Total gray matter volume is reduced in individuals with bipolar disorder currently treated with atypical antipsychotics

Journal of Affective Disorders 260 (2020) 722–727

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Research paper

Total gray matter volume is reduced in individuals with bipolar disorder currently treated with atypical antipsychotics

T

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Armin Birnera, Susanne A. Bengessera, , Stephan Seilerb, Nina Dalknera, Robert Queissnera, Martina Platzera, Frederike T. Fellendorfa, Carlo Hamma, Alexander Mageta, Rene Pilza, Melanie Lengera, Bernd Reininghausa, Lukas Pirpamerc, Stefan Ropelec,d, Nicole Hintereggerd, Marton Magyard, Hannes Deutschmannd, Christian Enzingerc,d, Hans-Peter Kapfhammera, Eva Z. Reininghausa a

Department of Psychiatry and Psychotherapy, Medical University of Graz, Auenbruggerplatz 31, A-8036, Graz, Austria Imaging of Dementia and Aging (IDeA), Laboratory Department of Neurology and Center for Neuroscience, University of California, Davis, USA Department of Neurology, Medical University of Graz, Austria d Division of Neuroradiology, Department of Radiology, Medical University of Graz, Austria b c

A R T I C LE I N FO

A B S T R A C T

Keywords: Bipolar disorder Gray matter volume Magnetic resonance imaging Psychopharmacology Antipsychotics

Background/Aims: Recent evidence indicates that the intake of atypical antipsychotics (AAP) is associated with gray matter abnormalities in patients with psychiatric disorders. We explored if patients with bipolar disorder (BD) who are medicated with AAP exhibit total gray matter volume (TGV) reduction compared to BD individuals not medicated with AAP and healthy controls (HC). Methods: In a cross-sectional design, 124 individuals with BD and 86 HC underwent 3T-MRI of the brain and clinical assessment as part of our BIPFAT-study. The TGV was estimated using Freesurfer. We used univariate covariance analysis (ANCOVA) to test for normalized TGV differences and controlled for covariates. Results: ANCOVA results indicated that 75 BD individuals taking AAP had significantly reduced normalized TGV as compared to 49 BD not taking AAP (F = 9.995, p = .002., Eta = 0.084) and 86 HC (F = 7.577, p = .007, Eta = 0.046). Limitations: Our cross-sectional results are not suited to draw conclusions about causality. We have no clear information on treatment time and baseline volumes before drug treatment in the studied subjects. We cannot exclude that patients received different psychopharmacologic medications prior to the study point. We did not included dosages into the calculation. Many BD individuals received combinations of psychopharmacotherapy across drug classes. We did not have records displaying quantitative alcohol consumption and drug abuse in our sample. Conclusions: Our data provide further evidence for the impact of AAP on brain structure in BD. Longitudinal studies are needed to investigate the causal directions of the proposed relationships.

1. Introduction Bipolar disorder (BD) is an affective disorder with a high disease burden (Fleishman, 2003) and is accompanied by structural brain changes (Hibar et al., 2016; Hibar et al., 2018; Arnone et al., 2009; Beyer et al., 2009; Kempton et al., 2008; Sexton et al., 2009; Ladouceur et al., 2008; Hallahana et al., 2011; EllisonWright, Bullmore, 2010; Foland-Ross et al., 2011; Rimol et al., 2010; Lim et al., 2013; Konopaske et al., 2014). Volume reduction and cortical thinning are found in several brain regions in BD (Hibar et al., 2018; ⁎

Arnone et al., 2009; Hallahana et al., 2011; Ellison-Wright, Bullmore, 2010; Abramovic et al., 2016; Wise et al., 2017). The lateral ventricles are approximately fifteen percent larger compared to healthy controls (Hibar et al., 2016; Arnone et al., 2009; Kempton et al., 2008; Abramovic et al., 2016). These morphological changes relate to illness duration (Arnone et al., 2009; Hallahana et al., 2011) and are differentially associated with intake of mood stabilizing medication. Lithium seems to have neuroprotective effects, while antiepileptic drugs relate to accelerated cortical thinning (Hibar et al., 2016; Hibar et al., 2018; Kempton et al., 2008; Hallahana et al., 2011; Abramovic et al., 2016).

Corresponding author. E-mail address: [email protected] (S.A. Bengesser).

https://doi.org/10.1016/j.jad.2019.09.068 Received 4 June 2019; Received in revised form 13 September 2019; Accepted 18 September 2019 Available online 19 September 2019 0165-0327/ © 2019 Published by Elsevier B.V.

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Effects of atypical antipsychotics (AAP), the third big group of “state of the art” - mood stabilizers on brain morphology is less well understood (McDonald, 2015), partly due to small sample sizes (Hallahana et al., 2011). The ENIGMA studies, however, increased sample sizes considerably and reported significantly reduced cortical surface areas of large prefrontal areas in 504 individuals with BD on AAP compared to 994 individuals with BD off AAP (Hibar et al., 2018). Hibar et al. found reduced thalamic volumes in BD individuals treated with AAP (Hibar et al., 2016). In a recent single-center analysis of cortical and subcortical measures, 114 BD individuals on AAP showed reduced hippocampal volumes and enlarged third ventricle sizes compared to 142 BD Individuals off AAP (Abramovic et al., 2016). In contrast, in schizophrenia it is suggested that treatment with AAP may ameliorate hippocampal volume deficits (van Erp et al., 2016). In schizophrenia patients, ENIGMA researchers reported global cortical thinning to be more pronounced in individuals on AAP compared to non-medicated subjects (van Erp et al., 2018). In longitudinal studies, antipsychotic treatment was associated with gray matter reduction over time (Ho et al., 2011; Borgwardt et al., 2009). A recent meta- analysis showed that gray matter reduction was associated with higher doses of antipsychotic medication in schizophrenia subjects (Haijma et al., 2013). Antipsychotics are, however, the only treatment option in schizophrenia and patients not treated with Antipsychotics are scarce. In BD, three different first line treatments exist. Therefore, BD patients provide an opportunity to study the effects of AAP on brain structure by comparing MRI measures among different treatment groups. Because cortical-and subcortical gray matter volumes are integrated in a complex brain network and share intricate relationships (Mesulam, 1990), we chose to assess the effects of AAP on total gray matter volume (TGV). We hypothesized that TGV in BD patients would be lower as compared to BD patients without AAP treatment and healthy controls (HC). It is important to keep in mind that our study is cross-sectional and therefore results are not suited to draw conclusions about causality.

psychiatric disorders. The study has been approved by the local ethics committee (Medical University of Graz, Austria) in compliance with the current revision of the Declaration of Helsinki, ICH guideline for Good Clinical Practice and current regulations (EK-number: 24–123 ex 11/ 12). 2.2. Magnetic resonance tomography (MRI) MRI data was collected with a 3T whole body scanner (TimTrio; Siemens Healthcare, Erlangen, Germany). The protocol included an axial FLAIR sequence (TR = 10,000 ms, TE = 69 ms, inversion time = 2500 ms, number of slices = 40, slice thickness = 3 mm, inplane resolution = 0.86 × 0.86 mm²) and a high resolution T1 weighted 3D sequence with magnetization prepared rapid gradient echo (MPRAGE) and whole brain coverage (TR = 1900 ms, TE = 2.19 ms, inversion time = 900 ms, flip angle = 9°, isotropic resolution of 1 mm). Cortical reconstruction and volumetric segmentation was performed with the Freesurfer image analysis suite (version 5.3), which is documented and freely available for download online (http:// surfer.nmr.mgh.harvard.edu) (Fischl, 2012; Dale et al., 1999; Reuter et al., 2012). The processing includes motion correction and averaging of volumetric T1 weighted images, removal of non-brain tissue (including stripping of the skull) using a watershed/surface deformation procedure (Segonne et al., 2007), automated Talairach transformation, segmentation of the subcortical white matter and deep gray matter volumetric structures (including hippocampus, amygdala, caudate, putamen) intensity normalization, tessellation of the gray/ white matter boundary, automated topology correction (Segonne et al., 2007), and surface deformation following intensity gradients to place the gray/white and gray/cerebrospinal fluid borders at the location where the greatest shift in intensity is defining the transition to the other class of tissue (Dale et al., 1999). Procedures for these measurements have been validated against histological analysis (Rosas et al., 2002) and manual measurements (Kuperberg et al., 2003). Freesurfer morphometrics have shown good reliability across scanner brands and across magnetic field strengths (Reuter et al., 2012). For quality control, a neuroradiology specialist, who is trained in and familiar with Freesurfer, checked the appropriate registration, subcortical segmentations and cortical parcellations. Incorrectly registered data sets (6) were removed. Because of favourable visual inspection results, no manual editing was done. After this quality check, the cortical and subcortical volumes among other variables of each individual were computed. TGV and total intracranial volume were used for statistical analysis.

2. Methods 2.1. Participants The study of brain structural characteristics in BD was conducted as part of the cross-sectional, prospective, single center BIPFAT (Bipolar Disorder and Fat-Metabolism) study at the Medical University of Graz, Department of Psychiatry and Psychotherapy, exploring shared pathophysiologic pathways between overweight and BD. The BIPFAT study assesses demographic parameters, complete actual and lifetime psychiatric history using the Structured Clinical Interview (SCID I) according to DSM-IV, history of medication, anthropometric measure, fasting blood and magnetic resonance imaging (MRI) of the brain. All patients included were former in- or outpatients of the Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz and had a diagnosis of BD I or BD II according to the DSM-IV criteria. Participants were recruited by psychiatrists or psychologists at our university clinic. Inpatients were recruited if they matched inclusion criteria. Furthermore, participants were recruited from our BD outpatient clinic. This outpatient clinic offers continuing appointments with trained psychiatrists or psychiatry residents under supervision and offers psychoeducation among other services (https://www. medunigraz.at/bipolar/). All participants signed informed consent. Participants did not receive monetary compensation. Exclusion criteria were the presence of chronic obstructive pulmonary disease, rheumatoid arthritis, systemic lupus erythematosus, inflammatory bowel disease, neurodegenerative and neuroinflammatory disorders (i.e. Alzheimer's, Huntington's and Parkinson's disease, multiple sclerosis), haemodialysis and interferon-α-based immunotherapy. Further exclusion criteria for controls were the presence of lifetime psychiatric diagnoses (verified by SCID I) and first and second-degree relatives with

2.3. Statistics For the calculation of differences in demographic characteristics between BD on and off AAP, Student´s t-tests were performed for group comparisons of normally distributed variables. Categorical data sets were analyzed using chi-squared tests. Tests were two tailed and a value of p < .05 was considered statistically significant. For group differences, tests for normal distribution were obtained using the Kolmogorov Smirnov test. In case of violation of normality, MannWhitney-U-tests were performed. For all statistical calculations, to ensure comparability between different head-sized subjects (Wolf et al., 2003), TGV was divided by individual total intracranial volume multiplied by 10,000(Buckner et al., 2004). To investigate potential volume differences between BD patients with and without medication, a univariate covariance analysis (ANCOVA) set to AAP (yes/no) controlling for the confounders age (Enzinger et al., 2005), sex (male/female) (Ruigrok et al., 2014; Sacher et al., 2013), actual intake of lithium (yes/no) (Hibar et al., 2018), antiepileptics (yes/no) (Hibar et al., 2018), body mass index (BMI) (Gustafson et al., 2004), alcohol dependency and illness duration 723

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Table 1 Demographic characteristics, clinical parameters and brain volumes in BD. BD off AAP (n = 49)

BD on AAP (n = 75)

Statistics BD off – on AAP

HC (n = 86)

Statistics HC- BD off AAP

Statistics HC-BD on AAP

Male (%) Age [years] (mean, sd)

55% 43 (14)

45% 42 (13)

χ2 = 1.13 p = .287 U=−0.65 p = .515

37% 42 (17)

Body Mass Index (mean, sd) Illness Duration [years] (mean, sd)

28 (6) 20 (13)

28 (6) 18 (11)

25 (4) –

χ2 = 1.09 p = .296 U = −0.27 p = .789 t = 5.78 p = .017 –

BD subtype I (%) Manic/Hypomanic Episodes (mean, sd)

61% 10 (10)

65% 10 (13)

– –

– –

– –

Depressive Episodes (mean, sd)

14 (13)

15 (15)

–

–

–

Psychotic Symptoms (%) Alcohol Dependency (%) Lithium (%) Anticonvulsants (%) Antidepressants (%) Low potent Antipsychotics (%) Intracranial Volume [mm3] (mean, sd)

20% 18% 45% 40% 76% 18% 1.498.946 (149.348) 594.329 (69.291) 3.967 (267)

27% 16% 25% 21% 60% 19% 1.539.721 (139.107) 596.173 (63.519) 3.874 (240)

t = 0.64 p = .834 U = −0.52 p = .606 χ2 = 0.22 p = .642 U = −0.82 p = .419 U = −0.06 p = .949 χ2 = 0.63 p = .426 χ2 = 0.12 p = .731 χ2 = 5.13 p = .024 χ2 = 5.46 p = .019 χ2 = 4.60 p = .032 χ2 = 0.00 p = .967 F = 2.85 p = .094

χ2 = 4.06 p = .044 U = −0.87 p = .385 t = 6.14 p = .012 –

– – – – – – 1.518.573 (151.361) 604.710 (71.520) 3.984 (271)

– – – – – – –

– – – – – – F = 0.03 p = .856

–

F = 7.577 p = .007

Total Gray Matter Volume [mm3] (mean, sd) [nValue] (mean, sd)

F = 9.995 p = .002

Results from t-test (t), Chi square test (χ 2), Mann-Whitney-U test (U) and ANCOVA (F). For the ANCOVAs between BD on and off AAP Total Gray Matter Volume (normalization on Intracranial Volume, displayed as nValue) and on Intracranial Volume, controlling for all displayed variables was performed. For the ANCOVAs between BD and HC, Total Gray Matter Volume (normalization on Intracranial Volume) and on Intracranial Volume, controlling for age, sex (male/female) and body mass index was performed. (sd = standard deviation, BD = bipolar disorder, AAP = atypical antipsychotic)

subcortical structures in the same way they are applicable to the cortex. Thus, TGV represents the only single and unified outcome measure that captures both, cortical and subcortical gray matter. Second, corticaland subcortical regions are integrated in a complex brain network and AAP effects might be seen across levels of brain organization (Mesulam, 1990). Atlas-based parcellation methods suffer from arbitrarily drawn boundaries among brain regions, which might not reflect the true “functional” boundaries. Functional MRI based cortical parcellation might be a way to overcome these limitations and future MRI studies in BD might want to deploy this technique. To limit the influence of spurious effects from these limitations, we decided to use total GMV as outcome measure. Our results are in line with recent publications that indicate that the intake of AAP is associated with gray matter abnormalities in patients with psychiatric disorders (see Introduction) (Hibar et al., 2016; Hibar et al., 2018; Abramovic et al., 2016; van Erp et al., 2018). In patients with first episode-schizophrenia, subtle antipsychotic-dependent brain volume decrease was observed longitudinally (Ho et al., 2011). A recent systematic review concluded that there is evidence for a relationship between brain volume reduction and use of antipsychotics (Moncrieff, Leo, 2010). Because BMI has shown to be related to brain volume reductions (McIntyre et al., 2010; Leboyer et al., 2012; Fagiolini et al., 2008; Goldstein et al., 2013), we controlled for the influence of BMI in our statistical models. Indeed, BMI was a significant confounding factor (see supplemental ANCOVA's). It is not clear if AAP lead to gray matter volume loss beyond the disease process itself. One argument against the idea of neurodegenerative effects of AAP might be that patients taking AAP were more severely affected by the disease and that disease related factors contribute to these different brain structural characteristics instead of the medication. Our data, however, argue against this argument, as illness duration, mood episodes and history of psychosis, did not differ between individuals with and without AAPs. An older study of Owen and colleagues (Owens et al., 1985) using cerebral computed tomography in 112 chronic schizophrenia patients, revealed a correlation between involuntary movements (a typical side effect of antipsychotics) and ventricle enlargement suggesting an influence of medication. In rodents, studies using serial imaging with postmortem confirmation showed that haloperidol (typical antipsychotic) as well as olanzapine

(Hibar et al., 2018) was performed. To control for disease severity, we included lifetime number of manic and depressive episodes, history of psychosis, diagnosis (BD I/BD II) as well as the current intake of antidepressants and low potent antipsychotics as covariates in our model. To compare normalized TGV in BD patients on AAP and HC (no medication, no illness duration), we performed an additional ANCOVA, including less covarites (age, BMI and sex) could be applied to the model. Normal distribution of normalized TGV was confirmed with Kolmogorov-Smirnov test (K-S = 0.029; p = n.s.). After Bonferroni correction for two ANCOVA's, the p < .025 was considered statistically significant. 3. Results BD individuals taking AAP had significantly reduced normalized TGV compared to BD individuals not taking AAP (F = 9.995, p = .002., Eta = 0.084) and compared to HC (F = 7.577, p = .007, Eta = 0.046). For inspection and information on effects of the confounding variables, see Supplemental Material ANCOVA's. Results and descriptive baseline characteristics are displayed in Table 1. For Visualization of the data distribution see Fig. 1. 4. Discussion We report significantly reduced total gray matter volume in individuals with BD receiving a continuous treatment of AAP at the time point of the study as compared to BD not receiving AAP and controls, even after adjusting for age, sex, BMI, lithium and anticonvulsive medication, alcohol dependency and illness duration. We did not include typical antipsychotics into the statistics, as not a single individual took a high potent antipsychotic like haloperidol. We focused on AAP for two reasons; first, among the three big therapeutic groups (AAP, lithium and anticonvulsants), the effect of AAP on brain structure in BD seems to be the least well known. Second, our sample did not provide the number of participants treated with lithium or anticonvulsants to create reasonably comparable groups. We believe that TGV was the best choice as an outcome measure of this first analysis for the following reasons. First, thickness and surface measures are not applicable to 724

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Fig. 1. Boxplots of total gray matter volume; normalized on the individuals total intracranial volume (y-axis) in 124 bipolar disorder subjects. BD on APP (n = 75) had significantly reduced normalized total gray matter volume compared to BD off APP (n = 49) and controls (n = 86). (BD = bipolar disorder, AAP = atypical antipsychotics).

into question their clinical use, which is undoubtedly beneficial in treating patients with severe psychiatric disorders, including BD and schizophrenia.

(AAP) led to significant decrease of brain volumes. Curiously, brain volume loss seemed to resolve after withdrawal of the antipsychotic agent (Vernon et al., 2014; Vernon et al., 2012; Vernon et al., 2011). A study of macaque monkeys treated over 18 months with haloperidol or olanzapine detected an 8–11% fresh brain weight reduction, compared to control monkeys (Dorph-Petersen et al., 2005). This weight reduction was detected ubiquitous in the brain, but was more pronounced in frontal and parietal lobes. A potential mechanism for the cortical volume loss might be that the blockade of dopamine receptors results in increased turnover of dopamine and free radical generation accompanied by oxidative damage leading to neurodegeneration (Vernon et al., 2012). Additional evidence from rodent studies suggests that continuous administration of risperidone may promote the remodeling of the structure of presumably GABA-ergic interneurons and that it may evoke the rearrangement of synaptic contact and therefore interact with neuroplasticity (Mackowiak et al., 2009). Conversely, the intake of antipsychotics has also been related to increases of subcortical volumes (Huhtaniska et al., 2017) including hippocampal (van Erp et al., 2016) and left pallidum (Hashimoto et al., 2017) volume. A possible explanation of those findings might be a high occurrence of dopamine 2 receptors in striato-pallidal regions (Black et al., 1997) leading to increased cell proliferation following dopamine blocking neuroleptic therapy (Beaulieu et al., 2007). Another possible cause might be increased blood flow in the striatum after antipsychotic administration (Lahti et al., 2005). Compared to schizophrenia, whose treatment relies on antipsychotics, BD has three different groups of first-line mood stabilizing agents (lithium, antiepileptic agents and atypical antipsychotics) to choose from, which puts BD on an advantage to compare different groups of medications. As patients with BD are more vulnerable to the side effect of involuntary movements on antipsychotic drug therapy compared to schizophrenia (Gao et al., 2008), they might also be more vulnerable to drug dependent brain changes (Owens et al., 1985). Furthermore, decreases in brain structural measurements are less pronounced in BD than in schizophrenia (Hibar et al., 2016; Hibar et al., 2018; Ellison-Wright, Bullmore, 2010; van Erp et al., 2016; van Erp et al., 2018) and early, volume reducing, neurodevelopmental processes seem to be present far more likely in schizophrenia (Haijma et al., 2013) than in BD. Thus, disease activity is less effecting brain structural characteristics in BD and therefor relatively higher effects of medication influence might be visble. Most importantly, we point out that these observed associations on a group level of antipsychotic medications with very subtle gray matter abnormalities in individuals with psychiatric disorders should not call

4.1. Limitations and advantages As our analysis is cross-sectional, we have no clear information on duration of treatment and baseline pretreatment gray matter volumes. Additionally, we do not have records of the individual medication history of our patients. The group of AAP consists of seven different agents (amisulpride, aripiprazole, clozapine, olanzapine, quetiapine, risperidone and ziprasidone) and we did not include dosages into the calculation, which might vary among subjects. Furthermore, many BD individuals received combinations of psychopharmacotherapy across drug classes and according to the latest meta-analysis results each class was associated with different kind of brainstructural changes (Hibar et al., 2016; Hibar et al., 2018). While none of the patients received high potent typical antipsychotics, benzodiazepines were rarely used as continuous treatment or for sleep disturbances or unrest. We decided, however, against including benzodiazepines in our analysis because complete information on their use among patients could not be obtained. Finally, we did not have records displaying quantitative alcohol consumption and drug abuse in our sample. Our cross-sectional results are not suited to draw conclusions about causality. TGV is a global measurement and future studies might want to use brain connectivity analysis, including tractography, to disentangle the intricate relationships among gray matter volumes and the effects of AAP use on gray matter network disruption. Advantages of our study are the acquisition of data in a single center, large sample size, and a consistent study protocol including identical clinical and MRI assessments across patients. Participants were selected by diagnosis only and represent the naturally occuring diversity of BD in- and outpatients of a public university clinic. 5. Conclusions Our results provide additional evidence for gray matter volume reductions in BD patients treated with AAP. As our study is singlecenter and limited by a cross-sectional design, our results need to be validated in longitudinal studies. Funding There has been no significant financial support for this work that 725

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could have influenced its outcome.

Borgwardt, S.J., Smieskova, R., Fusar-Poli, P., Bendfeldt, K., Riecher-Rossler, A., 2009. The effects of antipsychotics on brain structure: what have we learnt from structural imaging of schizophrenia? Psychol. Med. 39 (11), 1781–1782. Buckner, R.L., Head, D., Parker, J., Fotenos, A.F., Marcus, D., Morris, J.C., Snyder, A.Z., 2004. A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. Neuroimage 23 (2), 724–738. Dale, A.M., Fischl, B., Sereno, M.I., 1999. Cortical surface-based analysis. I. segmentation and surface reconstruction. Neuroimage 9 (2), 179–194. Dorph-Petersen, K.A., Pierri, J.N., Perel, J.M., Sun, Z., Sampson, A.R., Lewis, D.A., 2005. The influence of chronic exposure to antipsychotic medications on brain size before and after tissue fixation: a comparison of haloperidol and olanzapine in macaque monkeys. Neuropsychopharmacology 30 (9), 1649–1661. Ellison-Wright, I., Bullmore, E., 2010. Anatomy of bipolar disorder and schizophrenia: a meta-analysis. Schizophr. Res. 117 (1), 1–12. Enzinger, C., Fazekas, F., Matthews, P.M., Ropele, S., Schmidt, H., Smith, S., Schmidt, R., 2005. Risk factors for progression of brain atrophy in aging: six-year follow-up of normal subjects. Neurology 64 (10), 1704–1711. Fagiolini, A., Chengappa, K.N., Soreca, I., Chang, J., 2008. Bipolar disorder and the metabolic syndrome: causal factors, psychiatric outcomes and economic burden. CNS Drugs 22 (8), 655–669. Fischl, B., 2012. FreeSurfer. Neuroimage 62 (2), 774–781. Fleishman, M., 2003. Economic grand rounds: psychopharmacosocioeconomics and the global burden of disease. Psychiatr. Serv. 54 (2), 142–144. Foland-Ross, L.C., Thompson, P.M., Sugar, C.A., Madsen, S.K., Shen, J.K., Penfold, C., Ahlf, K., Rasser, P.E., Fischer, J., Yang, Y., Townsend, J., Bookheimer, S.Y., Altshuler, L.L., 2011. Investigation of cortical thickness abnormalities in lithium-free adults with bipolar i disorder using cortical pattern matching. Am. J. Psychiatry 168 (5), 530–539. Gao, K., Kemp, D.E., Ganocy, S.J., Gajwani, P., Xia, G., Calabrese, J.R., 2008. Antipsychotic-induced extrapyramidal side effects in bipolar disorder and schizophrenia: a systematic review. J. Clin. Psychopharmacol. 28 (2), 203–209. Goldstein, B.I., Liu, S.M., Schaffer, A., Sala, R., Blanco, C., 2013. Obesity and the threeyear longitudinal course of bipolar disorder. Bipolar Disord. 15 (3), 284–293. Gustafson, D., Lissner, L., Bengtsson, C., Bjorkelund, C., Skoog, I., 2004. A 24-year followup of body mass index and cerebral atrophy. Neurology 63 (10), 1876–1881. Haijma, S.V., Van Haren, N., Cahn, W., Koolschijn, P.C., Hulshoff Pol, H.E, Kahn, R.S., 2013. Brain volumes in schizophrenia: a meta-analysis in over 18 000 subjects. Schizophr. Bull. 39 (5), 1129–1138. Hallahana, B., Newellb, J., Soaresp, J., Brambillac, P., Strakowski, S., Flecke, E., 2011. Structural magnetic resonance imaging in bipolar disorder: an international collaborative mega-analysis of individual adult patient data. Biol. Psychiatry 69 (4), 326–335. Hashimoto, N., Ito, Y.M., Okada, N., Yamamori, H., Yasuda, Y., Fujimoto, M., Kudo, N., Takemura, A., Son, S., Narita, H., Yamamoto, M., Tha, K.K., Katsuki, A., Ohi, K., Yamashita, F., Koike, S., Takahashi, T., Nemoto, K., Fukunaga, M., Onitsuka, T., Watanabe, Y., Yamasue, H., Suzuki, M., Kasai, K., Kusumi, I., Hashimoto, R., COCORO, 2017. The effect of duration of illness and antipsychotics on subcortical volumes in schizophrenia: analysis of 778 subjects. NeuroImage Clin. 17, 563–569. Hibar, D.P., Westlye, L.T., Doan, N.T., Jahanshad, N., Cheung, J.W., Ching, C.R.K., Versace, A., Bilderbeck, A.C., Uhlmann, A., Mwangi, B., Kramer, B., Overs, B., Hartberg, C.B., Abe, C., Dima, D., Grotegerd, D., Sprooten, E., Boen, E., Jimenez, E., Howells, F.M., Delvecchio, G., Temmingh, H., Starke, J., Almeida, J.R.C., Goikolea, J.M., Houenou, J., Beard, L.M., Rauer, L., Abramovic, L., Bonnin, M., Ponteduro, M.F., Keil, M., Rive, M.M., Yao, N., Yalin, N., Najt, P., Rosa, P.G., Redlich, R., Trost, S., Hagenaars, S., Fears, S.C., Alonso-Lana, S., van Erp, T.G.M., Nickson, T., ChaimAvancini, T.M., Meier, T.B., Elvsashagen, T., Haukvik, U.K., Lee, W.H., Schene, A.H., Lloyd, A.J., Young, A.H., Nugent, A., Dale, A.M., Pfennig, A., McIntosh, A.M., Lafer, B., Baune, B.T., Ekman, C.J., Zarate, C.A., Bearden, C.E., Henry, C., Simhandl, C., McDonald, C., Bourne, C., Stein, D.J., Wolf, D.H., Cannon, D.M., Glahn, D.C., Veltman, D.J., Pomarol-Clotet, E., Vieta, E., Canales-Rodriguez, E.J., Nery, F.G., Duran, F.L.S., Busatto, G.F., Roberts, G., Pearlson, G.D., Goodwin, G.M., Kugel, H., Whalley, H.C., Ruhe, H.G., Soares, J.C., Fullerton, J.M., Rybakowski, J.K., Savitz, J., Chaim, K.T., Fatjo-Vilas, M., Soeiro-de-Souza, M.G., Boks, M.P., Zanetti, M.V., Otaduy, M.C.G., Schaufelberger, M.S., Alda, M., Ingvar, M., Phillips, M.L., Kempton, M.J., Bauer, M., Landen, M., Lawrence, N.S., van Haren, N.E.M., Horn, N.R., Freimer, N.B., Gruber, O., Schofield, P.R., Mitchell, P.B., Kahn, R.S., Lenroot, R., MachadoVieira, R., Ophoff, R.A., Sarro, S., Frangou, S., Satterthwaite, T.D., Hajek, T., Dannlowski, U., Malt, U.F., Arolt, V., Gattaz, W.F., Drevets, W.C., Caseras, X., Agartz, I., Thompson, P.M., Andreassen, O.A., 2018. Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA bipolar disorder working group. Mol. Psychiatry 23 (4), 932. Hibar, D.P., Westlye, L.T., van Erp, T.G., Rasmussen, J., Leonardo, C.D., Faskowitz, J., Haukvik, U.K., Hartberg, C.B., Doan, N.T., Agartz, I., Dale, A.M., Gruber, O., Kramer, B., Trost, S., Liberg, B., Abe, C., Ekman, C.J., Ingvar, M., Landen, M., Fears, S.C., Freimer, N.B., Bearden, C.E., Costa Rica/Colombia Consortium for Genetic Investigation of Bipolar Endophenotypes,Sprooten, E, Glahn, D.C, Pearlson, G.D, Emsell, L, Kenney, J, Scanlon, C, McDonald, C, Cannon, D.M, Almeida, J, Versace, A, Caseras, X, Lawrence, N.S, Phillips, M.L, Dima, D, Delvecchio, G, Frangou, S, Satterthwaite, T.D, Wolf, D, Houenou, J, Henry, C, Malt, U.F, Boen, E, Elvsashagen, T, Young, A.H, Lloyd, A.J, Goodwin, G.M, Mackay, C.E, Bourne, C, Bilderbeck, A, Abramovic, L, Boks, M.P, van Haren, N.E, Ophoff, R.A, Kahn, R.S, Bauer, M, Pfennig, A, Alda, M, Hajek, T, Mwangi, B, Soares, J.C, Nickson, T, Dimitrova, R, Sussmann, J.E, Hagenaars, S, Whalley, H.C, McIntosh, A.M, Thompson, P.M, Andreassen, O.A, 2016. "Subcortical volumetric abnormalities in bipolar disorder" Mol. Psychiatry 21

CRediT authorship contribution statement Armin Birner: Conceptualization, Data curation, Investigation, Methodology, Project administration, Supervision, Writing - original draft, Writing - review & editing. Susanne A. Bengesser: Conceptualization, Data curation, Investigation, Methodology, Project administration, Supervision, Writing - review & editing. Stephan Seiler: Conceptualization, Data curation, Investigation, Methodology, Project administration, Supervision, Writing - original draft, Writing review & editing. Nina Dalkner: Conceptualization, Data curation, Investigation, Methodology, Project administration, Supervision, Writing - review & editing. Robert Queissner: Conceptualization, Data curation, Investigation, Methodology. Martina Platzer: Conceptualization, Data curation, Investigation, Methodology. Frederike T. Fellendorf: Conceptualization, Data curation, Investigation, Methodology. Carlo Hamm: Conceptualization, Data curation, Investigation, Methodology. Alexander Maget: Conceptualization, Data curation, Investigation, Methodology. Rene Pilz: Conceptualization, Data curation, Investigation, Methodology. Melanie Lenger: Conceptualization, Data curation, Investigation, Methodology, Project administration, Supervision, Writing - review & editing. Bernd Reininghaus: Project administration, Supervision, Writing - review & editing. Lukas Pirpamer: Conceptualization, Data curation, Investigation, Methodology. Stefan Ropele: Conceptualization, Data curation, Investigation, Methodology. Nicole Hinteregger: Conceptualization, Data curation, Investigation, Methodology. Marton Magyar: Conceptualization, Data curation, Investigation, Methodology. Hannes Deutschmann: Conceptualization, Data curation, Investigation, Methodology. Christian Enzinger: Conceptualization, Data curation, Investigation, Methodology, Project administration, Supervision, Writing - review & editing. Hans-Peter Kapfhammer: Project administration, Supervision, Writing - review & editing. Eva Z. Reininghaus: Conceptualization, Data curation, Investigation, Methodology, Project administration, Supervision, Writing - review & editing. Declaration of Competing Interest The authors declare that there is no conflict of interest. Acknowledgements The authors thank all participants in the study as well as staff of the Department of Psychiatry Graz, Medical University of Graz, Austria with special thanks to Renate Unterweger for her additional support. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jad.2019.09.068. References Abramovic, L., Boks, M.P., Vreeker, A., Bouter, D.C., Kruiper, C., Verkooijen, S., van Bergen, A.H., Ophoff, R.A., Kahn, R.S., van Haren, N.E., 2016. The association of antipsychotic medication and lithium with brain measures in patients with bipolar disorder. Eur. Neuropsychopharmacol. 26 (11), 1741–1751. Arnone, D., Cavanagh, J., Gerber, D., Lawrie, S.M., Ebmeier, K.P., McIntosh, A.M., 2009. Magnetic resonance imaging studies in bipolar disorder and schizophrenia: metaanalysis. BrJ Psychiatry 195 (3), 194–201. Beaulieu, J.M., Tirotta, E., Sotnikova, T.D., Masri, B., Salahpour, A., Gainetdinov, R.R., Borrelli, E., Caron, M.G., 2007. Regulation of Akt signaling by D2 and D3 dopamine receptors in vivo. J. Neur 27 (4), 881–885. Beyer, J.L., Young, R., Kuchibhatla, M., Krishnan, K.R., 2009. Hyperintense MRI lesions in bipolar disorder: a meta-analysis and review. Int. Rev. Psychiatry 21 (4), 394–409. Black, K.J., Gado, M.H., Perlmutter, J.S., 1997. PET measurement of dopamine D2 receptor-mediated changes in striatopallidal function. J. Neurosci 17 (9), 3168–3177.

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A. Birner, et al.

O.A., Agartz, I., Westlye, L.T., Haukvik, U.K., Dale, A.M., Melle, I., Hartberg, C.B., Gruber, O., Kraemer, B., Zilles, D., Donohoe, G., Kelly, S., McDonald, C., Morris, D.W., Cannon, D.M., Corvin, A., Machielsen, M.W., Koenders, L., de Haan, L., Veltman, D.J., Satterthwaite, T.D., Wolf, D.H., Gur, R.C., Gur, R.E., Potkin, S.G., Mathalon, D.H., Mueller, B.A., Preda, A., Macciardi, F., Ehrlich, S., Walton, E., Hass, J., Calhoun, V.D., Bockholt, H.J., Sponheim, S.R., Shoemaker, J.M., van Haren, N.E., Pol, H.E., Ophoff, R.A., Kahn, R.S., Roiz-Santianez, R., Crespo-Facorro, B., Wang, L., Alpert, K.I., Jonsson, E.G., Dimitrova, R., Bois, C., Whalley, H.C., McIntosh, A.M., Lawrie, S.M., Hashimoto, R., Thompson, P.M., Turner, J.A., 2016. Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the enigma consortium. Mol. Psychiatry 21 (4), 585. van Erp, T.G.M., Walton, E., Hibar, D.P., Schmaal, L., Jiang, W., Glahn, D.C., Pearlson, G.D., Yao, N., Fukunaga, M., Hashimoto, R., Okada, N., Yamamori, H., Bustillo, J.R., Clark, V.P., Agartz, I., Mueller, B.A., Cahn, W., de Zwarte, S.M.C., Hulshoff Pol, H.E., Kahn, R.S., Ophoff, R.A., van Haren, N.E.M., Andreassen, O.A., Dale, A.M., Doan, N.T., Gurholt, T.P., Hartberg, C.B., Haukvik, U.K., Jorgensen, K.N., Lagerberg, T.V., Melle, I., Westlye, L.T., Gruber, O., Kraemer, B., Richter, A., Zilles, D., Calhoun, V.D., Crespo-Facorro, B., Roiz-Santianez, R., Tordesillas-Gutierrez, D., Loughland, C., Carr, V.J., Catts, S., Cropley, V.L., Fullerton, J.M., Green, M.J., Henskens, F.A., Jablensky, A., Lenroot, R.K., Mowry, B.J., Michie, P.T., Pantelis, C., Quide, Y., Schall, U., Scott, R.J., Cairns, M.J., Seal, M., Tooney, P.A., Rasser, P.E., Cooper, G., Shannon Weickert, C., Weickert, T.W., Morris, D.W., Hong, E., Kochunov, P., Beard, L.M., Gur, R.E., Gur, R.C., Satterthwaite, T.D., Wolf, D.H., Belger, A., Brown, G.G., Ford, J.M., Macciardi, F., Mathalon, D.H., O'Leary, D.S., Potkin, S.G., Preda, A., Voyvodic, J., Lim, K.O., McEwen, S., Yang, F., Tan, Y., Tan, S., Wang, Z., Fan, F., Chen, J., Xiang, H., Tang, S., Guo, H., Wan, P., Wei, D., Bockholt, H.J., Ehrlich, S., Wolthusen, R.P.F., King, M.D., Shoemaker, J.M., Sponheim, S.R., De Haan, L., Koenders, L., Machielsen, M.W., van Amelsvoort, T., Veltman, D.J., Assogna, F., Banaj, N., de Rossi, P., Iorio, M., Piras, F., Spalletta, G., McKenna, P.J., Pomarol-Clotet, E., Salvador, R., Corvin, A., Donohoe, G., Kelly, S., Whelan, C.D., Dickie, E.W., Rotenberg, D., Voineskos, A.N., Ciufolini, S., Radua, J., Dazzan, P., Murray, R., Reis Marques, T., Simmons, A., Borgwardt, S., Egloff, L., Harrisberger, F., Riecher-Rossler, A., Smieskova, R., Alpert, K.I., Wang, L., Jonsson, E.G., Koops, S., Sommer, I.E.C., Bertolino, A., Bonvino, A., Di Giorgio, A., Neilson, E., Mayer, A.R., Stephen, J.M., Kwon, J.S., Yun, J.Y., Cannon, D.M., McDonald, C., Lebedeva, I., Tomyshev, A.S., Akhadov, T., Kaleda, V., FatourosBergman, H., Flyckt, L., Karolinska Schizophrenia Project,Busatto, G.F., Rosa, P.G.P., Serpa, M.H., Zanetti, M.V., Hoschl, C., Skoch, A., Spaniel, F., Tomecek, D., Hagenaars, S.P., McIntosh, A.M., Whalley, H.C., Lawrie, S.M., Knochel, C., Oertel-Knochel, V., Stablein, M., Howells, F.M., Stein, D.J., Temmingh, H.S., Uhlmann, A., LopezJaramillo, C., Dima, D., McMahon, A., Faskowitz, J.I., Gutman, B.A., Jahanshad, N., Thompson, P.M., Turner, J.A., 2018. Cortical brain abnormalities in 4474 individuals with schizophrenia and 5098 control subjects via the enhancing neuro imaging genetics through meta analysis (ENIGMA) consortium. Biol. Psychiatry 84 (9), 644–654. Vernon, A.C., Crum, W.R., Lerch, J.P., Chege, W., Natesan, S., Modo, M., Cooper, J.D., Williams, S.C., Kapur, S., 2014. Reduced cortical volume and elevated astrocyte density in rats chronically treated with antipsychotic drugs-linking magnetic resonance imaging findings to cellular pathology. Biol. Psychiatry 75 (12), 982–990. Vernon, A.C., Natesan, S., Crum, W.R., Cooper, J.D., Modo, M., Williams, S.C., Kapur, S., 2012. Contrasting effects of haloperidol and lithium on rodent brain structure: a magnetic resonance imaging study with postmortem confirmation. Biol. Psychiatry 71 (10), 855–863. Vernon, A.C., Natesan, S., Modo, M., Kapur, S., 2011. Effect of chronic antipsychotic treatment on brain structure: a serial magnetic resonance imaging study with ex vivo and postmortem confirmation. Biol. Psychiatry 69 (10), 936–944. Wise, T., Radua, J., Via, E., Cardoner, N., Abe, O., Adams, T.M., Amico, F., Cheng, Y., Cole, J.H., de Azevedo Marques Perico, C., Dickstein, D.P., Farrow, T.F.D., Frodl, T., Wagner, G., Gotlib, I.H., Gruber, O., Ham, B.J., Job, D.E., Kempton, M.J., Kim, M.J., Koolschijn, P.C.M.P., Malhi, G.S., Mataix-Cols, D., McIntosh, A.M., Nugent, A.C., O'Brien, J.T., Pezzoli, S., Phillips, M.L., Sachdev, P.S., Salvadore, G., Selvaraj, S., Stanfield, A.C., Thomas, A.J., van Tol, M.J., van der Wee, N.J.A., Veltman, D.J., Young, A.H., Fu, C.H., Cleare, A.J., Arnone, D., 2017. Common and distinct patterns of grey-matter volume alteration in major depression and bipolar disorder: evidence from voxel-based meta-analysis. Mol. Psychiatry 22 (10), 1455–1463. Wolf, H., Kruggel, F., Hensel, A., Wahlund, L.O., Arendt, T., Gertz, H.J., 2003. The relationship between head size and intracranial volume in elderly subjects. Brain Res. 973 (1), 74–80.

(12), 1710–1716. Ho, B.C., Andreasen, N.C., Ziebell, S., Pierson, R., Magnotta, V., 2011. Long-term antipsychotic treatment and brain volumes: a longitudinal study of first-episode schizophrenia. Arch. Gen. Psychiatry 68 (2), 128–137. Huhtaniska, S., Jaaskelainen, E., Hirvonen, N., Remes, J., Murray, G.K., Veijola, J., Isohanni, M., Miettunen, J., 2017. Long-term antipsychotic use and brain changes in schizophrenia - a systematic review and meta-analysis. Hum. Psychopharmacol. 32 (2). https://doi.org/10.1002/hup.2574. Kempton, M.J., Geddes, J.R., Ettinger, U., Williams, S.C., Grasby, P.M., 2008. Metaanalysis, database, and meta-regression of 98 structural imaging studies in bipolar disorder. Arch. Gen. Psychiatry 65 (9), 1017–1032. Konopaske, G.T., Lange, N., Coyle, J.T., Benes, F.M., 2014. Prefrontal cortical dendritic spine pathology in schizophrenia and bipolar disorder. JAMA Psychiatry 71 (12), 1323–1331. Kuperberg, G.R., Broome, M.R., McGuire, P.K., David, A.S., Eddy, M., Ozawa, F., Goff, D., West, W.C., Williams, S.C., van der Kouwe, A.J., Salat, D.H., Dale, A.M., Fischl, B., 2003. Regionally localized thinning of the cerebral cortex in schizophrenia. Arch. Gen. Psychiatry 60 (9), 878–888. Ladouceur, C., Almeida, J., Birmaher, B., 2008. Subcortical gray matter volume abnormalities in healthy bipolar offspring: potential neuroanatomical risk marker for bipolar disorder ? J. Am. Acad. Child. Adolesc. Psychiatry 47, 532–539. Lahti, A.C., Weiler, M.A., Medoff, D.R., Tamminga, C.A., Holcomb, H.H., 2005. Functional effects of single dose first- and second-generation antipsychotic administration in subjects with schizophrenia. Psychiatry Res. 139 (1), 19–30. Leboyer, M., Soreca, I., Scott, J., Frye, M., Henry, C., Tamouza, R., Kupfer, D.J., 2012. Can bipolar disorder be viewed as a multi-system inflammatory disease? J. Affect. Disord. 141 (1), 1–10. Lim, C.S., Baldessarini, R.J., Vieta, E., Yucel, M., Bora, E., Sim, K., 2013. Longitudinal neuroimaging and neuropsychological changes in bipolar disorder patients: review of the evidence. Neurosci. Biobehav. Rev. 37 (3), 418–435. Mackowiak, M., Dudys, D., Chocyk, A., Wedzony, K., 2009. Repeated risperidone treatment increases the expression of NCAMand PSA-NCAM protein in the rat medial prefrontal cortex. Euro Neuropsychopharmacol 19 (2), 125–137. McDonald, C., 2015. Brain structural effects of psychopharmacological treatment in bipolar disorder. Curr. Neuropharmacol. 13 (4), 445–457. McIntyre, R.S., Danilewitz, M., Liauw, S.S., Kemp, D.E., Nguyen, H.T., Kahn, L.S., Kucyi, A., Soczynska, J.K., Woldeyohannes, H.O., Lachowski, A., Kim, B., Nathanson, J., Alsuwaidan, M., Taylor, V.H., 2010. Bipolar disorder and metabolic syndrome: an international perspective. J. Affect. Disord. 126 (3), 366–387. Mesulam, M.M., 1990. Large-scale neurocognitive networks and distributed processing for attention, language, and memory. Ann. Neurol. 28 (5), 597–613. Moncrieff, J., Leo, J., 2010. A systematic review of the effects of antipsychotic drugs on brain volume. Psychol. Med. 40 (9), 1409–1422. Owens, D.G., Johnstone, E.C., Crow, T.J., Frith, C.D., Jagoe, J.R., Kreel, L., 1985. Lateral ventricular size in schizophrenia: relationship to the disease process and its clinical manifestations. Psychol. Med. 15 (1), 27–41. Reuter, M., Schmansky, N.J., Rosas, H.D., Fischl, B., 2012. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61 (4), 1402–1418. Rimol, L.M., Hartberg, C.B., Nesvag, R., Fennema-Notestine, C., Hagler Jr, D.J., Pung, C.J, Jennings, R.G, Haukvik, U.K, Lange, E, Nakstad, P.H, Melle, I, Andreassen, O.A, Dale, A.M, Agartz, I., 2010. Cortical thickness and subcortical volumes in schizophrenia and bipolar disorder. Biol. Psychiatry 68 (1), 41–50. Rosas, H.D., Liu, A.K., Hersch, S., Glessner, M., Ferrante, R.J., Salat, D.H., van der Kouwe, A., Jenkins, B.G., Dale, A.M., Fischl, B., 2002. Regional and progressive thinning of the cortical ribbon in Huntington's disease. Neurology 58 (5), 695–701. Ruigrok, A.N., Salimi-Khorshidi, G., Lai, M.C., Baron-Cohen, S., Lombardo, M.V., Tait, R.J., Suckling, J., 2014. A meta-analysis of sex differences in human brain structure. Neurosci. Biobehav. Rev. 39, 34–50. Sacher, J., Neumann, J., Okon-Singer, H., Gotowiec, S., Villringer, A., 2013. Sexual dimorphism in the human brain: evidence from neuroimaging. Magn. Reson. Imaging 31 (3), 366–375. Segonne, F., Pacheco, J., Fischl, B., 2007. Geometrically accurate topology-correction of cortical surfaces using nonseparating loops. IEEE Trans. Med Imaging 26 (4), 518–529. Sexton, C., Mackay, C., Ebmeier, K., 2009. A systematic review of diffusion tensor imaging studies in affective disorders. Biol. Psychiatry 66 (9), 814–823. van Erp, T.G., Hibar, D.P., Rasmussen, J.M., Glahn, D.C., Pearlson, G.D., Andreassen,

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