Medial prefrontal cortex pathology in schizophrenia as revealed by convergent findings from multimodal imaging.

Abstract

Neuroimaging studies have found evidence of altered brain structure and function in schizophrenia, but have had complex findings regarding the localization of abnormality. We applied multimodal imaging (voxel-based morphometry (VBM), functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) combined with tractography) to 32 chronic schizophrenic patients and matched healthy controls. At a conservative threshold of P=0.01 corrected, structural and functional imaging revealed overlapping regions of abnormality in the medial frontal cortex. DTI found that white matter abnormality predominated in the anterior corpus callosum, and analysis of the anatomical connectivity of representative seed regions again implicated fibres projecting to the medial frontal cortex. There was also evidence of convergent abnormality in the dorsolateral prefrontal cortex, although here the laterality was less consistent across techniques. The medial frontal region identified by these three imaging techniques corresponds to the anterior midline node of the default mode network, a brain system which is believed to support internally directed thought, a state of watchfulness, and/or the maintenance of one's sense of self, and which is of considerable current interest in neuropsychiatric disorders.

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ORIGINAL ARTICLE
Medial prefrontal cortex pathology in schizophrenia as
revealed by convergent findings from multimodal imaging
E Pomarol-Clotet1,2, EJ Canales-Rodrı ´guez1,2, R Salvador1,2, S Sarro ´1,2,3, JJ Gomar1,2, F Vila4,
J Ortiz-Gil1,2, Y Iturria-Medina5, A Capdevila4,6and PJ McKenna1,2
1Benito Menni Complex Assistencial en Salut Mental, Barcelona, Spain;2CIBERSAM, Spain;3Psychiatry and Clinical
Psychology Programme, Universitat Auto `noma de Barcelona, Barcelona, Spain;4Fundacio ´ Sant Joan de De ´u, Barcelona,
Spain;5Neuroimaging Department, Cuban Neuroscience Center, Havana, Cuba and6CIBER-BBN, Spain
Neuroimaging studies have found evidence of altered brain structure and function in
schizophrenia, but have had complex findings regarding the localization of abnormality. We
applied multimodal imaging (voxel-based morphometry (VBM), functional magnetic resonance
imaging (fMRI) and diffusion tensor imaging (DTI) combined with tractography) to 32 chronic
schizophrenic patients and matched healthy controls. At a conservative threshold of P=0.01
corrected, structural and functional imaging revealed overlapping regions of abnormality in
the medial frontal cortex. DTI found that white matter abnormality predominated in the anterior
corpus callosum, and analysis of the anatomical connectivity of representative seed regions
again implicated fibres projecting to the medial frontal cortex. There was also evidence of
convergent abnormality in the dorsolateral prefrontal cortex, although here the laterality was
less consistent across techniques. The medial frontal region identified by these three imaging
techniques corresponds to the anterior midline node of the default mode network, a brain
system which is believed to support internally directed thought, a state of watchfulness, and/or
the maintenance of one’s sense of self, and which is of considerable current interest in
neuropsychiatric disorders.
Molecular Psychiatry (2010) 15, 823–830; doi:10.1038/mp.2009.146; published online 12 January 2010
Keywords: schizophrenia; voxel-based morphometry; fMRI; diffusion tensor imaging; default
mode network; anterior cingulate cortex
Introduction
Among the many lines of investigation undertaken to
characterize the brain pathology of schizophrenia,
one of the most productive has been neuroimaging.
Early studies using computed tomography estab-
lished beyond doubt that there is lateral ventricular
enlargement in the disorder,1and magnetic resonance
imaging (MRI) studies have shown that this is
coupled with a small degree of brain tissue volume
reduction of around 2%.2MRI studies have also
suggested that brain volume loss in schizophrenia,
although widespread, is not homogeneous, but is
instead most pronounced in medial temporal lobe
structures2and in the grey matter of the superior
temporal cortex.3Recent studies using voxel-based
morphometry (VBM), which identifies clusters of
difference between groups of subjects in an unbiased
way without the necessity of preselecting regions of
interest, have confirmed that cortical grey matter
volume reductions are prominent in parts of the
temporal lobe cortex.4However, these studies have
also found evidence of volume reductions in other
areas, including the anterior cingulate cortex, insular
cortex, left middle frontal gyrus and postcentral
gyrus.5
Although structural imaging has implicated a
variety of brain regions in schizophrenia, the empha-
sis in functional imaging studies has been and
continues to be on the frontal lobes. The original
functionalimagingfinding
although not consistently replicated, has been sup-
ported by meta-analysis both at rest and under
conditions of neuropsychological task activation.6
At the same time, it is increasingly clear that the
prefrontal cortex dysfunction in schizophrenia goes
beyond any simple concept of hypofunction. Thus,
rather than hypofrontality, a number of studies have
found evidence of increased activation in the pre-
frontal cortex during performance of working memory
tasks.7–10This ‘hyperfrontality’ has been found in the
dorsolateral and ventral prefrontal regions, and
whether it is seen seems to depend on the nature of
ofhypofrontality,
Received 21 August 2009; revised 8 November 2009; accepted 24
November 2009; published online 12 January 2010
Correspondence: Dr E Pomarol-Clotet, Research unit, Benito
Menni Complex Assistencial en Salut Mental, Germanes Hospi-
tala `ries del Sagrat Cor de Jesu ´s, C/Dr Antoni Pujadas 38-C, Sant
Boi de Llobregat, Barcelona 08830, Spain.
E-mail: edith.pomarol@gmail.com
Molecular Psychiatry (2010) 15, 823–830
& 2010 Macmillan Publishers Limited All rights reserved 1359-4184/10
www.nature.com/mp

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the task used, and how well the patients perform it.9–13
Complicating matters further, two recent studies
have found that schizophrenic patients show a failure
of task-related deactivation in the medial frontal
cortex.14,15This medial frontal area corresponds to part
of the so-called default mode network, a series of
interconnected regions that are highly active at rest
but which deactivate during performance of a wide
range of cognitive tasks.16
A third imaging technique that is increasingly being
applied to schizophrenia is diffusion tensor imaging
(DTI). This indexes white matter abnormality by
means of decreased fractional anisotropy (FA);17
tractography algorithms can also be used to identify
which particular tracts are affected. Over 20 studies
have indicated that schizophrenia is associated with
decreased FA, particularly in the white matter tracts
connecting temporal and prefrontal regions. However,
to date there is a lack of convergence in the findings,
and the majority of studies have used a region-of-
interest (ROI) approach rather than whole-brain-based
examination.17
It has been suggested that further understanding of
brain pathology in schizophrenia will depend on the
integration of findings from voxel-based structural
techniques with those from functional imaging and
white matter tractography using DTI.18To date,
however, relatively few such ‘multimodal’ imaging
studies have been carried out, and these have either
used only two imaging techniques19–21
focused on discrete brain structures such as the
hippocampus and amygdala.22–24In this study, we
report for the first time the use of a whole-brain
approach combining all three imaging modalities, to
determine what, if any, common sites of pathology
they identify in the disorder.
or have
Methods
Subjects
The patient sample consisted of 32 schizophrenic
patients recruited from two hospitals. They all met
Diagnostic and Statistical Manual of Mental Disor-
ders, 4th Edition (DSM-IV) criteria for schizophrenia,
based on interview and review of clinical history.
Patients were excluded if (a) they were <18 or >65
years of age, (b) had a history of brain trauma or
neurological disease and (c) had shown alcohol/
substance abuse within 12 months before participa-
tion. Altogether, 27 were in-patients and 5 were living
outside the hospital with relatives or in supported
accommodation. They all had chronic illnesses
(range 5–39 years), were symptomatic (mean Positive
andNegativeSymptoms
71.97±17.01) and had on an average moderately
severeillness(GlobalAssessment
score=44.03±10.89). They were all scanned when
in relatively stable condition (that is, outside any
period of acute relapse). All were taking neuroleptic
medication (clozapine N=7, other atypicals N=10,
typical neuroleptics N=3, combined typical and
Scale (PANSS)score
ofFunction
atypical treatment N=12). Functional MRI (fMRI)
findings on this sample have previously been de-
scribed by Pomarol-Clotet et al.14
The controls consisted of 32 healthy individuals
selected to be age- and sex-matched to the patients
and meeting the same exclusion criteria. Subjects
were recruited from nonmedical staff working in the
hospital, their relatives and acquaintances, and also
from independent sources in the community. They
were questioned and excluded if they reported
a history of mental illness and/or treatment with
psychotropic medication. Written informed consent
was obtained from all participants. The study was
approved by the local hospital ethics committee.
Procedure
All subjects underwent structural and functional MRI
scanning in a single session, using the same 1.5T GE
Signa scanner (General Electric Medical Systems,
Milwaukee, WI, USA), at the Sant Joan de De ´u
Hospital in Barcelona (Spain).
Structural imaging.
MRIdata
acquisition parameters: matrix size 512?512; 180
contiguousaxialslices;
0.47?0.47?1mm3; echo (TE), repetition (TR) and
inversion (TI) times, (TE/TR/TI)=3.93ms/2000ms/
710ms, respectively; flip angle 151.
Structural data were analysed with FSL-VBM, an
optimized VBM style analysis25,26carried out with
FMRIB Software Library (FSL) tools;27this yields a
measure of difference in the local grey matter volume.
In a first step, structural images were brain-extracted
using BET.28Next, tissue-type segmentation was car-
ried out and the resulting grey matter partial volume
images were aligned to the MNI152 standard space
using the FSL tools FLIRT and FNIRT. The resulting
images were averaged to create a study-specific
template, to which the native grey matter images were
nonlinearly re-registered. These images were modu-
lated (to correct for local expansion or contraction) by
dividing by the Jacobian of the warp field, and they
were later smoothed with an isotropic Gaussian kernel
with a sigma of 4mm. Finally, group comparisons
between patients and controls were carried out with
permutation-based nonparametric tests. These were
made with the randomize function implemented in
FSL, using the recently developed threshold-free
cluster-enhancement method, for proper statistical
inference of spatially distributed patterns.29
High-resolution structural T1
acquired withwere thefollowing
voxel resolution
fMRI.
Pomarol-Clotet et al.14Scanning was carried out
whileparticipants performed
version of the n-back task.30Two levels of memory
load (1- and 2-back) were presented in a block design
manner. Each block consisted of 24 letters that were
shown every 2s (1s on, 1s off) and all blocks
contained 5 repetitions (1- and 2-back depending on
the block) located randomly within block. Individuals
The paradigm used has been described in
asequential-letter
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had to detect them and inform by pressing a button.
Four 1-back and four 2-back blocks were presented in
an interleaved way, and between them, a baseline
stimulus (anasteriskflashing
frequency as the letters) was always presented for
16s. To identify which task had to be performed,
characters were shown in green in 1-back blocks and
in red in the 2-back blocks. All participants first went
through a training session, outside the scanner.
In each individual scanning session 266 volumes
were acquired. A gradient-echo echo-planar (EPI)
sequence depicting the blood-oxygenation-level-de-
pendent (BOLD) contrast was used. Each volume
contained 16 axial planes acquired with the following
parameters: TR=2000ms, TE=20ms, flip angle=701,
section thickness=7mm, section skip=0.7mm, in-
plane resolution=3?3mm2. The first 10 volumes
were discarded to avoid T1 saturation effects.
Functional MRI image analyses were performed
with the fMRI Expert Analysis Tool (FEAT) module,
included in the FSL software. At a first level, images
were corrected for movement, were coregistered to a
common stereotaxic space (Montreal Neurologic
Institute template) and were spatially smoothed with
a Gaussian filter (FWHM=5mm). To minimize
unwantedmovement-related
with an estimated maximum absolute movement
over 3.0mm, or an average absolute movement higher
than 0.3mm were discarded from the study. Finally,
group comparisons between patients and controls
were performed using the same FEAT module, by
means of mixed-effects GLM models. A z-threshold
of 2.3 (the default in FSL) was used to generate the
initial set of clusters. FEAT uses the Gaussian
Random Field theory to properly account for the
spatially distributed patterns when performing sta-
tistical tests.
with thesame
effects,individuals
DTI.
recorded along 25 gradient directions using three
different b-values (500, 750 and 1000smm?2) together
with three unweighted (b=0) images (78 images in
total). Foreachimage,
parameters: field of view=289?289mm2; matrix size
128?128; number of slices 28; voxel resolution
1.13?1.13?5mm3; TE=107ms; TR=8000ms.
The DTI data sets were analysed with FSL-TBSS,26
a Tract-Based Spatial Statistics analysis carried out
with FSL tools. In a first step, FA images were created
by fitting a tensor model to the raw diffusion data
using FMRIB’s Diffusion Toolbox (FDT), and then
brain-extracted using BET.28All subjects’ FA data
were then aligned into a common space using the
nonlinear registration tool FNIRT (Andersson et al.31;
Andersson et al.32), which uses a b-spline representa-
tion of the registration warp field.33Next, the mean
FA image was created and thinned to create a mean
FA skeleton, which represents the centres of all tracts
common to the group. Each subject’s aligned FA data
were then projected onto this skeleton. As in the VBM
analysis, group comparisons between patients and
The whole brain diffusion-weighted images were
we usedthe following
controls were performed using the randomize func-
tion implemented in FSL, which carries out permuta-
tion-based, nonparametric comparisons.
In addition, the output from the DTI analysis was
used to explore the potential differences in white
matter structural connectivity. For this we used the
voxel-based methodology described by Iturria-Medi-
na et al.,34,35which uses Dijkstra’s graph theory
algorithm to find the most probable route of connec-
tion between any pairs of voxels in the brain. First,
the statistical map resulting from TBSS was auto-
matically partitioned into nonoverlapping clusters.
Next, four of these clusters were selected as repre-
sentative ROIs, after considering their central location
in each of the significant tracts, and avoiding other
clusters containing fibre intersections (which would
make the interpretation of results difficult). Every ROI
was used separately as a seed mask by an optimized
tractographyalgorithm,34
sional images that quantified the degree of anatomical
connection between a particular ROI and each voxel
in the brain.35Finally, all individual maps were
aligned into a common space and they were smoothed
with an isotropic Gaussian kernel with a sigma of
4mm. This allowed comparing connectivity patterns
between patients and controls using the same permu-
tation-based randomize function from FSL.
delivering three-dimen-
Statistical thresholds
In the three principal analyses comparing patients
and controls, a threshold of P=0.01, corrected for
multiple comparisons across space, was used. We
applied this relatively conservative threshold in order
to minimize false-positive findings arising from the
analysis of several modalities and from different sets
of images. Using this threshold on the three main
comparisons leads to an overall probability of finding
one (or more) false positive of 0.03. Results applying a
corrected P=0.05 in the individual tests are reported
in the Supplementary Material. However, these
results should be treated with caution, as they imply
an overall probability of 0.14 for false positives in the
three main comparisons.
Results
Demographic findings
Demographic findings are shown in Table 1. The
patients and controls were selected to be matched for
age and sex. There were also no significant differ-
ences between their score on the Word Accentuation
Test (Test de Acentuacio ´n de Palabras, TAP), a test
designed to estimate intelligence quotient (IQ) (pre-
morbid IQ in the patients) analogous to the National
Adult Reading Test in English,36on the basis of
pronunciationoflow-frequency
whose accents are removed.37As typically found in
schizophrenia, the patients, however, had a lower
current IQ than the controls.
Spanishwords
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VBM
Because of poor image quality, two individuals
(1 control and 1 patient) were excluded from the
VBM analyses. At a P=0.01, only two areas of the
brain showed significant differences in volume
between patients and controls (see Figure 1a). One
of these was centred in the medial frontal cortex (peak
in Brodmann area 32 (BA 32), MNI (?8, 14, 46),
z-score=5.95, P<0.001), bilaterally including parts of
the gyrus rectus, anterior cingulate, medial superior
frontal gyrus, and extending posteriorly to the
supplementary motor area. The second significant
area was located in the right hemisphere, partially
covering the dorsolateral prefrontal cortex and ex-
tending to inferior frontal cortical regions and the
precentral gyrus with a maximum in BA 44 (MNI (50,
8, 24), z-score=4.7, P<0.0197).
fMRI
Although the 1-back vs baseline contrast did not
reveal any significant differences in activation, the
2-back vs baseline contrast revealed several areas
of differential activation between the two groups. The
patients showed significantly reduced activation
compared with controls in two areas (Figure 1b,
shown in blue). One of these areas comprised two
distinct but clearly related clusters (cluster1: peak
activation in BA 8, MNI (2, 26, 50), z-score=4.35,
P<1.19?10?7; cluster2: peak activation in BA 6, MNI
(?46, ?2, 28), z-score=4.12, P<0.00131). These
clusters included parts of the frontal operculum
bilaterally, both the precentral gyri and the supple-
mentary motor areas. They also reached some sections
of the right dorsolateral prefrontal cortex and left
basal ganglia. The other area was located in the
cerebellum (peak activation in MNI coordinates (2,
?56, ?24), z-score=5.96, P<1.91?10?8).
In addition, the patients showed significant failure
to deactivate in two clusters (Figure 1b, shown in
orange). One of these extended over a large medial
frontal area including, bilaterally, the gyrus rectus,
anterior cingulate cortex, and parts of the superior
medial frontal cortex and related frontomedial struc-
tures (peak activation in BA 11, MNI (?2, 38, ?2),
z-score=4.91, P<1.78?10?9). This cluster clearly over-
lapped with the region of volume reduction identified
in the VBM analysis (Figure 1a). The other cluster
where there was significant failure to deactivate was
smaller, and contained parts of the hippocampal
complex and neighbouring anterior temporal regions
with a maximum activation in BA 48 (MNI (46, ?8,
?12), z-score=4.04, P<0.000798).
DTI
Owing to the excessive movement, only 25 patients
and 28 controls could be included in this analysis. At
P=0.01 corrected, the major FA differences between
the patients and controls were found in the corpus
callosum (maximum in MNI space (22, 37, ?1),
P<0.00701] (see Figure 2a). In particular, alterations
were seen in the anterior portion of this structure,
extending from below the genu to include the
Figure 1
findings. Regions showing significant volume reduction
thresholded at P=0.01 in the schizophrenic patients are
shown in orange. Bottom panel: (b) functional magnetic
resonance imaging (fMRI) findings. Regions are shown
where there were significant differences between patients
and controls during performance of the n-back task (2-back
vs baseline comparison), thresholded at P=0.01. Blue
indicates hypoactivation, that is, areas where controls
activated significantly more than the patients. Orange
indicates areas where the schizophrenic patients showed
failure to deactivate in comparison to controls. The right
side of the images represents the left side of the brain.
Top panel: (a) voxel-based morphometry (VBM)
Table 1
Demographic characteristics of patients and controls
Patients (n=32)Controls (n=32) P-value
Age
Sex (M/F)
TAP correct words
Current IQ (WAIS-III)
Duration of illness (years)
GAF score
PANSS score
41.56±8.79 (28–60)41.03±11.04 (range 21–59)
21/11
22.93±4.63
100.52±9.10 (range 80–110)
—
—
—
0.83
1
0.32
0.01
—
—
—
21/11
21.55±5.58
94.31±9.02 (range 80–110)
21.79±9.09 (range 5–39)
44.03±10.89 (range 22–65)
71.97±17.01 (range 38–116)
Abbreviations: F, female; GAF, Global Assessment of Function; IQ, intelligence quotient; M, male; WAIS, Wechsler Adult
Intelligence Scale.
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rostral and anterior midbody. This area of significant
differencealsoincluded
radiata bilaterally (visible particularly in the left-
hand figure).
A further anomalous region was located posteriorly
in the splenium of the corpus callosum on the left,
also involving the posterior thalamic radiation (in-
cluding the optic radiation) and, to a lesser extent, the
posterior corona radiata (maximum at MNI coordi-
nates (?21, ?83, 10), P<0.007).
To examine the relationship of the anterior callosal
FA differences to the overlapping areas of structural
and functional MRI abnormality in the medial
prefrontal cortex, we performed a connectivity/tracto-
graphy analysis (see Methods). For this, four ROIs
were selected from an automatic parcellation of all
areas with significant differences in FA at P=0.01.
ROI#1 corresponded to the genu of the corpus
callosum in the midline; ROI2# and ROI#3 were
components of the left and right parts of the body of
the corpus callosum anteriorly; ROI#4 was located
theanterior corona
within the left posterior thalamic radiation. In this
analysis, we first applied a less conservative thresh-
old of 0.05 rather than the 0.01 used in all other
analyses: we expected higher levels of noise inherent
to limitations in both the diffusion tensor model38and
the tractography algorithm to reduce the statistical
power of the connectivity analyses.
The three anteriorly placed seed regions (ROI#1,
ROI#2 and ROI#3) showed significant connectivity
differences between patients and controls, which
affected principally a large expanse of the medial
frontal cortex (see Figure 2b). It can be noted that
there is a clear overlap between these results and
areas showing failure to deactivate in fMRI and
regions where there was volume reduction in VBM
(Figures 1a and b). The seed placed in the left side of
the body of the corpus callosum also showed
differential connectivity with the left dorsolateral
prefrontal cortex. The fourth seed, located posteriorly
in the posterior thalamic radiation, did not result in
any differential connectivity between the schizophre-
nic patients and the controls.
Repeating the analyses at a threshold of P=0.01
corrected had similar results, but the area of differ-
ential structural connectivity was less extensive. In
particular, only the seed region ROI#3 in the right part
of the body of the corpus callosum continued to show
significant differences between patients and controls.
These affected areas were very similar to those
depicted in the lower panel of Figure 2b, but
unilaterally on the right side of the brain.
Discussion
In this study, three different whole-brain voxel-based
imaging techniques identified the medial prefrontal
cortex as a prominent site of abnormality in schizo-
phrenia. This region has not previously been a focus
of interest in the disorder, although some post-
mortem studies have noted microstructural abnorm-
alities in the anterior cingulate cortex,39and it is
an area where apparent hyperactivation,12and more
recently failure of deactivation14,15has been found
(see below). We also found evidence of convergent
abnormality in the dorsolateral prefrontal cortex,
which is an area of long-standing interest in schizo-
phrenia. Here, however, the laterality was less
consistent across techniques.
Our findings differ from two other studies that have
applied whole-brain multimodal imaging to schizo-
phrenia.19,20Calhoun et al.19examined 15 chronic
schizophrenic patients and 15 controls using VBM
and fMRI while performing an auditory oddball task.
They used joint independent component analysis
(jICA) of both modalities to determine areas of
difference between the groups. Significant differences
for VBM and fMRI were located in clearly different
cortical regions, and there was no anatomical con-
vergence. Lui et al.20carried out VBM on 68 patients
with first-episode schizophrenia and 68 controls.
Similar to our study, they found a cluster of grey
Figure 2
panel (a) shows areas of significant fractional anisotropy
(FA) reduction in the schizophrenic patients identified
using TBSS thresholded at P=0.01. Bottom panels (b) show
areas of structural connectivity that differed significantly
between the schizophrenic patients and controls, on the
basis of the seed placed in the genu of the corpus callosum
(upper, shown in red), and the seeds placed in the body of
the corpus callosum, right and left (lower, shown in orange).
A threshold of P=0.05 corrected was used for this analysis.
The right side of the images represents the left side of the
brain.
Diffusion tensor imaging (DTI) findings. Top
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matter volume reduction in the anterior cingulate
cortex (although on the right only), along with two
other clusters in the right superior and right middle
temporal gyri. However, when the authors used these
three regions as seeds for a functional connectivity
analysis using resting fMRI, no differences between
the patients and the controls emerged.
Our findings are also different from those of other
studies using single modalities of brain imaging. In
particular, we found a considerably less extensive
pattern of brain abnormality in the VBM and DTI
analyses than in previous studies. On the one hand,
this undoubtedly reflects our use of a conservative
threshold of P=0.01 corrected. Thus, while two
recent meta-analyses of VBM studies in schizophre-
nia5,40found, similar to this study, clusters of reduced
volume in the medial prefrontal cortex and the
dorsolateral prefrontal cortex, these were accompa-
nied by other clusters, notably in the insula/inferior
frontal cortex and medial temporal lobes. Some of
these additional areas were also evident in our
analysis using a threshold of P=0.05 (see Supple-
mentary Material).
On the other hand, there are grounds for believing
that VBM abnormality in schizophrenia may not be as
widespread as originally thought. When the techni-
que was originally introduced in 2000–2001,41,42it
utilized a measure of grey matter concentration or
density, the proportion of grey matter within a given
voxel after spatial normalization of the images. Later,
a modulated or ‘optimized’ method became available,
which gives a more intuitive estimate of grey matter
volume. Fornito et al.40meta-analysed studies using
measures of grey matter concentration in schizophre-
nia and found clusters of significant reduction in the
medial and lateral frontal cortex, temporal cortex and
insula bilaterally. However, meta-analysis of studies
using the volume measure resulted in a more
restricted pattern of differences: changes in the
temporal lobe and insula were no longer seen, and
the largest areas of reduced volume were in the
medial aspect of the left superior frontal gyrus plus
the left orbitofrontal and fusiform regions.
DTI studies of schizophrenia have found evidence
of abnormality in many different white matter areas,
although these include the corpus callosum,17espe-
cially in recent studies.21,43Once again, however,
there are grounds for believing that the true pattern
may be more restrictive: Ellison-Wright and Bull-
more44meta-analysed 15 studies which used a whole-
brain voxel-based approach, and found that only two
locations were consistently identified across studies.
One of these was in the deep white matter of the left
frontal lobe, close to the genu of the corpus callosum.
The other area was in the left temporal lobe white
matter. These areas are strikingly similar to those we
found using a threshold of P=0.01 corrected.
The linchpin for the convergence of evidence in our
study is the failure of deactivation in the medial
frontal cortex, which was equally prominent at
thresholds of P=0.05 or 0.01. One of the most
important functional imaging findings to emerge in
recent years has been that prefrontal cortex dysfunc-
tion in schizophrenia goes beyond any simple con-
cept of hypofrontality, with a series of studies
documenting areas of increased activation (‘hyper-
frontality’) during performance of working memory
tasks.7–10The leading interpretation of this finding
has been that of cortical inefficiency: even when
schizophrenic patients are able to perform a cognitive
task normally, they have to ‘work harder to keep up’
with its demands, and this leads to a compensatory
brain functional response characterized by greater
and/or wider activation of the dorsolateral prefrontal
cortex than in healthy subjects.10,13However, a meta-
analysis of fMRI studies using the n-back task12casts
doubts on this interpretation, as (a) hyperfrontality
was found in the medial prefrontal cortex, not the
dorsolateral and other lateral areas where hypofron-
tality was seen; and (b) this medial frontal area was
not activated in the meta-analysis of either patients
alone or controls alone. Subsequently, it has been
argued that the hyperfrontality seen in the medial
frontal cortex in schizophrenia actually represents a
failure to deactivate14(because of ‘reverse subtraction’
from a high baseline, this can result in apparent
hyperactivation, see Gusnard and Raichle45).
What is the function of this medial prefrontal area
identified by multimodal imaging? As mentioned
above, it includes, but is not limited to, the anterior
cingulate cortex, an area which has been implicated
in mood, attention, emotional regulation, error detec-
tion and decision making.39,46Perhaps more signifi-
cantly, it corresponds closely to one of the two
midline nodes of the default mode network, a system
of brain regions that are active at rest but deactivate
during performance of a wide range of attention
demanding cognitive tasks.16The default mode net-
work has been proposed to be involved in functions
such as conceiving the perspectives of others,
retrieving autobiographical memories and envision-
ing the future, processes that can be subsumed under
a broad heading of mentation detached from the
external world.47Going further, it has been argued
that such functions underlie the experience and
maintenance of one’s sense of self.48A further
possibility is that the default mode network underlies
a state of ‘watchfulness’, a passive, low-level mon-
itoring of the external environment for unexpected
events in conditions when active attention is re-
laxed.47Any and all of these possibilities seem to
have scope for explaining the symptoms of schizo-
phrenia.
Conflict of interest
The authors declare no conflict of interest.
Acknowledgments
This work was supported by the Instituto de Salud
Carlos III, Centro de Investigacio ´n en Red de Salud
Medial prefrontal cortex pathology in schizophrenia
E Pomarol-Clotet et al
828
Molecular Psychiatry

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Mental, CIBERSAM. Edith Pomarol-Clotet was sup-
ported by a Marie Curie European Reintegration Grant
(MERG-CT-2004-511069). Raymond Salvador was sup-
ported by grants from the Instituto de Salud Carlos III
(CP04/00322 and PI05/2693).
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Medial prefrontal cortex pathology in schizophrenia
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Molecular Psychiatry