Journal of Affective Disorders 155 (2014) 223–233

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Research report

Frontal white matter alterations are associated with executive cognitive function in euthymic bipolar patients Viola Oertel-Knöchel a,n,1, Britta Reinke a,1, Gilberto Alves b, Alina Jurcoane c,d, Sofia Wenzler a, David Prvulovic a, David Linden e, Christian Knöchel a a Laboratory of Neurophysiology and Neuroimaging, Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University, Frankfurt/Main 60528, Germany b Center for Alzheimer's Disease and Related Disorders, Universidade Federal, do Rio de Janeiro, Brazil c Institute for Neuroradiology, Goethe University, Frankfurt/Main, Germany d Center for Individual Development and Adaptive Education of Children at Risk, Frankfurt, Germany e MRC Centre for Neuropsychiatric Genetics & Genomics, Institute of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, CF, United Kingdom

art ic l e i nf o

a b s t r a c t

Article history: Received 9 August 2013 Received in revised form 6 November 2013 Accepted 7 November 2013 Available online 16 November 2013

Background: Bipolar affective disorder (BD) is often associated with cognitive dysfunction in executive domains. However the biological underpinnings of cognitive deficits in BD are not sufficiently understood. A growing body of evidence indicates a loss of microstructural integrity in various white matter (WM) fiber tracts in BD. The aim of the current study was to assess potential links between WM structural abnormalities and cognitive performance in euthymic middle-aged BD patients (n ¼30) and matched healthy controls (n ¼ 32). Methods: Diffusion tensor imaging (DTI) data was carried out with both voxelwise (tract based spatial statistics, TBSS) and region-of-interest (ROI) based analysis. We compared multiple indices of diffusion including fractional anisotropy (FA), radial (DR), axial (DA) and mean diffusivities (MD). Results: Increased mean diffusivity was found in the fornix, anterior thalamic radiation, splenium and the truncus of the corpus callosum in BD patients compared with controls. These diffusion changes were significantly associated with poorer performance in executive tasks in BD patients. Conclusions: Our results indicate a direct link between executive cognitive functioning and abnormal WM microstructural integrity of fronto-limbic tracts in remitted BD patients, and add evidence to the neuronal disruption that underlies the residual symptomatology of BD. & 2013 Elsevier B.V. All rights reserved.

Keywords: DTI Bipolar disorders ROI-analysis Cognition VBM

1. Introduction Bipolar disorder (BD) is a devastating psychiatric disease, characterized by recurrent depressive and manic phases, disturbances of mood, affect, activity, thought, sleep and appetite. Cognitive functions are frequently impaired as well, particularly attention, memory and executive functioning (Arts et al., 2008; Bora et al., 2009; Deckersbach et al., 2004; Torres et al., 2007; Torres and Malhi, 2010). While in clinical practice cognitive symptoms are generally considered to recover with clinical remission of manic or depressive phases, there is increasing evidence

n Correspondence to: Laboratory of Neurophysiology and Neuroimaging, Dept. of Psychiatry, Psychosomatic Medicine and Psychotherapy; Heinrich-Hoffmann-Str. 10, Goethe-University, 60528 Frankfurt. Tel.: þ 49 69 6301 83780; fax: þ49 69 6301 3833. E-mail address: [email protected] (V. Oertel-Knöchel). 1 Equal contribution.

0165-0327/$ - see front matter & 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jad.2013.11.004

that bipolar patients do suffer from residual cognitive deficits even during fully remitted or euthymic states without clinically relevant symptoms of mania or depression (Robinson et al., 2006). This cognitive dysfunction can become progressively worse in some cases, leading to chronic functional impairment (Martinez-Aran et al., 2000; Schneider et al., 2012). The biological underpinnings of this chronic and state-independent cognitive dysfunction in BD have not been systematically investigated so far. Frontal–limbic neuronal networks have been suggested to play an important role in cognitive and emotional processing (Torgerson et al., 2012). Recent systematic reviews and meta-analyses of morphometric studies in mood disorders including BD suggest structural brain changes mainly in frontal, temporal and limbic white matter (WM) regions (Delaloye et al., 2011; Ellison-Wright and Bullmore, 2010; Selvaraj et al., 2012). The pathophysiology of these DTI changes in subjects with affective disorders is yet unknown (Schneider et al., 2012). It has been proposed that loss of fiber integrity and increased diffusivity

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may be caused by a loss of axonal density, axonal diameter, a loss of neurons, localized water content and a reduced myelinisation or changes in the orientation of the axons within the fibers (Benedetti et al., 2011; Beyer et al., 2005; Chaddock et al., 2009; Kafantaris et al., 2009; Mahon et al., 2010; Regenold et al., 2007; Tkachev et al., 2003)). Although microstructural WM alterations have indeed been found in euthymic BD patients (Chaddock et al., 2009; Yurgelun-Todd et al., 2007), there is some evidence for state-dependent changes in WM integrity as well (Zanetti et al., 2009). In a sample of 19 euthymic geriatric BD patients (68 75.8y) and matched controls, (Haller et al., 2011; Kafantaris et al., 2009) reported that decreased orbito-frontal WM integrity was associated with slower visuomotor processing in adolescent BD patients (average age: 16y). In the current study, we investigated micro-structural properties of WM in middle-aged euthymic BD patients using diffusion tensor imaging (DTI). We analyzed parameters which are thought to reflect directional WM integrity (fractional anisotropy, FA), tissue breakdown with increased water content (mean diffusivity, MD), and the integrity of axons versus their adjacent myelin sheaths (axial diffusivity, DA; radial diffusivity, DR) (Basser, Mattiello, and LeBihan, 1994; Pierpaoli and Basser, 1996; Sun et al., 2006; Pierpaoli and Basser, 1996; Torgerson et al., 2012; Beaulieu, 2009). We used multiple indices of diffusion in order to assess WM integrity as comprehensively and sensitively as possible and correlated the findings with the clinical and cognitive scores. Moreover, we assessed any underlying WM volume differences. We hypothesized that alterations of WM integrity parameters as assessed by DTI and volumetry would correlate with performance in cognitive tests and with subclinical symptoms in euthymic BD patients.

2. Methods 2.1. Participants We investigated 30 outpatients with bipolar disorder I as defined by DSM-IV criteria (APA, 1994) and diagnosed by a board-certified

psychiatrist at the Dept. of Psychiatry and Psychotherapy of Frankfurt University Medical School (mean age [M]¼39.23 years [SD¼ 12.37], mean duration of illness M¼ 10.20 [SD¼7.10] years; [see Table 1 for further details]). At the time of the measurements all patients were in disease remission (see definition in the clinical testing section), were without any comorbid axis I or II disorders and were on stable medication (mean duration of medication: M¼8.30 [SD¼7.40] years) (see Supplemental material). We also enrolled a group of healthy controls (n ¼32; M¼39.22 years [SD¼ 10.35]), who were matched for sociodemographic criteria (age, gender and education). Exclusion criteria for control participants were current drug-abuse, neurological disease, any personal or familial history of psychiatric disorders and an inability to provide informed consent. All participants received a study description and consented to participate in the experiments and tests as part of a larger imaging -project of the research group (Oertel-Knochel et al., 2013, 2012). The ethical board of the Medical School of the Goethe-University, Frankfurt/Main, Germany approved the study. 2.2. Clinical testing All subjects were right handed and they all underwent a set of tests for general intelligence, episodic memory, problem solving ability, and psychiatric pathology. These tests were part of a larger test battery. General intelligence was tested with MWT-B MehrfachwahlWortschatz-Test, (Lehrl, 2005), the German equivalent of the “Spot-the-Word test”. We assessed episodic memory with the California verbal learning test (CVLT; (Niemann et al., 2008)), which includes a learning, an interference and a recognition list. We used the following parameters of the CVLT assessment: discriminality (DW), delayed free recall I (VFW I) and delayed free recall II (VFW II). Problem solving ability was assessed using the subscale number of problem solving of the Tower of London test (TL-D p) (Tuche and Lange, 2004) which assesses executive functioning through transformation tasks. We evaluated psychiatric history, including DSM-IV axis I and axis II disorders, and affective or psychotic disorders by applying a Structured Clinical Interview for DSM-IV (SCID-I and SCID-II;

Table 1 Sociodemographic, clinical and cognitive characteristics of the patient group (PAT; n¼ 30) and the control group (CON; n¼ 32). Statistics of the group comparison appear in the fourth column and, depending on the reported score, was done with Chi-Quadrat-test (χ²), t-test (t), or Mann–Whitney-U-Test (z). CVLT DFR I¼ CVLT—delayed free recall I, CVLT DFR II ¼ CVLT—delayed free recall II, CVLT DW ¼ CVLT discriminality, TL-D p¼ number of solved problems of the Tower of London, MWT-B ¼ MehrfachwahlWortschatztest, PANAS PA ¼ PANAS positive subscale, PANAS NA ¼PANAS negative subscale. Variable

PAT

CON

Significance

Number Gender Age Education (years) Education Parents (years)

30 14 w/16 m 39.22 (12.37) 15.40 (2.34) Mother: 13.13 (3.37) Father: 14.47 (3.68) All right handed 10.20 (7.19) 9.79 (12.62) 9.00 (10.23) 13.75 (12.00) 8.30 (7.40) 10.66 (9.63) 0.73 (1.89) 26.27 (7.93) 16.57 (7.50) 30.07 (3.22) 5.83 (1.31) 11.43 (2.65) 12.33 (2.93) 15.80 (1.94)

32 16 w/16 m 39.22 (10.35) 16.31 (1.75) Mother: 12.97 (3.26) Father: 14.66 (2.44) All right handed

– χ² ¼0.07, p ¼ 0.79 t¼  0.01, p ¼ 0.99 z ¼  1.482, p ¼ 0.18 z ¼  0.236, p ¼0.81 z ¼  0.007, p¼ 0.99

2.28 (4.36) 0.59 (1.07) 34.44 (5.52) 13.88 (5.18) 31.53 (2.58) 6.28 (1.34) 13.28 (2.19) 13.69 (2.16) 16.53 (1.98)

z ¼  4.45, p o 0.001 z ¼  0.36, p ¼0.67 z ¼4.73, p o0.01 z ¼  1.65, p 40.05 t¼  1.83, p ¼0.07 t¼ 2.34, p ¼0.03 t¼ 3.00, po 0.01 t¼ 2.08, p ¼0.04 t¼ 2.47, p ¼ 0.03

Handedness Duration of illness (years) Nr. of dep. episodes Nr. of manic episodes Nr. of episode of illness Duration of me-dication (years) BDI II BRMAS PANAS PA PANAS NA MWT-B CVLT DW CVLT DFR I CVLT DFR II TL-D p

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German version: (Wittchen et al., 1996). Current depressive or manic symptoms were assessed using the German version of the Beck Depression Inventory II (BDI II; (Hautzinger et al., 2006)) and the German version of the Bech Rafaelsen Mania Scale (BRMAS; (Bech, 1981)). Remitted state was defined by BDI II scores of o 18 and BRMAS scores of o7. All participants were also screened for their current mental state using the Positive and Negative Affect Schedule (PANAS; (Krohne et al., 1996). For the PANAS, two scores were computed: the positive affect (PANAS PA) and the negative affect (PANAS NA). 2.3. Data acquisition and image processing We acquired Diffusion MRI and anatomical MR data using a Trio 3-T scanner (Siemens, Erlangen, Germany) with a standard transmit-receive head coil. Participants wore protective earplugs to reduce scanner noise and held a response device in the hand. Diffusion MRI data was acquired with an echo planar imaging (EPI) sequence with generalised auto-calibrating parallel acquisitions (GRAPPA; Griswold et al., 2002a, 2002b) (TR ¼8760 ms; TE ¼ 100 ms; bandwith ¼1302 Hz/pixel, acquisition voxel size ¼2  2  2 mm3; 60 axial adjacent slices; slice thickness¼2 mm (no gap); FOV ¼192 mm  192 mm  120 mm; acquisition matrix ¼96  96; averages of 10 images without (b0) and 60 images with diffusion weighting (b1000 ¼1000 s/mm2 60 noncolinear directions) (total acquisition time ¼ 10 min 31 s). The anatomical data consisted of one MDEFT (Modified Driven Equilibrium Fourier Transform) sequence (Deichmann et al., 2004) with 1  1  1 mm3 voxel size and 176 slices. 2.4. DTI processing—Whole brain and region of interest (ROI) based approaches Diffusion MRI data were analysed with FSL 4.1 (Oxford Centre for Functional MRI of the Brain—FMRIB software library (FSL, http://www.fmrib.ox.ac.uk/fsl) (Smith et al., 2006) and an inhouse script pipeline (MRIST: MR Imaging and Spectroscopy Toolbox, Institute for Neuroradiology). First, all Diffusion MRI images were quality checked (Chavez et al., 2009) by two investigators (B.R., V.O.). Particularly images with distinct motion distortion or spikes have been removed. Then all three Diffusion MRI datasets of a subject were merged and each diffusion-weighted volume was affine-aligned to the first b0 image, thus correcting the images for possible minimal motion artefacts and eddy-current distortions. After affine-alignment, the merged data was split back in three files, which were then averaged to obtain one Diffusion MRI dataset per subject. Tensor fitting was then done on the averaged Diffusion MRI data and resulted in the following parametric maps: fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (DR), axial diffusivity (DA). These maps were then used in further analysis for whole brain as well as region of interest approaches. For the whole brain approach we used TBSS (Tract Based Spatial Statistics, (Smith et al., 2006) which projects individual DTI parameters of each participant on a mean skeleton of a white matter mask. First all subjects' maps were transformed to Montral Neurological institute (MNI) space by nonlinear alignment to the FMRIB58_FA, a standard template provided by FSL and then a mean FA image was created and thinned to obtain a mean FA skeleton representing the centres of all tracts common to the group. Each subject's aligned FA (and consequently also MD, DR and DA) map was then projected onto this skeleton and fed into voxelwise cross-subject statistics with permutation testing (randomise tool in FSL, 5.000 permutations, (Nichols and Holmes, 2002)). We used the permuted p-values, which included the threshold-free cluster enhancement (tfce) provided by FSL, and

225

set the statistical threshold at a p value of p o0.01. Age was included as covariate. We explored the regions that showed a significant group contrast for FA further in order to assess the underlying diffusivity changes in more detail. To this end we anatomically defined and localized masks using the Jülich Histological Atlas (Hua et al., 2008) or the HarvardOxford subcortical structural Atlas around the significant clusters of the FA group analysis. For two ROIs, the truncus and the splenium of the corpus callosum, there were no atlas-based masks available, and therefore they were manually generated by two independent raters. For the resulting regions we extracted all four DTI parameters (FA, MD, DR, DA) which were extracted from each individual ROI; group comparisons were performed with ANCOVAs (covariate: age), using the SPSS 19.0 software. This analysis was followed by Scheffe post-hoc contrasts. We also performed bivariate correlation analysis between DTI parameters and cognitive and clinical parameters. Here, the FA, MD, DR and DA values of all significant ROIs from the ROI analysis were correlated with cognitive parameters (CVLT; Pearson Product Moment Correlation), clinical scores (years of illness, PANAS, BDI, BRMAS, SCL 90-R; Spearman rank correlation) and medication doses and duration (Pearson Product Moment correlation, (Almeida et al., 2009) in each group individually. Due to the high number of comparisons, we corrected all correlations for multiple comparisons using the Bonferroni correction. 2.5. VBM—ROI analysis The VBM preprocessing and statistical analysis were performed with SPM8 (statistical parametric mapping [Wellcome Department of Imaging Neuroscience, London, UK]) running on MATLAB version 7.7.0. First, all images were checked for artefacts, structural abnormalities and pathologies. Second, customized T1 templates and images of GM, WM and CSF provided by SPM were created from all participants in order to use it for the group analysis. We used modulated data and prior probability maps (voxel intensity) to guide segmentation in SPM. The segmentation included six different tissue types, light bias regularisation (0.001), 60 mm bias FWHM cut-off, warping regularisation of 4, affine regularisation to the ICBM European brain template (linear registration) and a sampling distance of 3. The segmentation was checked for quality before further analysis. Finally, the images were smoothed (Ashburner and Friston, 2000) with a Gaussian kernel of 8  8  8 mm3 (FWHM), whereby the intensity of each voxel was replaced by the weighted average of the surrounding voxels, in essence blurring the segmented image. We used the predefined masks from the WFU PickAtlas toolbox in SPM8 (Maldjian et al., 2003) and 10 mm3 manually defined ROIs sphere centered around the peak voxels from the significant regions resulting from DTI whole brain analysis. WM volume differences between groups were compared using voxelwise t-statistics in the following ROIs: anterior thalamic radiation, fornix and truncus and splenium of the corpus callosum. The respective global volumes of grey and WM and CSF as obtained during segmentation were included as nuisance variables. We used no grand mean scaling, an absolute threshold masking of 0.2, implicit but no explicit masking, omitted global calculation and applied a non-sphericity correction with replications over subjects for non-correlated repeated measures because there was only one measurement from each subject. This was done to assess morphological changes in the WM regions connected with the fiber tracts which showed significant group differences of diffusion parameters in the TBSS group analyses. The resulting statistical maps show all voxels of the ROIs that show a significant group difference at a (minimum cluster size ¼100 mm3) p-value thresholded at pr 0.001. The significant results of the analysis are

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interpreted as volume difference between the groups (Ashburner and Friston, 2000).

WM volume differences of the thalamus and the splenium of the corpus callosum were not significant (p Z0.05). 3.5. Correlations between clinical scores and DTI parameters

3. Results 3.1. Clinical and cognitive tests BD patients scored significantly lower than controls in CVLT subscales and problem solving abilities (TL-D p) (po 0.05). The patient group also showed significantly lower positive affect (PANAS PA) (po 0.05), but no significant group differences were found in the negative affect subscale (PANAS NA) (p 40.05) (see Table 1). The clinical and cognitive data of this sample have been reported in part in other studies of the working group (OertelKnochel et al., 2013, 2012). BDI II scores for depression were also different between the groups (p o0.01) which indicate subclinical depressive symptoms in BD patients although they did not fulfil the full criteria for a depressive episode. There were no group differences in the BRMAS scores (p4 0.05). 3.2. DTI whole-brain analysis The results of the exploratory whole-brain FA analysis were shown on a mean FA skeleton, using the standard MNI152 1  1  1 mm brain template, with a cluster size 410 voxel and on a p r0.01 (tfce) significance level (see Fig. 1, Table 2). The whole-brain analysis of FA values revealed significant group differences in the white matter underlying the right middle frontal gyrus and the left parahippocampal gyrus, the fornix and the medial thalamus (underneath the anterior radiation thalami) in these areas lower FA values was found in BD patients relative to controls. (p r0.01 significance level, voxel size 410) (see Table 2). Additionally, an exploratory analysis with a threshold of a pr 0.05 significance level revealed significant differences between groups for FA indices in the corpus callosum.

The correlation analyses between FA from ROI and clinical scores showed significant associations between BDI II scores and FA in the corpus callosum body (negative correlations) (see Table 3 for further details) in the control group. PANAS NA scores in the control group correlated significantly (negative) with FA of the left anterior thalamic radiation. There was no significant correlation with age in any of the computed correlations in the control group (p Z0.05). In contrast to the control group, age was significantly associated in the patient group with DTI values of the fornix (FA) and the right anterior thalamic radiation (MD, DR, DA). There was no significant correlation between the BDI II, BRMAS, PANAS and SCL90 scores and the DTI scores (p Z0.05) in the patient group. There was no significant correlation between any of the DTI scores and years of illness, number of episodes or duration of medication in the patient group (p Z0.05). There was also no significant association between the medication indices and the DTI scores (p Z0.05). There was no significant correlation between the global score of the SCL-90R and the PANAS PA scores in any of the groups (p Z0.05) (see Table 3). 3.6. Correlations between cognitive scores and DTI parameters The DW scores of the CVLT showed significant correlations in the control group in the DTI scores of the left thalamic radiation (MD, DR) and the splenium of the corpus callosum (FA). The scores of the TL p were not significantly associated with DTI scores in the control group. The patient group showed significant correlations between the TL p scores and the DTI scores of the fornix (MD, DR) and the right thalamic radiation (MD, DR, DA). The scores of the CVLT scale were not significantly associated with any of the DTI scores in patients.

3.3. DTI ROI analysis

4. Discussion

The fornix had significantly higher MD, DR and DA and significantly lower FA values in BD patients than in controls (see Table 2). The corpus callosum taken as a whole tended to show lower FA and higher DR in BD patients compared with controls (p r0.10). The splenium and the truncus showed significantly lower FA and the truncus showed higher DR in BD patients compared with controls. MD and DA scores were not significantly different between groups in the whole corpus callosum, the truncus and the splenium (p 40.05). FA-values were significantly reduced in BD patients in the right thalamic radiation (p r0.05) and showed trend-level significance in the left anterior thalamic radiation (p r0.10). In addition, MD, DR and DA scores of the anterior thalamic radiation bilaterally were significantly higher in the patient group compared with controls (pr 0.05) (see Fig. 1, Table 2).

The current study examined white matter fiber integrity in remitted middle-aged bipolar patients in comparison with healthy controls and possible associations with cognitive and subclinical scores. As a highlight of the current study, we did region-ofinterest analyses for all four common DTI parameters. Our findings revealed lower fractional anisotropy (or higher mean diffusivity, radial and axial diffusity) in the fornix, parts of the corpus callosum and the anterior thalamic radiation bilaterally in BD patients in comparison with healthy controls. This was accompanied by reduced white matter volumes of the fornix, the callosal body and the truncus of the corpus callosum assessed with VBM analyses. As a core finding of the current study, episodic memory performance (as indicated by the discriminative scores of the CVLT) was significantly associated with DTI parameters in the left anterior thalamic radiation and in the splenium of the corpus callosum in the control group. Problem solving (TL p) was related with DTI parameters in the patient, but not in the control group. This finding is in line with findings by recent meta-analyis and reviews, indicating aberrant executive functions in BD patients (Arts et al., 2008; Bora et al., 2009; Deckersbach et al., 2004; Torres et al., 2007; Torres and Malhi, 2010). Accordingly, Wintermute et al. (2012) showed that a brain network including the angular gyrus, middle temporal gyrus and anterior prefrontal regions is mainly engaged by the need to modify solution procedures. This

3.4. White matter volumes: ROI analysis with VBM Statistical tests for group differences (t-tests, cluster-size: 100 mm³, po 0.001) between BD patients and controls in the volumes of the ROIs (thalamic radiation, fornix, corpus callosum body, truncus, splenium) revealed significant lower WM volumes in BD patients compared with controls in the fornix, in the callosal body and in the truncus of the corpus callosum (see Table 2, Fig. 2).

V. Oertel-Knöchel et al. / Journal of Affective Disorders 155 (2014) 223–233

227

Fornix Fornix *

Ant. thalamic radiation *

*

*

* * *

*

Anterior thalamic radiation FA

MD

DR

FA

DA

Callosal Body

MD

DR

DA

Corpus Callosum: Splenium

*

Corpus Callosum

FA

MD

DR

DA

FA

MD

DR

DA

Corpus Callosum: Truncus Corpus Callosum - Splenium *

*

FA

MD

DR

DA

Corpus Callosum - Truncus

Fig. 1. (A) ROI DTI masks: fornix mask (yellow–red; threshold: 35) from the Jülich Histological [cyto- and myelo-architectonic] Atlas (Eickhoff et al., 2005), anterior thalamic radiation (red–yellow; threshold: 20) from the JHU White-Matter Tractography Atlas (Hua et al., 2008), corpus callosum mask (callosal body; red-yellow; threshold: 70) from the Jülich Histological [cyto- and myelo-architectonic] Atlas (Eickhoff et al., 2005), splenium mask (red), truncus mask (red). The FA skeleton is marked in green, using the standard MNI152 1  1  1 mm brain template. All images are shown in radiological convention (left is right). (B) Beta scores across groups for the ROIS, indicating the significant group effects (thresholded at a cluster size 410 voxel and on a p r 0.01 (tfce) significance level. Colour-code: black: CON, white: PAT. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

network includes brain regions which are known to be involved in the pathophysiology of BD. Few findings in bipolar patients revealed significant associations between FA scores and WM and executive functions and visuo-motor speed (Kafantaris et al., 2009). In contrast, no significant correlations between DTI values and neuropsychological tests was reported in an investigation with elderly bipolar patients (Haller et al., 2011). However, studies including adolescents or geriatric acute BD patients are of limited power to compare with middle-aged euthymic bipolar patients, as confounds like neurodevelopmental or neurodegenerative influences may affect the results. Recent studies in other psychiatric disorders, including dementia, alcoholism and late-life depression, indicate that performance

in certain cognitive domains, e.g., speed of processing and executive functioning, can be linked to WM density in frontal–subcortical networks (Alexopoulos et al., 2002; Chanraud et al., 2009; Malloy et al., 2007; Murphy et al., 2007; Perez-Iglesias et al., 2010; Turken et al., 2008; Yuan et al., 2007). Moreover, reduced fiber integrity in the corpus callosum has been related to cognitive impairment in Alzheimer's disease (Thillainadesan et al., 2012). The finding that episodic memory performance was significantly associated with DTI parameters in the control group, whereas problem solving performance was related with DTI parameters in the patient group is somewhat surprising. We may suggest that the correlation between DTI parameters and memory performance in the control group reflect there a high

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Table 2 Significant group effect of ROI analysis (threshold: p r 0.05) and for white matter (WM) volume differences (threshold: pr 0.001) between BD patients (n¼ 30) and controls (n¼32). FA ¼ fractional anisotropy, MD¼ mean diffusivity, WM¼ white matter. PAT¼ BD patients, CON¼ controls. All values combined for both hemispheres without anterior thalamic radiation scores. For the DTI scores, we used the permuted p-values, which included the threshold-free cluster enhancement (tfce) provided by FSL. Age was included as covariate. ROI mask

Tal. Koord. (x, y, z)

Fornix

1,  21, 9

Ant. Thalamic radiation

Left:  14, 3, 7 Right: 14, 6, 6

Cluster-size (voxel) 6163

11457 10253

Parameter

CON DTI beta scores (SD)

PAT DTI beta scores (SD)

F

FA MD DR DA GM FA

0.27 (0.03) 1.44 (0.18) 1.27 (0.19) 1.78 (0.17) 0.56 (0.21) 0.37 (0.01) 0.37 (0.01) 0.75 (0.05) 0.76 (0.05) 0.61 (0.05) 0.61 (0.05) 1.04 (0.05) 1.04 (0.05) 0.55 (0.13) 0.61 (0.09) 0.57 (0.02) 0.77 (0.03) 0.51 (0.01) 1.30 (0.04) 0.34 (0.17) 0.53 (0.02) 1.03 (1.04) 0.76 (0.04) 1.56 (0.05) 0.65 (0.34) 0.47 (0.02) 0.70 (0.02) 0.50 (0.03) 1.09 (0.03) 0.64 (0.25)

0.25 (0.04) 1.54 (0.20) 1.38 (2.14) 1.87 (0.17) 0.44 (0.18) 0.35 (0.02) 0.35 (0.03) 0.80 (0.09) 0.81 (0.10) 0.66 (0.09) 0.67 (0.10) 1.08 (0.08) 1.08 (0.09) 0.50 (0.08) 0.64 (0.14) 0.56 (0.03) 0.78 (0.03) 0.52 (0.04) 1.31 (0.04) 0.21 (0.22) 0.52 (0.02) 1.04 (0.05) 0.78 (0.05) 1.56 (0.07) 0.60 (0.22) 0.45 (0.04) 0.72 (0.03) 0.52 (0.04) 1.09 (0.04) 0.51 (0.21)

4.122 4.513 4.414 4.615 3.23 3.979 4.826 7.008 6.336 7.285 6.683 6.079 5.164 2.02 2.13 3.827 2.079 3.214 0.024 3.23 4.160 0.869 2.020 0.003 2.18 6.637 2.266 5.231 0.438 4.45

MD DR DA GM Callosal Body

Splenium Corpus Callosum

Truncus Corpus Callosum

1,  7, 17

2,  40, 15

17, 6, 30

35240

12658

6426

FA MD DR DA GM FA MD DR DA GM FA MD DR DA GM

level of interindividual variability in those fiber tracts connected with the individual memory performance of the control group. In contrast, the association between altered DTI parameters and executive functioning in the patient group may indicate underlying pathology of the disease. Direct relations between diffusion parameters of certain brain areas known to be involved in cognitive functioning and the neurocognitive performance are of interest for current pathophysiological models of the disease (Oishi et al., 2011). The association between white matter integrity and cognitive functioning in bipolar disorder has to be validated through further studies, but the results indicate that specific cognitive domains are associated with patients' diffusion parameters. Comparable results of altered fractional anisotropy (lower) and radial diffusivity (higher) of BD patients mainly in subparts of the corpus callosum, splenium and truncus (right-sided) have been previously reported (Atmaca et al., 2007; Barnea-Goraly et al., 2009; Brambilla et al., 2003; Bruno et al., 2008; Chaddock et al., 2009; Ha et al., 2011; Haller et al., 2010; Pavuluri et al., 2009; Sussmann et al., 2009; Torgerson et al., 2012; Wang et al., 2012). The corpus callosum has also been found to have reduced size (Atmaca et al., 2007; Brambilla et al., 2003) in BD which conforms to our findings of reduced VBM scores underneath the callosal body and the truncus of the corpus callosum. The fornix is a projection tract which is located underneath the corpus callosum and connects the hippocampus with the mamillary body (and further cortical and subcortical structures) (Förstl and Hautzinger, 2006). Both structures are part of the limbic system and known to be involved in memory processes (Bähr and Frotscher, 2009; Ulfig, 2008). We showed significant higher MD, DR and DA scores as well as lower FA scores in the patient

CON4 PAT (p)

PAT 4CON (p)

0.04n 0.03n 0.04n 0.03n o 0.01n 0.051 0.032n 0.010n 0.015n 0.009n 0.012n 0.017n 0.027n 40.05 40.05 0.05 0.155 0.078 0.878 o 0.05n 0.04n 0.35 0.160 0.953 40.05 0.01n 0.138 0.026n 0.512 o 0.01n

group. (Barnea-Goraly et al., 2009) showed lower fiber integrity within the fornix in a group of adolescent bipolar patients which may point to a very early involvement of the fornix in the course of the disease. The lack of significant differences in the other existing DTI studies may be caused by methodological issues, e.g., a small number of participants (Heng et al., 2010) and generally smaller effects in studies investigating remitted bipolar patients (Schneider et al., 2012). Barnea-Goraly et al. (2009) suggested that the lack of previous reports of fornix alterations in BD may be due to its anatomic characteristics, as the fornix is a very thin structure, challenging the spatial resolution of current MRI methods. However, in this study, we selected a more exploratory form of analysis, using permutation analysis to correct for multiple comparisons. Although we did not use more conservative approaches provided by FSL (e.g., FWE correction), so that we cannot completely eliminate the point that this may be false positives. Considering the different methodological approaches, it is difficult to directly compare our results to previous studies. In general, taking into account its connections with the hippocampus, reduced fiber integrity in the fornix may have implications for memory function deficits and emotional dysregulation. This conforms to current pathophysiological models of BD, indicating abnormalities in frontal–limbic networks (Strakowski et al., 2012). Another affected WM structure in BD patients in our study was the anterior thalamic radiation (lower FA left-sided, higher MD, DR, DA bilaterally), which is a central node for cognitive (attention), motor and sensory information processing (Oishi et al., 2011). The anterior parts of the thalamic radiation are connected with the hippocampus through the fornix (Birbaumer and Schmidt, 2006). Alterations in the fiber integrity of the anterior thalamic

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229

Fig. 2. ROI VBM analysis (volumes of interest). Left-sided: ROI masks from the WFU PickAtlas toolbox in SPM8 (Maldjian et al., 2003). We show only those volumes which were extracted from the exploratory FA whole-brain group contrast (p o 0.001, uncorrected). Right-sided: significant group contrast (CON4PAT) regarding the ROIs (marked in yellow). ATR ¼ anterior thalamic radiation. Lower row: beta scores across groups for the ROIs, indicating significant group effects. Colour code: black: CON, white: PAT. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

radiation in BD have been reported previously (Chan et al., 2010; Ha et al., 2011; Heng et al., 2010; Lin et al., 2011; Mahon et al., 2009), in particular in the right hemisphere (Chan et al., 2010; Cui et al., 2011; Ha et al., 2011; Vederine et al., 2011). In accordance with fMRI and structural findings (Strakowski et al., 2012), alterations of the connections between the thalamus and limbic areas may be relevant to cognitive processing and clinical symptoms

observed in BD (Townsend and Altshuler, 2012). The anterior thalamic radiation is also one of the classical targets of stereotactic surgery for affective disorders (anterior capsulotomy) (Greenberg et al., 2010). Previous investigations have hypothesized that microstructural changes in the WM of frontal–subcortical circuits lead to a disconnection syndrome between frontal and subcortical regions.

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Table 3 Bivariate correlation analysis between individual psychopathology (BDI II, BRMAS, SCL 90, PANAS), cognitive scores (TMT, MWT, TL) and episodic memory performance (verbal and non-verbal episodic memory) and significant areas in the group contrast of ROI DTI analysis. The p-values are corrected for multiple comparisons using the Bonferroni correction. All tests which showed no significant differences across the correlation analyses were excluded from the table due to space limitation. CVLT DW ¼ CVLT —discriminality. TL p¼ number of solved problems of the Tower of London, PANAS NA ¼ PANAS negative subscale. Significant threshold (Bonferroni correction): p o 0.015. ROI mask

Parameter

Fornix

FA MD DR DA FA MD

PANAS NA: r ¼  0.46, p o 0.001 left: CVLT DW: r ¼  0.47, p o0.001

DR

left: CVLT DW: r ¼  0.46, p ¼ 0.007

Ant.thal. radiation

Callosal Body

Splenium CC

Truncus CC

DA FA MD DR DA FA MD DR DA FA MD DR DA

CON

PAT Age: r ¼  0.49, p ¼ 0.006 TL p: r ¼0.47, p ¼0.009 TL p: r ¼0.47, p ¼0.009

right: Age: r¼ 0.47, p ¼ 0.009 TL p: r ¼0.48, p¼ 0.008 right: Age: r¼ 0.47, p ¼ 0.008 TL p: r ¼0.48, p¼ 0.007 right: TL p: r¼ 0.47, p ¼ 0.008

BDI II: r ¼  0.52, p o 0.001

CVLT DW: r ¼ 0.48, p ¼0.005

Schneider et al. (2012) reviewed recent articles of DTI findings in young BD patients and suggested that WM alterations in the corpus callosum, the cingulate–paracingulate white matter, prefrontal regions, fornix and superior longitudinal fasciculus are related to emotional regulation. Moreover, white matter abnormalities of frontal–subcortical circuits in affective disorders might be related to a network dysfunction between frontal and subcortical regions (O'Dwyer et al., 2011) as also named “disconnection syndrome” (Pierpaoli and Basser, 1996; Sexton et al., 2009; Smith et al., 2002). These network alterations have been associated with clinical symptoms in BD (Schneider et al., 2012). 4.1. Association with clinical parameters None of the assessed clinical parameters (BDI II [depressive symptoms], BRMAS [manic symptoms], years of illness, number of episodes) was significantly related to the ROI DTI parameters in the patient group. This conforms to findings of other studies, showing relatively consistent findings independent of acute symptoms (Cui et al., 2011) (Chaddock et al., 2009; Haller et al., 2010; Wessa et al., 2009; Yurgelun-Todd et al., 2007; Regenold et al., 2005). Only a minority of studies reported a relation between depressive symptoms and DTI scores in regional WM regions (e.g., anterior thalamic radiation) (Sussmann et al., 2009; Versace et al., 2008; Zanetti et al., 2009). While a number studies suggest that WM alterations may largely represent trait rather than state markers of BD, there is some evidence that changes of microstructural WM integrity may recover with clinical remission. Being limited to only remitted BD patients, our study has not revealed any correlations between sublicinical symptom scores and DTI parameters. 4.2. Limitations Some limitations of the current study need to be addressed. One of them is the lack of specificity of TBSS results, as pointed by previous studies (Bae et al., 2006); not every tract within a ROI belongs to a particular circuit and some results may be confounded by crossing fibers (Wiegell et al., 2003; Bae et al., 2006).

Therefore, explorations based on DTI probabilistic tractography would be more suitable for the identification of WM pathways associated with BD pathology. However, the use of four different DTI indices has offered a more detailed investigation of WM integrity. It has been suggested that FA should not be interpreted alone, because this index lacks sensitivity when diffusion changes proportionally along all three eigenvectors (Acosta-Cabronero et al., 2010; O'Dwyer et al., 2011). Another advantage of the current study is the use of a combined approach which has included voxel-based DTI, ROI DTI and VBM of white matter volumes. A combined approach has been done in few previous studies (Chen et al., 2012; Haller et al., 2010) and may also support the main DTI findings with additional approaches to assess faulty brain anatomy in psychiatric diseases. In addition, the high number of encoding directions of our DTI sequence is of benefit: there is evidence that a higher number of directions may enhance the accuracy of results (Mahon et al., 2009), even though it has not been used systematically in DTI investigations with BD subjects. A direct comparison between the existing DTI studies in BD disorders is difficult, as the number of participants per group (between n ¼9 (Adler et al., 2004) and n¼ 42 (Sussmann et al., 2009) BD patients), the illness state (acute manic, acute depressive, remitted), the correlation analyses (with age, years of illness, symptomatology) and the approach used (voxel-based, ROI) are not concordant across studies. We investigated a relatively high number of patients (n ¼30) in a remitted state of illness. A larger sample size than the majority of the previous studies and the lack of acute mood symptoms, as present in our study, may have led to robust findings, supporting the validity of the study. The interpretation of results may be limited caused by the lack of follow up of results. Hence, conclusions on the effect of age in bipolar disorders in the current study are restricted by the crosssectional nature of the study. Longitudinal study designs should be used to investigate possible developmental changes in white matter integrity. Although we cannot draw any firm conclusions about the neurodegenerative, long-term nature of the observed anatomical changes, the significant associations between age and DTI scores in the fornix (FA) and the right anterior thalamic

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radiation (MD, DR) in the patient group only speaks for age- and illness-related decreases of all DTI parameters. Overall, current knowledge points towards the idea that developmental alterations of key neural structures are associated with abnormal connectivity between certain brain regions in bipolar disorders (Schneider et al., 2012). Another methodological limitation was the lack of drug-naive patients. All patients have been treated with psychiatric medication along the time of measurement. Nonetheless, the potential influence of psychiatric medication on the structural brain changes has been tested in our study and failed to show any association with DTI parameters, particularly in relation to medication status or duration of medication use. This result conforms to previous reports of no influence or a positive effect (reduced group differences between patients and controls) of psychopharmacological treatment on structural and functional findings in BD patients (Hafeman et al., 2012).

5. Conclusions The current study provides new insights into quantitative associations between aberrant WM fiber integrity and cognitive deficits in patients with bipolar disorders. The altered fiber tracts in the corpus callosum, the fornix and the thalamus as connections of the hippocampus and the limbic system, together with connecting white matter alterations, may be associated with cognitive and clinical representation of BD disorders (Strakowski et al., 2012). For future studies, assessing bipolar patients in distinct illness states (manic, depressive, remitted) may offer further insights into the understanding of WM abnormalities in BD and how disrupted connectivity may lead to cognitive and clinical symptoms in this disease. Finally, future studies are necessary to clarify the role of WM changes as a biological indicator of BD, particularly if they represent a developmental or trait marker of the disease. Role of funding source There were no funding for this manuscript.

Conflict of interest The authors report no conflict of interest.

Acknowledgements MRI was performed at the Frankfurt Brain Imaging Centre, supported by the German Research Council (DFG) and the German Ministry for Education and Research (BMBF; Brain Imaging Center Frankfurt/Main, DLR 01GO0203). Viola Oertel-Knöchel was supported by the “Adolf Messer Preis” from the “Freunde der Universität”, Frankfurt, Germany. The authors report no conflict of interest.

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