Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, and Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, USA
Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, Connecticut, USA, and Institute of Mathematics and Statistics, University of São Paulo, Brazil
Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, and Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, USA
Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, Connecticut, USA
Biomedical Engineering Department, University of Alberta, Edmonton, Canada
Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, and Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, USA
Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, Connecticut, USA
Department of Psychiatry and Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, and Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, USA
Correspondence: Dr Fei Wang, Department of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 901, New Haven, CT 06511, USA. Email: fei.wang{at}yale.edu
H.P.B. has been consultant to Pfizer Inc. and has received honoraria from Eli Lilly and Abbott Laboratories.
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Convergent evidence implicates white matter abnormalities in bipolar disorder. The cingulum is an important candidate structure for study in bipolar disorder as it provides substantial white matter connections within the corticolimbic neural system that subserves emotional regulation involved in the disorder.
Aims
To test the hypothesis that bipolar disorder is associated with abnormal white matter integrity in the cingulum.
Method
Fractional anisotropy in the anterior and posterior cingulum was compared between 42 participants with bipolar disorder and 42 healthy participants using diffusion tensor imaging.
Results
Fractional anisotropy was significantly decreased in the anterior cingulum in the bipolar disorder group compared with the healthy group (P=0.003); however, fractional anisotropy in the posterior cingulum did not differ significantly between groups.
Conclusions
Our findings demonstrate abnormalities in the structural integrity of the anterior cingulum in bipolar disorder. They extend evidence that supports involvement of the neural system comprising the anterior cingulate cortex and its corticolimbic gray matter connection sites in bipolar disorder to implicate abnormalities in the white matter connections within the system provided by the cingulum.
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Evidence from post-mortem histological and structural neuro-imaging studies supports the involvement of white matter in the pathophysiology of bipolar disorder. Decreases in glial density in anterior cingulate and orbitofrontal cortical subregions were observed in bipolar disorder.12,13 Oligodendrocyte abnormalities in bipolar disorder are increasingly implicated by reports of decreased oligodendrocyte density, as well as reduced expression of oligodendrocyte- and myelination-related genes, in the frontal cortex.14,15 Magnetic resonance imaging (MRI) studies of bipolar disorder provide further evidence for white matter abnormalities, including abnormalities in the volume and structural integrity of frontal white matter.16–20
Diffusion tensor imaging presents the opportunity to measure the organisation of fibres within specific white matter tracts.21 Fractional anisotropy is an indirect measure of the coordinated directionality and coherence of fibres within a white matter fibre bundle.22 Decreases in fractional anisotropy have been detected in disorders of central nervous system myelination,23–25 suggesting that fractional anisotropy is a measure sensitive to myelination abnormalities. In this study, a diffusion tensor imaging method that provides accurate isolation of the cingulum bundle was used to measure fractional anisotropy in the anterior and posterior cingulum to test the hypothesis that bipolar disorder is associated with abnormalities in the structural integrity of the cingulum bundle. Anterior cingulum deficits in bipolar disorder were anticipated.
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Twenty-five (60%) participants with bipolar disorder met criteria for rapid cycling. At the time of scanning, 11 (26%) participants with bipolar disorder met DSM–IV criteria for a current manic/mixed or hypomanic episode, 9 (21%) for a depressive episode and 22 (52%) were euthymic. Comorbidity included panic disorder (4 participants with bipolar disorder, 10%) and post-traumatic stress disorder (2 participants, 5%). Seven (17%) participants with bipolar disorder were unmedicated. Psychotropic medications prescribed to the remaining participants with bipolar disorder included lithium carbonate (n=11, 26%), anticonvulsants (n=20, 48%), atypical antipsychotics (n=19, 45%), antidepressants (n=17, 40%), benzodiazepines (n=8, 19%) and levothyroxine sodium (n=5, 12%).
Magnetic resonance imaging acquisition
Diffusion-weighted images were acquired on a 3T Trio MR scanner (Siemens,
Erlangen, Germany) with a single-shot echo planar imaging sequence in
alignment with the anterior commissure–posterior commissure plane.
Diffusion sensitising gradients were applied along 32 non-colinear directions
uniformly distributed on a unit sphere, with b-value=1000 s/mm2,
together with an acquisition without diffusion weighting (b-value=0)
(repetition time (TR)=7400 ms, time to echo (TE)=115 ms, field of view
(FOV)=256 x 256 mm2, matrix=128 x 128, slice thickness=
3 mm without gap, 40 slices, 1 average).
Diffusion tensor imaging processing
Diffusion tensor imaging data were processed with BioImage Suite for
Windows
(www.bioimagesuite.org).
Diffusion-weighted data were first interpolated to 2 mm thickness along the
coronaloblique direction with a within-plane resolution of 1 mm x 1 mm,
and denoised by a three-dimensional isotropic Gaussian kernel with sigma 1 mm
full-width-at-half-maximum. After diagonalisation of diffusion tensor imaging,
diffusion eigenvectors and corresponding eigenvalues (
1,
2,
3) were acquired. Fractional
anisotropy was calculated according to the following
formula:21
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The absolute red-green-blue colour-encoding scheme defined the directionality of the principal eigenvector:27 left–right fibres in red, anterior–posterior fibres in green and superior–inferior fibres in blue. The colour-coded diffusion tensor images provided excellent distinction between the cingulum and nearby bundles such as the corpus callosum. The cingulum bundle was delineated to include voxels containing fibres travelling in the anterior–posterior direction that also exhibited fractional anisotropy greater than 0.2 in order to exclude gray matter.28 The cingulum was further subdivided into anterior and posterior sections by the coronal-oblique slice perpendicular to the anterior commissure–posterior commissure line and passing through its midpoint (the mid-anterior commissure–posterior commissure slice) (see online Fig. DS1). Then, mean fractional anisotropy was calculated separately for anterior cingulum and posterior cingulum regions of interest, each in the right and left hemispheres. Specifically, fractional anisotropy for the anterior cingulum regions of interest was calculated as the mean cingulum fractional anisotropy over five coronal slices (sampled every three slices with 6 mm intervals between sampled slices) anterior to the mid-anterior commissure–posterior commissure slice; fractional anisotropy in the posterior cingulum region of interest was calculated as the mean cingulum fractional anisotropy over five coronal slices (sampled every three slices with 6 mm intervals between sampled slices) posterior to and including the midanterior commissure–posterior commissure slice. High interrater reliability for manual delineation on the coronal slices was obtained with intraclass correlation coefficients of 0.92–0.95.
Statistical analysis
All data were analysed using SAS, version 9.1 for Windows. Fractional
anisotropy values were tested for normality using Kolmogorov–Smirnov
test statistics and normal probability plots. The primary statistical mixed
model (PROC MIXED) tested whether the bipolar disorder and healthy control
groups differed in regional fractional anisotropy values. The model included
data from all participants (n=84), a fixed effect of diagnosis
(bipolar disorder and healthy controls) and random participant effects.
Repeated measures were performed over the spatial domain of region (anterior
and posterior cingulum) and hemisphere (right and left) and were included as
within-participant factors in the model. Age and gender served as covariates,
and all twoand three-way interactions were fitted in the final models. The
correlation structure of the data was modelled by random effects for
participant and by unstructured variance–covariance matrix for
observations on the two cingulums within each hemisphere. The latter
variance–covariance structure was the best fitting according to the
Akaike Information Criterion. Only significant results (P<0.05)
involving diagnosis are reported below. Least squares means and standard
errors were calculated from the mixed model for regional fractional anisotropy
values and plotted to interpret diagnosis effects.
Post hoc exploratory analyses were performed for potential main effects of clinical variables among bipolar disorder participants. Clinical factors examined included presence or absence of rapid cycling, mood state at the time of scanning and medication status at scanning.
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The main effect of diagnosis was significant (F(1,240)=6.33, P=0.013), as was the diagnosis by region interaction (F(1,240)=5.1, P=0.025). The difference of least squares means between the diagnostic groups (Fig. 1) indicated that the stronger contribution to group differences was derived from smaller anterior cingulum fractional anisotropy values in the bipolar disorder group compared with the healthy control group. Anterior cingulum fractional anisotropy was decreased significantly in the bipolar disorder group compared with the healthy control group (F(1,240)=9.36, P=0.003); posterior cingulum fractional anisotropy was decreased to a lesser extent in the bipolar disorder group compared with the healthy control group, and the difference was not significant (F(1,240)=2.81, P=0.10). Exploratory analyses did not reveal any significant main effects of clinical factors within the bipolar disorder group on anterior cingulum fractional anisotropy values, including presence or absence of rapid cycling (P=0.40), mood state at the time of scanning (P=0.91) and medication status at scanning (P=0.77).
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Fig. 1 Least squares mean fractional anisotropy values in the anterior and
posterior cingulum and standard errors for the bipolar disorder group
(n=42) and the healthy control group (n=42). Means are adjusted for age, gender and hemisphere. *The effect of diagnosis was significant in the anterior cingulum (P<0.05).
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The findings are consistent with previous diffusion tensor imaging reports consistent with abnormalities in individuals with bipolar disorder in the structural integrity of frontal white matter including ventral regions, as well as areas that contain frontostriato-thalamic projections.16–18,20 This, however, is the first report that we are aware of to specifically examine the cingulum bundle with diffusion tensor image methodology in individuals with bipolar disorder and to report anterior cingulum fractional anisotropy abnormalities. The region of interest method employed has the strong advantage of providing excellent, reliable delineation of the cingulum. However, it is possible that regional abnormalities extend to subgenual subregions further ventral than studied herein.
The specific cellular abnormalities that underlie differences in fractional anisotropy cannot be concluded from this study. Although the organisation of myelinated fibres within white matter bundles is thought to be the major contribution to fractional anisotropy values, and the findings are consistent with reports of decreases in frontal oligodendrocytes in the disorder,15 other microstructural components of white matter fibres such as axonal membranes, microtubules and neurofilaments could potentially affect fractional anisotropy measures.22 Further, a recent study by Houenou et al29 that employed diffusion tensor image tractography methodology demonstrated an increased number of reconstructed fibres between the left subgenual cingulate and left amygdalo-hippocampal, supporting the presence of macrostructural abnormalities in connectivity in bipolar disorder.29 This suggests the importance of examination of both micro- and macrostructure of white matter connectivity in future studies of bipolar disorder.
We did not detect significant main effects of clinical variables such as presence or absence of rapid cycling, mood state or medication status within the bipolar disorder group on the anterior cingulum fractional anisotropy values. However, our ability to detect effects of these factors might have been limited by inadequate power and heterogeneous bipolar disorder participant samples. A previous diffusion tensor imaging report of frontal white matter abnormalities in medication-naïve adolescents with bipolar disorder suggests that white matter abnormalities may be early manifestations of the disorder that are not related to repeated episodes or medication exposure.17
Conclusions
Our findings indicate the presence of abnormalities in the structural
integrity of the anterior cingulum in bipolar disorder. Further understanding
of abnormalities in anterior cingulum white matter may prove important in the
treatment of mood disorders. For example, a deep brain stimulation study that
targeted white matter proximal to the anterior cingulum, albeit in a more
ventral region, showed effectiveness in treating
depression.30 This
suggests that a focus of future research on white matter in the anterior
cingulum may help to elucidate the pathophysiology underlying neural circuitry
abnormalities in bipolar disorder and point to new treatment strategies.
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This article is dedicated to Ms Kathleen Colonese who was devoted to helping those suffering from psychiatric illnesses and to advancing the field of bipolar disorder research. The authors thank Cheryl Lacadie, Karen Martin, Terry Hickey and Hedy Sarofin for their technical expertise, Allison McDonough and Lindsay Warren for assistance with the study, and the people who participated in this study.
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