Regional brain gray matter volume differences in patients with bipolar disorder as assessed by optimized voxel-based morphometry.

Department of Neuroscience, New York State Psychiatric Institute, New York, New York 10032, USA.
Biological Psychiatry (Impact Factor: 10.26). 07/2004; 55(12):1154-62. DOI: 10.1016/j.biopsych.2004.02.026
Source: PubMed


Structural magnetic resonance imaging (MRI) studies of regions of interest in brain have been inconsistent in demonstrating volumetric differences in subjects with bipolar disorder (BD). Voxel-based morphometry (VBM) provides an unbiased survey of the brain, can identify novel brain areas, and validates previously hypothesized regions. We conducted both optimized VBM, comparing MRI gray matter volume, and traditional VBM, comparing MRI gray matter density, in 11 BD subjects and 31 healthy volunteers. To our knowledge, these are the first VBM analyses of BD.
Segmented MRI gray matter images were normalized into standardized stereotactic space, modulated to allow volumetric analysis (optimized only), smoothed, and compared at the voxel level with statistical parametric mapping.
Optimized VBM showed that BD subjects had smaller volume in left ventromedial temporal cortex and bilateral cingulate cortex and larger volume in left insular/frontoparietal operculum cortex and left ventral occipitotemporal cortex. Traditional VBM showed that BD subjects had less gray matter density in left ventromedial temporal cortex and greater gray matter density in left insular/frontoparietal operculum cortex and bilateral thalamic cortex. Exploratory analyses suggest that these abnormalities might differ according to gender.
Bipolar disorder is associated with volumetric and gray matter density changes that involve brain regions hypothesized to influence mood.


Available from: Maria A. Oquendo
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    • "The second finding of this study was that BD patients had reduced GM volumes in the bilateral thalamus compared with HC, and this finding was also present after controlling for possible confounding effects of age and gender. This result is consistent with some (Dasari et al., 1999; McIntosh et al., 2004; Frazier et al., 2005; Hallahan et al., 2011), but not all structural neuroimaging studies in BD (Dupont et al., 1995; Strakowski et al., 1999; Caetano et al., 2001; Strakowski et al., 2002; Lochhead et al., 2004; McDonald et al., 2005; Adler et al., 2007; Ivleva et al., 2013; Amann et al., 2015). The thalamus is a difficult structure to study using neuroimaging techniques, due to the heterogeneity of its nuclei and the difficulty in isolating the subthalamic nuclei with the most relevant connections with other fronto-limbic areas (Blond et al., 2012). "
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    ABSTRACT: Bipolar disorder (BD) is highly heritable. First-degree relatives of BD patient have an increased risk to develop the disease. We investigated abnormalities in gray matter (GM) volumes in healthy first-degree relatives of BD patients to identify possible brain structural endophenotypes for the disorder. 3D T1-weighted magnetic resonance images were obtained from 25 DSM-IV BD type I patients, 23 unaffected relatives, and 27 healthy controls (HC). A voxel-based morphometry protocol was used to compare differences in GM volumes between groups. BD patients presented reduced GM volumes bilaterally in the thalamus compared with HC. Relatives presented no global or regional GM differences compared with HC. Our negative results do not support the role of GM volume abnormalities as endophenotypes for BD. Thalamic volume abnormalities may be associated the pathophysiology of the disease.
    Psychiatry Research: Neuroimaging 09/2015; 234(2). DOI:10.1016/j.pscychresns.2015.09.005 · 2.42 Impact Factor
    • "Another VBM study reported greater GM density in the ACC of bipolar patients than in controls and lithium-treated patients showed significantly greater GM density in the right ACC than patients not-taking lithium (Bearden et al., 2007). Decreased (Atmaca et al., 2007; Chiu et al., 2008; Kaur et al., 2005; Lochhead et al., 2004; Lyoo et al., 2004) or unchanged (Biederman et al., 2008; Brambilla et al., 2002; Zimmerman et al., 2006) GM density/volume in the ACC of bipolar patients have also been reported. Although an association between GM deficits in the right ACC and the genetic risk for bipolar disorder has been reported in one high-risk design MRI study using a quantitative measure of genetic liability and computational morphometric techniques (McDonald et al., 2004), we did not replicate this finding in our sample using VBM. "
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    ABSTRACT: Bipolar disorder (BD) is a highly heritable mental illness which is associated with neuroanatomical abnormalities. Investigating healthy individuals at high genetic risk for bipolar disorder may help to identify neuroanatomical markers of risk and resilience without the confounding effects of burden of illness or medication. Structural magnetic resonance imaging scans were acquired from 30 euthymic patients with BD-I (BP), 28 healthy first degree relatives of BD-I patients (HR), and 30 healthy controls (HC). Data was analyzed using DARTEL for voxel based morphometry in SPM8. Whole-brain analysis revealed a significant main effect of group in the gray matter volume in bilateral inferior frontal gyrus, left parahippocampal gyrus, left lingual gyrus and cerebellum, posterior cingulate gyrus, and supramarginal gyrus (alphasim corrected (≤0.05 FWE)). Post-hoc t-tests showed that inferior frontal gyrus volumes were bilaterally larger both in BP and HR than in HC. BP and HR also had smaller cerebellar volume compared with HC. In addition, BP had smaller left lingual gyrus volume, whereas HR had larger left parahippocampal and supramarginal gyrus volume compared with HC. This study was cross-sectional and the sample size was not large. All bipolar patients were on medication, therefore we were not able to exclude medication effects in bipolar group in this study. Our findings suggest that increased inferior frontal gyrus and decreased cerebellar volumes might be associated with genetic predisposition for bipolar disorder. Longitudinal studies are needed to better understand the predictive and prognostic value of structural changes in these regions. Copyright © 2015 Elsevier B.V. All rights reserved.
    Journal of Affective Disorders 07/2015; 186:110-118. DOI:10.1016/j.jad.2015.06.055 · 3.38 Impact Factor
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    • "behavioral measures across the naturalistic group- ings. Extant methods for machine-based classification of an individual brain using imaging data (Barnes et al., 2004; Davatzikos, Fan, Wu, Shen, & Resnick, 2008; Duchesnay et al., 2007; Efigueiredo et al., 1995; Fan, Shen, & Davatzikos, 2005; Herholz et al., 2002; Jack et al., 2004; Kawasaki et al., 2007; Klöppel et al., 2008a; Lao et al., 2004; Lerch et al., 2006; Liu et al., 2004; Lochhead, Parsey, Oquendo, & Mann, 2004; Mourao-Miranda, Bokde, Born, Hampel, & Stetter, 2005; Teipel et al., 2007; Wahlund et al., 2005) can generally be characterized as supervised. Imaging data from all participants are first spatially warped, or 'normalized', to a template brain to bring all corresponding points into a common spatial register (termed 'template space') and to compute the feature vectors of interest for each of the brains. "
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    ABSTRACT: Brain morphometry in recent decades has increased our understanding of the neural bases of psychiatric disorders by localizing anatomical disturbances to specific nuclei and subnuclei of the brain. At least some of these disturbances precede the overt expression of clinical symptoms and possibly are endophenotypes that could be used to diagnose an individual accurately as having a specific psychiatric disorder. More accurate diagnoses could significantly reduce the emotional and financial burden of disease by aiding clinicians in implementing appropriate treatments earlier and in tailoring treatment to the individual needs. Several methods, especially those based on machine learning, have been proposed that use anatomical brain measures and gold-standard diagnoses of participants to learn decision rules that classify a person automatically as having one disorder rather than another. We review the general principles and procedures for machine learning, particularly as applied to diagnostic classification, and then review the procedures that have thus far attempted to diagnose psychiatric illnesses automatically using anatomical measures of the brain. We discuss the strengths and limitations of extant procedures and note that the sensitivity and specificity of these procedures in their most successful implementations have approximated 90%. Although these methods have not yet been applied within clinical settings, they provide strong evidence that individual patients can be diagnosed accurately using the spatial pattern of disturbances across the brain.
    Journal of Child Psychology and Psychiatry 03/2012; 53(5):519-35. DOI:10.1111/j.1469-7610.2012.02539.x · 6.46 Impact Factor
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