Structural Abnormalities in Bipolar Euthymia: A Multicontrast Molecular Diffusion Imaging Study

doi:10.1016/j.biopsych.2013.09.027

Biological Psychiatry

Volume 76, Issue 3, 1 August 2014, Pages 239-248

https://doi.org/10.1016/j.biopsych.2013.09.027 Get rights and content

Background

Evidence from decades of magnetic resonance imaging (MRI) research in bipolar disorder has been summarized in meta-analyses of various MRI modalities. Notably, although structural MRI studies suggest gray matter reductions are restricted to specific cortical regions, functional MRI has also shown involvement of subcortical dysfunction. Such disparity in results is open to discussion and requires further exploration with additional MRI modalities.

Methods

We applied whole-brain high angular resolution molecular diffusion imaging to compare different properties of the water diffusion process in brain tissues, using different contrasts. Specifically, we looked at fractional anisotropy, mean diffusivity, probability of return to the origin, and generalized fractional anisotropy in a sample of 40 euthymic patients with bipolar disorder and 40 well-matched healthy control subjects.

Results

Convergent abnormalities were detected by contrasts in various tissue types. Apart from alterations in white matter (in corpus callosum, cingulum bundle, corona radiata, and superior fronto-occipital fasciculus) and cortical gray matter (in medial frontal cortex, left insula, Heschl’s gyrus, and cerebellum), three of the contrasts (fractional anisotropy, mean diffusivity, and generalized fractional anisotropy) revealed abnormalities in subcortical structures, including the hippocampus, the thalamus and the caudate nucleus.

Conclusions

Our findings point to a wider pattern of axonal pathology in bipolar disorder than previously thought. Although findings related to cortical gray matter are consistent with structural meta-analyses, subcortical abnormalities suggest a cytoarchitectonic basis for previously reported subcortical dysfunction. Diffusion results could be interpreted in terms of loss of tissue volume and/or altered membrane permeability, agreeing with both hypotheses of mitochondrial malfunction and neuroinflammation.

Section snippets

Subjects

A sample of 40 patients with bipolar I disorder, diagnosed using the DSM-IV, were recruited from two psychiatric hospitals in the Barcelona area. The diagnosis was based on a clinical interview by members of the research team and review of case notes. All patients were scanned in the same scanner during euthymia, defined as patients having a Young Mania Rating Scale score less than 8 and a Hamilton Rating Scale for Depression score less than 8, during the 3 months before the scanning session

Demographic and Clinical Data

Demographic data for the patients with bipolar disorder and healthy control subjects are listed in Table 1. The groups were matched for age, sex, and estimated premorbid IQ (TAP score).

Imaging Results

The findings obtained from the whole-brain statistical analyses performed for each diffusion contrast separately are reported in Table 2 and Figure 2. Briefly, no significant increases in FA were found in patients, but significant reductions were observed in the splenium of corpus callosum (CC) and right insula.

Discussion

In this study, the perigenual areas of the frontomedial cortex, the anterior cingulate cortex, and the anterior insula of individuals with bipolar disorder showed altered microstructural organization as revealed by different molecular diffusion imaging contrasts. This result is consistent with previous structural meta-analyses 1, 2, 3, 4, which have found gray matter reductions in these regions.

Similarly, there is a notable degree of overlap between our results and those reported in fMRI

References (68)

E. Bora et al.
Voxelwise meta-analysis of gray matter abnormalities in bipolar disorder
Biol Psychiatry
(2010)
I. Ellison-Wright et al.
Anatomy of bipolar disorder and schizophrenia: A meta-analysis
Schizophr Res
(2010)
J. Houenou et al.
Neuroimaging-based markers of bipolar disorder: Evidence from two meta-analyses
J Affect Disord
(2011)
G. Delvecchio et al.
Common and distinct neural correlates of emotional processing in bipolar disorder and major depressive disorder: A voxel-based meta-analysis of functional magnetic resonance imaging studies
Eur Neuropsychopharmacol
(2012)
F.E. Vederine et al.
A meta-analysis of whole-brain diffusion tensor imaging studies in bipolar disorder
Prog Neuropsychopharmacol Biol Psychiatry
(2011)
J.X. Liu et al.
Differences in white matter abnormalities between bipolar I and II disorders
J Affect Disord
(2010)
Z. Chen et al.
Voxel based morphometric and diffusion tensor imaging analysis in male bipolar patients with first-episode mania
Prog Neuropsychopharmacol Biol Psychiatry
(2012)
F. Wang et al.
Functional and structural connectivity between the perigenual anterior cingulate and amygdala in bipolar disorder
Biol Psychiatry
(2009)
J. Sui et al.
Discriminating schizophrenia and bipolar disorder by fusing fMRI and DTI in a multimodal CCA+ joint ICA model
Neuroimage
(2011)
T. Del Ser et al.
Estimation of premorbid intelligence in Spanish people with the Word Accentuation Test and its application to the diagnosis of dementia
Brain Cogn
(1997)

J.J. Gomar et al.

Validation of the Word Accentuation Test (TAP) as a means of estimating premorbid IQ in Spanish speakers

Schizophr Res

(2011)

S.M. Smith et al.

Advances in functional and structural MR image analysis and implementation as FSL

Neuroimage

(2004)

P.J. Basser et al.

Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI

J Magn Reson B

(1996)

P.P. Mitra et al.

Pulsed-field-gradient NMR measurements of restricted diffusion and the return-to-the-origin probability

J Magn Reson, Ser A

(1995)

E.J. Canales-Rodriguez et al.

Diffusion orientation transform revisited

Neuroimage

(2010)

J. Cohen-Adad et al.

Detection of multiple pathways in the spinal cord using q-ball imaging

Neuroimage

(2008)

Y. Assaf et al.

White matter changes in multiple sclerosis: Correlation of q-space diffusion MRI and 1H MRS

Magn Reson Imaging

(2005)

Y.C. Wu et al.

Hybrid diffusion imaging

Neuroimage

(2007)

A.P. Hosseinbor et al.

Bessel Fourier Orientation Reconstruction (BFOR): An analytical diffusion propagator reconstruction for hybrid diffusion imaging and computation of q-space indices

Neuroimage

(2013)

J. Cohen-Adad et al.

Demyelination and degeneration in the injured human spinal cord detected with diffusion and magnetization transfer MRI

Neuroimage

(2011)

B.B. Avants et al.

Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain

Med Image Anal

(2008)

B.B. Avants et al.

Multivariate analysis of structural and diffusion imaging in traumatic brain injury

Acad Radiol

(2008)

A. Yildiz-Yesiloglu et al.

Neurochemical alterations of the brain in bipolar disorder and their implications for pathophysiology: A systematic review of the in vivo proton magnetic resonance spectroscopy findings

Prog Neuropsychopharmacol Biol Psychiatry

(2006)

L.M. Rimol et al.

Cortical thickness and subcortical volumes in schizophrenia and bipolar disorder

Biol Psychiatry

(2010)

B. Hallahan et al.

Structural magnetic resonance imaging in bipolar disorder: An international collaborative mega-analysis of individual adult patient data

Biol Psychiatry

(2011)

C.D. Ladouceur et al.

Subcortical gray matter volume abnormalities in healthy bipolar offspring: Potential neuroanatomical risk marker for bipolar disorder?

J Am Acad Child Adolesc Psychiatry

(2008)

L. Emsell et al.

Limbic and callosal white matter changes in euthymic bipolar I disorder: An advanced diffusion magnetic resonance imaging tractography study

Biol Psychiatry

(2013)

A.H. Miller et al.

Inflammation and its discontents: The role of cytokines in the pathophysiology of major depression

Biol Psychiatry

(2009)

L. Xu et al.

Joint source based morphometry identifies linked gray and white matter group differences

Neuroimage

(2009)

Y.I. Sheline

Neuroimaging studies of mood disorder effects on the brain

Biol Psychiatry

(2003)

L. Clark et al.

Sustained attention-deficit confirmed in euthymic bipolar disorder but not in first-degree relatives of bipolar patients or euthymic unipolar depression

Biol Psychiatry

(2005)

J.D. Tournier et al.

Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution

Neuroimage

(2007)

E.J. Canales-Rodriguez et al.

Deconvolution in diffusion spectrum imaging

Neuroimage

(2010)

B.A. Landman et al.

Effects of diffusion weighting schemes on the reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T

Neuroimage

(2007)

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Archival ReportStructural Abnormalities in Bipolar Euthymia: A Multicontrast Molecular Diffusion Imaging Study

Background

Methods

Results

Conclusions

Section snippets

Subjects

Demographic and Clinical Data

Imaging Results

Discussion

Biol Psychiatry

Schizophr Res

J Affect Disord

Eur Neuropsychopharmacol

Prog Neuropsychopharmacol Biol Psychiatry

J Affect Disord

Prog Neuropsychopharmacol Biol Psychiatry

Biol Psychiatry

Neuroimage

Brain Cogn

Schizophr Res

Neuroimage

J Magn Reson B

J Magn Reson, Ser A

Neuroimage

Neuroimage

Magn Reson Imaging

Neuroimage

Neuroimage

Neuroimage

Med Image Anal

Acad Radiol

Prog Neuropsychopharmacol Biol Psychiatry

Biol Psychiatry

Biol Psychiatry

J Am Acad Child Adolesc Psychiatry

Biol Psychiatry

Biol Psychiatry

Neuroimage

Biol Psychiatry

Biol Psychiatry

Neuroimage

Neuroimage

Neuroimage

Archival Report
Structural Abnormalities in Bipolar Euthymia: A Multicontrast Molecular Diffusion Imaging Study