Psychiatry Research: Neuroimaging 221 (2014) 58–62

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A comparative diffusion tensor imaging study of corpus callosum subregion integrity in bipolar disorder and schizophrenia Jian Li a,1, Elliot Kale Edmiston b,1, Kaiyuan Chen c,1, Yanqing Tang c, Xuan Ouyang d, Yifeng Jiang f, Guoguang Fan a, Ling Ren a, Jie Liu e, Yifang Zhou c, Wenyan Jiang c, Zhening Liu d, Ke Xu a,n, Fei Wang a,e,nn a

Department of Radiology, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang 110001, Liaoning, PR China Vanderbilt Neuroscience Graduate Program, Vanderbilt Brain Institute, Vanderbilt University, 465 21st Avenue South, Nashville, TN 37232, United States c Department of Psychiatry, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang 110001, Liaoning, PR China d The Institute of Mental Health, Second Xiangya Hospital of Central South University, Changsha, PR China e Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, United States f Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, CT 06511, United States b

art ic l e i nf o

a b s t r a c t

Article history: Received 30 August 2012 Received in revised form 18 August 2013 Accepted 25 October 2013 Available online 7 November 2013

Structural magnetic resonance imaging (MRI) studies have provided evidence for corpus callosum (CC) white matter abnormalities in bipolar disorder (BD) and schizophrenia (SZ). These findings include alterations in shape, volume, white matter intensity and structural integrity compared to healthy control populations. Although CC alterations are implicated in both SZ and BD, no study of which we are aware has investigated callosal subregion differences between these two patient populations. We used diffusion tensor imaging (DTI) to assess CC integrity in patients with BD (n ¼16), SZ (n ¼ 19) and healthy controls (HC) (n ¼ 24). Fractional anisotropy (FA) of CC subregions was measured using region of interest (ROI) analysis and compared in the three groups. Significant group differences of FA values were revealed in five CC subregions, including the anterior genu, middle genu, posterior genu, posterior body and anterior splenium. FA values of the same subregions were significantly reduced in patients with SZ compared with HC. FA values were also significantly reduced in patients with BD compared to the HC group in the same subregions, excepting the middle genu. No significant difference was found between patient groups in any region. Most of the alterations in CC subregions were present in both the BD and SZ groups. These results imply an overlap in potential pathology, possibly relating to risk factors common to both disorders. The one region that differed between patient groups, the middle genu area, may serve as an illness marker and is perhaps involved in the different cognitive impairments observed in BD and SZ. & 2013 Elsevier Ireland Ltd. All rights reserved.

Keywords: Bipolar disorder Schizophrenia Corpus callosum Diffusion tensor imaging Fractional anisotropy

1. Introduction Bipolar disorder (BD) and schizophrenia (SZ) have long been considered as two distinct disorders; for example, BD and SZ often differ in their clinical course, associated levels of functional impairment, and response to medications (Goldberg et al., 1993; Gourovitch

n Corresponding author at: Department of Radiology, The First Affiliated Hospital, China Medical University, 155 Nanjing North Street, Shenyang, Liaoning 110001, PR China. Tel.: þ86 24 8328 2999; fax: þ 86 24 8328 2997. nn Corresponding author at: Department of Radiology, The First Affiliated Hospital, China Medical University, 155 Nanjing North Street, Shenyang, Liaoning 110001, PR China and Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT 06511, United States. Tel.: þ 1 203 737 2507; fax: þ1 203 737 2513. E-mail addresses: [email protected] (K. Chen), [email protected] (K. Xu), [email protected] (F. Wang). 1 Co-first authors.

0925-4927/$ - see front matter & 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.pscychresns.2013.10.007

et al., 1999). Although the Kraepelinian dichotomy classifies BD and SZ as distinct entities, this concept has recently been challenged (Craddock and Owen, 2005), convergent evidence increasingly suggests that BD and SZ have overlapping features, such as symptomatology, persistent neurocognitive deficits, and shared susceptibility genes that frequently co-occur within relatives (Murray et al., 2004; Kato et al., 2005; Benes, 2007;Owen et al., 2007; Schretlen et al., 2007; Maier, 2008). Increasing evidence for greater commonalities in identified disorder mechanisms presents challenges for elucidating distinct neuropathophysiologies of BD and SZ. The corpus callosum (CC) plays a pivotal role in higher cognitive functions via the integration of interhemispheric information. Since CC alterations may already be present in the early stages of BD (Atmaca et al., 2007; Lopez-Larson et al., 2010) and SZ (Douaud et al., 2007; Kyriakopoulos et al., 2008; White et al., 2009; Davenport et al., 2010; Henze et al., 2012), the CC has become a major structure of interest in BD and SZ research. Subsequent

J. Li et al. / Psychiatry Research: Neuroimaging 221 (2014) 58–62

structural magnetic resonance imaging (MRI) studies have provided evidence for CC white matter abnormalities in BD (Brambilla et al., 2004; Atmaca et al., 2007; Yurgelun-Todd et al., 2007; Walterfang et al., 2009) and SZ (Downhill et al., 2000; Shenton et al., 2001; Bachmann et al., 2003; Innocenti et al., 2003; Nemes et al., 2005) including alterations in volume, signal intensity and structural integrity. Homologous findings in studies comparing either BD or SZ to healthy control populations have suggested alterations in callosal subregions that provide interhempshieric connections to the cortex, such as the genu (prefrontal areas), body (inferior temporal and superior parietal lobes) and isthmus (superior temporal and posterior parietal cortices) (Brambilla et al., 2003, 2004; Arnone et al., 2008; Bastin et al., 2008; White et al., 2008; BarneaGoraly et al., 2009; Bellani et al., 2009; Walterfang et al., 2009; Henze et al., 2012). Diffusion tensor imaging (DTI) (Basser et al., 1994) has made it possible to study microscopic characteristics of white matter and orientation of neural tissue in vivo by measuring the degree of water diffusion in the brain. DTI studies regularly employ a region of interest (ROI) methodology to measure fractional anisotropy (FA), an index of white matter integrity. Reduced FA indicates disruption of the organization of fiber tracts (Werring et al., 1999) and may be related to efficiency of interhemispheric signal transfer in the case of the CC. Given the longstanding implication of callosal volume, morphometry, and signal intensity differences in BD and SZ, many DTI studies have investigated CC FA in both disorders separately (Price et al., 2007; Wang et al., 2008 Gasparotti et al., 2009; Ha et al., 2011), results indicate that CC FA alterations are present in both BD and SZ. Meanwhile recent studies using DTI, which aim to investigate the specific white matter integrity in both disorders, have reported inconsistent findings including lower FA in posterior CC in BD (Lu et al., 2011), and no significant differences in CC regions (Sussmann et al., 2009; Cui et al., 2011). However, there is no study of which we are aware that uses DTI to compare BD and SZ directly to investigate the underlying subregion or subregions that differentiate the two disorders. In the current DTI study, we applied a CC semi-automated segmentation approach to examine potential regionally and diagnostically specific CC abnormalities in patients with BD versus SZ, as indexed by ROI FA values, to discover possible distinct neural markers for both disorders.

2. Materials and methods 2.1. Ethics statement We confirm that the research has been conducted in compliance with the appropriate ethical guidelines of the declaration of Helsinki. The study was approved by the Ethics Committee of the China Medical University. After complete description of the study, written informed consent was obtained from all participants. We confirm that all potential participants who declined to participate were not disadvantaged in any way by not participating in the study.

2.2. Subjects Participants included 16 subjects with BD I (mean age 30.3 7 standard deviation [S.D.] 5.6 years, mean age of onset 25 7 S.D.5.88 years, 9 females), 19 with paranoid SZ (mean age 29.2 7 S.D. 8.8 years, mean age of onset 24.897 S.D.8.04 years,10 females), and 24 healthy control comparisons (HC) (mean age 29.1 7 S. D.7.3, 14 females). Both outpatients and inpatients were recruited from the First Affiliated Hospital of China Medical University and Shenyang Mental Health Center. All participants were evaluated by two psychiatrists for diagnosis using the Structured Clinical Interview for DSM-IV. Mood states in BD were assessed on the basis of the Hamilton Depression Rating Scale (HDRS) (Hamilton, 1960) and Young Mania Rating Scale (YMRS) (Young et al., 1978). Psychotic symptoms in SZ were assessed on the basis of the Brief Psychiatric Rating Scale (BPRS) (Overall and Gorham, 1962). Three BD patients were on lithium, six were on anticonvulsants, 10

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Table 1 Demographic and clinical characteristics. Characteristics

HC (n¼24) BD (n¼ 16) SZ (n¼ 19)

Age (years, mean 7 S.D.) Gender (male: female) Course of disease (months, mean 7 S.D.) BPRS (mean7 S.D.) HDRS (mean7 S.D.) YMRS (mean7 S.D.) Medication (Yes, N)

29.17 7.3 10:14 N/A N/A 0.7 70.9 0 N/A

30.3 75.6 7:9 65.9 782.0 N/A 5.17 6.2 5.7 79.2 15

29.2 7 8.8 9:10 52.9 7 79.5 27.2 7 8.9 N/A N/A 13

BD: Bipolar disorder. SZ: Schizophrenia. S.D.: Standard deviation. BPRS: Brief psychiatric rating scale. HDRS: Hamilton depression rating scale. YMRS: Young mania rating scale. were on atypical antipsychotics, four were on antidepressants, and one was unmedicated, as well as one patient with unclear medication. One SZ patient was on typical antipsychotics, 13 were on atypical antipsychotics and six were unmedicated. No presence of DSM-IV Axis I was confirmed in the HC participants and their first-degree family members. All participants were right handed, except two SZ patients, one BD patient and two HCs who had mixed hand dominance. Detailed demographic and clinical data are presented in Table 1. Exclusion criteria for all participants included (1) general MRI-contraindications, (2) history of head injury with loss of consciousness over 5 min or any neurological disorders, (3) any concomitant major medical disorders, and (4) IQ o70. 2.3. MRI acquisition Diffusion-weighted images were acquired on a GE Signa HDX 3.0T magnetic resonance image (MRI) scanner at the First Affiliated Hospital of China Medical University, Shenyang, China. Head motion was minimized with restraining foam pads. A standard head coil was used for radiofrequency transmission and reception of the MRI signal. DTI data were acquired using spin-echo planar imaging sequence, parallel to the anterior–posterior (AC–PC) plane. The diffusion sensitizing gradients were applied along 25 non-collinear directions (b¼ 1000 s/mm2), together with an axial acquisition without diffusion weighting (b¼0). Scan parameters were repetition time (TR)¼ 17,000 ms; echo time (TE)¼ 85.4 ms; field of view (FOV)¼ 240  240 mm2; image matrix¼ 120  120; 65 contiguous slices of 2 mm without gap. A 3D Fast Spoiled Gradient-Echo (FSPGR) T1-weighted sequence was used to acquire high resolution structural images for anatomical determinations (TR¼ 7.1 ms, TE¼ 3.2 ms, FOV¼ 240  240 mm2, matrix¼ 240  240, slice thickness¼ 1.0 mm without gap, 176 slices, one average). 2.4. DTI processing and analysis Images were processed with FSL (FMRIB Software Library, http://www.fmrib.ox. ac.uk/fsl/) and BioImage Suite software (http://www.bioimagesuite.org) using our previous protocol (Xu et al., 2012). Briefly, motion and eddy current correction were performed with FSL (FMRIB Software Library, http://www.fmrib.ox.ac.uk/fsl/). Linear motion (x, y, z planes) for all participants was below 2 mm and rotational motion (pitch, roll, yaw) was below 21. A tri-linear interpolation was then followed by resampling the image from 2  2  2 mm3 to 1  1  1 mm3 resolution. Additional DTI data processing, such as diffusion tensor matrices, FA and color tensor maps, and CC tracing were done using BioImage Suite software (http://www.bioimagesuite. org). First, the mid-sagittal slice was determined using AC–PC aligned high resolution T1-images. DTI data was coregistered to high resolution T1-images which was used to identify the mid-sagittal slice. Then, the entire CC was delineated manually on the mid-sagittal color tensor map with two raters blind to participant characteristics. The corpus callosum was then subdivided into the genu, body, isthmus and splenium according to the definition by Keshavan et al. (2002) (Fig. 1). Inter-rater reliabilities for FA values in the nine subregions including the anterior, middle and posterior genu, anterior body, posterior body, isthmus, as well as the anterior, middle and posterior splenium, presented as intra-class correlation coefficients, ranged from 0.86 to 0.97. 2.5. Statistical analysis All statistical analyses were conducted using SPSS for Windows software, version 16.0 (SPSS Inc., Chicago, 2008). FA values were tested for normality using Kolmogorov–Smirnov test statistics and normal probability plots. Nine analysis of variance (ANOVAs) were performed to test the three groups for differences in FA values across the nine subregions and p o0.05/9 (Bonferroni corrected) was

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considered significant. Once significant three-group differences were determined in specific subregions, posthoc ANOVAs were applied to explore FA value differences in these regions in pairwise group comparisons. Pearson's correlations analyses of symptom assessments (HDRS, YMRS and BPRS) and duration of illness with FA values in the subregions showing significant differences between BD versus HC or SZ versus HC were performed. Bonferroni correction was used in these analyses as well.

3. Results No significant differences in age or sex were found among the three groups (HC, BD and SZ). ANOVAs with Bonferroni correction indicated significant group differences of FA values in five CC subregions, including the anterior genu, middle genu, posterior genu, posterior body and anterior splenium (po 0.0056, corrected for nine subregions) (Table 2). Furthermore, posthoc pairwise ANOVAs showed that SZ patients had significant FA reductions in all these five subregions while BD patients had FA reductions in the anterior genu, posterior genu, posterior body and anterior splenium, but not in

Fig. 1. Sagittal images from the tensor color map (top panel) and the fractional anisotropy map (bottom panel) display the scheme used to subdivide the corpus callosum; left–right coursing fibers of the corpus callosum are in red (top panel). Based on the classic 7-subregion CC division defined in Witelson (1989), a line was further drawn to connect the midpoint of the A–P line within the genu and the midpoint of the line separating the genu from the body. This line was trisected by two perpendiculars, dividing the genu into its anterior, middle, and posterior regions. The same method is used to divide splenium into the anterior, middle, and posterior regions. (1:anterior genu, 2:middle genu, 3:posterior genu, 4:anterior body, 5:posterior body, 6:isthmus, 7:anterior splenium, 8:middle splenium, and 9: posterior splenium).

the middle genu. No significant differences were detected between the BD and SZ groups (p o0.01, Bonferroni corrected, Table 3); however a trend for significance in the middle genu CC region was present between the two patient groups (p ¼0.045, Table 3). The results remained the same when the illness duration and chlorpromazine equivalent doses were taken as covariates. There were no significant correlations between HDRS, YMRS, duration of illness and FA values in anterior and posterior genu, posterior body and anterior splenium in BD group, BPRS and duration of illness and FA values in anterior, middle and posterior genu, posterior body and anterior splenium in SZ group.

4. Discussion The current study is the first study comparing CC subregions between BD and SZ. Compared to the HC group, SZ patients had significant FA reductions in all five subregions, including the anterior genu, posterior genu, posterior body and anterior splenium, while BD patients had similar FA reductions in all subregions except middle genu. These findings suggest that cerebral regions interconnected by the genu (prefrontal areas), posterior body (inferior temporal and superior parietal lobes) and splenium (temporo-parietal cortices) may be hypoconnected in both BD and SZ compared to normal populations. Furthermore, altered FA values were presented in common CC subregions in both disorders, except for the middle genu subregion, which did not show alterations in BD. These similar findings might implicate a potential shared neurobiological mechanism, such as altered axonal migration or myelination caused by shared genetic susceptibility in both BD and SZ (Craddock et al., 2006), suggesting that defects in interhemispheric communication might be diagnostically nonspecific in psychotic disorders. Subtle abnormalities in the genu, which connects the prefrontal lobes, are well-documented and related to impaired emotional and cognitive performance in BD and SZ (Hubl et al., 2004; Buchsbaum et al., 2006; Price et al., 2007; Shergill et al., 2007; Bruno et al., 2008; Rotarska-Jagiela et al., 2008; Wang et al., 2008). Consistent with previous reports, our findings provide evidence of decreased FA in three subregions of the genu in both disorders, suggest that SZ and BD might share greater phenotypic similarity in terms of the pattern rather than the severity of their neurocognitive deficits (Seidman et al., 2002; Berrettini, 2004; Dickerson et al., 2004). The two disorders diverge significantly as regards neuropsychological function. Whilst some qualitative similarities have been described (Schretlen et al., 2007), illness features diverge on severity and outcome, with SZ patients being most impaired (Goldberg, 1999). In the current study, FA values in the genu subregions were lower in the SZ group than in the BD group. Although these differences were not significant, the findings are

Table 2 Fractional anisotropy of corpus callosum subregions cross the groups. Subregion (FA)

HC (mean7 S.D.)

BD (mean 7 S.D.)

SZ (mean 7 S.D.)

Statistical value

Anterior genu Middle genu Posterior genu Anterior body Posterior body Isthmus Anterior splenium Middle splenium Posterior splenium

0.6817 0.033 0.5477 0.060 0.5177 0.051 0.528 7 0.056 0.5647 0.066 0.554 7 0.062 0.6417 0.090 0.6867 0.050 0.656 7 0.035

0.6427 0.055 0.5117 0.045 0.460 7 0.055 0.4857 0.038 0.5067 0.069 0.5017 0.052 0.554 7 0.063 0.6777 0.063 0.656 7 0.045

0.621 70.049 0.478 70.047 0.4477 0.053 0.481 70.053 0.492 70.077 0.5147 0.076 0.559 7 0.095 0.6677 0.055 0.6667 0.036

F ¼9.904; p ¼0.0002n F ¼9.229; p¼ 0.0003n F ¼10.588; p ¼ 0.0001n F ¼5.502; p¼ 0.007 F ¼6.411; p ¼ 0.003n F ¼3.738; p ¼ 0.030 F ¼7.003; p ¼ 0.002n F ¼0.647; p ¼0.528 F ¼0.455; p¼ 0.637

n

Significant at Bonferroni-corrected p o 0.0056.

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Table 3 Altered fractional anisotropy of corpus callosum subregions in BD compared with HC, SZ compared with HC and BD compared with SZ. Subregion

Anterior genu Middle genu Posterior genu Posterior body Anterior splenium n

Statistical value BD versus HC

SZ versus HC

BD versus SZ

F ¼7.688; p ¼0.009n F ¼4.212; p ¼0.047 F ¼11.195; p ¼0.002n F ¼7.350; p¼ 0.010n F ¼11.287; p¼ 0.002n

F¼ 23.642; p ¼ 0.00002n F¼ 16.741; p ¼ 0.00002n F¼ 18.784; p¼ 0.00001n F¼ 10.945; p ¼0.002n F¼ 8.498; p ¼ 0.006n

F¼ 1.448; p ¼ 0.237 F¼ 4.328; p ¼ 0.045 F¼ 0.481; p ¼ 0.493 F¼ 0.283; p ¼ 0.598 F¼ 0.028; p ¼ 0.869

Significant at Bonferroni-corrected p o 0.01.

consistent with the viewpoint that FA values underlie differences in symptom severity along the proposed psychosis continuum. When comparing the BD and SZ groups directly, there was no significant difference in the middle genu. This may result from strict statistical correction (pbonf o0.01). Nevertheless, the negative finding provides a trend to indicate the possibility of identifying bio-behavioral correlates that may distinguish SZ and BD. It is possible that this trend might reach significance in larger sample sizes. The qualitative similarity of cognitive deficits in the two disorders suggests that measures of other biological characteristics, such as electrophysiological or neuroanatomic abnormalities, may yield useful biological illness markers which may be too subtle to detect with structural MRI. This study has several limitations that should be taken into account. First, the subjects of our study were under diverse medication treatment. Populations with longstanding antipsychotic drug treatment may not accurately reflect original microstructural brain abnormalities representative of the disorder. The effects of psychotropic drugs on the CC are still unclear, and a recent review suggested that few significant effects of medication are shown in DTI studies (Hafeman et al., 2012). It is suggested that lithium may affected myelin gene expression (McQuillin et al., 2007; Brambilla et al., 2009) and mood stabilizers as well as antidepressants may be associated with neurotrophic effects (Manji et al., 2000; Banasr et al., 2004). In addition, a study reported after six weeks of antipsychotic treatment, drug-naive schizophrenia patients showed a progressive change in white matter FA value but the authors also point out that the underlying progression of illness may affect this result (Wang et al., 2013). Therefore, to some extent, it is difficult to make a distinction in the current study between effects of primary disease, medications, duration of illness, or the interaction of these factors. Future studies are needed to eliminate these confounding factors by recruiting drug-naive, first-episode, short-duration patients to better characterize differences between BD and SZ. Furthermore, future studies should examine behavioral, symptom, or cognitive correlates of the subtle CC microstructural alterations in both BD and SZ. In summary, our results demonstrate most of the abnormalities of white matter integrity in CC subregions were present in both BD and SZ, suggesting an overlap in potential pathology, possibly relating to risk factors common to both disorders. The subtle differences of white matter integrity deficits in the corpus CC might serve as an speculative illness marker and is perhaps involved in the clinical distinctions observed in BD and SZ. References Arnone, D., McIntosh, A.M., Chandra, P., Ebmeier, K.P., 2008. Meta-analysis of magnetic resonance imaging studies of the corpus callosum in bipolar disorder. Acta Psychiatrica Scandinavica 118, 357–362. Atmaca, M., Ozdemir, H., Yildirim, H., 2007. Corpus callosum areas in first-episode patients with bipolar disorder. Psychological Medicine 37, 699–704. Bachmann, S., Pantel, J., Flender, A., Bottmer, C., Essig, M., Schroder, J., 2003. Corpus callosum in first-episode patients with schizophrenia—A magnetic resonance imaging study. Psychological Medicine 33, 1019–1027.

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A comparative diffusion tensor imaging study of corpus callosum subregion integrity in bipolar disorder and schizophrenia.

Structural magnetic resonance imaging (MRI) studies have provided evidence for corpus callosum (CC) white matter abnormalities in bipolar disorder (BD...
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