controls.
Regarding the correlative relationships between BIS impulsivity
measures and local grey matter volumes, the most distinct
associations could be observed for the left dorsolateral prefrontal cortex volumes (BA 9, Montreal Neurological Institute
coordinates: 56, 22, 33), which were negatively correlated
with the BIS total score (k = 53, FDR P50.03; r = 0.439,
P50.01) and all BIS subscales (motor impulsivity: k = 161, FDR
P50.02; r = 0.454, P50.01; non-planning impulsivity: k = 479,
FDR P50.05; r = 0.466, P50.01) except attentional impulsivity.
However, the attentional impulsivity was negatively correlated
with the left supplementary motor cortex volume (BA 6,
Montreal Neurological Institute coordinates:
23;
17; 61,
k = 123, FDR P50.05; r = 0.478, P50.01). Besides the dorsolateral prefrontal cortex volume correlations, the motor impulsivity was additionally negatively correlated with right medial
orbitofrontal cortex volumes (BA 11, Montreal Neurological
Institute coordinates: 17; 42;
28, k = 74, FDR P50.02;
r = 0.438, P50.01) and the non-planning impulsivity showed
an additional negative correlation with left frontopolar volumes
(BA 10). This corresponds to the finding that compared with
healthy controls and non-addicted schizophrenics, addicted
groups showed both the highest non-planning impulsivity and
grey matter volume decreases.
Scatter plots illustrating the correlation patterns between local
grey matter volumes and inhibition and planning performance as
well as non-planning impulsivity are provided in Supplementary
Fig. 1.
VBM in schizophrenia-addiction comorbidity
Brain 2010: 133; 3093–3103
| 3099
Table 2 Grey matter volume differences between healthy controls and clinical groups (n = 51) as demonstrated by separate
two sample t-tests (t44.13) covaried for age
Group
Brain region
BA
MNI coordinates
x
HC
Medial orbitofrontal cortex
Lateral orbitofrontal cortex
Ventrolateral prefrontal cortex
Dorsolateral prefrontal cortex
Anterior cingulate cortex
Frontopolar cortex
Parahippocampal gyrus
Middle temporal cortex
Inferior temporal cortex
Superior temporal cortex
Angular gyrus
Superior parietal lobule
Cuneus
Middle occipital cortex
11,47
11,47
47
47
47
45,47
6,8
6,8
6
24,32
24,32
9,10
10
10
10
27,30
28,35
39
20,21
20
20
20
20
38
38
39
7,19
18
18,19
19
y
13
13
35
42
51
52
14
11
7
7
2
8
24
13
30
13
22
49
65
34
57
33
60
31
45
35
35
5
10
40
Side
Z
29
26
25
27
24
27
33
35
6
37
33
53
56
60
51
40
20
71
38
14
31
8
21
4
5
75
72
83
91
81
4 SUD
4 SZ
P
Cluster
size
24
26
15
20
6
2
52
54
73
22
19
27
25
16
11
0
19
8
4
32
28
44
34
43
17
33
48
8
22
10
L
R
R
R
L
L
R
L
R
R
L
R
R
L
L
R
L
L
R
R
L
L
L
R
R
L
L
L
R
R
3476
714
Cluster
size
4 SZ+A
P
0.005*
0.006*
823
13136
0.006*
0.005*
0.005*
615
1000
205
2395
498
452
0.009*
0.009*
0.008*
0.008*
0.005*
0.005*
0.006*
231
209
0.008*
0.008*
390
0.005*
13748
0.005*
0.005*
0.005*
2584
3131
3675
0.033
0.033
237
0.033
489
334
0.033
0.033
990
221
0.033
0.033
217
1291
171
517
0.033
0.033
0.033
0.033
Cluster
size
P
539
1002
498
312
0.021
0.021
0.021
0.022
214
0.024
974
636
261
234
0.020
0.038
0.040
0.040
337
0.034
327
9534
0.022
0.013
719
0.021
957
0.021
HC = healthy controls; SUD = non-schizophrenic addicted patients; MNI = Montreal Neurological Institute.
*FWEcorrected P50.05.
For the reversed contrasts (SUD 4 HC, SZ 4 HC and SZ+A 4 HC) significant differences were absent.
Table 3 Grey matter volume differences between all clinical groups (n = 37) as demonstrated by separate two sample t-tests
co-varied for age
Contrast
Brain regions
Side
BA
MNI coordinates
x
SZ 4 SZ+A
Superior parietal lobule
Anterior cingulate cortex
L
L
7
32
SZ+A 4 SZ
Frontopolar cortex
Superior temporal cortex
L
R
10
38
SUD 4 SZ+A
SZ+A 4 SUD
No region
Frontopolar cortex
SUD 4 SZ
SZ 4 SUD
Superior temporal cortex
Frontopolar cortex
Dorsolateral prefrontal cortex
Lingual gyrus
Cuneus
Supplementary motor cortex
y
Cluster size
*P
647
187
152
282
385
0.042
0.047
0.048
0.025
0.048
z
37
7
15
41
32
65
17
31
59
4
54
45
27
12
50
R
10
9
60
4
511
0.038
R
R
R
R
L
L
R
R
L
38
10
46
9
46
18
18
6
6
32
41
45
36
49
9
2
5
1
3
53
37
46
36
69
90
21
28
45
6
19
34
9
0
12
68
67
455
397
722
624
731
686
360
387
282
0.044
0.039
0.039
0.039
0.039
0.039
0.039
0.039
0.039
SUD = non-schizophrenic addicted patients; MNI = Montreal Neurological Institute.
*FDRcorrected P50.05.
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| Brain 2010: 133; 3093–3103
B. Schiffer et al.
Figure 2 Grey matter volume differences between schizophrenic patients with and without comorbid substance use disorder covaried for
age (corrected for multiple comparisons, FDR P50.05; extent threshold = 113 = expected voxels per cluster), as resulted from the independent sample t-test. The colour intensity represents t-statistic values at the voxel level. The results are visualized on standard normalized
T1-weighted images in selected slices and displayed in neurological convention (right is right).
Discussion
While previous studies assessing brain volumes in schizophreniaaddiction comorbidity focused on structural effects, here we also
examined the structure–function relationship between local grey
matter volumes and impulsivity.
Grey matter volume deficits were found in all clinical groups
compared with healthy controls, but were largest in both addicted
groups (8%). However, while temporal and lateral orbitofrontal
cortex deficits rather correspond to the schizophrenia diagnosis,
the medial orbitofrontal cortex, anterior cingulate cortex, dorsolateral prefrontal cortex and frontopolar volume decreases show
more corresponding elements with addiction. Furthermore, as
hypothesized, we observed enhanced addiction-related impulsivity
which was correlated mainly with the same local grey matter volumes as addiction-related decreased. In accordance with this,
comorbid patients compared with non-addicted schizophrenics
showed significant volume decreases in the left anterior cingulate
cortex and frontopolar cortex. Additionally, and in line with previous studies (Matsuo et al., 2009), the medial orbitofrontal
cortex, which showed volume decreases in both addicted groups
compared with healthy controls, was also correlated with most of
the impulsivity-related measures.
As indicated by both the self-report measure (BIS score:
non-planning impulsivity) and the behaviourally assessed executive
domain planning, comorbid patients showed the largest abnormality regarding non-planning impulsivity. These were, except the
dorsolateral prefrontal cortex, related to the same grey matter
volumes as the inhibition performance, where the addicted patients group showed their largest impairments: orbitofrontal
cortex, anterior cingulate cortex and frontopolar volumes
(Matsuo et al., 2009; Minzenberg et al., 2009). However, the
result that both addicted groups showed significant structural
decreases implies a structure–function relationship between these
areas and two dimensions of impulsivity (disinhibition and
non-planning) which seem to be strongly related to addiction, as
has been reported previously for drug abuse (Jentsch and Taylor,
1999).
All of these areas are part of different behaviourally relevant
frontosubcortical circuits, the superior medial frontosubcortical circuit (anterior cingulate and frontopolar cortex) and the medial
orbitofrontal cortex frontosubcortical circuit (Chow and
Cummings, 2007). Each of these interconnected networks comprises projections to the ventral striatum (including ventromedial
caudate, ventral putamen, nucleus accumbens), globus pallidus
and substantia nigra as well as ventroanterior and dorsomedial
nuclei of the thalamus, and closes with projections back to the
anterior cingulate cortex and the medial orbitofrontal cortex.
While the medial orbitofrontal cortex frontosubcortical circuit
was associated with dysregulation of affect, social behaviour and
impulsivity in previous studies (Chow and Cummings, 2007), the
superior medial frontosubcortical circuit, especially the anterior cingulate cortex, is known to be of central importance for cognitive
control (Kerns et al., 2004; Ridderinkhof et al., 2004) and apathy
or amotivational syndrome (Chow and Cummings, 2007). As suggested previously (Spanagel and Heilig, 2005) the disruption of
parts of the frontosubcortical circuits in (comorbid) addiction
might contribute to the more negative outcome in terms of
enhanced impulsivity and antisocial/violent behaviours in addicted
and dual-diagnosis patients (Fazel et al., 2009).
Although not significant, comorbid patients had increased grey
matter total volumes compared with addicted non-schizophrenic
patients. This might be due to the fact that compared with
patients with dual diagnosis, addicted non-schizophrenic patients
showed a more severe substance use problem, especially with
illegal drugs, and were abstinent only for half as long as comorbid
VBM in schizophrenia-addiction comorbidity
patients. This, in combination with the fact that both addicted
groups in the present study had not used substances for at least
one year, may also explain the differences between our results and
those reported previously (Mathalon et al., 2003), where this was
not a prerequisite (abstinence duration: alcoholics median = 33
days, comorbid patients = none abstinent). In the present study,
the addicted patients group showed the largest volume decreases,
thus contrasting with the results of Mathalon et al. (2003), which
showed that the comorbid group suffered from the largest volume
decreases.
It is known that alcohol-induced volume decreases partially recover after a 6–9 month period after cessation of drinking
(Cardenas et al., 2007), thus showing early effects of abstinence
(after a few weeks) (Bartsch et al., 2007). However, it is questionable whether complete brain regeneration is possible after
having been abstinent for a longer period, or whether the brain
recovers from long-term illegal drug use. To clarify this issue, we
conducted additional correlation analyses between local grey
matter volumes and substance abuse related measures that,
except for dependency duration, failed to show significant relationships. Even though the sample size was small, our results
indicate a progressive process of grey matter volume losses in
frontopolar, middle temporal and cerebellar regions over dependency duration. Additionally, in absence of a positive correlation
between abstinence duration and grey matter volumes, they also
indicate a steady recovery state in both addicted groups after
cessation of drinking or consuming illegal drugs within at least
one year.
In agreement with previous findings (Honea et al. 2005), the
most consistent grey matter volume reduction in patients with
chronic schizophrenia was found in the right superior temporal
cortex (including the temporal pole). However, non-addicted
schizophrenics, compared with healthy subjects (healthy controls
4 SZ) and with comorbid patients (SZ+A 4 SZ), showed a significant volume decrease in the temporal pole known to be crucial for
theory of mind or social cognition (Frith and Frith, 2003). While
structural and theory of mind-related functional abnormalities in
the temporal poles have been previously observed in schizophrenia
(Benedetti et al., 2009), the absence of such theory of
mind-related structural impairments in comorbid patients might
indicate that comorbid addiction would be associated with preserved mentalizing abilities in schizophrenia, especially as there is
evidence showing that dual-diagnosis patients have better social
skills than non-addicted schizophrenic patients (Carey et al. 2003).
Yet, some methodological concerns of this study should be considered. Firstly, with respect to correlation analyses in voxel-based
morphometry studies, performance in specific cognitive domains
may only be modulated in part by structural alterations. Secondly,
the sample sizes of the subgroups in our experiment were rather
small and the variation of substance categories may contribute
variation in some measures (e.g. neuropsychological performance).
While this design enables the conjoined analysis of two major
clinical dimensions, the variations of substance categories may preclude replication of some effects of comorbidity noted from the
existing literature and to that extent have to be replicated in larger
samples without such a variation. A further specific characteristic
of our sample was that both addicted groups had been abstinent
Brain 2010: 133; 3093–3103
| 3101
for at least 1 year. Assuming that major recovery processes occur
within the first year of abstinence, our results suggest a larger
stability over time compared with results of patient groups that
had been abstinent for shorter periods. Therefore, the reported
grey matter volume decreases in these groups are limited to
those that persist in spite of abstinence for longer than at least
1 year.
Conclusions
As hypothesized, the results of the present study indicate an
increased non-planning impulsivity in addicted, especially
dual-diagnosis patients, which is related to grey matter volume
losses in medial orbitofrontal cortex, dorsolateral prefrontal
cortex, anterior cingulate cortex and frontopolar regions.
However, in contrast to non-addicted schizophrenics, comorbid
patients showed exacerbated volume decreases only in the anterior cingulate cortex and frontopolar region but not in the medial
orbitofrontal cortex or the dorsolateral prefrontal cortex. The previously reported finding, that substance abuse comorbidity represents the main risk factor of schizophrenic patients to become
violent (Fazel et al., 2009), corresponds to the hypothesis that
violence is associated with impulsivity that was increased in both
addicted groups. Finding this structural relationship between substance abuse, non-planning impulsivity and anterior cingulate and
frontopolar volumes in dual-diagnosis patients is of clinical importance and requires further investigation with functional MRI
approaches.
Acknowledgements
Christina Pawliczek, MSc, and Alexander Wormit, cand. med.,
assisted with the data collection and analysis.
Funding
This work was supported by a grant from the Landschaftsverband
Rheinland, Germany (Dr Schiffer). The Landschaftsverband
Rheinland participated in the design and conduct of the study
and the collection and analysis of the data with the support of
Dr Schiffer.
Supplementary material
Supplementary material is available at Brain online.
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