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Gray matter volumes in patients with bipolar disorder and their first-degree relatives
Author’s Accepted Manuscript Gray matter volumes in patients with bipolar disorder and their first-degree relatives Fabiano G. Nery, Alexandre Duarte Gigante, Jose A. Amaral, Francy B.F. Fernandes, Mariangeles Berutti, Karla M. Almeida, Camila de Godoi Carneiro, Fabio Luis Souza Duran, Maria G. Otaduy, Claudia Costa Leite, Geraldo Busatto, Beny Lafer
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S0925-4927(15)30076-7 http://dx.doi.org/10.1016/j.pscychresns.2015.09.005 PSYN10440
To appear in: Psychiatry Research: Neuroimaging Received date: 12 May 2015 Revised date: 24 July 2015 Accepted date: 1 September 2015 Cite this article as: Fabiano G. Nery, Alexandre Duarte Gigante, Jose A. Amaral, Francy B.F. Fernandes, Mariangeles Berutti, Karla M. Almeida, Camila de Godoi Carneiro, Fabio Luis Souza Duran, Maria G. Otaduy, Claudia Costa Leite, Geraldo Busatto and Beny Lafer, Gray matter volumes in patients with bipolar disorder and their first-degree relatives, Psychiatry Research: Neuroimaging, http://dx.doi.org/10.1016/j.pscychresns.2015.09.005 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Nery FG Title: Gray matter volumes in patients with bipolar disorder and their first-degree relatives
Authors: Fabiano G. Nery,*a,b Alexandre Duarte Gigante,a Jose A. Amaral,a Francy B.F. Fernandes,a Mariangeles Berutti,a Karla M. Almeida,a Camila de Godoi Carneiro,c Fabio Luis Souza Duran,c Maria G. Otaduy,d Claudia Costa Leite,d Geraldo Busatto,c Beny Lafera
Affiliations: a
Bipolar Disorder Program, Department of Psychiatry, University of Sao Paulo Medical
School, Sao Paulo/SP, Brazil b
Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of
Medicine, Cincinnati/OH, USA c
Laboratory of Neuroimage in Psychiatry (LIM 21) and Research in Applied Neuroscience
Support Core of the University of Sao Paulo (NAPNA-USP) d
Laboratory of Neuroradiology (LIM 44), Department of Radiology and Oncology,
University of Sao Paulo Medical School, Sao Paulo/SP, Brazil
Corresponding author: Fabiano G. Nery, M.D., Ph.D. Department of Psychiatry, University of Sao Paulo Medical School, Sao Paulo/SP, Brazil. Rua Dr. Ovidio Pires de Campos, 785. Sao Paulo/SP, Brazil. 05403-010 Phone: 55 11 2661 7928. Fax: 55 11 2661 7928. Email: [email protected]; [email protected]
Word count: Abstract: 116 Article body: 2809 Figures: 1 Tables: 1
Abstract
Nery FG 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.
Key words: bipolar disorder, magnetic resonance imaging, endophenotypes, cerebral cortex, thalamus
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Nery FG 1. Introduction
Bipolar disorder (BD) is highly heritable (Winokur et al., 1995; McGuffin et al., 2003; Kieseppa et al., 2004). First-degree relatives of BD patients have an approximate 11% risk of developing the disorder compared with 1-3% of the general population (Goodwin and Jamisom, 2007). Due to this strong hereditability, first-degree relatives may share genetic underlying influences which can cause brain alterations without the manifestation of the disease. If these alterations are potentially involved in the pathophysiology of the disease, they are called endophenotypes (Gottesman and Gould, 2003; Hasler et al., 2006). One strategy to identify endophenotypes is to compare BD patients with their relatives and healthy controls. (Brambilla and Tansella, 2009). The aim of this comparisson is to identify biological alterations in nonaffected family members wich are also found in affected members, both at a higher rate than in the general population (Glahn et al., 2013).This design is important to identify brain changes which are a manifestation of the underlying disease liability and separate from those that occur as a consequence of the disease or treatment. Gray matter (GM) abnormalities, probably associated with the pathophysiology of the disease, have been found in BD patients (Bora et al., 2010).Those findings have stimulated the investigation of GM alterations as endophenotypes in BD. However, the results of the studies in relatives are less consistents and have yielded varying results. They have found, in both patients and relatives, increased volumes of the caudate (Noga et al., 2001; Hajek et al., 2009), left insula (Kempton et al., 2009), and right inferior frontal gyrus (Hajek et al., 2013) and decreased GM volume in the left insula (Matsuo et al., 2012), orbitofrontal cortex and bilateral anterior thalamus (McIntosh et al., 2004). There are also studies which did not find differences between the groups when comparing global, hemispheric, frontal and temporal GM volumes, or subgenual anterior cingulate cortex, amygdala, hippocampus, basal ganglia and pituitary volumes. (Noga et al., 2001; Kieseppa et al., 2002; Kieseppa et al., 2003; 3
Nery FG McDonald et al., 2006; Hajek et al., 2008; Singh et al., 2008; Hajek et al., 2008; Hajek et al., 2010; Takahashi et al., 2010). The significance of these findings is still not clear, mainly because there was no replication among the studies. It is important to notice, however, that the areas identified have been associated with BD in previous studies. Thus, the objective of the present study was to further investigate the differences in whole and regional GM volumes among BD patients, their unaffected relatives, and healthy controls (HC) looking for biological alterations that might be identified as endophenotypes. Our hypothesis was that BD patients and their unaffected first-degree relatives would present decreased GM volumes in brain areas pertaining to the anterior limbic network, which is postulated to be disturbed in BD (Strakowski et al., 2012).
2. Patients and Methods Sample The sample was comprised of 25 patients with BD, 23 unaffected relatives, and 27 HC. Subjects were recruited from outpatient facilities at the Institute of Psychiatry of the University of Sao Paulo Medical School, or from the community through media advertisements. All subjects gave written informed consent to participate in the study. All the procedures were carried out according to the Declaration of Helsinki and the study was approved by the University of Sao Paulo Medical School Ethics Committee. Inclusion criteria for patients with BD were a diagnosis of BD type I, according to DSM-IV criteria, age above 18 years old, having at least one first-degree relative older than 18 years old and willing to participate in the study, and being in remission. We defined remission as not meeting criteria for any mood episode in the last 2 months, and presenting Hamilton Depression Rating Scale (HDRS) scores and Young Mania Rating Scale (YMRS) scores below 8 on the day of participation (Tohen et al., 2009). Patients were allowed to be on
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Nery FG their current medication. Inclusion criteria for unaffected relatives were being a first-degree relative of the proband, age over 18 years old, and absence of any lifetime Axis I diagnosis. Inclusion criteria for HC were age over 18 years old, absence of any lifetime DSM-IV Axis I diagnosis, and absence of any lifetime DSM-IV Axis I diagnosis among first-degree relatives. Exclusion criteria for all subjects were pregnancy (in female subjects), severe and/or decompensated medical diseases with repercussions in the central nervous system, such as diabetes mellitus, hypertension, or hypothyroidism, and neurological disorders, such as epilepsy or stroke. Additional exclusion criteria for patients with BD were presence of active alcohol/drug use disorders in the last 12 months. Psychiatric assessments We used the Structured Clinical Interview for DSM-IV diagnosis (SCID), versions for patients and non-patients, to confirm the diagnosis of BD type I in patients and to exclude psychiatric diagnosis in unaffected relatives and HC (First et al., 2002). We used the 17-item HDRS (Hamilton, 1976) and the YRMS (Young et al., 1978) to evaluate the presence of depressive and manic symptoms, respectively. Board-certified psychiatrists (FGN, JAA) with extensive research experience in mood disorders administered the SCID, HDRS, and YMRS in all subjects. Image acquisition Imaging data were obtained using a 3 T Phillips scanner (Philips Medical Systems, Best, The Netherlands) and an eight-channel head coil. Contiguous sagittal images across the entire brain were acquired using a 3D T1-FFE sequence with the following parameters: TE=3.2 ms, TR=7 ms, flip angle=8°, SENSE=2, acquisition matrix=240x240, and voxel size of 1mm×1mm×1mm (180 slices). Images processing and analysis
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Nery FG The voxel-based morphometry (VBM) analysis was carried out using Statistical Parametric Mapping, version 8 (SPM8; http://www.fil.ion.ucl.ac.uk/spm) running under Matlab r2009b (http://www.mathworks.com/index.html). Briefly, all MRI datasets were first oriented manually to place the anterior commissure at the origin of the three-dimensional Montreal Neurological Institute (MNI) coordinate system. Images were then segmented into grey and white matter partitions using the unified segmentation procedure described in Ashburner and Friston (Ashburner and Friston, 2005). The Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) algorithm (Ashburner, 2007) was then used to spatially normalize the segmented images; this procedure maximizes sensitivity and accuracy of localization by registering individual structural images to an asymmetric custom T1-weighted template derived from the participants' structural images rather than a standard T1-weighted template based on a different sample (Ashburner, 2007). An additional “modulation” step consisted of multiplying each spatially normalized GM image by its relative volume before and after normalization; this ensured that the total amount of GM in each voxel was preserved. Finally, the resulting GM images were smoothed using a 8-mm isotropic kernel at full width half maximum (FWHM) to ensure normal data distribution as required by subsequent parametric tests. Between-group statistical comparisons of mean GM volumes were performed with the general linear model, based on random Gaussian field theory (Friston et al., 1995). Only voxels with values above an absolute GM threshold of .05 entered the analyses, resulting in a search volume of approximately 550,000 voxels. A measure of the total volume of GM of each subject was entered as a covariate in an analysis of covariance. Resulting statistics at each voxel were transformed to Z scores and displayed as SPMs into standard space, at a threshold of Z=3.09. Subsequently, the analyses with positive findings were repeated using
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Nery FG age and sex as covariates because these variables may affect brain structure (Good et al., 2001; Sowell et al. 2003). To investigate GM volume differences between groups, three different 2-group comparisons were performed: GM volumes in BD patients versus unaffected relatives, BD patients versus HC, and unaffected relatives versus HC. GM volume differences (increased or decreased) were tested between each group. The voxel-wise set of results for each of the above contrasts constituted an SPM t statistic {SPM (t)} map, using an uncorrected threshold of p≤0.001. Based on previous literature about GM differences in relatives of BD patients, the following a priori regions of interests (ROIs) were selected for identification of any significant findings on the SPM(t) maps: orbitofrontal cortex, anterior cingulate cortex, amygdala, hippocampus, parahyppocampal gyrus, insula, caudate and thalamus. Such voxelwise search of each map was performed using the small volume correction (SVC) method with the purpose of constraining the total number of voxels included in the analyses. Each region was circumscribed by merging the spatially normalized ROI masks that are available within the Anatomical Automatic Labeling SPM toolbox. Findings of these hypothesisdriven, SVC-analyses were reported as significant if they survived family-wise error (FWE) correction for multiple comparisons (p≤0.05), with further criteria for voxel clusters to comprise at least 20 voxels over the respective ROI. Statistical analysis Regarding demographic and clinical data, Chi-square tests were conducted for crosstabulated qualitative data and analysis of variance (ANOVA) for ordinal and interval scale data to compare the BD patients, unaffected relatives, and HC. The analysis was repeated with age and gender as covariates. Statistical analyses were conducted using SPSS version 14.0 (SPSS, Inc, Chicago, IL, USA). Differences of p<0.05 were considered statistically significant.
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Nery FG
3. Results
Demographic and clinical information for BD patients, unaffected relatives, and HC are displayed in Table 1.
GM volume differences among BD patients, unaffected relatives, and HC The sum of GM volume in the modulated, segmented images totaled 646.64 ml ± 71.87 in the BD patients group, 645.97 ml ± 48.20 in unaffected relatives and 637.87 ml ± 62.50 in HC (F(2,71)=0.162, p=0.85), indicating that there was no significant differences in GM volume in BD patients, unaffected relatives, and HC. In the whole brain comparison, there were no differences in GM volumes between BD patients and unaffected relatives, between BD patients and HC, or between unaffected relatives and HC. SVC analyses guided by a priori selected brain regions showed that BD patients presented reduced GM volumes in the bilateral thalamus (medial dorsal nucleus, 225 voxels, Z score=4.03, peak coordinates x,y,z=-2 -11 9, p=0.007) compared with HC (Figure 1a). There were no differences in any of the other a priori selected brain regions between groups. Repeating the analyses using age and gender as covariates, BD patients presented reduced GM volume in the bilateral thalamus compared with HC (medial dorsal nucleus, 142 voxels, Z score=3.62, peak coordinates x,y,z=-2 -12 9, p=0.027).
4. Discussion
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Nery FG In this study, no abnormalities in GM volumes in unaffected relatives of BD patients were found compared with HC. Unaffected relative and HC groups were well matched on mean age and gender, decreasing the relative influence of these variables on the results. Furthermore, the relatives were all subjects who have never suffered any Axis I psychiatric disorder or received psychiatric treatment during their lives. Therefore, these results are not confounded by previous or current psychopathology or exposure to psychiatric medication in the relatives. Our negative findings are consistent with some (McDonald et al., 2006; van der Schot et al., 2009; Hulshoff Pol et al., 2012) but not all studies that used morphometric methods to study relatives of BD patients (McIntosh et al., 2004; Kempton et al. 2009; Matsuo et al., 2012; Hajek et al., 2013). Methodological differences between those studies and ours may explain, at least in part, the differences in results. For instance, some of the earlier VBM studies have used previous versions of the SPM software (McIntosh et al., 2004; Ladouceur et al., 2008; Kempton et al., 2009) as opposed to our use of SPM8 with DARTEL Toolbox. Some authors have demonstrated that the use of the DARTEL Toolbox provides superior results when compared to previous versions of the program (Klein et al., 2009; Tahmasebi et al., 2009; Ashburner, 2009). The clinical heterogeneity of BD may also be an important factor accounting for discrepancies across studies of high risk populations. Several clinical aspects of BD, including BD subtype, psychotic features, and comorbid alcohol use disorders, have familial aggregation and, therefore, may be the expression of specific genetic susceptibilities (Schulze et al., 2006; Saunders et al., 2008; Hua et al., 2011). Other sources of discrepancies between our study and previous studies include the developmental stages of brain structure, the genetic loading for BD within the families recruited, and the inclusion of relatives with other Axis I psychiatric disorders. These factors
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Nery FG could potentially affect the detection of the endophenotype in the relatives of BD patients (Smoller and Finn, 2003; Birmaher et al., 2009; Schneider et al., 2012), thus limiting direct comparison of findings. 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 frontolimbic areas (Blond et al., 2012). It is a midline structure within the brain, shared by the different frontal-subcortical circuits that mediate motor activity and human behavior (Tekin and Cummings, 2002). Its functions are very diverse, as it contains several nuclei, each with different functions that include limbic, sensory or associative functions. The limbic nuclei of the thalamus are involved in regulating motivation, drive, and emotional experience and expression (Schmahmann, 2003). Vascular lesions of the thalamus may be accompanied by behavioral changes that resemble symptoms of BD, including abnormal energy (apathy or motor hyperactivity), psychomotor acceleration, mood swings, social inadequacy, and psychotic manic states (Schmahmann, 2003; Carrera and Bogousslavsky, 2006; De Witte et al., 2011). Thus, our finding of decreased thalamus GM in BD patients compared with HC is consistent with the theory that thalamus may be involved in the neurocircuitry responsible for the clinical manifestations of BD (Strakowski et al., 2012).
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Nery FG Some limitations of this study should be considered. All patients were medicated, and potential confounding effects of psychotropic medications on GM volumes (Hafeman et al., 2012) may not be excluded and can partially explain some of the discrepancies between ours findings and those from previous studies. However, the more relevant results of this study are the comparisons between relatives and HC, which are not confounded by medication effects. Furthermore, our study had relatively small sample sizes in the comparison groups. It should also be noted that we did not include premorbid IQ as a confounding covariate in our between-group comparisons. The three groups had similar educational level, but there might have been mean IQ differences that could have influenced our results; therefore, the absence of IQ measurements is one further limitation of our study. The heritability of BD likely involves a multifactorial transmission pattern, and hundreds or thousands of genes of small effect may act together to predispose the person to develop BD (Barnett and Smoller, 2009; Schulze, 2010). Thus, when recruiting first-degree relatives of BD, we may have included individuals that share only some of the putative BD genes, and therefore, the effect size of these few genes on brain structure may be so small as to be nearly undetectable by neuroimaging studies with small samples. Furthermore, GM volume reduction in BD is small at most (when compared e.g. to schizophrenia), as have shown some recent studies (Ivleva et al., 2013; Amann et al., 2015) and such changes are likely to be even subtler in unaffected relatives of BD patients. Therefore, studies with large samples are more likely to detect subtle but relevant structural abnormalities. New studies should also investigate white matter abnormalities in relatives of BD patients, since initial evidence for this has been found in this population (Arat et al., 2015) and it has been postulated that changes in white matter might be a vulnerability trait factor for BD rather than changes in GM (Schneider et al., 2012). On the other hand, our study has considerable strengths. Our sample was clinically well characterized and reasonably well matched, minimizing any potential confounding effect
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Nery FG of demographic characteristics. Moreover, in our analysis, we have used age and gender as covariates to control for the effects of these potential confounders. We have also used very stringent criteria for euthymia, to decrease the possibility of mood symptomatology affecting our findings. In addition, we carefully scrutinized the presence of previous psychopathology in the unaffected relatives of BD patients, in an effort to exclude the confounding effects of another Axis I disorder. In conclusion, the present study found that unaffected relatives of BD patients do not present brain global or regional GM differences compared with HC. BD patients present decreased GM volumes in the right and left thalamus compared with HC. These results suggest that there is no structural endophenotype for BD and support the role of the thalamus in the pathophysiology of BD.
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Nery FG 5. Acknowledgments This study was partly supported by Conselho Nacional de Pesquisa (CNPq) grant #478466/2009, Conselho de Aperfeicoamento de Pessoal de Nivel Superior (CAPES) grant #2632/09-8, Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) grant #2005/56464-9, Instituto Nactional de Ciencia e Tecnologia (INCT) Translacional em Medicina do CNPq grant #573671/2008-7, and by a generous private donation from the family of Thompson Motta (to PROMAN).
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Nery FG 6. Disclosures Dr. Nery held a temporary position as a medical advisor in Eli Lilly and Company from 2012 to 2013. Dr. Stertz is a recipient of a scholarship from CAPES. Dr. Kapczinski has received grants/research support from AstraZeneca, Eli Lilly, Janssen-Cilag, Servier, CNPq, CAPES, NARSAD, and the Stanley Medical Research Institute; he has also been a member of the speakers boards for AstraZeneca, Eli Lilly, Janssen-Cilag and Servier, and has served as a consultant for Servier. All other co-authors reported no biomedical financial interests or potential conflicts of interest.
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Nery FG 7. References
Adler, C.M., DelBello, M.P., Jarvis, K., Levine, A., Adams, J., Strakowski, S.M., 2007. Voxel-based study of structural changes in first-episode patients with bipolar disorder. Biological Psychiatry 61, 776-781. Amann, B.L., Canales-Rodríguez, E.J., Madre, M., Radua, J., Monte, G., Alonso-Lana, S., Landin-Romero, R., Moreno-Alcázar, A., Bonnin, C.M., Sarró, S., Ortiz-Gil, J., Gomar, J.J., Moro, N., Fernandez-Corcuera, P., Goikolea, J.M., Blanch, J., Salvador, R., Vieta, E., McKenna, P.J., Pomarol-Clotet, E., 2015. Brain structural changes in schizoaffective disorder compared to schizophrenia and bipolar disorder. Acta Psychiatrica Scandinavica. doi: 10.1111/acps.12440. [Epub ahead of print] Arat, H.E., Chouinard, V.A., Cohen, B.M., Lewandowski, K.E., Öngür, D., 2015. Diffusion tensor imaging in first degree relatives of schizophrenia and bipolar disorder patients. Schizophrenia Research 161, 329-339. Ashburner, J., 2007. A fast diffeomorphic image registration algorithm. NeuroImage 38, 95113. Ashburner, J., 2009. Computational anatomy with the SPM software. Magnetic Resonance Imaging 27, 1163-1174. Ashburnerm J., Friston, K.J., 2005. Unified segmentation. Neuroimage 26, 839-851. Barnett, J.H., Smoller, J.W., 2009. The genetics of bipolar disorder. Neuroscience 164, 331343. Birmaher, B., Axelson, D., Monk, K., Kalas, C., Goldstein, B., Hickey, M.B., Obreja, M., Ehmann, M., Iyengar, S., Shamseddeen, W., Kupfer, D., Brent, D., 2009. Lifetime psychiatric disorders in school-aged offspring of parents with bipolar disorder: the Pittsburgh Bipolar Offspring study. Archives of General Psychiatry 66, 287-296.
15
Nery FG Blond, B.N., Fredericks, C.A., Blumberg, H.P., 2012. Functional neuroanatomy of bipolar disorder: structure, function, and connectivity in an amygdala-anterior paralimbic neural system. Bipolar Disorders 14, 340-355. Bora, E., Fornito, A., Yücel, M., Pantelis, C., 2010. Voxelwise metaanalysis of gray matter abnormalities in bipolar disorder. Biological Psychiatry 67, 1097–1105. Brambilla, P., Tansella, M., 2009. What are the perspectives of human brain mapping in the field of bipolar disorder? International Review of Psychiatry 21, 295–296. Caetano, S.C., Sassi, R., Brambilla, P., Harenski, K., Nicoletti, M., Mallinger, A.G., Frank, E., Kupfer, D.J., Keshavan, M.S., Soares, J.C., 2001. MRI study of thalamic volumes in bipolar and unipolar patients and healthy individuals. Psychiatry Research 108, 161168. Carrera, E., Bogousslavsky, J., 2006. The thalamus and behavior: effects of anatomically distinct strokes. Neurology 66, 1817-1823. Dasari, M., Friedman, L., Jesberger, J., Stuve, T.A., Findling, R.L., Swales, T.P., Schulz, S.C., 1999. A magnetic resonance imaging study of thalamic area in adolescent patients with either schizophrenia or bipolar disorder as compared to healthy controls. Psychiatry Research 91, 155-162. De Witte, L., Brouns, R., Kavadias, D., Engelborghs, S., De Deyn, P.P., Mariën, P., 2011. Cognitive, affective and behavioural disturbances following vascular thalamic lesions: a review. Cortex 47, 273-319. Dupont, R.M., Jernigan, T.L., Heindel, W., Butters, N., Shafer, K., Wilson, T., Hesselink, J., Gillin, J.C., 1995. Magnetic resonance imaging and mood disorders. Localization of white matter and other subcortical abnormalities. Archives of General Psychiatry 52, 747-755.
16
Nery FG First, M.B., Gibbon, M., Spitzer, R.L., Williams, J.B.W., 2002. Structured Clinical Interview for DSM-IV-TR Axis I Disorders. Biometrics Research, New York. Frazier, J.A., Chiu, S., Breeze, J.L., Makris, N., Lange, N., Kennedy, D.N., Herbert, M.R., Bent, E.K., Koneru, V.K., Dieterich, M.E., Hodge, S.M., Rauch, S.L., Grant, P.E., Cohen, B.M., Seidman, L.J., Caviness, V.S., Biederman, J., 2005. Structural brain magnetic resonance imaging of limbic and thalamic volumes in pediatric bipolar disorder. American Journal of Psychiatry 162, 1256-1265. Friston, K.J., Holmes, A.P., Poline, J.B., Grasby, P.J., Williams, S.C., Frackowiak, R.S., Turner, R., 1995. Analysis of fMRI time-series revisited. NeuroImage 2, 45-53. Glahn, D.C., Knowles, E.E., McKay, D.R., Sprooten, E., Raventós, H., Blangero, J., Gottesman, I.I., Almasy, L., 2014. Arguments for the sake of endophenotypes: examining common misconceptions about the use of endophenotypes in psychiatric genetics. American Journal of Medical Genetics 165B, 122-130. Good, C.D., Johnsrude, I.S., Ashburner, J., Henson, R.N., Friston, K.J., Frackowiak, R.S., 2001. A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage 14, 21-36. Goodwin, F.K., Jamison, K.R., 2007. Manic-Depressive Illness: Bipolar Disorders and Recurrent Depression. Oxford University Press, New York. Gottesman, I.I., Gould, T.D., 2003. The endophenotype concept in psychiatry: etymology and strategic intentions. American Journal of Psychiatry 160, 636-645. Hafeman, D.M., Chang, K.D., Garrett, A.S., Sanders, E.M., Phillips, M.L., 2012. Effects of medication on neuroimaging findings in bipolar disorder: an updated review. Bipolar Disorders 14, 375-410. Hajek, T., Cullis, J., Novak, T., Kopecek, M., Blagdon, R., Propper, L., Stopkova, P., Duffy, A., Hoschl, C., Uher, R., Paus, T., Young, L.T., Alda, M., 2013. Brain structural
17
Nery FG signature of familial predisposition for bipolar disorder: replicable evidence for involvement of the right inferior frontal gyrus. Biological Psychiatry 73, 144-152. Hajek, T., Gunde, E., Bernier, D., Slaney, C., Propper, L., Grof, P., Macqueen, G., Duffy, A., Alda, M., 2008. Subgenual cingulate volumes in affected and unaffected offspring of bipolar parents. Journal of Affective Disorders 108, 263-269. Hajek, T., Gunde, E., Bernier, D., Slaney, C., Propper, L., Macqueen, G., Duffy, A., Alda, M., 2008. Pituitary volumes in relatives of bipolar patients: high-risk study. European Archives of Psychiatry and Clinical Neuroscience 258, 357-362. Hajek, T., Gunde, E., Slaney, C., Propper, L., MacQueen, G., Duffy, A., Alda, M., 2009. Striatal volumes in affected and unaffected relatives of bipolar patients--high-risk study. Journal of Psychiatric Research 43, 724-729. Hajek, T., Novak, T., Kopecek, M., Gunde, E., Alda, M., Höschl, C., 2010. Subgenual cingulate volumes in offspring of bipolar parents and in sporadic bipolar patients. European Archives of Psychiatry and Clinical Neuroscience 260, 297-304. Hallahan, B., Newell, J., Soares, J.C., Brambilla, P., Strakowski, S.M., Fleck, D.E., Kieseppa, T., Altshuler, L.L., Fornito, A., Malhi, G.S., McIntosh, A.M., Yurgelun-Todd, D.A., Labar, K.S., Sharma, V., MacQueen, G.M., Murray, R.M., McDonald, C., 2011. Structural magnetic resonance imaging in bipolar disorder: an international collaborative mega-analysis of individual adult patient data. Biological Psychiatry 69, 326-335. Hamilton, M., 1976. Hamilton Psychiatric Rating Scale for Depression. In: Guy, W. (Ed.), Assessment Manual for Psychopharmacology. Education and Welfare, Washington DC, pp. 179-192.
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Nery FG Hasler, G., Drevets, W.C., Gould, T.D., Gottesman, I.I., Manji, H.K., 2006. Toward constructing an endophenotype strategy for bipolar disorders. Biological Psychiatry 60, 93-105. Hua, L.L., Wilens, T.E., Martelon, M., Wong, P., Wozniak, J., Biederman, J., 2011. Psychosocial functioning, familiality, and psychiatric comorbidity in bipolar youth with and without psychotic features. Journal of Clinical Psychiatry 72, 397-405. Hulshoff Pol, H.E., van Baal, G.C.M., Schnack, H.G., Brans, R.G.H., van der Schot, A.C., Brouwer, R.M., van Haren, N.E.M., Lepage, C., Collins, D.L., Evans, A.C., Boomsma, D.I., Nolen, W., Kahn, R.S., 2012. Overlapping and segregating structural brain abnormalities in twins with schizophrenia or bipolar disorder. Archives of General Psychiatry 69, 349-359. Ivleva, E.I., Bidesi, A.S., Keshavan, M.S., Pearlson, G.D., Meda, S.A., Dodig, D., Moates, A.F., Lu, H., Francis, A.N., Tandon, N., Schretlen, D.J., Sweeney, J.A., Clementz, B.A., Tamminga, C.A., 2013. Gray matter volume as an intermediate phenotype for psychosis: Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP). American Journal of Psychiatry 170, 1285-1296. Kempton, M.J., Haldane, M., Jogia, J., Grasby, P.M., Collier, D., Frangou, S., 2009. Dissociable brain structural changes associated with predisposition, resilience, and disease expression in bipolar disorder. The Journal of Neuroscience 29, 10863-10868. Kieseppa, T., Partonen, T., Haukka, J., Kaprio, J., Lonnqvist, J., 2004. High concordance of bipolar I disorder in a nationwide sample of twins. American Journal of Psychiatry 161, 1814-1821. Kieseppa, T., van Erp, T.G., Haukka, J., Partonen, T., Cannon, T.D., Poutanen, V.P., Kapri, J., Lönnqvist, J., 2002. The volumetric findings in MRI brain study of bipolar twins and their healthy co-twins. Bipolar Disorders 4 Suppl 1, 29-30.
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Nery FG Kieseppa, T., van Erp, T.G.M., Haukka, J., Partonen, T., Cannon, T.D., Poutanen, V.-P., Kaprio, J., Lonnqvist, J., 2003. Reduced left hemispheric white matter volume in twins with bipolar I disorder. Biological Psychiatry 54, 896-905. Klein, A., Andersson, J., Ardekani, B.A., Ashburner, J., Avants, B., Chiang, M.-C., Christensen, G.E., Collins, D.L., Gee, J., Hellier, P., Song, J.H., Jenkinson, M., Lepage, C., Rueckert, D., Thompson, P., Vercauteren, T., Woods, R.P., Mann, J.J., Parsey, R.V., 2009. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage 46, 786-802. Ladouceur, C.D., Almeida, J.R.C., Birmaher, B., Axelson, D.A., Nau, S., Kalas, C., Monk, K., Kupfer, D.J., Phillips, M.L., 2008. Subcortical gray matter volume abnormalities in healthy bipolar offspring: potential neuroanatomical risk marker for bipolar disorder? Journal of the American Academy of Child & Adolescent Psychiatry 47, 532-539. Lochhead, R.A., Parsey, R.V., Oquendo, M.A., Mann, J.J., 2004. Regional brain gray matter volume differences in patients with bipolar disorder as assessed by optimized voxelbased morphometry. Biological Psychiatry 55, 1154-1162. Matsuo, K., Kopecek, M., Nicoletti, M.A., Hatch, J.P., Watanabe, Y., Nery, F.G., ZuntaSoares, G., Soares, J.C., 2012. New structural brain imaging endophenotype in bipolar disorder. Molecular Psychiatry 17, 412-420. McDonald, C., Bullmore, E., Sham, P., Chitnis, X., Suckling, J., MacCabe, J., Walshe, M., Murray, R.M., 2005. Regional volume deviations of brain structure in schizophrenia and psychotic bipolar disorder: computational morphometry study. British Journal of Psychiatry 186, 369-377. McDonald, C., Marshall, N., Sham, P.C., Bullmore, E.T., Schulze, K., Chapple, B., Bramon, E., Filbey, F., Quraishi, S., Walshe, M., Murray, R.M., 2006. Regional brain
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Nery FG morphometry in patients with schizophrenia or bipolar disorder and their unaffected relatives. American Journal of Psychiatry 163, 478-487. McGuffin, P., Rijsdijk, F., Andrew, M., Sham, P., Katz, R., Cardno, A., 2003. The heritability of bipolar affective disorder and the genetic relationship to unipolar depression. Archives of General Psychiatry 60, 497-502. McIntosh, A.M., Job, D.E., Moorhead, T.W.J., Harrison, L.K., Forrester, K., Lawrie, S.M., Johnstone, E.C., 2004. Voxel-based morphometry of patients with schizophrenia or bipolar disorder and their unaffected relatives. Biological Psychiatry 56, 544-552. Noga, J.T., Vladar, K., Torrey, E.F., 2001. A volumetric magnetic resonance imaging study of monozygotic twins discordant for bipolar disorder. Psychiatry Research 106, 25-34. Saunders, E.H., Scott, L.J., McInnis, M.G., Burmeister, M., 2008. Familiality and diagnostic patterns of subphenotypes in the National Institutes of Mental Health bipolar sample. American Journal of Medical Genetics 147B, 18-26. Schmahmann, J.D., 2003. Vascular syndromes of the thalamus. Stroke 34, 2264-2278. Schneider, M.R., Delbello, M.P., McNamara, R.K., Strakowski, S.M., Adler, C.M., 2012. Neuroprogression in bipolar disorder. Bipolar Disorders 14, 356-374. Schulze, T.G., 2010. Genetic research into bipolar disorder: the need for a research framework that integrates sophisticated molecular biology and clinically informed phenotype characterization. Psychiatric Clinics of North America 33, 67-82. Schulze, T.G., Hedeker, D., Zandi, P., Rietschel, M., McMahon, F.J., 2006. What is familial about familial bipolar disorder? Resemblance among relatives across a broad spectrum of phenotypic characteristics. Archives of General Psychiatry 63, 1368-1376. Singh, M.K., Delbello, M.P., Adler, C.M., Stanford, K.E., Strakowski, S.M., 2008. Neuroanatomical characterization of child offspring of bipolar parents. Journal of the American Academy of Child and Adolescent Psychiatry 47, 526-531.
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Nery FG Smoller, J.W., Finn, C.T., 2003. Family, twin, and adoption studies of bipolar disorder. American Journal of Medical Genetics 123C, 48-58. Sowell, E.R., Peterson, B.S., Thompson, P.M., Welcome, S.E., Henkenius, A.L., Toga, A.W., 2003. Mapping cortical change across the human life span. Nature Neuroscience 6, 309315. Strakowski, S.M., Adler, C.M., Almeida, J., Altshuler, L.L., Blumberg, H.P., Chang, K.D., DelBello, M.P., Frangou, S., McIntosh, A., Phillips, M.L., Sussman, J.E., Townsend, J.D., 2012. The functional neuroanatomy of bipolar disorder: a consensus model. Bipolar Disorders 14, 313-325. Strakowski, S.M., DelBello, M.P., Sax, K.W., Zimmerman, M.E., Shear, P.K., Hawkins, J.M., Larson, E.R., 1999. Brain magnetic resonance imaging of structural abnormalities in bipolar disorder. Archives of General Psychiatry 56, 254-260. Strakowski, S.M., DelBello, M.P., Zimmerman, M.E., Getz, G.E., Mills, N.P., Ret, J., Shear, P., Adler, C.M., 2002. Ventricular and periventricular structural volumes in first- versus multiple-episode bipolar disorder. American Journal of Psychiatry 159, 1841-1847. Tahmasebi, A.M., Abolmaesumi, P., Zheng, Z.Z., Munhall, K.G., Johnsrude, I.S., 2009. Reducing inter-subject anatomical variation: effect of normalization method on sensitivity of functional magnetic resonance imaging data analysis in auditory cortex and the superior temporal region. NeuroImage 47, 1522-1531. Takahashi, T., Walterfang, M., Wood, S.J., Kempton, M.J., Jogia, J., Lorenzetti, V., Soulsby, B., Suzuki, M., Velakoulis, D., Pantelis, C., Frangou, S., 2010. Pituitary volume in patients with bipolar disorder and their first-degree relatives. Journal of Affective Disorders 124, 256-261. Tekin, S., Cummings, J.L., 2002. Frontal-subcortical neuronal circuits and clinical neuropsychiatry: an update. Journal of Psychosomatic Research 53, 647-654.
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Nery FG Tohen, M., Frank, E., Bowden, C.L., Colom, F., Ghaemi, S.N., Yatham, L.N., Malhi, G.S., Calabrese, J.R., Nolen, W.A., Vieta, E., Kapczinski, F., Goodwin, G.M., Suppes, T., Sachs, G.S., Chengappa, K.R., Grunze, H., Mitchell, P.B., Kanba, S., Berk, M., 2009. The International Society for Bipolar Disorders (ISBD) Task Force report on the nomenclature of course and outcome in bipolar disorders. Bipolar Disorders 11, 453473. van der Schot, A.C., Vonk, R., Brans, R.G.H., van Haren, N.E.M., Koolschijn, P.C.M.P., Nuboer, V., Schnack, H.G., van Baal, G.C.M., Boomsma, D.I., Nolen, W.A., Hulshoff Pol, H.E., Kahn, R.S., 2009. Influence of genes and environment on brain volumes in twin pairs concordant and discordant for bipolar disorder. Archives of General Psychiatry 66, 142-151. Winokur, G., Coryell, W., Keller, M., Endicott, J., Leon, A., 1995. A family study of manicdepressive (bipolar I) disease. Is it a distinct illness separable from primary unipolar depression? Archives of General Psychiatry 52, 367-373. Young, R.C., Biggs, J.T., Ziegler, V.E., Meyer, D.A., 1978. A rating scale for mania: reliability, validity and sensitivity. British Journal of Psychiatry 133, 429-435. Table 1 legend: Demographic and clinical characteristics of BD patients, unaffected relatives, and HC
Table 1 footnote: Abbreviations: BD (bipolar disorder); HC (healthy controls); HDRS (Hamilton Depression Rating Scale 17 items); YMRS (Young Mania Rating Scale) *Information about number of episodes was reliably available for 14 patients. For the remaining 11 patients, number of episodes was unreliable or too many to count.
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Nery FG
Figure 1 footnote: Brain regions where there were foci of significant differences in gray matter volumes between BD patients and HC (at the Z > 3.09 cutoff, uncorrected for multiple comparisons and using an extent threshold of 20 voxels). Foci of significance were overlaid on axial brain slices spatially normalized into an approximation to the Talairach and Tournoux stereotactic atlas. (Talairach J, Tornoux P (1988): Co-Planar Stereotaxic Atlas of the Human Brain, Thieme Medical Publishers, New York). The numbers associated with each frame represent standard coordinates in the y-axis. Reaching statistical significance at the p<0.05 level after family-
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Nery FG wise error correction for multiple comparisons using the SVC tool in SPM. (a) Clusters (highlighted in yellow) of decreased GM volume in BD patients relative to HC in the bilateral thalamus, Abbreviations: GM (gray matter); BD (bipolar disorder); HC (healthy controls); SVC (small volume correction); SPM (Statistical Parametric Mapping); R (right).
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Nery FG Table 1: Demographic and clinical characteristics of the sample Unaffected BD patients
HC
Statistics,
(n=27)
p value
relatives (n=25) (n=23) Age, y (mean±S.D.)
35.7±8.9
31.6±6.7
31.2±9.5
0.07
Gender (male), n (%)
8 (32%)
9 (39.1%)
11 (40.7%)
0.79
Right-handed, n (%)
21 (84%)
22 (95.7%)
26 (96.3%)
0.12
Family history of BD, n (%)
7 (28%)
23 (100%)
-
-
Relative participating in the study, n (%)
19 (76%)
18 (78%)
-
-
6.6±4
-
-
-
HDRS (mean±S.D.)
2.9±2.6
-
-
-
YMRS (mean±S.D.)
1.2±2.1
-
-
-
Age at disease onset, y (mean±S.D.)
22.1±8.5
-
-
-
Length of illness, y (mean±S.D.)
13.6±8.1
-
-
-
6±4
-
-
-
Time in remission, mo (mean±S.D.)
Number of mood episodes,* (mean±S.D.) Current medication
-
Lithium, n (%)
12 (48%)
-
-
-
Anticonvulsants, n (%)
10 (40%)
-
-
-
Atypical antipsychotics, n (%)
13 (52%)
-
-
-
Antidepressants, n (%)
5 (20%)
-
-
-
Sedative-hypnotics, n (%)
2 (8%)
-
-
-
Highlights
We investigated brain structure endophenotypes in bipolar disorder. We compared gray matter volumes in patients, their relatives, and controls. Patients presented smaller bilateral gray matter volumes than controls.
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Nery FG
Relatives did not differ from patients and controls on gray matter volumes. Abnormal gray matter volumes may not be an endophenotype for bipolar disorder.
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PII: DOI: Reference:
www.elsevier.com
S0925-4927(15)30076-7 http://dx.doi.org/10.1016/j.pscychresns.2015.09.005 PSYN10440
To appear in: Psychiatry Research: Neuroimaging Received date: 12 May 2015 Revised date: 24 July 2015 Accepted date: 1 September 2015 Cite this article as: Fabiano G. Nery, Alexandre Duarte Gigante, Jose A. Amaral, Francy B.F. Fernandes, Mariangeles Berutti, Karla M. Almeida, Camila de Godoi Carneiro, Fabio Luis Souza Duran, Maria G. Otaduy, Claudia Costa Leite, Geraldo Busatto and Beny Lafer, Gray matter volumes in patients with bipolar disorder and their first-degree relatives, Psychiatry Research: Neuroimaging, http://dx.doi.org/10.1016/j.pscychresns.2015.09.005 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Nery FG Title: Gray matter volumes in patients with bipolar disorder and their first-degree relatives
Authors: Fabiano G. Nery,*a,b Alexandre Duarte Gigante,a Jose A. Amaral,a Francy B.F. Fernandes,a Mariangeles Berutti,a Karla M. Almeida,a Camila de Godoi Carneiro,c Fabio Luis Souza Duran,c Maria G. Otaduy,d Claudia Costa Leite,d Geraldo Busatto,c Beny Lafera
Affiliations: a
Bipolar Disorder Program, Department of Psychiatry, University of Sao Paulo Medical
School, Sao Paulo/SP, Brazil b
Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of
Medicine, Cincinnati/OH, USA c
Laboratory of Neuroimage in Psychiatry (LIM 21) and Research in Applied Neuroscience
Support Core of the University of Sao Paulo (NAPNA-USP) d
Laboratory of Neuroradiology (LIM 44), Department of Radiology and Oncology,
University of Sao Paulo Medical School, Sao Paulo/SP, Brazil
Corresponding author: Fabiano G. Nery, M.D., Ph.D. Department of Psychiatry, University of Sao Paulo Medical School, Sao Paulo/SP, Brazil. Rua Dr. Ovidio Pires de Campos, 785. Sao Paulo/SP, Brazil. 05403-010 Phone: 55 11 2661 7928. Fax: 55 11 2661 7928. Email: [email protected]; [email protected]
Word count: Abstract: 116 Article body: 2809 Figures: 1 Tables: 1
Abstract
Nery FG 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.
Key words: bipolar disorder, magnetic resonance imaging, endophenotypes, cerebral cortex, thalamus
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Nery FG 1. Introduction
Bipolar disorder (BD) is highly heritable (Winokur et al., 1995; McGuffin et al., 2003; Kieseppa et al., 2004). First-degree relatives of BD patients have an approximate 11% risk of developing the disorder compared with 1-3% of the general population (Goodwin and Jamisom, 2007). Due to this strong hereditability, first-degree relatives may share genetic underlying influences which can cause brain alterations without the manifestation of the disease. If these alterations are potentially involved in the pathophysiology of the disease, they are called endophenotypes (Gottesman and Gould, 2003; Hasler et al., 2006). One strategy to identify endophenotypes is to compare BD patients with their relatives and healthy controls. (Brambilla and Tansella, 2009). The aim of this comparisson is to identify biological alterations in nonaffected family members wich are also found in affected members, both at a higher rate than in the general population (Glahn et al., 2013).This design is important to identify brain changes which are a manifestation of the underlying disease liability and separate from those that occur as a consequence of the disease or treatment. Gray matter (GM) abnormalities, probably associated with the pathophysiology of the disease, have been found in BD patients (Bora et al., 2010).Those findings have stimulated the investigation of GM alterations as endophenotypes in BD. However, the results of the studies in relatives are less consistents and have yielded varying results. They have found, in both patients and relatives, increased volumes of the caudate (Noga et al., 2001; Hajek et al., 2009), left insula (Kempton et al., 2009), and right inferior frontal gyrus (Hajek et al., 2013) and decreased GM volume in the left insula (Matsuo et al., 2012), orbitofrontal cortex and bilateral anterior thalamus (McIntosh et al., 2004). There are also studies which did not find differences between the groups when comparing global, hemispheric, frontal and temporal GM volumes, or subgenual anterior cingulate cortex, amygdala, hippocampus, basal ganglia and pituitary volumes. (Noga et al., 2001; Kieseppa et al., 2002; Kieseppa et al., 2003; 3
Nery FG McDonald et al., 2006; Hajek et al., 2008; Singh et al., 2008; Hajek et al., 2008; Hajek et al., 2010; Takahashi et al., 2010). The significance of these findings is still not clear, mainly because there was no replication among the studies. It is important to notice, however, that the areas identified have been associated with BD in previous studies. Thus, the objective of the present study was to further investigate the differences in whole and regional GM volumes among BD patients, their unaffected relatives, and healthy controls (HC) looking for biological alterations that might be identified as endophenotypes. Our hypothesis was that BD patients and their unaffected first-degree relatives would present decreased GM volumes in brain areas pertaining to the anterior limbic network, which is postulated to be disturbed in BD (Strakowski et al., 2012).
2. Patients and Methods Sample The sample was comprised of 25 patients with BD, 23 unaffected relatives, and 27 HC. Subjects were recruited from outpatient facilities at the Institute of Psychiatry of the University of Sao Paulo Medical School, or from the community through media advertisements. All subjects gave written informed consent to participate in the study. All the procedures were carried out according to the Declaration of Helsinki and the study was approved by the University of Sao Paulo Medical School Ethics Committee. Inclusion criteria for patients with BD were a diagnosis of BD type I, according to DSM-IV criteria, age above 18 years old, having at least one first-degree relative older than 18 years old and willing to participate in the study, and being in remission. We defined remission as not meeting criteria for any mood episode in the last 2 months, and presenting Hamilton Depression Rating Scale (HDRS) scores and Young Mania Rating Scale (YMRS) scores below 8 on the day of participation (Tohen et al., 2009). Patients were allowed to be on
4
Nery FG their current medication. Inclusion criteria for unaffected relatives were being a first-degree relative of the proband, age over 18 years old, and absence of any lifetime Axis I diagnosis. Inclusion criteria for HC were age over 18 years old, absence of any lifetime DSM-IV Axis I diagnosis, and absence of any lifetime DSM-IV Axis I diagnosis among first-degree relatives. Exclusion criteria for all subjects were pregnancy (in female subjects), severe and/or decompensated medical diseases with repercussions in the central nervous system, such as diabetes mellitus, hypertension, or hypothyroidism, and neurological disorders, such as epilepsy or stroke. Additional exclusion criteria for patients with BD were presence of active alcohol/drug use disorders in the last 12 months. Psychiatric assessments We used the Structured Clinical Interview for DSM-IV diagnosis (SCID), versions for patients and non-patients, to confirm the diagnosis of BD type I in patients and to exclude psychiatric diagnosis in unaffected relatives and HC (First et al., 2002). We used the 17-item HDRS (Hamilton, 1976) and the YRMS (Young et al., 1978) to evaluate the presence of depressive and manic symptoms, respectively. Board-certified psychiatrists (FGN, JAA) with extensive research experience in mood disorders administered the SCID, HDRS, and YMRS in all subjects. Image acquisition Imaging data were obtained using a 3 T Phillips scanner (Philips Medical Systems, Best, The Netherlands) and an eight-channel head coil. Contiguous sagittal images across the entire brain were acquired using a 3D T1-FFE sequence with the following parameters: TE=3.2 ms, TR=7 ms, flip angle=8°, SENSE=2, acquisition matrix=240x240, and voxel size of 1mm×1mm×1mm (180 slices). Images processing and analysis
5
Nery FG The voxel-based morphometry (VBM) analysis was carried out using Statistical Parametric Mapping, version 8 (SPM8; http://www.fil.ion.ucl.ac.uk/spm) running under Matlab r2009b (http://www.mathworks.com/index.html). Briefly, all MRI datasets were first oriented manually to place the anterior commissure at the origin of the three-dimensional Montreal Neurological Institute (MNI) coordinate system. Images were then segmented into grey and white matter partitions using the unified segmentation procedure described in Ashburner and Friston (Ashburner and Friston, 2005). The Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) algorithm (Ashburner, 2007) was then used to spatially normalize the segmented images; this procedure maximizes sensitivity and accuracy of localization by registering individual structural images to an asymmetric custom T1-weighted template derived from the participants' structural images rather than a standard T1-weighted template based on a different sample (Ashburner, 2007). An additional “modulation” step consisted of multiplying each spatially normalized GM image by its relative volume before and after normalization; this ensured that the total amount of GM in each voxel was preserved. Finally, the resulting GM images were smoothed using a 8-mm isotropic kernel at full width half maximum (FWHM) to ensure normal data distribution as required by subsequent parametric tests. Between-group statistical comparisons of mean GM volumes were performed with the general linear model, based on random Gaussian field theory (Friston et al., 1995). Only voxels with values above an absolute GM threshold of .05 entered the analyses, resulting in a search volume of approximately 550,000 voxels. A measure of the total volume of GM of each subject was entered as a covariate in an analysis of covariance. Resulting statistics at each voxel were transformed to Z scores and displayed as SPMs into standard space, at a threshold of Z=3.09. Subsequently, the analyses with positive findings were repeated using
6
Nery FG age and sex as covariates because these variables may affect brain structure (Good et al., 2001; Sowell et al. 2003). To investigate GM volume differences between groups, three different 2-group comparisons were performed: GM volumes in BD patients versus unaffected relatives, BD patients versus HC, and unaffected relatives versus HC. GM volume differences (increased or decreased) were tested between each group. The voxel-wise set of results for each of the above contrasts constituted an SPM t statistic {SPM (t)} map, using an uncorrected threshold of p≤0.001. Based on previous literature about GM differences in relatives of BD patients, the following a priori regions of interests (ROIs) were selected for identification of any significant findings on the SPM(t) maps: orbitofrontal cortex, anterior cingulate cortex, amygdala, hippocampus, parahyppocampal gyrus, insula, caudate and thalamus. Such voxelwise search of each map was performed using the small volume correction (SVC) method with the purpose of constraining the total number of voxels included in the analyses. Each region was circumscribed by merging the spatially normalized ROI masks that are available within the Anatomical Automatic Labeling SPM toolbox. Findings of these hypothesisdriven, SVC-analyses were reported as significant if they survived family-wise error (FWE) correction for multiple comparisons (p≤0.05), with further criteria for voxel clusters to comprise at least 20 voxels over the respective ROI. Statistical analysis Regarding demographic and clinical data, Chi-square tests were conducted for crosstabulated qualitative data and analysis of variance (ANOVA) for ordinal and interval scale data to compare the BD patients, unaffected relatives, and HC. The analysis was repeated with age and gender as covariates. Statistical analyses were conducted using SPSS version 14.0 (SPSS, Inc, Chicago, IL, USA). Differences of p<0.05 were considered statistically significant.
7
Nery FG
3. Results
Demographic and clinical information for BD patients, unaffected relatives, and HC are displayed in Table 1.
GM volume differences among BD patients, unaffected relatives, and HC The sum of GM volume in the modulated, segmented images totaled 646.64 ml ± 71.87 in the BD patients group, 645.97 ml ± 48.20 in unaffected relatives and 637.87 ml ± 62.50 in HC (F(2,71)=0.162, p=0.85), indicating that there was no significant differences in GM volume in BD patients, unaffected relatives, and HC. In the whole brain comparison, there were no differences in GM volumes between BD patients and unaffected relatives, between BD patients and HC, or between unaffected relatives and HC. SVC analyses guided by a priori selected brain regions showed that BD patients presented reduced GM volumes in the bilateral thalamus (medial dorsal nucleus, 225 voxels, Z score=4.03, peak coordinates x,y,z=-2 -11 9, p=0.007) compared with HC (Figure 1a). There were no differences in any of the other a priori selected brain regions between groups. Repeating the analyses using age and gender as covariates, BD patients presented reduced GM volume in the bilateral thalamus compared with HC (medial dorsal nucleus, 142 voxels, Z score=3.62, peak coordinates x,y,z=-2 -12 9, p=0.027).
4. Discussion
8
Nery FG In this study, no abnormalities in GM volumes in unaffected relatives of BD patients were found compared with HC. Unaffected relative and HC groups were well matched on mean age and gender, decreasing the relative influence of these variables on the results. Furthermore, the relatives were all subjects who have never suffered any Axis I psychiatric disorder or received psychiatric treatment during their lives. Therefore, these results are not confounded by previous or current psychopathology or exposure to psychiatric medication in the relatives. Our negative findings are consistent with some (McDonald et al., 2006; van der Schot et al., 2009; Hulshoff Pol et al., 2012) but not all studies that used morphometric methods to study relatives of BD patients (McIntosh et al., 2004; Kempton et al. 2009; Matsuo et al., 2012; Hajek et al., 2013). Methodological differences between those studies and ours may explain, at least in part, the differences in results. For instance, some of the earlier VBM studies have used previous versions of the SPM software (McIntosh et al., 2004; Ladouceur et al., 2008; Kempton et al., 2009) as opposed to our use of SPM8 with DARTEL Toolbox. Some authors have demonstrated that the use of the DARTEL Toolbox provides superior results when compared to previous versions of the program (Klein et al., 2009; Tahmasebi et al., 2009; Ashburner, 2009). The clinical heterogeneity of BD may also be an important factor accounting for discrepancies across studies of high risk populations. Several clinical aspects of BD, including BD subtype, psychotic features, and comorbid alcohol use disorders, have familial aggregation and, therefore, may be the expression of specific genetic susceptibilities (Schulze et al., 2006; Saunders et al., 2008; Hua et al., 2011). Other sources of discrepancies between our study and previous studies include the developmental stages of brain structure, the genetic loading for BD within the families recruited, and the inclusion of relatives with other Axis I psychiatric disorders. These factors
9
Nery FG could potentially affect the detection of the endophenotype in the relatives of BD patients (Smoller and Finn, 2003; Birmaher et al., 2009; Schneider et al., 2012), thus limiting direct comparison of findings. 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 frontolimbic areas (Blond et al., 2012). It is a midline structure within the brain, shared by the different frontal-subcortical circuits that mediate motor activity and human behavior (Tekin and Cummings, 2002). Its functions are very diverse, as it contains several nuclei, each with different functions that include limbic, sensory or associative functions. The limbic nuclei of the thalamus are involved in regulating motivation, drive, and emotional experience and expression (Schmahmann, 2003). Vascular lesions of the thalamus may be accompanied by behavioral changes that resemble symptoms of BD, including abnormal energy (apathy or motor hyperactivity), psychomotor acceleration, mood swings, social inadequacy, and psychotic manic states (Schmahmann, 2003; Carrera and Bogousslavsky, 2006; De Witte et al., 2011). Thus, our finding of decreased thalamus GM in BD patients compared with HC is consistent with the theory that thalamus may be involved in the neurocircuitry responsible for the clinical manifestations of BD (Strakowski et al., 2012).
10
Nery FG Some limitations of this study should be considered. All patients were medicated, and potential confounding effects of psychotropic medications on GM volumes (Hafeman et al., 2012) may not be excluded and can partially explain some of the discrepancies between ours findings and those from previous studies. However, the more relevant results of this study are the comparisons between relatives and HC, which are not confounded by medication effects. Furthermore, our study had relatively small sample sizes in the comparison groups. It should also be noted that we did not include premorbid IQ as a confounding covariate in our between-group comparisons. The three groups had similar educational level, but there might have been mean IQ differences that could have influenced our results; therefore, the absence of IQ measurements is one further limitation of our study. The heritability of BD likely involves a multifactorial transmission pattern, and hundreds or thousands of genes of small effect may act together to predispose the person to develop BD (Barnett and Smoller, 2009; Schulze, 2010). Thus, when recruiting first-degree relatives of BD, we may have included individuals that share only some of the putative BD genes, and therefore, the effect size of these few genes on brain structure may be so small as to be nearly undetectable by neuroimaging studies with small samples. Furthermore, GM volume reduction in BD is small at most (when compared e.g. to schizophrenia), as have shown some recent studies (Ivleva et al., 2013; Amann et al., 2015) and such changes are likely to be even subtler in unaffected relatives of BD patients. Therefore, studies with large samples are more likely to detect subtle but relevant structural abnormalities. New studies should also investigate white matter abnormalities in relatives of BD patients, since initial evidence for this has been found in this population (Arat et al., 2015) and it has been postulated that changes in white matter might be a vulnerability trait factor for BD rather than changes in GM (Schneider et al., 2012). On the other hand, our study has considerable strengths. Our sample was clinically well characterized and reasonably well matched, minimizing any potential confounding effect
11
Nery FG of demographic characteristics. Moreover, in our analysis, we have used age and gender as covariates to control for the effects of these potential confounders. We have also used very stringent criteria for euthymia, to decrease the possibility of mood symptomatology affecting our findings. In addition, we carefully scrutinized the presence of previous psychopathology in the unaffected relatives of BD patients, in an effort to exclude the confounding effects of another Axis I disorder. In conclusion, the present study found that unaffected relatives of BD patients do not present brain global or regional GM differences compared with HC. BD patients present decreased GM volumes in the right and left thalamus compared with HC. These results suggest that there is no structural endophenotype for BD and support the role of the thalamus in the pathophysiology of BD.
12
Nery FG 5. Acknowledgments This study was partly supported by Conselho Nacional de Pesquisa (CNPq) grant #478466/2009, Conselho de Aperfeicoamento de Pessoal de Nivel Superior (CAPES) grant #2632/09-8, Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) grant #2005/56464-9, Instituto Nactional de Ciencia e Tecnologia (INCT) Translacional em Medicina do CNPq grant #573671/2008-7, and by a generous private donation from the family of Thompson Motta (to PROMAN).
13
Nery FG 6. Disclosures Dr. Nery held a temporary position as a medical advisor in Eli Lilly and Company from 2012 to 2013. Dr. Stertz is a recipient of a scholarship from CAPES. Dr. Kapczinski has received grants/research support from AstraZeneca, Eli Lilly, Janssen-Cilag, Servier, CNPq, CAPES, NARSAD, and the Stanley Medical Research Institute; he has also been a member of the speakers boards for AstraZeneca, Eli Lilly, Janssen-Cilag and Servier, and has served as a consultant for Servier. All other co-authors reported no biomedical financial interests or potential conflicts of interest.
14
Nery FG 7. References
Adler, C.M., DelBello, M.P., Jarvis, K., Levine, A., Adams, J., Strakowski, S.M., 2007. Voxel-based study of structural changes in first-episode patients with bipolar disorder. Biological Psychiatry 61, 776-781. Amann, B.L., Canales-Rodríguez, E.J., Madre, M., Radua, J., Monte, G., Alonso-Lana, S., Landin-Romero, R., Moreno-Alcázar, A., Bonnin, C.M., Sarró, S., Ortiz-Gil, J., Gomar, J.J., Moro, N., Fernandez-Corcuera, P., Goikolea, J.M., Blanch, J., Salvador, R., Vieta, E., McKenna, P.J., Pomarol-Clotet, E., 2015. Brain structural changes in schizoaffective disorder compared to schizophrenia and bipolar disorder. Acta Psychiatrica Scandinavica. doi: 10.1111/acps.12440. [Epub ahead of print] Arat, H.E., Chouinard, V.A., Cohen, B.M., Lewandowski, K.E., Öngür, D., 2015. Diffusion tensor imaging in first degree relatives of schizophrenia and bipolar disorder patients. Schizophrenia Research 161, 329-339. Ashburner, J., 2007. A fast diffeomorphic image registration algorithm. NeuroImage 38, 95113. Ashburner, J., 2009. Computational anatomy with the SPM software. Magnetic Resonance Imaging 27, 1163-1174. Ashburnerm J., Friston, K.J., 2005. Unified segmentation. Neuroimage 26, 839-851. Barnett, J.H., Smoller, J.W., 2009. The genetics of bipolar disorder. Neuroscience 164, 331343. Birmaher, B., Axelson, D., Monk, K., Kalas, C., Goldstein, B., Hickey, M.B., Obreja, M., Ehmann, M., Iyengar, S., Shamseddeen, W., Kupfer, D., Brent, D., 2009. Lifetime psychiatric disorders in school-aged offspring of parents with bipolar disorder: the Pittsburgh Bipolar Offspring study. Archives of General Psychiatry 66, 287-296.
15
Nery FG Blond, B.N., Fredericks, C.A., Blumberg, H.P., 2012. Functional neuroanatomy of bipolar disorder: structure, function, and connectivity in an amygdala-anterior paralimbic neural system. Bipolar Disorders 14, 340-355. Bora, E., Fornito, A., Yücel, M., Pantelis, C., 2010. Voxelwise metaanalysis of gray matter abnormalities in bipolar disorder. Biological Psychiatry 67, 1097–1105. Brambilla, P., Tansella, M., 2009. What are the perspectives of human brain mapping in the field of bipolar disorder? International Review of Psychiatry 21, 295–296. Caetano, S.C., Sassi, R., Brambilla, P., Harenski, K., Nicoletti, M., Mallinger, A.G., Frank, E., Kupfer, D.J., Keshavan, M.S., Soares, J.C., 2001. MRI study of thalamic volumes in bipolar and unipolar patients and healthy individuals. Psychiatry Research 108, 161168. Carrera, E., Bogousslavsky, J., 2006. The thalamus and behavior: effects of anatomically distinct strokes. Neurology 66, 1817-1823. Dasari, M., Friedman, L., Jesberger, J., Stuve, T.A., Findling, R.L., Swales, T.P., Schulz, S.C., 1999. A magnetic resonance imaging study of thalamic area in adolescent patients with either schizophrenia or bipolar disorder as compared to healthy controls. Psychiatry Research 91, 155-162. De Witte, L., Brouns, R., Kavadias, D., Engelborghs, S., De Deyn, P.P., Mariën, P., 2011. Cognitive, affective and behavioural disturbances following vascular thalamic lesions: a review. Cortex 47, 273-319. Dupont, R.M., Jernigan, T.L., Heindel, W., Butters, N., Shafer, K., Wilson, T., Hesselink, J., Gillin, J.C., 1995. Magnetic resonance imaging and mood disorders. Localization of white matter and other subcortical abnormalities. Archives of General Psychiatry 52, 747-755.
16
Nery FG First, M.B., Gibbon, M., Spitzer, R.L., Williams, J.B.W., 2002. Structured Clinical Interview for DSM-IV-TR Axis I Disorders. Biometrics Research, New York. Frazier, J.A., Chiu, S., Breeze, J.L., Makris, N., Lange, N., Kennedy, D.N., Herbert, M.R., Bent, E.K., Koneru, V.K., Dieterich, M.E., Hodge, S.M., Rauch, S.L., Grant, P.E., Cohen, B.M., Seidman, L.J., Caviness, V.S., Biederman, J., 2005. Structural brain magnetic resonance imaging of limbic and thalamic volumes in pediatric bipolar disorder. American Journal of Psychiatry 162, 1256-1265. Friston, K.J., Holmes, A.P., Poline, J.B., Grasby, P.J., Williams, S.C., Frackowiak, R.S., Turner, R., 1995. Analysis of fMRI time-series revisited. NeuroImage 2, 45-53. Glahn, D.C., Knowles, E.E., McKay, D.R., Sprooten, E., Raventós, H., Blangero, J., Gottesman, I.I., Almasy, L., 2014. Arguments for the sake of endophenotypes: examining common misconceptions about the use of endophenotypes in psychiatric genetics. American Journal of Medical Genetics 165B, 122-130. Good, C.D., Johnsrude, I.S., Ashburner, J., Henson, R.N., Friston, K.J., Frackowiak, R.S., 2001. A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage 14, 21-36. Goodwin, F.K., Jamison, K.R., 2007. Manic-Depressive Illness: Bipolar Disorders and Recurrent Depression. Oxford University Press, New York. Gottesman, I.I., Gould, T.D., 2003. The endophenotype concept in psychiatry: etymology and strategic intentions. American Journal of Psychiatry 160, 636-645. Hafeman, D.M., Chang, K.D., Garrett, A.S., Sanders, E.M., Phillips, M.L., 2012. Effects of medication on neuroimaging findings in bipolar disorder: an updated review. Bipolar Disorders 14, 375-410. Hajek, T., Cullis, J., Novak, T., Kopecek, M., Blagdon, R., Propper, L., Stopkova, P., Duffy, A., Hoschl, C., Uher, R., Paus, T., Young, L.T., Alda, M., 2013. Brain structural
17
Nery FG signature of familial predisposition for bipolar disorder: replicable evidence for involvement of the right inferior frontal gyrus. Biological Psychiatry 73, 144-152. Hajek, T., Gunde, E., Bernier, D., Slaney, C., Propper, L., Grof, P., Macqueen, G., Duffy, A., Alda, M., 2008. Subgenual cingulate volumes in affected and unaffected offspring of bipolar parents. Journal of Affective Disorders 108, 263-269. Hajek, T., Gunde, E., Bernier, D., Slaney, C., Propper, L., Macqueen, G., Duffy, A., Alda, M., 2008. Pituitary volumes in relatives of bipolar patients: high-risk study. European Archives of Psychiatry and Clinical Neuroscience 258, 357-362. Hajek, T., Gunde, E., Slaney, C., Propper, L., MacQueen, G., Duffy, A., Alda, M., 2009. Striatal volumes in affected and unaffected relatives of bipolar patients--high-risk study. Journal of Psychiatric Research 43, 724-729. Hajek, T., Novak, T., Kopecek, M., Gunde, E., Alda, M., Höschl, C., 2010. Subgenual cingulate volumes in offspring of bipolar parents and in sporadic bipolar patients. European Archives of Psychiatry and Clinical Neuroscience 260, 297-304. Hallahan, B., Newell, J., Soares, J.C., Brambilla, P., Strakowski, S.M., Fleck, D.E., Kieseppa, T., Altshuler, L.L., Fornito, A., Malhi, G.S., McIntosh, A.M., Yurgelun-Todd, D.A., Labar, K.S., Sharma, V., MacQueen, G.M., Murray, R.M., McDonald, C., 2011. Structural magnetic resonance imaging in bipolar disorder: an international collaborative mega-analysis of individual adult patient data. Biological Psychiatry 69, 326-335. Hamilton, M., 1976. Hamilton Psychiatric Rating Scale for Depression. In: Guy, W. (Ed.), Assessment Manual for Psychopharmacology. Education and Welfare, Washington DC, pp. 179-192.
18
Nery FG Hasler, G., Drevets, W.C., Gould, T.D., Gottesman, I.I., Manji, H.K., 2006. Toward constructing an endophenotype strategy for bipolar disorders. Biological Psychiatry 60, 93-105. Hua, L.L., Wilens, T.E., Martelon, M., Wong, P., Wozniak, J., Biederman, J., 2011. Psychosocial functioning, familiality, and psychiatric comorbidity in bipolar youth with and without psychotic features. Journal of Clinical Psychiatry 72, 397-405. Hulshoff Pol, H.E., van Baal, G.C.M., Schnack, H.G., Brans, R.G.H., van der Schot, A.C., Brouwer, R.M., van Haren, N.E.M., Lepage, C., Collins, D.L., Evans, A.C., Boomsma, D.I., Nolen, W., Kahn, R.S., 2012. Overlapping and segregating structural brain abnormalities in twins with schizophrenia or bipolar disorder. Archives of General Psychiatry 69, 349-359. Ivleva, E.I., Bidesi, A.S., Keshavan, M.S., Pearlson, G.D., Meda, S.A., Dodig, D., Moates, A.F., Lu, H., Francis, A.N., Tandon, N., Schretlen, D.J., Sweeney, J.A., Clementz, B.A., Tamminga, C.A., 2013. Gray matter volume as an intermediate phenotype for psychosis: Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP). American Journal of Psychiatry 170, 1285-1296. Kempton, M.J., Haldane, M., Jogia, J., Grasby, P.M., Collier, D., Frangou, S., 2009. Dissociable brain structural changes associated with predisposition, resilience, and disease expression in bipolar disorder. The Journal of Neuroscience 29, 10863-10868. Kieseppa, T., Partonen, T., Haukka, J., Kaprio, J., Lonnqvist, J., 2004. High concordance of bipolar I disorder in a nationwide sample of twins. American Journal of Psychiatry 161, 1814-1821. Kieseppa, T., van Erp, T.G., Haukka, J., Partonen, T., Cannon, T.D., Poutanen, V.P., Kapri, J., Lönnqvist, J., 2002. The volumetric findings in MRI brain study of bipolar twins and their healthy co-twins. Bipolar Disorders 4 Suppl 1, 29-30.
19
Nery FG Kieseppa, T., van Erp, T.G.M., Haukka, J., Partonen, T., Cannon, T.D., Poutanen, V.-P., Kaprio, J., Lonnqvist, J., 2003. Reduced left hemispheric white matter volume in twins with bipolar I disorder. Biological Psychiatry 54, 896-905. Klein, A., Andersson, J., Ardekani, B.A., Ashburner, J., Avants, B., Chiang, M.-C., Christensen, G.E., Collins, D.L., Gee, J., Hellier, P., Song, J.H., Jenkinson, M., Lepage, C., Rueckert, D., Thompson, P., Vercauteren, T., Woods, R.P., Mann, J.J., Parsey, R.V., 2009. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage 46, 786-802. Ladouceur, C.D., Almeida, J.R.C., Birmaher, B., Axelson, D.A., Nau, S., Kalas, C., Monk, K., Kupfer, D.J., Phillips, M.L., 2008. Subcortical gray matter volume abnormalities in healthy bipolar offspring: potential neuroanatomical risk marker for bipolar disorder? Journal of the American Academy of Child & Adolescent Psychiatry 47, 532-539. Lochhead, R.A., Parsey, R.V., Oquendo, M.A., Mann, J.J., 2004. Regional brain gray matter volume differences in patients with bipolar disorder as assessed by optimized voxelbased morphometry. Biological Psychiatry 55, 1154-1162. Matsuo, K., Kopecek, M., Nicoletti, M.A., Hatch, J.P., Watanabe, Y., Nery, F.G., ZuntaSoares, G., Soares, J.C., 2012. New structural brain imaging endophenotype in bipolar disorder. Molecular Psychiatry 17, 412-420. McDonald, C., Bullmore, E., Sham, P., Chitnis, X., Suckling, J., MacCabe, J., Walshe, M., Murray, R.M., 2005. Regional volume deviations of brain structure in schizophrenia and psychotic bipolar disorder: computational morphometry study. British Journal of Psychiatry 186, 369-377. McDonald, C., Marshall, N., Sham, P.C., Bullmore, E.T., Schulze, K., Chapple, B., Bramon, E., Filbey, F., Quraishi, S., Walshe, M., Murray, R.M., 2006. Regional brain
20
Nery FG morphometry in patients with schizophrenia or bipolar disorder and their unaffected relatives. American Journal of Psychiatry 163, 478-487. McGuffin, P., Rijsdijk, F., Andrew, M., Sham, P., Katz, R., Cardno, A., 2003. The heritability of bipolar affective disorder and the genetic relationship to unipolar depression. Archives of General Psychiatry 60, 497-502. McIntosh, A.M., Job, D.E., Moorhead, T.W.J., Harrison, L.K., Forrester, K., Lawrie, S.M., Johnstone, E.C., 2004. Voxel-based morphometry of patients with schizophrenia or bipolar disorder and their unaffected relatives. Biological Psychiatry 56, 544-552. Noga, J.T., Vladar, K., Torrey, E.F., 2001. A volumetric magnetic resonance imaging study of monozygotic twins discordant for bipolar disorder. Psychiatry Research 106, 25-34. Saunders, E.H., Scott, L.J., McInnis, M.G., Burmeister, M., 2008. Familiality and diagnostic patterns of subphenotypes in the National Institutes of Mental Health bipolar sample. American Journal of Medical Genetics 147B, 18-26. Schmahmann, J.D., 2003. Vascular syndromes of the thalamus. Stroke 34, 2264-2278. Schneider, M.R., Delbello, M.P., McNamara, R.K., Strakowski, S.M., Adler, C.M., 2012. Neuroprogression in bipolar disorder. Bipolar Disorders 14, 356-374. Schulze, T.G., 2010. Genetic research into bipolar disorder: the need for a research framework that integrates sophisticated molecular biology and clinically informed phenotype characterization. Psychiatric Clinics of North America 33, 67-82. Schulze, T.G., Hedeker, D., Zandi, P., Rietschel, M., McMahon, F.J., 2006. What is familial about familial bipolar disorder? Resemblance among relatives across a broad spectrum of phenotypic characteristics. Archives of General Psychiatry 63, 1368-1376. Singh, M.K., Delbello, M.P., Adler, C.M., Stanford, K.E., Strakowski, S.M., 2008. Neuroanatomical characterization of child offspring of bipolar parents. Journal of the American Academy of Child and Adolescent Psychiatry 47, 526-531.
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Nery FG Smoller, J.W., Finn, C.T., 2003. Family, twin, and adoption studies of bipolar disorder. American Journal of Medical Genetics 123C, 48-58. Sowell, E.R., Peterson, B.S., Thompson, P.M., Welcome, S.E., Henkenius, A.L., Toga, A.W., 2003. Mapping cortical change across the human life span. Nature Neuroscience 6, 309315. Strakowski, S.M., Adler, C.M., Almeida, J., Altshuler, L.L., Blumberg, H.P., Chang, K.D., DelBello, M.P., Frangou, S., McIntosh, A., Phillips, M.L., Sussman, J.E., Townsend, J.D., 2012. The functional neuroanatomy of bipolar disorder: a consensus model. Bipolar Disorders 14, 313-325. Strakowski, S.M., DelBello, M.P., Sax, K.W., Zimmerman, M.E., Shear, P.K., Hawkins, J.M., Larson, E.R., 1999. Brain magnetic resonance imaging of structural abnormalities in bipolar disorder. Archives of General Psychiatry 56, 254-260. Strakowski, S.M., DelBello, M.P., Zimmerman, M.E., Getz, G.E., Mills, N.P., Ret, J., Shear, P., Adler, C.M., 2002. Ventricular and periventricular structural volumes in first- versus multiple-episode bipolar disorder. American Journal of Psychiatry 159, 1841-1847. Tahmasebi, A.M., Abolmaesumi, P., Zheng, Z.Z., Munhall, K.G., Johnsrude, I.S., 2009. Reducing inter-subject anatomical variation: effect of normalization method on sensitivity of functional magnetic resonance imaging data analysis in auditory cortex and the superior temporal region. NeuroImage 47, 1522-1531. Takahashi, T., Walterfang, M., Wood, S.J., Kempton, M.J., Jogia, J., Lorenzetti, V., Soulsby, B., Suzuki, M., Velakoulis, D., Pantelis, C., Frangou, S., 2010. Pituitary volume in patients with bipolar disorder and their first-degree relatives. Journal of Affective Disorders 124, 256-261. Tekin, S., Cummings, J.L., 2002. Frontal-subcortical neuronal circuits and clinical neuropsychiatry: an update. Journal of Psychosomatic Research 53, 647-654.
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Nery FG Tohen, M., Frank, E., Bowden, C.L., Colom, F., Ghaemi, S.N., Yatham, L.N., Malhi, G.S., Calabrese, J.R., Nolen, W.A., Vieta, E., Kapczinski, F., Goodwin, G.M., Suppes, T., Sachs, G.S., Chengappa, K.R., Grunze, H., Mitchell, P.B., Kanba, S., Berk, M., 2009. The International Society for Bipolar Disorders (ISBD) Task Force report on the nomenclature of course and outcome in bipolar disorders. Bipolar Disorders 11, 453473. van der Schot, A.C., Vonk, R., Brans, R.G.H., van Haren, N.E.M., Koolschijn, P.C.M.P., Nuboer, V., Schnack, H.G., van Baal, G.C.M., Boomsma, D.I., Nolen, W.A., Hulshoff Pol, H.E., Kahn, R.S., 2009. Influence of genes and environment on brain volumes in twin pairs concordant and discordant for bipolar disorder. Archives of General Psychiatry 66, 142-151. Winokur, G., Coryell, W., Keller, M., Endicott, J., Leon, A., 1995. A family study of manicdepressive (bipolar I) disease. Is it a distinct illness separable from primary unipolar depression? Archives of General Psychiatry 52, 367-373. Young, R.C., Biggs, J.T., Ziegler, V.E., Meyer, D.A., 1978. A rating scale for mania: reliability, validity and sensitivity. British Journal of Psychiatry 133, 429-435. Table 1 legend: Demographic and clinical characteristics of BD patients, unaffected relatives, and HC
Table 1 footnote: Abbreviations: BD (bipolar disorder); HC (healthy controls); HDRS (Hamilton Depression Rating Scale 17 items); YMRS (Young Mania Rating Scale) *Information about number of episodes was reliably available for 14 patients. For the remaining 11 patients, number of episodes was unreliable or too many to count.
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Nery FG
Figure 1 footnote: Brain regions where there were foci of significant differences in gray matter volumes between BD patients and HC (at the Z > 3.09 cutoff, uncorrected for multiple comparisons and using an extent threshold of 20 voxels). Foci of significance were overlaid on axial brain slices spatially normalized into an approximation to the Talairach and Tournoux stereotactic atlas. (Talairach J, Tornoux P (1988): Co-Planar Stereotaxic Atlas of the Human Brain, Thieme Medical Publishers, New York). The numbers associated with each frame represent standard coordinates in the y-axis. Reaching statistical significance at the p<0.05 level after family-
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Nery FG wise error correction for multiple comparisons using the SVC tool in SPM. (a) Clusters (highlighted in yellow) of decreased GM volume in BD patients relative to HC in the bilateral thalamus, Abbreviations: GM (gray matter); BD (bipolar disorder); HC (healthy controls); SVC (small volume correction); SPM (Statistical Parametric Mapping); R (right).
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Nery FG Table 1: Demographic and clinical characteristics of the sample Unaffected BD patients
HC
Statistics,
(n=27)
p value
relatives (n=25) (n=23) Age, y (mean±S.D.)
35.7±8.9
31.6±6.7
31.2±9.5
0.07
Gender (male), n (%)
8 (32%)
9 (39.1%)
11 (40.7%)
0.79
Right-handed, n (%)
21 (84%)
22 (95.7%)
26 (96.3%)
0.12
Family history of BD, n (%)
7 (28%)
23 (100%)
-
-
Relative participating in the study, n (%)
19 (76%)
18 (78%)
-
-
6.6±4
-
-
-
HDRS (mean±S.D.)
2.9±2.6
-
-
-
YMRS (mean±S.D.)
1.2±2.1
-
-
-
Age at disease onset, y (mean±S.D.)
22.1±8.5
-
-
-
Length of illness, y (mean±S.D.)
13.6±8.1
-
-
-
6±4
-
-
-
Time in remission, mo (mean±S.D.)
Number of mood episodes,* (mean±S.D.) Current medication
-
Lithium, n (%)
12 (48%)
-
-
-
Anticonvulsants, n (%)
10 (40%)
-
-
-
Atypical antipsychotics, n (%)
13 (52%)
-
-
-
Antidepressants, n (%)
5 (20%)
-
-
-
Sedative-hypnotics, n (%)
2 (8%)
-
-
-
Highlights
We investigated brain structure endophenotypes in bipolar disorder. We compared gray matter volumes in patients, their relatives, and controls. Patients presented smaller bilateral gray matter volumes than controls.
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Nery FG
Relatives did not differ from patients and controls on gray matter volumes. Abnormal gray matter volumes may not be an endophenotype for bipolar disorder.
27