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. Author manuscript; available in PMC: 2019 Aug 1.
Published in final edited form as: J Affect Disord. 2018 Apr 4;235:15–19. doi: 10.1016/j.jad.2018.04.010

Neurochemical Differences Between Bipolar Disorder Type I and II in Superior Temporal Cortices: A Proton Magnetic Resonance Spectroscopy Study

Murat İlhan Atagün 1,2,*, Elif Muazzez Şıkoğlu 3, Serdar Süleyman Can 1,2, Görkem Karakaş Uğurlu 1,2, Semra Ulusoy Kaymak 2, Ali Çayköylü 1,2, Oktay Algın 4,5, Mary L Phillips 6, Constance M Moore 3, Dost Öngür 7
PMCID: PMC5951770  NIHMSID: NIHMS958082  PMID: 29631202

Abstract

Background

Despite the diagnostic challenges in categorizing bipolar disorder subtypes, bipolar I and II disorders (BD-I and BD-II respectively) are valid indices for researchers. Subtle neurobiological differences may underlie clinical differences between mood disorder subtypes. The aims of this study were to investigate neurochemical differences between bipolar disorder subtypes.

Methods

Euthymic BD-II patients (n=21) are compared with BD-I (n=28) and healthy comparison subjects (HCs, n=30). Magnetic Resonance Imaging (MRI) and proton spectroscopy (1H MRS) were performed on a 3T Siemens Tim Trio system. MRS voxels were located in the left/right superior temporal cortices, and spectra acquired with the single voxel Point REsolved Spectroscopy Sequence (PRESS). The spectroscopic data were analyzed with LCModel (Version 6.3.0) software.

Results

There were significant differences between groups in terms of glutamate [F=6.27, p=0.003], glutamate+glutamine [F=6.08, p=0.004], inositol containing compounds (Ino) (F=9.25, p<0.001), NAA [F=7.63, p=0.001] and creatine+phosphocreatine [F=11.06, p<0.001] in the left hemisphere and Ino [F=5.65, p=0.005] in the right hemisphere. Post-hoc comparisons showed that the BD-I disorder group had significantly lower metabolite levels in comparison to the BD-II and the HC groups.

Limitations

This was a cross-sectional study with a small sample size. In addition, patients were on various psychotropic medications, which may have impacted the results.

Conclusions

Neurochemical levels, in the superior temporal cortices, measured with 1H-MRS discriminated between BD-II and BD-I. Although further studies are needed, one may speculate that the superior temporal cortices (particularly left hemispheric) play a critical role, whose pathology may be related to subtyping bipolar disorder.

Keywords: Magnetic resonance spectroscopy, bipolar disorder, superior temporal gyrus

Introduction

Despite the diagnostic challenges in categorizing bipolar disorder subtypes, bipolar I disorder (BD-I) and bipolar II disorder (BD-II) are valid categorical indices for researchers (Phillips and Kupfer, 2013; Parker et al., 2014). Along with depressive episodes, presence of manic or hypomanic episodes is the major difference between BD-I and BD-II disorders. Despite the valid diagnostic boundaries, only limited studies have reported neurobiological differences between the subtypes (Phillips and Kupfer, 2013; Parker et al., 2014; McGrath et al., 2014).

Genetic studies have identified differences between BD-I and BD-II (Lee et al., 2011; Uemura et al., 2011). Psychosis (Parker et al., 2014), neurotoxic processes (Yumru et al., 2009; Uemura et al., 2011) and cognitive dysfunction (Bora et al., 2011) are more prevalent in BD-I compared to BD-II. Magnetic resonance imaging (MRI) studies have reported that BD-I had more severe findings in comparison to BD-II in volumes (Hauser et al., 2000), white matter lesions (Ambrosi et al., 2013) and functional connectivity (Li et al., 2012). Neurochemistry may also differ between the subtypes.

Proton magnetic resonance spectroscopy (1H-MRS) can quantify specific neurochemical compounds in the brain in vivo. It has been observed in 1H-MRS that mood state shifts and medications alter metabolites in BD-I (Moore et al., 2007). However, only a few MRS studies have compared BD-I and BD-II. No consistent finding has been reported between the subtypes in the frontal (Winsberg et al., 2000) or temporal (Hamakawa et al., 1998; Atagun et al., 2017) lobes, or the basal ganglia (Silverstone et al., 2004). Only one study detected increased choline concentrations in the basal ganglia in BD-II in comparison to BD-I (Kato et al., 1996).

Patients with bipolar disorder showed fronto-temporal dysconnectivity (Ozerdem et al., 2011) and cortical thinning was specific to superior temporal cortex in BD-I (Hanford et al., 2016). Furthermore, auditory processing deficits in bipolar I disorder are related to left hemisphere superior temporal cortices (McCarley et al., 2008). Moreover, it has been postulated that shared neurochemical abnormalities in superior temporal cortices by schizophrenia and bipolar disorder might be a common feature of psychosis (Nudmamud et al., 2003). On the other hand, superior temporal cortices may have unique local metabolic disturbances in both schizophrenia and bipolar disorder (McNamara et al., 2014). These findings have highlighted the pathology of superior temporal lobes in bipolar disorder. However, it is not clear whether there is difference between subtypes of bipolar disorder.

In this study, we aimed to compare neurochemical differences between BD-I and BD-II disorders utilizing 1H-MRS in superior temporal cortices. Our hypothesis was that there might be neurochemical differences between the subtypes of bipolar disorder. To our knowledge, this is the first 1H-MRS study to assess superior temporal lobes in BD-I and BD-II. In vivo assessment of metabolite levels in superior temporal lobes may show differences in pathogenesis of the subtypes of bipolar disorder.

Methods

All participants provided a written informed consent before study participation. This study was approved by the local Ethical Committee. In a recent article, we have reported abnormal metabolite levels in the superior temporal lobes of participants with BD-I (Atagün et al., 2015). We further enrolled patients with BD-II (n=21, mean age= 38.38±13.84, 12 women/ 9 men and compared with our BD-I sample and healthy comparison subjects (HCs). All patients were euthymic during the study participation. Clinical evaluation tools were Structured Clinical Interview according to the DSM-IV (SCID-I) (First et al., 1996), Young Mania Rating Scale (YMRS) (Young et al., 1978) and Hamilton Depression Rating Scale (HDRS) (Hamilton, 1960).

Data acquisition processes were performed with a 3.0 T Siemens MAGNETOM TIM Trio MR system (Siemens, Erlangen, Germany) and a 32 channel phased-array head coil. T1 anatomical MRI [MPRAGE sequence, parameters were TE: 3.02, TR: 2000 ms, FOV phase: 100, FOV read: 215, slice thickness: 0.84, number of slices: 192] and spectroscopy voxels [single voxel Point REsolved Spectroscopy Sequence (PRESS); 8 cm3 voxels (2 cm X 2 cm X 2 cm); TE: 30 ms, TR: 2000 ms, 128 averages] were recorded to evaluate left and right hemisphere superior temporal lobes which host Heschl’s gyrus and planum temporale (Supplementary Figure 1a). B0 shimming was manually applied around the voxel. LCModel software (Version 6.3.0) was used to quantify the metabolite levels (Provencher, 2001), using water as internal reference. Representative spectra (supplementary Figure 1b) and quantified metabolites are presented in the supplement. Gray matter, white matter and cerebro-spinal fluid (CSF) contributions to the voxels were analyzed in T1 weighted structural MRI with Statistical Parametric Mapping-8 (SPM-8) software [Welcome Department of Imaging Neuroscience, London, UK; (http://www.fil.ion.ucl.ac.uk/spm/software/spm8/)] (for further details please see Atagün et al., 2015).

Statistical Analysis

Statistical analyses were performed with SPSS 21 (IBM, Armonk, NY, USA) software. Distribution characteristics of the data are assessed with Kolmogorov-Smirnov and Shapiro-Wilks’ Tests. Independent samples t Test or Mann-Whitney U Tests were performed for two independent group comparisons. Categorical variables were compared with chi-square test. Correlations were examined with Pearson’s and Spearman’s Correlation Tests. Univariate Analysis of Variance (ANOVA) and Analysis of Covariance (ANCOVA) was performed for three group comparisons for main effects. Chlorpromazine dose equivalents of antipsychotics, age, gender, age at onset, number of episodes, smoking status and alcohol consuming were available variables for covariate analysis. Interaction effects for grey matter, white matter and CSF contributions to the voxels were checked with interaction effect analysis in univariate ANOVA. Kruskal-Wallis Test was used for the comparison of non-Gaussian distributed variables. Post-hoc tests were Bonferroni or Tamhane Tests according to Levene’s Homogeneity of Variance Test. Since 6 ANOVAs were performed for 6 metabolites (Glutamate, glutamate+glutamine, inositol containing compounds, creatine+phosphocreatine, choline containing compounds, n-Acetyl Aspartate) in the two hemispheres, a Bonferroni correction was applied to the p values (6 metabolites X 2 hemispheres= 12; 0.05 / 12 = 0.0041) and level for statistical significance was 0.0041 for metabolite level comparisons. For all other comparisons, cut-off for statistical significance was 0.05.

Results

Groups were similar in terms of age, gender, and education (Table 1). Smokers were more frequent in the patient groups [F=6.99, p=0.030] and alcohol consumers were more frequent in the BD-II patient group [χ2 =9.69, p=0.008].

Table 1.

Sociodemographic characteristics of the groups

BD-I (n=28) BD-II (n=21) HCs (n=30) t / F / χ2
Age 35.32 ± 9.12 38.38 ± 13.84 32.90 ± 10.82 1.47
Gender [Women (%)] 15 (%53.6) 12 (%57.1) 17 (%56.7) 0.15
Education* 10.89 ± 4.86 12.10 ± 4.09 9.97 ± 3.16 1.66
Age at disease onset* 23.57 ± 8.66 24.71 ± 9.63 −0.44
Duration of the disease** 92.21 ± 107.69 153.43 ± 126.48 −1.83
Total Number of Episodes 7.82 ± 5.46 8.50 ± 6.87 −0.38
Mania ¥ 2.77 ± 2.18 3.50 ± 3.52 −0.87
Depression 4.22 ± 3.42 4.90 ± 4.15 −0.61
Psychosis during episodes n (%) 18 (%64.3) 8 (%38.1)¥ 3.31
Nicotine n (%) 18 (%64.3) 14 (%66.7) 10 (%33.3) 6.99
Alcohol n (%) 2 (%7.1) 9 (%40.9) 5 (%16.7) 9.69
YMRS 1.07 ± 1.61 1.48 ± 1.50 −0.90
HAM-D 3.46 ± 3.51 3.38 ± 3.20 0.09
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BD-I: Bipolar I Disorder, BD-II: Bipolar II Disorder, HCs: Healthy Controls,

*

Years,

**

Months,

¥

Hypomania for patients with bipolar II disorder. YMRS: Young Mania Rating Scale, HAM-D: Hamilton Depression Rating Scale. Bold characters indicate statistical significance.

¥

Psychotic symptoms were observed in depressive episodes of BD-II.

Comparison of the metabolite levels revealed that there were significant differences between groups in terms of glutamate [F=6.27, p=0.003], glutamate + glutamine [F=6.08, p=0.004], inositol containing compounds (Ino) [F=9.25, p<0.001], NAA [F=7.63, p=0.001] and creatine + phosphocreatine [F=11.06, p<0.001] in the left hemisphere (Table 2). Post-hoc comparisons showed that the BD-I disorder group had significantly lower metabolite levels in comparison to the BD-II and the HC groups (Table 2). There was no significant difference between the groups in terms of voxel segmentation data (gray / white matter and cerebrospinal fluid content, results are presented in Table 2).

Table 2.

1H-MRS findings

BD-I (n=28) BD-II (n=21) HCs (n=30) F p Partial η2 Post-hoc
Right Glutamate 1.31 ± 0.16 1.46 ± 0.24 1.43 ± 0.18 3.98 0.023 0.11
Glx 1.87 ± 0.26 1.98 ± 0.26 1.95 ± 0.26 1.19 0.311 0.04
Inositol CC 0.92 ± 0.15 1.02 ± 0.21 1.06 ± 0.09 5.65 0.005 0.25
Choline CC 0.29 ± 0.05 0.30 ± 0.05 0.28 ± 0.06 1.04 0.358 0.03
NAA 1.59 ± 0.18 1.72 ± 0.18 1.74 ± 0.20 4.94 0.010 0.13
Cr 1.12 ± 0.14 1.27 ± 0.19 1.18 ± 0.18 4.38 0.016 0.11
Gray Matter* 56.83 ± 4.78 57.50 ± 13.89 58.00 ± 4.87 0.14 0.870 <0.01
White Matter* 28.91 ± 8.09 29.01 ± 5.11 30.58 ± 6.47 0.51 0.604 0.01
CSF* 13.58 ± 7.20 13.00 ± 11.72 10.74 ± 5.49 0.93 0.401 0.02
Left Glutamate 0.82 ± 0.11 0.99 ± 0.25 0.95 ± 0.13 6.27 0.003 0.15 BD-I <HCs,BD-II
Glx 1.11 ± 0.18 1.36 ± 0.37 1.25 ± 0.18 6.08 0.004 0.15 BD-I<HCs,BD-II
Inositol CC 0.57 ± 0.12 0.72 ± 0.17 0.67 ± 0.09 9.25 <0.001 0.21 BD-I <HCs,BD-II
Choline CC 0.17 ± 0.03 0.20 ± 0.04 0.19 ± 0.03 4.18 0.019 0.11
NAA 1.02 ± 0.13 1.23 ± 0.29 1.17 ± 0.13 7.63 0.001 0.18 BD-I <HCs,BD-II
Cr 0.71 ± 0.11 0.90 ± 0.19 0.81 ± 0.10 11.0 6 <0.001 0.23 BD-I <HCs,BD-II
Gray Matter* 57.03 ± 4.42 57.05 ± 14.84 59.78 ± 5.73 0.85 0.432 0.22
White Matter* 31.06 ± 9.46 30.67 ± 7.77 30.51 ± 6.72 0.04 0.966 <0.01
CSF* 10.25 ± 7.48 11.85 ± 11.27 9.38 ± 4.56 0.59 0.556 0.02
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All metabolite levels are in institutional units and rounded up (X 1000). Univariate ANOVA, Bonferroni or Tamhane Test were picked for post-hoc comparisons. Glx: Glutamate + Glutamine, NAA: N-Acetyl-Aspartate, Inositol CC: Inositol containing compounds, Choline CC: Choline Containing Compounds, Cr: Creatine + Phosphocreatine, CSF: Cerebrospinal Fluid content.

*

Voxel Segmentation Data (percentage), Bold indicate statistical significance.

Correlation analysis was performed with Glu, Glx, NAA, Ino, Cr, age, age at onset, duration of the disease, number of episodes, serum lithium levels, serum valproate levels, chlorpromazine equivalent doses of antipsychotics, Young Mania Rating Scale and Hamilton Depression Rating Scale scores variables. There were significant correlations between right hemisphere N-Acetyl Aspartate (NAA) levels and age at disease onset (r2=−0.58, p=0.009), number of hypomanic episodes (r2=−0.51, p=0.032) in the BD-II group. ANCOVA showed that right hemisphere NAA levels were associated with the YMRS scores [F=5.36, p=0.027], the number of life-time hypomanic episodes [F=8.69, p=0.022], age of disease onset [F=5.86, p=0.006] in the BD-II group. No other significant correlation was observed. Because we presented the BD-I group findings in the past, here we only present correlation analyses within the BD-II group. Medications are presented in Table 3 (Supplementary Table 3). Metabolite levels did not differ between patients on/off lithium, valproate (p>0.05 for all). There were no significant differences between psychotic (n=26) and non-psychotic (n=23) patients independent of diagnostic subtypes (p>0.05 for all metabolites in both hemispheres). Psychosis, alcohol or nicotine consumption did not show any significant impact on the metabolite levels (p>0.05; Supplementary Table 4 and Table 5).

Discussion

In this study metabolite levels in the superior temporal cortices differed between the BD-I and BD-II groups. The BD-I group had significantly lower metabolite levels in comparison to the BD-II group. Further analysis showed that clinical factors such as psychosis, number of episodes and nicotine or alcohol consumption did not have any impact on metabolite levels. HDRS scores had inverse relationship between NAA and Cr in both right and left hemispheres and Ino and HDRS had correlation in the right hemisphere in BD-I (Atagun et al., 2015). Whereas NAA levels were negatively correlated with age at onset, number of hypomanic episodes and YMRS scores in the BD-II group.

Glutamatergic neurotransmission is maintained by glutamate to glutamine cycle and glial cells serve for the cycle. This cycle is differentially altered in manic and depressive states (Yüksel and Öngür, 2010). Globally Glx is elevated (Dager et al., 2004), however Glu levels are not significantly elevated in bipolar disorder (Yüksel and Öngür, 2010; Atagun et al., 2015). Since Glx consist of glutamate plus glutamine, elevation of Glx is probably associated with increased glutamine levels. Quantification of glutamine is not possible with low magnetic field scanners, and thus there are not much studies in the literature. Glutamine levels in anterior cingulate cortex were found to be increased in bipolar disorder (Ongur et al., 2008). These findings may indicate that glutamate to glutamine cycle is disturbed in bipolar disorder. There may be a difference in glial physiopathology in BD-I and BD-II.

Inositol levels are found to be changed in bipolar disorder and mood stabilizers alter inositol levels by influencing phospho-inositol cycle (Silverstone and McGrath, 2009). However, the 1H-MRS literature regarding Ino levels is mixed (Silverstone and Mcgrath, 2009). Controlling for voxel locations, medications, field strength might be useful in future studies. NAA is a marker of mitochondrial function for oxidative metabolism and neuronal function/viability; decrease of NAA levels indicate neuronal dysfunction (Moffett et al., 2007). Cr metabolites are involved in energy metabolism by transporting high energy phosphorous groups; Cr compounds are highly diffusible and are considered to be stable throughout the brain. However, it has been determined that Cr is not stable in bipolar disorder and schizophrenia (Ongur et al., 2009). Thus, the use of Cr as an internal reference might be erroneous in bipolar disorder and schizophrenia. Correlation analysis revealed differences between BD-I and BD-II. NAA levels are negatively correlated with number of hypomanic episodes and age at disease onset in BD-II. Whereas in BD-I HDRS scores were negatively correlated with Ino and NAA (reported in Atagun et al., 2015).

Small sample size and being a cross-sectional study are major limitations of this study. Furthermore, patients were on various psychotropic medications and medications are known to alter the metabolites measured by 1H-MRS (Moore et al., 2000; Moore et al., 2007). Because of its anatomical adjacency to cranium, B0 and B1 inhomogeneity are high in temporal lobe (Juchem and de Graff, 2016). B0 shimming was performed, however B1 shimming was not available in this study.

To conclude, levels of metabolites are decreased in BD-I in comparison to BD-II and healthy controls in this study. These findings may indicate that neuronal dysfunction and metabolic decline in the left (dominant) hemisphere superior temporal lobe is specific to BD-I. Although further studies are needed, it can be postulated that pathology of the left hemispheric superior temporal lobe might be more severe in BD-I in comparison to BD-II. Our findings indicate that the left hemispheric superior temporal lobe metabolite levels in BD-I are declined similar to psychotic spectrum disorders rather than BD-II. Further studies comparing manic and hypomanic episodes of BD-I and BD-II may reveal metabolic differences between the subtypes.

Supplementary Material

supplement

Highlights.

  • Despite the diagnostic challenges in categorizing bipolar disorder subtypes, bipolar I and II disorders are valid categorical indices

  • However, neurobiology studies failed to identify underpinnings of the clinical differences between bipolar spectrum disorders

  • We compared neurochemical levels measured with proton magnetic resonance spectroscopy in superior temporal cortices in bipolar disorder type I and II

  • Bipolar disorder type I group had significantly lower metabolite levels in comparison to the bipolar disorder type II and the healthy control group in the left hemisphere

  • Superior temporal cortices (particularly left hemispheric) may play a critical role, whose pathology may be related to subtyping bipolar disorder

Acknowledgments

The authors would like to thank to Prof. Ergin Atalar and UMRAM staff. The authors also appreciate techical help y Dinesh Deelchand, Dr. Uzay Emir and Gülin z (from the Center for Magnetic Resonance Research, Mineapolis, MN, USA) during voxel segmentation.

Role of Funding Source: The study was funded by Scientific Research Projects Committee of the Ankara Yıldırım Beyazıt University (Project Code: 803) and NIMH grants to CMM (MH073998) and NIH grant to DO K24 (MH104449). Dr. Phillips acknowledged the support of the Pittsburgh Foundation.

Footnotes

Publisher's Disclaimer: 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 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.

Conflict of Interest:

Dr. Kaymak participated in Otsuka’s clinical trial (ClinicalTrials.gov Identifier: NCT01129882.), Dr. Can participated in Janssen Cilag’s clinical trial (ClinicalTrials.gov Identifier: NCT02782104). Other authors declare no conflict of interest

Contributors:

MIA: Prepared the initial study protocol and procedures, data collection, interpretation of the results, wrote the manuscript.

EMS: Data analysis, interpretation of the results and preparation of the manuscript.

SSC: Initial study procedures and data collection

GKU: Initial study procedures and data collection

SUK: Initial study procedures and data collection

AC: Reviewed the protocol, initial study procedures and data collection

OA: Initial study procedures, data collection and data analysis

MLP: Scientific supervision during the preparation of the protocol, interpretation of the results and supervision for the manuscript

CMM: Scientific supervision during the preparation of the protocol, data analysis, interpretation of the results and supervision for the manuscript

DO: Scientific supervision during the preparation of the protocol, data analysis, interpretation of the results and supervision for the manuscript

References

  1. Ambrosi E, Rossi-Espagnet MC, Kotzalidis GD, Comparelli A, Del Casale A, Carducci F, Romano A, Manfredi G, Tatarelli R, Bozzao A, Girardi P. Structural brain alterations in bipolar disorder II: a combined voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) study. J Affect Disord. 2013;150:610–615. doi: 10.1016/j.jad.2013.02.023. [DOI] [PubMed] [Google Scholar]
  2. Atagün Mİ, Şıkoğlu EM, Can SS, Karakaş-Uğurlu G, Ulusoy-Kaymak S, Çayköylü A, Algın O, Phillips ML, Moore CM, Öngür D. Investigation of Heschl’s gyrus and planum temporale in patients with schizophrenia and bipolar disorder: A proton magnetic resonance spectroscopy study. Schizophr Res. 2015;161:202–209. doi: 10.1016/j.schres.2014.11.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Atagün Mİ, Şıkoğlu EM, Soykan Ç, Can SS, Ulusoy-Kaymak S, Çayköylü A, Algın O, Phillips ML, Öngür D, Moore CM. Perisylvian GABA levels in schizophrenia and bipolar disorder. Neurosci Lett. 2017;637:70–74. doi: 10.1016/j.neulet.2016.11.051. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bora E, Yücel M, Pantelis C, Berk M. Meta-analytic review of neurocognition in bipolar II disorder. Acta Psychiatr Scand. 2011;123:165–174. doi: 10.1111/j.1600-0447.2010.01638.x. [DOI] [PubMed] [Google Scholar]
  5. Dager SR, Friedman SD, Parow A, Demopulos C, Stoll AL, Lyoo IK, Dunner DL, Renshaw PF. Brain metabolic alterations in medication-free patients with bipolar disorder. Arch Gen Psychiatry. 2004;61:450–458. doi: 10.1001/archpsyc.61.5.450. [DOI] [PubMed] [Google Scholar]
  6. First MD, Gibbon M, Spitzer RL, Gibbon M, Williams JBW. User’s Guide for the Structured Interview for DSM-IV Axis I Disorders Research Version (SCID-I, version 2.0) Biometrics Research; New York: 1996. [Google Scholar]
  7. Hamakawa H, Kato T, Murashita J, Kato N. Quantitative proton magnetic resonance spectroscopy of the basal ganglia in patients with affective disorders. Eur Arch Psychiatr Clin Neurosci. 1998;248:53–58. doi: 10.1007/s004060050017. [DOI] [PubMed] [Google Scholar]
  8. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960;23:56–62. doi: 10.1136/jnnp.23.1.56. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Hanford LC, Nazarov A, Hall GB, Sassi RB. Cortical thickness in bipolar disorder: a systematic review. Bipolar Disord. 2016;18:4–18. doi: 10.1111/bdi.12362. [DOI] [PubMed] [Google Scholar]
  10. Hauser P, Matochik J, Altshuler LL, Denicoff KD, Conrad A, Li X, Post RM. MRI-based measurements of temporal lobe and ventricular structures in patients with bipolar I and bipolar II disorders. J Affect Disord. 2000;60:25–32. doi: 10.1016/s0165-0327(99)00154-8. [DOI] [PubMed] [Google Scholar]
  11. Juchem C, de Graaf RA. B0 magnetic field homogeneity and shimming for in vivo magnetic resonance spectroscopy. Anal Biochem. 2016;529:17–29. doi: 10.1016/j.ab.2016.06.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Kato T, Hamakawa H, Shioiri T, Murashita J, Takahashi Y, Takahashi S, Inubushi T. Choline-containing compounds detected by proton magnetic resonance spectroscopy in the basal ganglia in bipolar disorder. J Psychiatry Neurosci. 1996;21:248–254. [PMC free article] [PubMed] [Google Scholar]
  13. Lee SY, Chen SL, Chen SH, Huang SY, Tzeng NS, Chang YH, Wang CL, Lee IH, Yeh TL, Yang YK, Lu RB. The COMT and DRD3 genes interacted in bipolar I but not bipolar II disorder. World J Biol Psychiatry. 2011;12:385–391. doi: 10.3109/15622975.2010.505298. [DOI] [PubMed] [Google Scholar]
  14. Lee SY, Chen SL, Chang YH, Chu CH, Huang SY, Tzeng NS, Wang CL, Lin SH, Lee IH, Yeh TL, Yang YK, Lu RB. The ALDH2 and 5-HT2A genes interacted in bipolar-I but not bipolar-II disorder. Prog Neuropsychopharmacol Biol Psychiatry. 2012;38:247–251. doi: 10.1016/j.pnpbp.2012.04.005. [DOI] [PubMed] [Google Scholar]
  15. Li CT, Hsieh JC, Wang SJ, Yang BH, Bai YM, Lin WC, Lan CC, Su TP. Differential relations between frontolimbic metabolism and executive function in patients with remitted bipolar I and bipolar II disorder. Bipolar Disord. 2012;14:831–842. doi: 10.1111/bdi.12017. [DOI] [PubMed] [Google Scholar]
  16. McCarley RW, Nakamura M, Shenton ME, Salisbury DF. Combining ERP and structural MRI information in first episode schizophrenia and bipolar disorder. Clin EEG Neurosci. 2008;39(2):57–60. doi: 10.1177/155005940803900206. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. McGrath BM, Wessels PH, Bell EC, Ulrich M, Silverstone PH. Neurobiological findings in bipolar II disorder compared with findings in bipolar I disorder. Can J Psychiatry. 2014;49:794–801. doi: 10.1177/070674370404901202. [DOI] [PubMed] [Google Scholar]
  18. McNamara RK, Rider T, Jandacek R, Tso P. Abnormal Fatty Acid Pattern in the Superior Temporal Gyrus Distinguishes Bipolar Disorder from Major Depression and Schizophrenia and Resembles Multiple Sclerosis. Psychiatry Res. 2014;215(3):560–567. doi: 10.1016/j.psychres.2013.12.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Moffett JR, Ross B, Arun P, Madhavarao CN, Namboodiri AM. N-Acetylaspartate in the CNS: from neurodiagnostics to neurobiology. Prog Neurobiol. 2007;81:89–131. doi: 10.1016/j.pneurobio.2006.12.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Moore CM, Biederman J, Wozniak J, Mick E, Aleardi M, Wardrop M, Dougherty M, Harpold T, Hammerness P, Randall E, Lyoo IK, Renshaw PF. Mania, glutamate/glutamine and risperidone in pediatric bipolar disorder: a proton magnetic resonance spectroscopy study of the anterior cingulate cortex. J Affect Disord. 2007;99:19–25. doi: 10.1016/j.jad.2006.08.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Moore GJ, Bebchuk JM, Hasanat K, Chen G, Seraji-Bozorgzad N, Wilds IB, Faulk MW, Koch S, Glitz DA, Jolkovsky L, Manji HK. Lithium increases N-acetylaspartate in the human brain: in vivo evidence in support of bcl-2’s neurotrophic effects? Biol Psychiatry. 2000;48:1–8. doi: 10.1016/s0006-3223(00)00252-3. [DOI] [PubMed] [Google Scholar]
  22. Nudmamud S, Reynolds LM, Reynolds GP. N-acetylaspartate and N-Acetylaspartylglutamate deficits in superior temporal cortex in schizophrenia and bipolar disorder: a postmortem study. Biol Psychiatry. 2003;53:1138–1141. doi: 10.1016/s0006-3223(02)01742-0. [DOI] [PubMed] [Google Scholar]
  23. Ongur D, Jensen JE, Prescot AP, Stork C, Lundy M, Cohen BM, Renshaw PF. Abnormal glutamatergic neurotransmission and neuronal–glial interactions in acute mania. Biol Psychiatry. 2008;64(8):718–726. doi: 10.1016/j.biopsych.2008.05.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Ongur D, Prescot AP, Jensen JE, Cohen BM, Renshaw PF. Creatine abnormalities in schizophrenia and bipolar disorder. Psychiatry Res. 2009;172(1):44–48. doi: 10.1016/j.pscychresns.2008.06.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Özerdem A, Güntekin B, Atagün I, Turp B, Başar E. Reduced long distance gamma (28–48 Hz) coherence in euthymic patients with bipolar disorder. J Affect Disord. 2011;132(3):325–332. doi: 10.1016/j.jad.2011.02.028. [DOI] [PubMed] [Google Scholar]
  26. Parker G, Fletcher K. Differentiating bipolar I and II disorders and the likely contribution of DSM-5 classification to their cleavage. J Affect Disord. 2014:152–154. 57–64. doi: 10.1016/j.jad.2013.10.006. [DOI] [PubMed] [Google Scholar]
  27. Phillips ML, Kupfer DJ. Bipolar disorder diagnosis: challenges and future directions. Lancet. 2013;381:1663–1671. doi: 10.1016/S0140-6736(13)60989-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Provencher SW. Automatic quantitation of localized in vivo 1H spectra with LCModel. NMR Biomed. 2001;14:260–264. doi: 10.1002/nbm.698. [DOI] [PubMed] [Google Scholar]
  29. Silverstone PH, Asghar SJ, O’Donnell T, Ulrich M, Hanstock CC. Lithium and valproate protect against dextro-amphetamine induced brain choline concentration changes in bipolar disorder patients. World J Biol Psychiatry. 2004;5:38–44. doi: 10.1080/15622970410029906. [DOI] [PubMed] [Google Scholar]
  30. Silverstone PH, McGrath BM. Lithium and valproate and their possible effects on themyo-inositol second messenger system in healthy volunteers and bipolar patients. Int Rev Psychiatry. 2009;21(4):414–423. doi: 10.1080/09540260902962214. [DOI] [PubMed] [Google Scholar]
  31. Uemura T, Green M, Corson TW, Perova T, Li PP, Warsh JJ. Bcl-2 SNP rs956572 associates with disrupted intracellular calcium homeostasis in bipolar I disorder. Bipolar Disord. 2011;13:41–51. doi: 10.1111/j.1399-5618.2011.00897.x. [DOI] [PubMed] [Google Scholar]
  32. Winsberg ME, Sachs N, Tate DL, Adalsteinsson E, Spielman D, Ketter TA. Decreased dorsolateral prefrontal N-acetylaspartate in bipolar disorder. Biol Psychiatry. 2000;47:475–481. doi: 10.1016/s0006-3223(99)00183-3. [DOI] [PubMed] [Google Scholar]
  33. Young RC, Biggs JT, Ziegler VE, Meyer DA. A rating scale for mania: reliability, validity and sensitivity. Br J Psychiatry. 1978;133:429–435. doi: 10.1192/bjp.133.5.429. [DOI] [PubMed] [Google Scholar]
  34. Yüksel C, Öngür D. Magnetic resonance spectroscopy studies of glutamate-related abnormalities in mood disorders. Biol Psychiatry. 2010;68(9):785–794. doi: 10.1016/j.biopsych.2010.06.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Yumru M, Savas HA, Kalenderoglu A, Bulut M, Celik H, Erel O. Oxidative imbalance in bipolar disorder subtypes: A comparative study. Prog Neuropsychopharmacol Biol Psychiatry. 2009;33:1070–1074. doi: 10.1016/j.pnpbp.2009.06.005. [DOI] [PubMed] [Google Scholar]

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