Orbitofrontal cortex gray matter volumes in
bipolar disorder patients: a region-of-interest
Nery FG, Chen H-H, Hatch JP, Nicoletti MA, Brambilla P, Sassi RB,
Mallinger AG, Keshavan MS, Soares JC. Orbitofrontal cortex gray
matter volumes in bipolar disorder patients: a region-of-interest MRI
Bipolar Disord 2009: 11: 145–153. ª 2009 The Authors
Journal compilation ª 2009 Blackwell Munksgaard
Objectives: Functional and postmortem studies suggest that the
orbitofrontal cortex (OFC) is involved in the pathophysiology of bipolar
disorder (BD). This anatomical magnetic resonance imaging (MRI)
study examined whether BD patients have smaller OFC gray matter
volumes compared to healthy comparison subjects (HC).
Methods: Twenty-eight BD patients were compared to 28 age- and
gender-matched HC. Subjects underwent a 1.5T MRI with 3D spoiled
gradient recalled acquisition. Total OFC and medial and lateral
subdivisions were manually traced by a blinded examiner. Images were
segmented and gray matter volumes were calculated using an automated
Results: Analysis of covariance, with intracranial volume as covariate,
showed that BD patients and HC did not differ in gray matter volumes of
total OFC or its subdivisions. However, total OFC gray matter volume
was significantly smaller in depressed patients (n = 10) compared to
euthymic patients (n = 18). Moreover, total OFC gray matter volumes
were inversely correlated with depressive symptom intensity, as assessed
by the Hamilton Depression Rating Scale. OFC gray matter volumes
were not related to lithium treatment, age at disease onset, number of
episodes, or family history of mood disorders.
Conclusions: Our results suggest that abnormal OFC gray matter
volumes are not a pervasive characteristic of BD, but may be associated
with specific clinical features of the disorder.
Fabiano G Nerya,b,c, Hua-Hsuan
Chend, John P Hatcha,e, Mark A
Nicolettif, Paolo Brambillag,h,
Roberto B Sassic,i, Alan G
Mallingerj, Matcheri S Keshavank
and Jair C Soaresf
aDepartment of Psychiatry, The University of Texas
Health Science Center at San Antonio (UTHSCSA),
bSouth Texas Veterans Health Care System, Audie
L. Murphy, San Antonio, TX, USA,cDepartment of
Psychiatry, University of Sa ˜o Paulo Medical School,
Sa ˜o Paulo, Brazil,dDepartment of Radiology,
University of Cincinnati, Cincinnati, OH,
eDepartment of Orthodontics, UTHSCSA, San
Antonio, TX,fCenter of Excellence for Research
and Treatment of Bipolar Disorders (CERT-BD),
Department of Psychiatry, University of North
Carolina School of Medicine, Chapel Hill, NC, USA,
gDepartment of Pathology and Experimental &
Clinical Medicine, Section of Psychiatry, University
of Udine,hScientific Institute IRCCS ?E. Medea?,
Udine, Italy,iDepartment of Psychiatry, Harvard
Medical School, Boston, MA,jMood and Anxiety
Disorders Program, National Institute of Mental
Health, Bethesda, MD,kDepartment of Psychiatry
and Behavioral Sciences, Wayne State School of
Medicine, Detroit, MI, USA
Key words: bipolar disorder – magnetic
resonance imaging – neuronal plasticity –
prefrontal cortex – region-of-interest – stress
Received 22 August 2007, revised and accepted
for publication 6 June 2008
Corresponding author: Fabiano G. Nery, MD,
Bipolar Disorder Research Group, Institute and
Department of Psychiatry, University of Sa ˜o Paulo
Medical School, Rua Dr. Ovı ´dio Pires de Campos,
785, Sa ˜o Paulo ⁄SP, Brazil 04503-010.
Fax: +55 11 3069-7928;
The authors of this paper do not have any commercial associations that might pose a conflict of interest in connection with this manuscript.
Bipolar Disorders 2009: 11: 145–153
ª 2009 The Authors
Journal compilation ª 2009 Blackwell Munksgaard
The neuroanatomical substrate that underlies the
clinical manifestations of bipolar disorder (BD) is
still poorly understood. It has been hypothesized
that the emotional, cognitive, and behavioral
manifestations of BD are caused by a disruption
in a brain network that comprises areas of the
prefrontal cortex and limbic system, particularly
two inter-related circuits: a limbic-thalamic-corti-
cal circuit and a limbic-striatal-pallidal-thalamic
circuit (1–3). The orbitofrontal cortex (OFC), the
most inferior and ventral part of the prefrontal
cortex, is a key area in this neurocircuitry. The
OFC has extensive connections to the amygdala,
striatum, thalamus, hypothalamus, and brain stem,
and it is functionally responsible for the linkage of
sensory and visceromotor activity, the reappraisal
of affective stimuli, the evaluation of social cues,
and the decision-making process (4–6). Typically,
traumatic lesions of the OFC are accompanied by
behavioral changes that resemble many aspects of
BD, such as mood lability, manic ⁄hypomanic
states, profound changes in personality with pres-
ervation of memory and intellect, behavioral dis-
inhibition, and impairment in planning the future
and making socially adaptive choices (5, 7–9).
Mounting evidence supports a role for OFC
dysfunction in the pathophysiology of BD. Post-
mortem studies reveal that BD patients present
several neuropathological changes in the OFC,
such as reduced neuronal size (10); reduced glial
fibrillary acidic protein immunoreactivity (which
might represent a dysfunction in the astrocytic and
neuronal glutamate transporter functioning) (11);
upregulation of genes involved in the signal-
ing ⁄regulation of neurotransmitter system (G-pro-
tein coupled receptor signaling) and in immune
response; and downregulation of genes involved in
the intracellular transport and trafficking, endocy-
tosis, regulation of transcription, immune re-
sponse, DNA repair, and neural activity (12).
Functional studies have also consistently shown a
decrease in regional cerebral blood flow in OFC
areas of BD patients, either in manic or in remitted
states (13–18). Interestingly, in remitted patients,
the blood flow decrease is inversely correlated with
the intensity of subclinical depressive symptom-
atology (14). On the other hand, one study showed
an increase in left OFC metabolism in depressed
BD type II patients in comparison to healthy
controls (HC) (19).
Despite the increasing evidence for a disturbed
OFC function in BD and a close association
between mood symptomatology and OFC cerebral
flow, morphometric data about this brain area in
BD are scarce. Although several studies have
reported reductions in OFC gray matter volumes
in adult major depressive disorder (MDD) patients
(20–24), few have studied BD, and studies have
yielded inconsistent results. For instance, using a
voxel-by-voxel automated method, Frangou et al.
(25) found smaller bilateral OFC gray matter
volumes in medicated BD type I patients relative
to HC. Nugent et al. (26) reported smaller left
OFC gray matter volume in medicated BD patients
relative to HC but not in medication-free BD
patients relative to HC, also using voxel-based
morphometry (VBM). Using a manual tracing
method, Najt et al. (27) found a differential effect
of gender on OFC gray matter volumes, with male
patients having smaller OFC gray matter volumes
than male HC and female patients having larger
OFC gray matter volumes than female HC. On the
other hand, Dickstein et al. (28) found no differ-
ence in OFC gray matter volumes in children and
adolescents using VBM.
In the present study, we investigated whether
adult BD patients present abnormal OFC volumes
compared to HC. We used a manual tracing
method of region of interest (ROI), which ad-
vances VBM findings by providing absolute values
of volumes and by being anatomically validated
(29). Based on previous reports, our first hypo-
thesis was that BD patients present smaller OFC
gray matter volumes than HC. Secondarily, we
explored the relationship between OFC gray
matter volumes and certain clinical characteristics
of BD, including medication status, mood state,
age at disease onset, number of episodes, family
history of mood disorders, and depressive symp-
Materials and methods
The sample comprised 28 BD patients matched by
age and gender to 28 HC. All of the subjects were
residents of the Pittsburgh, PA, USA metropolitan
area or surrounding cities. The subjects were
recruited from the outpatient facilities of the
through local media advertisements. Subjects gave
written informed consent to participate in the
study after understanding all issues involved in
participating in the research. All the procedures
were carried out according to the Declaration of
Helsinki and the study was approved by the
University of Pittsburgh biomedical Institutional
Inclusion criteria for BD patients were a diag-
nosis of BD type I or type II, according to
DSM-IV criteria, and age 18-65 years. Patients
Nery et al.
could be in any mood state. BD patients were
eligible if they met either of two possible medica-
tion profiles. Patients either had not received any
psychotropic medications for at least two weeks
and had not received lithium for at least four weeks
at study entry or were currently receiving lithium
monotherapy. No medication treatment was with-
drawn or initiated for the purpose of this study;
rather, subjects were included if they met one of
these medication profiles at screening. All subjects
had normal physical examination results and no
history of neurologic problems. Exclusion criteria
for BD patients were any comorbid Axis I psychi-
atric disorder, alcohol ⁄substance abuse within six
months preceding study entry or lifetime alco-
hol ⁄substance dependence, and current significant
medical problems. Information about family his-
tory of psychiatric disorders, age at onset of illness,
length of illness, and number of previous affective
episodes defined by DSM-IV criteria was retrieved
from the patient interviews and medical charts.
The exclusion criteria for HC were the presence of
any past or current Axis I psychiatric disorder,
presence of significant neurological or medical
problems, and presence of any Axis I diagnosis in
first-degree relatives. The diagnostic assessments
were conducted using the Structured Clinical
Interview for Axis I DSM-IV Disorders (SCID),
versions for patients and non-patients (30). The
21-item version (31), was administered to BD
patients to assess intensity of depressive symptoms.
Magnetic resonance imaging (MRI) acquisition and
The images were acquired in a 1.5 T GE Signa
Imaging System running version Signa 5.4.3
Milwaukee, WI, USA). A sagittal scout series
was conducted to confirm image quality and
patients? head position and to find a midline
sagittal image. A T1-weighted sagittal scout image
was acquired in order to obtain a graphic
prescription of the coronal and axial images.
Three-dimensional gradient echo imaging (Spoiled
Gradient Recalled Acquisition) was conducted in
the coronal plane [repetition time (TR) = 25 ms,
echo time (TE) = 5 ms, nutation angle = 40?,
fieldof view (FOV) = 24 cm,
ness = 1.5 mm,number
(NEX) = 1, matrix size = 256 · 192] in order to
obtain 124 images covering the entire brain. To
rule out any neuroradiological abnormalities, we
obtained a T2 and proton density image in the
A Dell PC workstation (Dell Computers, Austin,
TX, USA) running BRAINS2 software, developed
at the University of Iowa Hospitals and Clinics,
was used to perform the anatomical measurements.
Before tracing, the T1- and T2-weighted images
were spatially realigned so that the brain anterior-
posterior axis was parallel to the intercommisural
line, which was horizontal in the sagittal plane, and
the interhemispheric fissure was vertical in the axial
plane. Six brain-limiting points (anterior, poste-
rior, superior, inferior, left, and right) were then
picked to place images into the standard Talairach
three-dimensional space (32). After co-registering
and fitting the two image sequences, a multimodal
Bayesian classifier based on discriminant analysis.
This segmentation method automatically generates
thresholds permitting the discrimination of gray
and white matter as well as cerebrospinal fluid
The OFC was manually traced in coronal view.
The OFC tracing consisted of two sections. Trac-
ing of the first part began at the tip of the genu of
the corpus callosum, which was located in sagittal
images and continued to be traced in coronal
images (moving in the anterior direction) until the
last slice where the corpus callosum was visible.
The superior limit for this section was represented
by a point 5 mm below the intersection of the
anterior commissure–posterior commissure (AC-
PC) line and the interhemispheric fissure. The
second section began on the first slice anterior to
the corpus callosum and continued in the anterior
direction and included the most anterior portion of
the brain tissue. Here, the intersection of the AC-
PC line and the interhemispheric fissure provided
the superior border (Fig. 1). To trace the OFC
volume, one horizontal and two vertical lines were
Fig. 1. Delimitation of the orbitofrontal cortex in coronal and
sagittal views according to the method proposed by Lacerda
et al. (33).
Orbitofrontal cortex in bipolar disorder
placed at the inferior and lateral surface of frontal
lobes to make right and left boundary markers.
The brain tissue within the area of the superior,
right, and left limit is defined as the OFC volume.
Detail of this tracing method was reported in a
previous study (33). Medial and lateral OFC were
divided by the olfactory sulcus and traced from the
tip of the genu of the corpus callosum to the slice
where the olfactory sulcus disappears. All volu-
metric measurements were manually traced by the
same evaluator, who was blinded to subjects?
identities and diagnoses (FGN). The intraclass
reliability of total and subregions of OFC were
Intracranial volume (ICV) was also traced man-
ually by two raters blinded to subjects? diagnoses
and with high inter-rater reliability (ICC ‡ 0.98).
The first slice that included the brain matter was
traced around the outside border of the brain and
included the total cerebral gray and white matter,
CSF, dura matter, and sinuses. The temporal lobes,
optic chiasma, pituitary, brain stem, and the cere-
bellar hemispheres were included. The base of the
cerebellum was the limitation of the inferior border.
Tracing was continued until no brain matter was
visible. Gray matter volumes were calculated from
the segmentated images using BRAINS2. The vol-
(ICC) for inter-rater
Fisher?s Exact test and v2test, used for cross-
tabulating qualitative data, and Mann-Whitney U-
test and Student?s t-test, for ordinal and interval
scale data, were used to compare the BD patients
and HC with respect to clinical and demographic
variables. First, we assessed the effect of diagnosis
(BD patients versus HC) or subdiagnosis according
to mood state (depressed BD patients versus
euthymic BD patients) or medication status
(unmedicated BD patients versus lithium-treated
BD patients) on the gray matter volumes of OFC
(total OFC, right and left OFC, medial and lateral
OFC) using analysis of covariance (ANCOVA),
with age, gender, and ICV as covariates. Because
the samples were well matched on age and gender,
and because these effects did not approach statis-
tical significance in the ANCOVA, we excluded age
and gender in the final model without any signif-
icant impact in the results. Therefore, we present
our results with ICV as the only covariate. Euthy-
mic BD patients had a younger age at onset of
disease than depressed BD patients, and as age at
disease onset might possibly affect brain anatomy,
we also explored the association of mood state and
gray matter volumes by adding age at onset of
disease as a covariate. We report partial eta
squared (gp2) as an estimate of effect size. The gp2
is interpreted as the proportion of variance in the
dependent variable (in this study, gray matter
volume) that is predicted by the independent
variable (diagnosis) while holding the covariates
constant. Spearman correlations were performed
to test the relationship between the OFC gray
matter volumes and some clinical variables, such as
age of disease onset, length of illness, number of
depressive episodes, and
intensity as measured by HDRS scores. Statistical
analyses were conducted using SPSS version 14.0
(SPSS, Inc., Chicago, IL, USA). Differences with
p < 0.05 were considered statistically significant.
No adjustment was made for the multiple ROIs
Demographic and clinical information for BD
patients and HC are displayed in Table 1. There
were no statistically significant differences between
BD patients and HC with respect to the gray
matter volumes of the total OFC or any of its
major subdivisions (left or right, medial or lateral).
There also was no interaction between gender and
diagnosis on total or regional OFC gray matter
volumes. Means and standard deviations for OFC
gray matter volumes are shown in Table 1.
At the time of study entry, 10 (35.7%) BD
patients were currently in a major depressive
episode and 18 (64.3%) were euthymic according
to SCID criteria. These subgroups of patients did
not statistically differ with respect to age (mean
± SD: 34.8 ± 10.2versus
Mann-Whitney U-test, p = 0.59), ICV (1465.1 ±
195.3 versus 1497.8 ± 125.8 cm3; t-test: df = 26,
p = 0.59) or total gray matter volumes (669.8 ±
113.7 versus 724.4 ± 77.0 cm3; t-test: df = 26,
p = 0.14). Gender, ethnicity, and handedness were
not statistically different between these two groups
(Fisher?s Exact test, all p = 1.0). Depressed and
euthymic BD patients also did not statistically
differ regarding subtype of BD, medication status,
length of illness, number of previous affective
episodes, or family history of mood disorders in
first-degree relatives (details in Table 2).
Depressed BD patients presented smaller gray
matter volumes than euthymic BD patients. Spe-
cifically, total OFC gray matter volume was
smaller in depressed patients compared to euthy-
mic patients (15.8 ± 4.8 versus 18.7 ± 3.9 cm3,
F(1, 25)= 4.3, p = 0.049 uncorrected for multiple
regions, gp2= 0.15). Depressed patients also
33.6 ± 12.7 years;
Nery et al.
presented smaller right OFC gray matter volumes,
and marginally smaller left and lateral OFC gray
matter volumes compared to euthymic patients (see
Table 3). The depressed patients were significantly
older at illness onset than the euthymic patients
(Table 2). We explored the role of age at disease
onset, adding this variable as a covariate in the
ANCOVA model. The results remained essentially
the same (i.e., differences in total, left, right, and
lateral OFC gray matter volumes were significantly
smaller in depressed compared to euthymic BD
Within the BD group, 18 patients were being
treated with lithium monotherapy (mean ± SD
Table 1. Demographic and clinical characteristics and orbitofrontal cortex (OFC) gray matter volumes of bipolar disorder (BD) patients and age and gender
matched healthy controls (HC)
Characteristics BD patients (n = 28) HC (n = 28)F(df)
Demographic and clinical
Age, years (mean ± SD)
Male, n (%)
Right-handed, n (%)
Ethnicity, n (%)
Educational level, n (%)
Graduated high school or equivalent
Graduated 2 years college
Graduated 4 years college
Part graduate ⁄professional school
Completed graduate ⁄professional school
Age at disease onset, years (mean ± SD)
Length of illness, years (mean ± SD)
Number of previous affective episodes (mean ± SD)
HDRS score (mean ± SD)
Medication status, n (%)
Total brain volumes (cm3)
Total gray matter
OFC gray matter volumes (cm3)
34.0 ± 11.9
32.5 ± 8.5
19.5 ± 7.1
14.0 ± 8.6
17.1 ± 24.0
8.5 ± 10.0
1468.1 ± 151.4
704.9 ± 93.6
1468.5 ± 116.2
688.0 ± 60
17.7 ± 4.4
8.9 ± 2.2
8.8 ± 2.3
6.8 ± 1.5
10.9 ± 3.2
17.1 ± 3.3
8.7 ± 2.2
8.4 ± 1.8
6.7 ± 1.2
10.4 ± 2.3
ANCOVA with intracranial volume (ICV) as covariate. No statistical correction for multiple regions.
HDRS = Hamilton Depression Rating Scale.
Table 2. Demographic and clinical characteristics of depressed and euthymic bipolar disorder (BD) patients
Characteristics Depressed BD patients (n = 10)Euthymic BD patients (n = 18)p value
Age, years (mean ± SD)
Gender [males, n (%)]
Handedness [right-handed, n (%)]
Subtype I, n (%)
Medication status, n (%)
Age at onset of illness, years (mean ± SD)
Length of illness, years (mean ± SD)
Number of affective episodes (mean ± SD)
Family history of mood disorders, n (%)
HDRS score (mean ± SD)
34.8 ± 10.3
33.6 ± 12.7
23.2 ± 7.5
11.7 ± 8.0
16.7 ± 17.5
17.3 ± 9.2
17.1 ± 5.9
15.4 ± 8.9
17.3 ± 26.9
2.6 ± 4.5
HDRS = Hamilton Depression Rating Scale.
Orbitofrontal cortex in bipolar disorder
dose: 1079.2 ± 332.6 mg ⁄d; mean ± SD duration
of lithium treatment: 121 ± 235.7 weeks) and 10
patients had not received any psychotropic medi-
cation for two weeks and had not received lithium
for four weeks prior to the study. Lithium-treated
patients presented slightly larger total and subre-
gional OFC gray matter volumes than unmedi-
cated patients (18.5 ± 4.0 versus 16.1 ± 4.7 cm3
for total OFC; 9.3 ± 2.0 versus 8.1 ± 2.4 cm3for
right OFC; 9.2 ± 2.1 versus 8.0 ± 2.4 cm3for left
OFC; 7.1 ± 1.4 versus 6.2 ± 1.5 cm3for medial
OFC; 11.4 ± 3.0 versus 9.9 ± 3.5 cm3for lateral
OFC). However, none of these differences were
statistically significant after adjusting for ICV
(F £ 0.83; p ‡ 0.37). Among the lithium-treated
patients, 14 were euthymic and four were depressed
at the time of the scan. Among the unmedicated
patients, four were euthymic and six were de-
pressed. There were no interactions between mood
state and lithium treatment for total or subregional
OFC volumes (F £ 0.49; p ‡ 0.18).
Within the BD group, there was an inverse
correlation between depressive symptom intensity
and total OFC gray matter volume (r = –0.43.
p = 0.03),rightOFC
(r = )0.45, p= 0.02), and medial OFC gray
matter volume (r = –0.41, p = 0.04). There was
a trend toward a significant correlation between
depressive symptom intensity and left OFC gray
matter volume (r = –0.39, p = 0.06) and lateral
OFC gray matter volume (r = –0.38, p = 0.06).
OFC gray matter volumes were not correlated with
age at disease onset, length of illness, or number of
episodes, and were not associated with family
history of mood disorders.
Contrary to our expectations, we found that BD
patients and HC did not statistically differ on
gray matter volumes of total OFC or any of its
subdivisions. Besides having an adequate age and
gender matching for the samples, we carefully
examined the statistical effects of these variables
on the ANCOVA models. There was no effect
for any of the analyses, and therefore, possible
confounding effects of age and gender on our
results are unlikely. Moreover, the partial eta
squared (gp2) statistics associated with the OFC
gray matter volume differences between BD
patients and HC were uniformly very small
Accordingly, the means and SDs of the gray
matter volumes of the two groups were very
similar. Such negligible sample mean differences
suggest that our failure to find significant differ-
ences probably was not merely a problem of
small sample size.
To the best of our knowledge, this is the first
report of OFC manual tracing in adult BD
patients. The hypothesis-driven manual tracing
method has some advantages over automated
voxel-based methods due to its better accuracy in
providing absolute volumes of gray matter and its
better anatomical validity (29). Although debated,
it is considered by some to be the gold standard
method for structural MRI studies (34).
A possible explanation for our negative results is
that small OFC gray matter volumes are not a
characteristic trait abnormality of BD. Smaller
OFC gray matter volumes have been reported in
MDD patients (20–24) but not consistently in BD
patients. Few studies have reported structural OFC
abnormalities in adult BD patients in comparison
to HC (25, 26, 34, 35), and most of them used an
automated voxel-based method, as opposed to our
manual tracing study. OFC volumetric differences
between BD and HC were also reported in children
and adolescents (27, 35, 36). Differences in brain
developmental stages according to age, gender, or
putative effects of psychotropic medications and
mood states might account for the differences
between these studies and our findings. In fact, in
the Blumberg et al. study (35), there were OFC
volumetric differences only between the young BD
patients compared to age-matched HC, but not
between adult BD patients compared to age-
matched HC. The authors postulated that these
Table 3. Orbitofrontal cortex (OFC) gray matter volumes of depressed and euthymic bipolar disorder (BD) patients
OFC gray matter
patients (n = 10)
patients (n = 18)F(df)
15.8 ± 4.7
7.9 ± 2.5
7.9 ± 2.3
6.3 ± 1.8
9.4 ± 3.3
1465.3 ± 195.3
18.7 ± 3.9
9.4 ± 1.8
9.3 ± 2.1
7.1 ± 1.2
11.6 ± 3.0
1497.8 ± 125.8
Volumes expressed as mean ± SD.
ANCOVA with intracranial volume as covariate. No statistical correction for multiple comparisons.
Nery et al.
discrepancies might be caused by a BD-related
acceleration of the natural age-related decline in
ventral prefrontal cortex volumes (a region that
includes the OFC).
In a secondary analysis, we found an associ-
ation between OFC gray matter volumes and
mood state in the BD group. Depressed BD
patients had smaller OFC gray matter volumes
than euthymic BD patients, and OFC gray
matter volumes were inversely correlated with
depressive symptom intensity as assessed by the
HDRS. The effect sizes for these differences were
modest, suggesting that only 15% of the differ-
ence between the two subgroups was associated
with mood state. We attempted to identify other
clinical characteristics of BD that potentially
could explain the relationship between mood
state and OFC volumes. The depressed and
euthymic subgroups presented similar mean age,
length of illness, number of affective episodes,
gender, BD subtype, and family history of mood
disorders (for details, see Table 2). There was
also no effect of interaction between mood state
and lithium treatment on OFC gray matter
volumes. The depressed BD subgroup was older
at disease onset, but adding this variable as a
covariate in the ANCOVA model resulted in
essentially the same results. Therefore, the differ-
ence in age at disease onset probably does not
explain why depressed and euthymic BD patients
are different on OFC gray matter volumes. On
the other hand, we found negative correlations of
moderate effect size (r ranging from –0.38 to
–0.45) between depressive symptom intensity and
gray matter volumes in most of the same areas,
which reinforces a possible association between
gray matter volumes and mood state.
Our tentative findings of OFC gray matter differ-
ences according to mood state are concordant with
several functional studies showing changes in OFC
metabolism in depressed or manic BD patients (13–
18). In interpreting these tentative findings, we
should consider what specific neuropathological
mechanism may possibly underlie depression-re-
lated OFC gray matter reduction. These differences
could be related to brain cellular changes in
depressed states, including atrophy and shrinkage
of neuronal cell bodies (37–40), or gray matter
decrement as a consequence of chronic dietary and
fluid restriction (41, 42). We should caution, how-
ever, that the association between mood state and
OFC gray matter volumes, although intriguing,
is modest and was not a predicted finding. If
Bonferroni correction were applied, the association
between mood state and OFC gray matter volumes
would not be significant. Therefore, these findings
need to be replicated and any potential explanation
is speculative at this point.
Part of the present sample has been included in
previous structural MRI reports that focused on
other brain structures (43–46). As opposed to some
of these reports (44–46), we did not find evidence
for effects of lithium treatment on gray matter
volumes. Rather, our results were in line with those
of Brambilla et al. (43), who did not observe
differences on subgenual prefrontal cortex gray
matter volumes between lithium-treated and un-
treated BD patients. Nonetheless, the hypothesis of
lithium having potential effects on gray matter
volumes could only be conclusively tested by a
longitudinal study evaluating patients before and
after lithium treatment.
Some limitations of this study should be
considered. Many factors, including subtype (I
or II), psychotic BD as opposed to nonpsychotic
duration of illness, and number of affective
influence the gray matter volumes of different
brain structures (45, 47–52). In this sense, the
study of subtypes of BD in larger samples that
would permit comparisons across subgroups of
patients and HC would be more suitable for
investigating OFC structural abnormalities. An-
other limitation is that this was a cross-sectional
study, so comparisons involving the temporal
trajectory of OFC development or degeneration
in BP and HC cannot be made. The findings
from our exploratory analysis of mood state are
tentative and would not survive a statistical
analysis that corrects for multiple comparisons.
Also, concerning these findings, no BD patient
was in a mixed or manic state, limiting general-
ization to an important segment of the BD
population. Studies are therefore needed that
target differences in mood states a priori and
include larger and more representative samples.
strengths. This is the first study to perform ROI
manual tracing of OFC gray matter volumes in
adult BD patients. Moreover, our sample was well
matched for demographic characteristics, also
In conclusion, we found that BD patients did not
differ statistically from HC on OFC gray matter
volumes. This is in contrast with postmortem and
functional studies showing OFC abnormalities in
BD patients. Studies combining anatomical and
functional methods and ⁄or a prospective design
to mood stabilizers,
also has considerable
Orbitofrontal cortex in bipolar disorder
are warranted to examine potential effects of mood
state on brain structure.
This research was partly supported by MH 68766, MH 068662,
RR 20571, NARSAD, Veterans Affairs (Merit Review), and
the Krus Endowed Chair in Psychiatry. The views expressed in
this paper do not necessarily reflect those of the NIH or the
1. Soares JC. Contributions from brain imaging to the
elucidation of pathophysiology of bipolar disorder. Int J
Neuropsychopharmacol 2003; 6: 171–180.
2. Monkul ES, Malhi GS, Soares JC. Anatomical MRI
abnormalities in bipolar disorder: do they exist and do they
progress? Aust N Z J Psychiatry 2005; 39: 222–226.
3. Strakowski SM, DelBello MP, Adler CM. The functional
neuroanatomy of bipolar disorder: a review of neuroim-
aging findings. Mol Psychiatry 2005; 10: 105–116.
4. Ongur D, Price JL. The organization of networks within
the orbital and medial prefrontal cortex of rats, monkeys
and humans. Cereb Cortex 2000; 10: 206–219.
5. Bechara A, Damasio H, Damasio AR. Emotion, decision
making and the orbitofrontal cortex. Cereb Cortex 2000;
6. Happaney K, Zelazo PD, Stuss DT. Development of
orbitofrontal function: current themes and future direc-
tions. Brain Cogn 2004; 55: 1–10.
7. Jorge RE, Robinson RG, Starkstein SE, Arndt SV, For-
rester AW, Geisler FH. Secondary mania following trau-
matic brain injury. Am J Psychiatry 1993; 150: 916–921.
8. Mega MS, Cummings JL. Frontal-subcortical circuits and
neuropsychiatric disorders. J Neuropsychiatry 1994; 6:
9. Bechara A. The role of emotion in decision-making:
evidence from neurological patients with orbitofrontal
damage. Brain Cogn 2004; 55: 30–40.
10. Cotter D, Hudson L, Landau S. Evidence for orbitofrontal
pathology in bipolar disorder and major depression, but
not in schizophrenia. Bipolar Disord 2005; 7: 358–369.
11. Toro CT, Hallak JEC, Dunham JS, Deakin JFW. Glial
fibrillary acid protein and glutamine synthetase in subre-
gions of prefrontal cortex in schizophrenia and mood
disorder. Neurosci Lett 2006; 404: 276–281.
12. Ryan MM, Lockstone HE, Huffaker SJ, Wayland MT,
Webster MJ, Bahn S. Gene expression analysis of bipolar
disorder reveals downregulation of the ubiquitin cycle and
alterations in synaptic genes. Mol Psychiatry 2006; 11:
13. Blumberg HP, Stern E, Ricketts S et al. Rostral and orbital
prefrontal cortex dysfunction in the manic state of bipolar
disorder. Am J Psychiatry 1999; 156: 1986–1988.
14. Kruger S, Seminowicz D, Goldapple K, Kennedy SH,
Mayberg HS. State and trait influences on mood regulation
in bipolar disorder: blood flow differences with an acute
mood challenge. Biol Psychiatry 2003; 54: 1274–1283.
15. Altshuler LL, Bookheimer SY, Townsend J et al. Blunted
activation in orbitofrontal cortex during mania: a func-
tional magnetic resonance imaging study. Biol Psychiatry
2005; 58: 763–769.
16. Altshuler L, Bookheimer S, Proenza MA et al. Increased
amygdala activation during mania: a functional magnetic
resonance imaging study. Am J Psychiatry 2005; 162:
17. Kruger S, Alda M, Young LT, Goldapple K, Parikh S,
Mayberg HS. Risk and resilience markers in bipolar
disorder: brain responses to emotional challenge in bipolar
patients and their healthy siblings. Am J Psychiatry 2006;
18. Kronhaus DM, Lawrence NS, Williams AM et al. Stroop
performance in bipolar disorder: further evidence for
abnormalities in the ventral prefrontal cortex. Bipolar
Disord 2006; 8: 28–39.
19. Mah L, Zarate CA Jr, Singh J et al. Regional cerebral
glucose metabolic abnormalities in bipolar II depression.
Biol Psychiatry 2007; 61: 765–775.
20. Lai T, Payne ME, Byrum CE, Steffens DC, Krishnan KR.
Reduction of orbital frontal cortex volume in geriatric
depression. Biol Psychiatry 2000; 48: 971–975.
21. Bremner JD, Vythilingam M, Vermetten E et al. Reduced
volume of orbitofrontal cortex in major depression. Biol
Psychiatry 2002; 51: 273–279.
22. Ballmaier M, Toga AW, Blanton RE, Sowell ER, Lavret-
sky H, Peterson J. Anterior cingulate, gyrus rectus, and
orbitofrontal abnormalities in elderly depressed patients:
an MRI-based parcellation of the prefrontal cortex. Am J
Psychiatry 2004; 161: 99–108.
23. Lacerda AL, Keshavan MS, Hardan AY et al. Anatomical
evaluation of the orbitofrontal cortex in major depressive
disorder. Biol Psychiatry 2004; 55: 353–358.
24. Hastings RS, Parsey RV, Oquendo MA, Arango V, Mann
JJ. Volumetric analysis of the prefrontal cortex, amygdala,
and hippocampus in major depression. Neuropsychophar-
macol 2004; 29: 952–959.
25. Frangou S. The Maudsley Bipolar Disorder Project.
Epilepsia 2005; 46 (Suppl. 4): 19–25.
26. Nugent AC, Milham MP, Bain EE et al. Cortical abnor-
malities in bipolar disorder investigated with MRI and
voxel-based morphometry. Neuroimage 2006; 30: 485–
27. Najt P, Nicoletti M, Chen HH et al. Anatomical measure-
ments of the orbitofrontal cortex in child and adolescent
patients with bipolar disorder. Neurosci Lett 2007; 413:
28. Dickstein DP, Milham MP, Nugent AC et al. Frontotem-
poral alterations in pediatric bipolar disorder: results of a
voxel-based morphometry study. Arch Gen Psychiatry
2005; 62: 734–741.
29. Giuliani NR, Calhoun VD, Pearlson GD, Francis A,
Buchanan RW. Voxel-based morphometry versus region
of interest: a comparison of two methods for analyzing
gray matter differences in schizophrenia. Schizophr Res
2005; 74: 135–147.
30. First MG, Spitzer RL, Gibbon M et al. Structured Clinical
Interview for DSM-IV Axis Disorders. New York, NY:
State Psychiatric Institute, Biometrics Research, 1995.
31. Hamilton M. A rating scale for depression. J Neurol
Neurosurg Psychiatry 1960; 23: 56–62.
32. Talairach J, Tournoux P. Co-planar Stereotaxic Atlas of
the Human Brain. Thieme: New York, 1988.
33. Lacerda ALT, Hardan AY, Yorbik O, Keshavan MS.
Measurement of the orbitofrontal cortex: a validation
study of a new method. Neuroimage 2003; 19: 665–673.
34. Lyoo IK, Kim MJ, Stoll AL et al. Frontal lobe gray matter
density decreases in bipolar I disorder. Biol Psychiatry
2004; 55: 648–651.
35. Blumberg HP, Krystal JH, Bansal R et al. Age, rapid-
cycling, and pharmacotherapy effects on ventral prefrontal
Nery et al.
cortex in bipolar disorder: a cross-sectional study. Biol Download full-text
Psychiatry 2006; 59: 611–618.
36. Wilke M, Kowatch RA, DelBello MP, Mills NP, Holland
SK. Voxel-based morphometry in adolescents with bipolar
disorder: first results. Psychiatry Res 2004; 131: 57–69.
37. Sapolsky RM. The possibility of neurotoxicity in the
hippocampus in major depression: a primer on neuron
death. Biol Psychiatry 2000; 48: 755–765.
38. Manji HK, Duman RS. Impairments of neuroplasticity
and cellular resilience in severe mood disorders: implica-
tions for the development of novel therapeutics. Psycho-
pharmacol Bull 2001; 35: 5–49.
39. Rajkowska G. Postmortem studies in mood disorders
indicated altered numbers of neurons and glial cells. Biol
Psychiatry 2000; 48: 766–777.
40. Castren E, Voikar V, Rantamaki T. Role of neurotrophic
factors in depression. Curr Opin Pharmacol 2007; 7: 18–21.
41. Wagner A, Greer P, Bailer UF et al. Normal brain tissue
volumes after long-term recovery in anorexia and bulimia
nervosa. Biol Psychiatry 2006; 59: 291–293.
42. Duning T, Kloska S, Steinstra ¨ ter O, Kugel H, Heindel W,
Knecht S. Dehydration confounds the assessment of brain
atrophy. Neurology 2005; 64: 548–550.
43. Brambilla P, Nicoletti MA, Harenski K et al. Anatomical
MRI study of subgenual prefrontal cortex in bipolar and
unipolar subjects. Neuropsychopharmacology 2002; 27:
44. Sassi RB, Nicoletti M, Brambilla P et al. Increased gray
matter volume in lithium-treated bipolar disorder patients.
Neurosci Lett 2002; 329: 243–245.
45. Sassi RB, Brambilla P, Hatch JP et al. Reduced left
anterior cingulate volumes in untreated bipolar patients.
Biol Psychiatry 2004; 56: 467–475.
46. Bearden CE, Thompson PM, Dalwani M et al. Greater
cortical gray matter density in lithium-treated patients with
bipolar disorder. Biol Psychiatry 2007; 62: 7–16.
47. Moore GJ, Bebchuk JM, Wilds IB, Chen G, Manji HK.
Lithium-induced increase in human brain grey matter.
Lancet 2000; 356: 1241–1242.
48. Brambilla P, Harenski K, Nicoletti MA et al. Anatomical
MRI study of basal ganglia in bipolar disorder patients.
Psychiatry Res 2001; 106: 65–80.
49. Lopez-Larson MP, DelBello MP, Zimmerman ME, Schw-
iers ML, Strakowski SM. Regional prefrontal gray and
white matter abnormalities in bipolar disorder. Biol
Psychiatry 2002; 52: 93–100.
50. McGrath BM, Wessels PH, Bell EC, Ulrich M, Silverstone
PH. Neurobiological findings in bipolar II disorder com-
pared with findings in bipolar I disorder. Can J Psychiatry
2004; 49: 794–801.
51. Chang K, Karchemskiy A, Barnea-Goraly N, Garrett A,
Simeonova DI, Reiss A. Reduced amygdalar gray matter
volume in familial pediatric bipolar disorder. J Am Acad
Child Adolesc Psychiatry 2005; 44: 567–573.
52. Strasser HC, Lilvestrom J, Ashby ER et al. Hippocampal
and ventricular volumes in psychotic and nonpsychotic
bipolar patients compared with schizophrenia patients and
community control subjects: a pilot study. Biol Psychiatry
2005; 57: 633–639.
Orbitofrontal cortex in bipolar disorder