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Reduced fractional anisotropy in bipolar disorder v. major depressive disorder independent of current symptoms

Published online by Cambridge University Press:  14 July 2022

Katharina Thiel ,
Susanne Meinert ,
Alexandra Winter ,
Hannah Lemke ,
Lena Waltemate ,
Fabian Breuer ,
Marius Gruber ,
Ramona Leenings ,
Lucia Wüste  and
Kathrin Rüb
...Show all authors
Katharina Thiel
Affiliation:
Institute for Translational Psychiatry, University of Münster, Münster, Germany
Susanne Meinert
Affiliation:
Institute for Translational Psychiatry, University of Münster, Münster, Germany Institute of Translational Neuroscience, University of Münster, Münster, Germany
Alexandra Winter
Affiliation:
Institute for Translational Psychiatry, University of Münster, Münster, Germany
Hannah Lemke
Affiliation:
Institute for Translational Psychiatry, University of Münster, Münster, Germany
Lena Waltemate
Affiliation:
Institute for Translational Psychiatry, University of Münster, Münster, Germany
Fabian Breuer
Affiliation:
Institute for Translational Psychiatry, University of Münster, Münster, Germany
Marius Gruber
Affiliation:
Institute for Translational Psychiatry, University of Münster, Münster, Germany
Ramona Leenings
Affiliation:
Institute for Translational Psychiatry, University of Münster, Münster, Germany
Lucia Wüste
Affiliation:
Institute for Translational Psychiatry, University of Münster, Münster, Germany
Kathrin Rüb
Affiliation:
Institute for Translational Psychiatry, University of Münster, Münster, Germany
Julia-Katharina Pfarr
Affiliation:
Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
Frederike Stein
Affiliation:
Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
Katharina Brosch
Affiliation:
Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
Tina Meller
Affiliation:
Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
Kai Gustav Ringwald
Affiliation:
Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
Igor Nenadić
Affiliation:
Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
Axel Krug
Affiliation:
Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany Department of Psychiatry and Psychotherapy, University Hospital Bonn, Bonn, Germany
Jonathan Repple
Affiliation:
Institute for Translational Psychiatry, University of Münster, Münster, Germany
Nils Opel
Affiliation:
Institute for Translational Psychiatry, University of Münster, Münster, Germany
Katharina Koch
Affiliation:
Institute for Translational Psychiatry, University of Münster, Münster, Germany
Elisabeth J. Leehr
Affiliation:
Institute for Translational Psychiatry, University of Münster, Münster, Germany
Jochen Bauer
Affiliation:
Department of Clinical Radiology, University of Muenster, Muenster, Germany
Dominik Grotegerd
Affiliation:
Institute for Translational Psychiatry, University of Münster, Münster, Germany
Tim Hahn
Affiliation:
Institute for Translational Psychiatry, University of Münster, Münster, Germany
Tilo Kircher
Affiliation:
Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
Udo Dannlowski*
Affiliation:
Institute for Translational Psychiatry, University of Münster, Münster, Germany
*
Author for correspondence: Udo Dannlowski, E-mail: dannlow@uni-muenster.de
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Abstract

Background

Patients with bipolar disorder (BD) show reduced fractional anisotropy (FA) compared to patients with major depressive disorder (MDD). Little is known about whether these differences are mood state-independent or influenced by acute symptom severity. Therefore, the aim of this study was (1) to replicate abnormalities in white matter microstructure in BD v. MDD and (2) to investigate whether these vary across depressed, euthymic, and manic mood.

Methods

In this cross-sectional diffusion tensor imaging study, n = 136 patients with BD were compared to age- and sex-matched MDD patients and healthy controls (HC) (n = 136 each). Differences in FA were investigated using tract-based spatial statistics. Using interaction models, the influence of acute symptom severity and mood state on the differences between patient groups were tested.

Results

Analyses revealed a main effect of diagnosis on FA across all three groups (ptfce-FWE = 0.003). BD patients showed reduced FA compared to both MDD (ptfce-FWE = 0.005) and HC (ptfce-FWE < 0.001) in large bilateral clusters. These consisted of several white matter tracts previously described in the literature, including commissural, association, and projection tracts. There were no significant interaction effects between diagnosis and symptom severity or mood state (all ptfce-FWE > 0.704).

Conclusions

Results indicated that the difference between BD and MDD was independent of depressive and manic symptom severity and mood state. Disruptions in white matter microstructure in BD might be a trait effect of the disorder. The potential of FA values to be used as a biomarker to differentiate BD from MDD should be further addressed in future studies using longitudinal designs.

Keywords

Bipolar disorderdiffusion tensor imagingmajor depressive disordermood statetraitwhite matter microstructure
Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 10 , July 2023 , pp. 4592 - 4602
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Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

Introduction

Bipolar disorder (BD) can be distinguished from major depressive disorder (MDD) by the presence of manic or hypomanic episodes. However, they are very similar in depressive psychopathology, as the same diagnostic criteria for depressive episodes apply to both disorders. In addition, the majority of BD patients present clinically primarily with depressive symptoms without reporting previous (hypo)manic episodes. This results in up to 70% of BD patients being initially misdiagnosed as MDD and an average of 5–10 years passing before the correct diagnosis is made (Berk et al., Reference Berk, Dodd, Callaly, Berk, Fitzgerald, de Castella and Kulkarni2007; Grande, Berk, Birmaher, & Vieta, Reference Grande, Berk, Birmaher and Vieta2016; Hirschfeld, Lewis, & Vornik, Reference Hirschfeld, Lewis and Vornik2003). This may cause a delay in the selection of effective psychotherapeutic and pharmacological interventions, resulting in poor disease course and prognosis as well as higher healthcare costs (De Almeida & Phillips, Reference De Almeida and Phillips2013; Hirschfeld et al., Reference Hirschfeld, Lewis and Vornik2003; Phillips & Kupfer, Reference Phillips and Kupfer2013). Therefore, identifying neurobiological markers of BD that aid in differential diagnosis between BD and MDD is of high clinical and scientific significance and has been subject to recent neuroimaging research (Han, De Berardis, Fornaro, & Kim, Reference Han, De Berardis, Fornaro and Kim2019; Phillips & Swartz, Reference Phillips and Swartz2014; Versace et al., Reference Versace, Almeida, Quevedo, Thompson, Terwilliger, Hassel and Phillips2010). One promising research subject is the microstructure of white matter (WM) (De Almeida & Phillips, Reference De Almeida and Phillips2013; Phillips & Kupfer, Reference Phillips and Kupfer2013). It can be studied by the magnetic resonance imaging (MRI) derived technique of diffusion tensor imaging (DTI), which quantifies water diffusion and the degree of its directionality in neuronal tissue. The most commonly examined measure in DTI studies is fractional anisotropy (FA), which is interpreted as a measure of WM integrity, myelination, coherence, and density of fiber bundling (Jones, Knösche, & Turner, Reference Jones, Knösche and Turner2013; Soares, Marques, Alves, & Sousa, Reference Soares, Marques, Alves and Sousa2013).

Alterations in WM microstructure have been shown to be present in both BD and MDD when compared to healthy controls (HC). Findings point toward a reduction of FA in both disorders, mainly in frontal and temporal WM tracts connecting the prefrontal cortex and anterior limbic structures (Chen et al., Reference Chen, Hu, Li, Huang, Lui, Kuang and Gong2016; Duarte, De Araújo e Silva, Goldani, Massuda, & Gama, Reference Duarte, De Araújo e Silva, Goldani, Massuda and Gama2016; Phillips & Swartz, Reference Phillips and Swartz2014; van Velzen et al., Reference van Velzen, Kelly, Isaev, Aleman, Aftanas, Bauer and Schmaal2020). A recent meta-analysis on WM abnormalities in BD and MDD patients compared the effect sizes between these disorders and found a greater reduction in FA in BD than in MDD compared to HC in the left posterior cingulum (Wise et al., Reference Wise, Radua, Nortje, Cleare, Young and Arnone2016). However, included studies contrasted both patient groups separately with HC, so this meta-analysis only compares these disorders indirectly. To date, DTI studies directly comparing MDD and BD have been rare, although they are crucial to identify changes in WM microstructure that clearly distinguish both disorders (De Almeida & Phillips, Reference De Almeida and Phillips2013; Han et al., Reference Han, De Berardis, Fornaro and Kim2019; Phillips & Swartz, Reference Phillips and Swartz2014). Consistent with the aforementioned meta-analysis (Wise et al., Reference Wise, Radua, Nortje, Cleare, Young and Arnone2016), most existing findings suggest that BD patients show reduced FA in WM compared to MDD patients. Affected tracts include the body and genu of the corpus callosum (Masuda et al., Reference Masuda, Okada, Takamura, Shibasaki, Yoshino, Yokoyama and Okamoto2020; Matsuoka et al., Reference Matsuoka, Yasuno, Kishimoto, Yamamoto, Kiuchi, Kosaka and Kudo2017; Repple et al., Reference Repple, Meinert, Grotegerd, Kugel, Redlich, Dohm and Dannlowski2017), cingulum bundles (Benedetti et al., Reference Benedetti, Absinta, Rocca, Radaelli, Poletti, Bernasconi and Filippi2011a; Repple et al., Reference Repple, Meinert, Grotegerd, Kugel, Redlich, Dohm and Dannlowski2017), uncinate fasciculi (Benedetti et al., Reference Benedetti, Absinta, Rocca, Radaelli, Poletti, Bernasconi and Filippi2011a; Deng et al., Reference Deng, Wang, Huang, Niu, Zhong, Zhao and Huang2018), corticospinal tracts (Metin, Altuglu, Metin, & Tarhan, Reference Metin, Altuglu, Metin and Tarhan2020; Repple et al., Reference Repple, Meinert, Grotegerd, Kugel, Redlich, Dohm and Dannlowski2017), and superior longitudinal fasciculi (Repple et al., Reference Repple, Meinert, Grotegerd, Kugel, Redlich, Dohm and Dannlowski2017; Versace et al., Reference Versace, Almeida, Quevedo, Thompson, Terwilliger, Hassel and Phillips2010), which are thought to form parts of the fronto-temporal system involved in the processing and regulation of emotion (Manelis et al., Reference Manelis, Soehner, Halchenko, Satz, Ragozzino, Lucero and Versace2021).

A number of researchers point out the importance of considering different mood states when comparing the two disorders on a neurobiological level to identify differences in neural structures that persist across all disease phases (Dvorak et al., Reference Dvorak, Hilke, Trettin, Wenzler, Hagen, Ghirmai and Oertel2019; Han et al., Reference Han, De Berardis, Fornaro and Kim2019; Phillips, Reference Phillips2019; Sacchet, Livermore, Iglesias, Glover, & Gotlib, Reference Sacchet, Livermore, Iglesias, Glover and Gotlib2015). However, the majority of studies limited their sample to patients of one mood state, with most of these studies examining currently depressed patients (Deng et al., Reference Deng, Wang, Huang, Niu, Zhong, Zhao and Huang2018; Lan et al., Reference Lan, Rubin-Falcone, Sublette, Oquendo, Stewart, Hellerstein and Mann2020; Repple et al., Reference Repple, Meinert, Grotegerd, Kugel, Redlich, Dohm and Dannlowski2017; Vai et al., Reference Vai, Parenti, Bollettini, Cara, Verga, Melloni and Benedetti2020; Versace et al., Reference Versace, Almeida, Quevedo, Thompson, Terwilliger, Hassel and Phillips2010) and only one looking at euthymic patients (Masuda et al., Reference Masuda, Okada, Takamura, Shibasaki, Yoshino, Yokoyama and Okamoto2020). Inclusion of euthymic as well as currently (hypo)manic patients is essential to determine whether these are transient differences associated with acute symptoms or stable, mood state-independent changes that can be regarded as trait differences and may help to distinguish both disorders (Benedetti et al., Reference Benedetti, Yeh, Bellani, Radaelli, Nicoletti, Poletti and Brambilla2011b; Dvorak et al., Reference Dvorak, Hilke, Trettin, Wenzler, Hagen, Ghirmai and Oertel2019; Masuda et al., Reference Masuda, Okada, Takamura, Shibasaki, Yoshino, Yokoyama and Okamoto2020; Phillips, Reference Phillips2019). Two DTI studies that directly compared BD patients with different mood states to HC suggest that WM microstructure varies with mood state, with currently depressed patients showing widely distributed reductions in FA when compared to HC that are less widespread or even absent in the manic and euthymic states (Magioncalda et al., Reference Magioncalda, Martino, Conio, Piaggio, Teodorescu, Escelsior and Amore2016; Zanetti et al., Reference Zanetti, Jackowski, Versace, Almeida, Hassel, Duran and Phillips2009). In contrast, Cui et al. (Reference Cui, Dong, Yang, Yu, Li, Liu and Jiang2020) reported no significant differences in FA between manic and depressed BD patients. To the best of our knowledge, only one DTI study examined differences in WM microstructure between BD and MDD while accounting for current mood (Matsuoka et al., Reference Matsuoka, Yasuno, Kishimoto, Yamamoto, Kiuchi, Kosaka and Kudo2017). In a sample including both currently depressed and euthymic – but no (hypo)manic – patients, this study found reduced FA in the anterior part of the corpus callosum in BD compared to MDD patients. Results remained significant after including the severity of depressive symptoms as a covariate (Matsuoka et al., Reference Matsuoka, Yasuno, Kishimoto, Yamamoto, Kiuchi, Kosaka and Kudo2017).

Thus, this study aimed to investigate differences in WM microstructure, particularly in FA, between BD and MDD patients and HC in a relatively large sample including patients in euthymic, depressed, and (hypo)manic mood state, and to explore whether these differences vary as a function of current mood. First, we expect alterations in WM microstructure, particularly a reduction of FA, in both BD and MDD patients compared to HC in fronto-temporal WM tracts (hypothesis 1). Second, we expect even more severe impairments of WM microstructure in these tracts in BD as compared to MDD patients (hypothesis 2). Given the paucity of findings, we have no specific hypothesis regarding the role of patients' current mood. We explore whether differences between BD and MDD patients vary as a function of their current mood, which we capture in two ways: First, using a dimensional approach, via current depressive and manic symptom severity, and second, using a categorical classification of euthymic, depressed, and manic mood states (hypothesis 3).

Materials and methods

Participants

The present study comprised data from the FOR2107-cohort which were collected at two scanning sites – the University of Marburg and the University of Münster [see earlier work for the general description of the study (Kircher et al., Reference Kircher, Wöhr, Nenadic, Schwarting, Schratt, Alferink and Dannlowski2019) and the MRI quality assurance protocol (Vogelbacher et al., Reference Vogelbacher, Möbius, Sommer, Schuster, Dannlowski, Kircher and Bopp2018)].

The FOR2107 study was approved by the Ethics Committees of the Medical Faculties, University of Marburg (AZ: 07/14) and University of Münster (2014-422-b-S). The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. All subjects provided written informed consent prior to examination and received financial compensation for participation. Participants were recruited in psychiatric hospitals or via newspaper advertisements.

See online Supplementary material 1 for detailed information on inclusion and exclusion criteria and the selection of the sample. In total, 408 participants were included in the study. N = 136 individuals with BD (n = 75 female, Mage = 41.17, s.d.age = 11.98, n = 73 BD type I, n = 63 BD type II) were selected. The same number of individuals with MDD as well as HC were matched to these subjects regarding age, sex, and site (MDD: n = 79 female, Mage = 41.59, s.d.age = 12.43, HC: n = 77 female, Mage = 42.26, s.d.age = 12.92), using the MatchIt package in R (2020, Version 4.0.1) (Ho, Imai, King, & Stuart, Reference Ho, Imai, King and Stuart2011). Table 1 provides detailed information on sociodemographic and clinical characteristics of the sample, Table 2 shows the latter separately for the euthymic, depressed, and (hypo)manic states. All participants underwent the Structured Clinical Interview for DSM-IV (SCID-I; Wittchen, Wunderlich, Gruschwitz, & Zaudig, Reference Wittchen, Wunderlich, Gruschwitz and Zaudig1997) to verify lifetime psychiatric diagnoses or the lack thereof. Remission status and current affective state were determined based on DSM-IV criteria (online Supplementary Table S2). Any lifetime psychiatric disorder according to the SCID-I as well as any intake of psychotropic medication resulted in exclusion from the study for HC. The 21-item Hamilton Depression Rating Scale (HDRS) (Hamilton, Reference Hamilton1960) and the Young Mania Rating Scale (YMRS) (Young, Biggs, Ziegler, & Meyer, Reference Young, Biggs, Ziegler and Meyer1978) were employed to assess the presence and severity of current depressive and manic symptoms, respectively. The Global Assessment of Functioning was used to rate the participants' overall functioning (Saß & Wittchen, Reference Saß and Wittchen2003). By using an established strategy as described earlier (Hassel et al., Reference Hassel, Almeida, Kerr, Nau, Ladouceur, Fissell and Phillips2008; Redlich et al., Reference Redlich, Almeida, Grotegerd, Opel, Kugel, Heindel and Dannlowski2014), we calculated the Medication Load Index (MedIndex) for each patient, a composite measure of individual total medication load reflecting the number and daily dose of all psychopharmacological medication (online Supplementary material 2).

Table 1. Demographic and clinical characteristics of the study sample

BD, bipolar disorder; GAF, Global Assessment of Functioning; HC, healthy controls; HDRS, Hamilton Depression Rating Scale; MDD, major depressive disorder; s.d., standard deviation; YMRS, Young Mania Rating Scale; n, number; n/a, not applicable.

Note: 1Two-sample t test assuming equal variance, 2Pearson χ2 test, 3two-sample t test assuming unequal variance, 4one-way analysis of variance (ANOVA) F-test, 5not all participants provided the necessary information, N min = 112 BD, N min = 108 MDD, see Table 2 for detailed information, 6see online Supplementary Table S1 for detailed information on medication intake.

Table 2. Clinical characteristics of patient subgroups

BD, bipolar disorder; GAF, Global Assessment of Functioning; HC, healthy controls; HDRS, Hamilton Depression Rating Scale; MDD, major depressive disorder; s.d., standard deviation; YMRS, Young Mania Rating Scale; n, number; n/a, not applicable.

Note: All patients being allocated to a depressed group (BD or MDD) fulfilled the criteria of an acute or partially remitted episode, the same holds for the manic patients who fulfilled the criteria for a (hypo-)manic episode, according to a SCID-Interview. Due to missing information not all BD patients could be assigned to a subgroup. 1Significant differences in post-hoc t tests: ab = BD euthymic v. BD depressed, ac = BD euthymic v. BD (hypo-)manic, ad = BD euthymic v. MDD euthymic, ae = BD euthymic v. MDD depressed, bc = BD depressed v. BD (hypo-)manic, bd = BD depressed v. MDD euthymic, be = BD depressed v. MDD depressed, cd = BD (hypo-)manic v. MDD euthymic, ce = BD (hypo-)manic v. MDD depressed, de = MDD euthymic v. MDD depressed. 2One-way analysis of variance (ANOVA) F-test. 3Pearson χ2 test.

DTI data acquisition

The DTI protocol and quality assurance protocol have already been described in detail elsewhere (Meinert et al., Reference Meinert, Repple, Nenadic, Krug, Jansen, Grotegerd and Dannlowski2019; Vogelbacher et al., Reference Vogelbacher, Möbius, Sommer, Schuster, Dannlowski, Kircher and Bopp2018). Data were acquired using 3T whole body MRI scanners. Detailed acquisition parameters are provided in online Supplementary material 3. For quality assurance, DTIPrep (Oguz et al., Reference Oguz, Farzinfar, Matsui, Budin, Liu, Gerig and Styner2014) was used. Individual images of a given participant were eliminated if affected by artifacts. The included participants had 64.20 images on average (s.d. = 1.32, range: 56–65).

Image processing

Preprocessing and analyses were implemented in FSL6.0.1 (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/) (Jenkinson, Beckmann, Behrens, Woolrich, & Smith, Reference Jenkinson, Beckmann, Behrens, Woolrich and Smith2012; Smith et al., Reference Smith, Jenkinson, Woolrich, Beckmann, Behrens, Johansen-Berg and Matthews2004; Woolrich et al., Reference Woolrich, Jbabdi, Patenaude, Chappell, Makni, Behrens and Smith2009). The DW images were corrected for motion and eddy current artifacts using ‘eddy’ from FSL (Andersson & Sotiropoulos, Reference Andersson and Sotiropoulos2016), and b-vectors were rotated following eddy current correction. After automated skull stripping using the Brain Extraction Tool (BET) in FSL (Smith, Reference Smith2002), the first b0 image was used as reference for alignment. Diffusion tensor was estimated using ‘DTIFIT’ within FMRIB's Diffusion Toolbox (FDT) (Behrens et al., Reference Behrens, Woolrich, Jenkinson, Johansen-Berg, Nunes, Clare and Smith2003) followed by the generation of tensor-derived maps. FA, mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD) were estimated for each voxel per participant (online Supplementary material 4).

Analysis

IBM SPSS Statistics 27 (SPSS Inc., Chicago, IL, USA) was used for analyses of sociodemographic data. For DTI analyses, tract-based spatial statistics (TBSS) (Smith et al., Reference Smith, Jenkinson, Johansen-Berg, Rueckert, Nichols, Mackay and Behrens2006) were used to reduce registration misalignments and partial volume effects. Registration was performed using FMRIB's non-linear image registration tool and reslicing all FA images to the FMRIB58_FA template [1 × 1 × 1 mm3 Montreal Neurological Institute (MNI) standard space]. A WM skeleton was created using a threshold of 0.2 for the mean of all aligned FA images and overlaid onto each participant's registered FA image. By searching orthogonally from the skeleton for maximum FA values, we moved individual FA values onto the mean skeleton mask. To test for statistical significance, we used the nonparametric permutation testing implemented in ‘randomise’ from FSL (Winkler, Ridgway, Webster, Smith, & Nichols, Reference Winkler, Ridgway, Webster, Smith and Nichols2014) with 5000 permutations. Threshold-Free Cluster Enhancement (TFCE) with default values provided by –T2 option optimized for TBSS was used to correct for multiple comparisons. The 95th percentile of the null distribution of permutated input data of the maximum TFCE scores was used for determination of significance, correcting estimated cluster sizes for the family-wise error (FWE) at p < 0.05 (Smith & Nichols, Reference Smith and Nichols2009). For figures, mean FA of the significant clusters were extracted using ‘fslstats’ in FSL. The total intracranial volume (TIV) was extracted from T1 images using the Computational Anatomy Toolbox (CAT12, http://www.neuro.uni-jena.de/cat, v1720). Results focus on FA, as most studies report findings on this DTI measure. However, as the consideration of other DTI metrics can support the interpretation of the results, the same registration steps and analyses were performed on MD, RD, and AD as well. The results derived from these three metrics are summarized in online Supplementary material 5.

For correction of scanner differences between the two MRI scanners and due to a body-coil change at the Marburg site during data acquisition two dummy coded variables (Marburg pre body-coil change: yes/no; Marburg post body-coil change: yes/no) with Münster as reference category were calculated (Vogelbacher et al., Reference Vogelbacher, Möbius, Sommer, Schuster, Dannlowski, Kircher and Bopp2018). These variables as well as age, sex, and TIV were included as nuisance variables in all analyses. Overall three analyses were conducted.

  1. (1) In a first step, to investigate diagnosis-specific differences in directional diffusion (hypotheses 1 and 2), a one-factorial Analysis of Covariance (ANCOVA) with FA as dependent variable and diagnosis (BD v. MDD v. HC) as independent variable was conducted. In case of significant effects, pairwise post hoc t-contrasts were performed between the three groups (analysis 1).

The following analyses, from which HC were excluded, aimed to further examine the differences in FA between BD and MDD patients in relation to their current mood (hypothesis 3).

  1. (2) To examine whether differences between BD and MDD patients were dependent on current symptom severity, the effect of diagnosis (BD v. MDD) on FA was reanalyzed together with the effect of depressive (HDRS) or manic (YMRS) symptom severity in two separate two-factorial ANCOVA models (analyses 2a, 2b). Due to missing values in the mentioned scales, three participants (n = 1 BD, n = 2 MDD) had to be excluded.

  2. (3) The third analysis aimed to explore the effect of mood state on the differences between BD and MDD patients in a categorical manner. Since there are naturally no manic patients within the MDD group, a subsample was created including only currently euthymic or depressed patients of both groups (BD: n = 39 euthymic, n = 53 depressed, MDD: n = 38 euthymic, n = 98 depressed). A 2 × 2 ANCOVA with FA as dependent variable and diagnosis (BD v. MDD) and mood state (euthymic v. depressed) as independent variables was conducted (analysis 3).

All analyses on patient groups were repeated adding the MedIndex, the number of lifetime depressive episodes as well as hospitalizations as nuisance variables in the model.

Results

Analysis 1: HC v. MDD v. BD

A significant main effect of diagnosis across BD, MDD, and HC on FA (ptfce-FWE = 0.003, total k = 2448 voxels in 13 clusters, peak voxel of largest cluster: x = −4, y = 5, z = 24, see online Supplementary Table S3 for the location and size of all significant clusters) was found (Fig. 1). Post hoc t tests revealed significantly reduced FA in the BD group compared to MDD and HC groups. In contrast, differences between MDD and HC groups only reached a trend level of significance (ptfce-FWE = 0.095). Specifically, BD patients had significantly lower FA values compared to HC in one large bilateral cluster (ptfce-FWE < 0.001, k = 38 575 voxels, peak voxel: x = −14, y = 11, z = 28) comprising the forceps minor and major, the inferior fronto-occipital fasciculi, the inferior longitudinal fasciculi, and bilateral superior longitudinal fasciculi among other regions (Fig. 2a, online Supplementary Table S4). The effect was most probably located in the forceps minor of the corpus callosum. Compared to MDD patients, BD patients showed reduced FA (ptfce-FWE = 0.005, total k = 17 689 voxels in eight clusters, peak voxel of largest cluster: x = −28, y = −17, z = 23) in several WM tracts including, amongst others, bilateral anterior thalamic radiation, left inferior fronto-occipital fasciculus, left inferior longitudinal fasciculus and left superior longitudinal fasciculus including the temporal part (Fig. 2b). There were no significant increases in FA in BD as compared to MDD or HC (all ptfce > 0.978). The differences between MDD and BD remained significant even after correcting for the MedIndex and the number of depressive episodes and hospitalizations, on top of age, sex, TIV, and scanner differences (online Supplementary Tables S3 and S4). For RD, we found significantly increased values in BD compared to MDD and HC, but again no significant differences between MDD and HC. No effects were found for MD and AD (online Supplementary material 5).

Fig. 1. Main effect of diagnosis across bipolar disorder (BD), major depressive disorder (MDD), and healthy controls (HC). (a) Mean fractional anisotropy (FA) across HC, patients with MDD and patients with BD. The mean FA value was obtained from FA values of all the voxels that showed a significant main effect of diagnosis (ptfce-FWE < 0.05). Error bars represent 95% confidence intervals. p values were obtained from pairwise post hoc t-contrasts, asterisks indicate significant differences between groups. (b) Effect displayed on the FMRIB58 template. Highlighted areas represent voxels (using FSL's ‘fill’ command for better visualization), where a significant main effect of diagnosis on FA was detected (ptfce-FWE < 0.05). MNI coordinates for the section plane: x = −4, y = 5, z = 24.

Fig. 2. Reduced fractional anisotropy (FA) in patients with bipolar disorder (BD) compared to healthy controls (HC) and major depressive disorder (MDD). MNI coordinates for the section plane: x = −28, y = −17, z = 23. Highlighted areas represent voxels (using FSL's ‘fill’ command for better visualization), where significant differences between groups (ptfce-FWE < 0.05) were detected. (a) Reduced FA in patients with BD compared to HC. (b) Reduced FA in patients with BD compared to patients with MDD.

Analysis 2a: ANCOVA with diagnosis, HDRS scores, and their interaction

In the model with diagnosis and HDRS scores as independent variables, only the main effect of diagnosis proved significant (ptfce-FWE = 0.004). As before, post hoc t tests showed a reduction of FA in BD compared to MDD (ptfce-FWE = 0.006, total k = 20 562 voxels in five clusters, peak voxel of largest cluster: x = −24, y = −24, z = 35) (online Supplementary Fig. S1a), affecting the same tracts as in analysis 1 (online Supplementary Table S4). There was neither a significant main effect of HDRS scores (ptfce-FWE = 0.388), nor a significant HDRS × diagnosis interaction (ptfce-FWE = 0.781).

Analysis 2b: ANCOVA with diagnosis, YMRS scores, and their interaction

The model with diagnosis and YMRS scores as independent variables again revealed a significant main effect of diagnosis (ptfce-FWE = 0.014), which was further determined by post hoc t-contrasts as a reduction in FA in BD patients (ptfce-FWE = 0.008, total k = 16 129 voxels in three clusters, peak voxel of largest cluster: x = −24, y = −25, z = 33) (online Supplementary Fig. S1b). The effect was located in several WM tracts, consistent with the results of previous analyses (online Supplementary Table S4). Neither the main effect of YMRS scores (ptfce-FWE = 0.931) nor the YMRS × diagnosis interaction was significant (ptfce-FWE = 0.959).

Main results of analyses 2a and 2b remained unchanged after inclusion of clinical covariates (MedIndex and the number of depressive episodes and hospitalizations) in the model (online Supplementary Tables S3 and S4). For RD, we also found a significant main effect of diagnosis in both models, but no significant main effect of HDRS or YMRS scores or an interaction (online Supplementary material 5).

Analysis 3: ANCOVA with diagnosis, mood state, and their interaction in the subsample without manic BD patients

In the subsample including only patients in a current euthymic or depressed mood state, neither an effect of mood state (ptfce-FWE = 0.906) nor an interaction of diagnosis and mood state (ptfce-FWE = 0.705) on FA reached level of significance. However, a significant main effect of diagnosis emerged (ptfce-FWE = 0.039). Post hoc t-contrasts again confirmed the previously observed reduction of FA in BD compared to MDD (ptfce-FWE = 0.036, total k = 2636 voxels in seven clusters, peak voxel of largest cluster: x = −19, y = −33, z = 32). Effects were primarily located in the left corticospinal tract and the left cingulate gyrus (online Supplementary Fig. S2). When MedIndex and the number of depressive episodes and hospitalizations were added to the model, the main effect of diagnosis remained significant (online Supplementary Tables S3 and S4). No significant effects were found for AD, MD, and RD (online Supplementary material 5). An additional analysis revealed no significant differences in any DTI measures between manic, depressed, and euthymic BD patients (see online Supplementary material 6).

Discussion

The present study found widespread alterations in WM integrity in BD patients compared to MDD patients and HC, confirming our hypothesis. MDD patients, on the other hand, had lower FA values than HC, which, however, only reached a trend level of significance. All findings remained unchanged after controlling for medication load and the number of depressive episodes and hospitalizations, supporting the conclusion of a robust change in WM microstructure in BD compared to both HC and MDD. Exploratory investigation regarding the role of patients' current mood state revealed that it did not affect the differences between patient groups. This was reflected in non-significant interactions between diagnosis and mood, both in a dimensional assessment of mood via depressive (HDRS) and manic (YMRS) symptom severity and in a categorical examination of euthymic and depressed state after excluding manic BD patients.

The present changes in WM microstructure in BD were widespread, with reduced FA and increased RD in multiple WM tracts not restricted to fronto-temporal regions. Our findings therefore point toward extensive and global rather than localized WM changes in BD, compared to both HC and MDD. Therefore, the present results might be interpreted in terms of global structural disconnectivity in BD, as has been discussed for schizophrenia (Kelly et al., Reference Kelly, Jahanshad, Zalesky, Kochunov, Agartz, Alloza and Donohoe2018). This conclusion is also supported by other studies comparing BD with HC, which reported affected tracts in frontal and temporal regions classically associated with emotion processing and regulation, but also other major WM pathways beyond these (Jenkins et al., Reference Jenkins, Barba, Campbell, Lamar, Shankman, Leow and Langenecker2016; Nortje, Stein, Radua, Mataix-Cols, & Horn, Reference Nortje, Stein, Radua, Mataix-Cols and Horn2013; Wise et al., Reference Wise, Radua, Nortje, Cleare, Young and Arnone2016). Among the former, impaired integrity of the cingulum is particularly associated with the pathophysiology of BD. As the most prominent pathway in the limbic system, it is implicated in numerous processes whose impairment is characteristic of BD, such as emotion processing and regulation as well as reward processing, cognition, and attention (Bracht, Linden, & Keedwell, Reference Bracht, Linden and Keedwell2015; Duarte et al., Reference Duarte, De Araújo e Silva, Goldani, Massuda and Gama2016; Mertse et al., Reference Mertse, Denier, Walther, Breit, Grosskurth, Federspiel and Bracht2022). In MDD, impaired microstructure in the cingulum has also already been associated with related specific symptoms such as anhedonia (Keedwell et al., Reference Keedwell, Chapman, Christiansen, Richardson, Evans and Jones2012; Yang et al., Reference Yang, Wang, Wang, Tian, Cheung, Xie and Chan2017) or rumination (Zhu et al., Reference Zhu, Wang, Xiao, Liao, Zhong, Wang and Yao2012). Similarly, disrupted microstructure in other major WM pathways has been related to specific aberrant functions and behaviors in BD, such as the CST with psychomotor retardation and agitation (Bracht et al., Reference Bracht, Steinau, Federspiel, Schneider, Wiest and Walther2018; Ji et al., Reference Ji, Godwin, Rutlin, Kandala, Shimony and Mamah2017; Sacchet et al., Reference Sacchet, Prasad, Foland-Ross, Joshi, Hamilton, Thompson and Gotlib2014) or the superior and inferior longitudinal fascicles with impairments in language, cognition, or visuospatial functions (El Nagar et al., Reference El Nagar, El Shahawi, Effat, El Sheikh, Adel, Ibrahim and Aufa2021; Ji et al., Reference Ji, Godwin, Rutlin, Kandala, Shimony and Mamah2017; Magioncalda et al., Reference Magioncalda, Martino, Conio, Piaggio, Teodorescu, Escelsior and Amore2016; Poletti et al., Reference Poletti, Bollettini, Mazza, Locatelli, Radaelli, Vai and Benedetti2015; Sprooten et al., Reference Sprooten, Barrett, Mckay, Knowles, Mathias, Winkler and Glahn2016). Overall, however, no definite interpretation of the reduced FA in these pathways can yet be made. Therefore, we interpret the differences as an indication of greater overall impairment in BD compared with MDD. Contrary to the widespread FA reductions in BD, our results indicate no large – albeit at a trend level of significance – differences between MDD and HC, which contradicts our hypothesis. This finding is in line with previous studies arguing that the inconsistently found differences between MDD patients and HC might be influenced by clinical heterogeneity or other clinical factors rather than the diagnosis itself (Choi et al., Reference Choi, Holtzheimer, Franco, Kelley, Dunlop, Hu and Mayberg2014; Meinert et al., Reference Meinert, Repple, Nenadic, Krug, Jansen, Grotegerd and Dannlowski2019; Olvet et al., Reference Olvet, Delaparte, Yeh, Delorenzo, McGrath, Weissman and Parsey2016). However, the smaller differences in MDD patients compared to HC support the notion that the neuropathological characteristics of MDD may differ from those of BD (De Almeida & Phillips, Reference De Almeida and Phillips2013; Koshiyama et al., Reference Koshiyama, Fukunaga, Okada, Morita, Nemoto, Usui and Hashimoto2020; Sexton, Mackay, & Ebmeier, Reference Sexton, Mackay and Ebmeier2009). In addition to the present brain structural differences between BD and MDD, this assumption is also supported by differences in clinical phenotype between the two groups, showing a more severe disease course for BD (Table 1).

So far, only one study exists that is comparable to our work, examining differences in FA between euthymic and depressed BD and MDD patients (Matsuoka et al., Reference Matsuoka, Yasuno, Kishimoto, Yamamoto, Kiuchi, Kosaka and Kudo2017). However, the authors did not include currently manic patients in their sample. Moreover, they did not examine the effect of depressive symptom severity in more detail but only included depressive symptom severity as a covariate in the model. Thus, ours was the first study to combine a categorical and a dimensional approach to examine the influence of current mood on the difference between BD and MDD regarding WM microstructure, including patients in the euthymic, depressed, and manic mood state.

Using the categorical approach, it was not possible to examine the effect of all mood states on the difference between BD and MDD in one model because there was no group of manic MDD patients. However, there were no differences within the BD group when the three mood states were tested against each other (online Supplementary material 6). Accordingly, the differences between BD and MDD emerged independently of the inclusion or exclusion of manic BD patients and unaffected by current euthymic or depressed mood state of either disorder. Complementing this analysis, dimensional measures of current depressive and manic symptoms allowed us to capture the full spectrum of symptoms, including subclinical manic symptoms in MDD (see online Supplementary Fig. S1b) or subclinical depressive symptoms in euthymic states. Central to this approach was that our sample included highly symptomatic patients as well as patients in partial remission and euthymic states, allowing us to analyze an adequate variance in symptom severity.

Although we cannot rule out that the lack of interaction between diagnosis and mood state could result from the rather small sample size per group, overall, our results suggest that reduced FA in BD v. MDD, previously observed in depressed patients (Deng et al., Reference Deng, Wang, Huang, Niu, Zhong, Zhao and Huang2018; Lan et al., Reference Lan, Rubin-Falcone, Sublette, Oquendo, Stewart, Hellerstein and Mann2020; Repple et al., Reference Repple, Meinert, Grotegerd, Kugel, Redlich, Dohm and Dannlowski2017; Vai et al., Reference Vai, Parenti, Bollettini, Cara, Verga, Melloni and Benedetti2020; Versace et al., Reference Versace, Almeida, Quevedo, Thompson, Terwilliger, Hassel and Phillips2010), may be generalized to the entire symptom spectrum of BD. The disrupted WM microstructure does not appear to be caused by state-dependent changes, but instead seems to be a rather stable neurobiological alteration that distinguishes the disorder from MDD, regardless of depressive or manic symptoms. Considering the high heritability of both BD (McGuffin et al., Reference McGuffin, Rijsdijk, Andrew, Sham, Katz and Cardno2003) and WM microstructure (Chiang et al., Reference Chiang, Barysheva, Shattuck, Lee, Madsen, Avedissian and Thompson2009; Kochunov et al., Reference Kochunov, Glahn, Lancaster, Winkler, Smith, Thompson and Blangero2010), these state-independent differences suggest the involvement of genetic factors. This assumption is also supported by studies that found reductions in FA even in unaffected subjects at familial risk of BD and interpreted these in terms of a potential endophenotype of BD (Foley et al., Reference Foley, Bracher-Smith, Tansey, Harrison, Parker and Caseras2018; Hu, Stavish, Leibenluft, & Linke, Reference Hu, Stavish, Leibenluft and Linke2020; Sarlçiçek et al., Reference Sarlçiçek, Zorlu, Yalln, Hldlroǧlu, Çavuşoǧlu, Ceylan and Özerdem2016; Sprooten et al., Reference Sprooten, Sussmann, Clugston, Peel, McKirdy, Moorhead and McIntosh2011). Thus, DTI metrics may have the potential for being used as a diagnostic tool for BD in the future, possibly as part of a multimodality diagnostic imaging approach including machine learning algorithms (Bürger et al., Reference Bürger, Redlich, Grotegerd, Meinert, Dohm, Schneider and Dannlowski2017; Grotegerd et al., Reference Grotegerd, Suslow, Bauer, Ohrmann, Arolt, Stuhrmann and Dannlowski2013; Lan et al., Reference Lan, Rubin-Falcone, Sublette, Oquendo, Stewart, Hellerstein and Mann2020; Vai et al., Reference Vai, Parenti, Bollettini, Cara, Verga, Melloni and Benedetti2020; Versace et al., Reference Versace, Almeida, Quevedo, Thompson, Terwilliger, Hassel and Phillips2010).

The neurobiological interpretation of these observed reductions in FA is not yet understood in detail and remains challenging. FA represents the strength of anisotropy and is considered as a measure of WM integrity. However, various factors are discussed in relation to FA reductions, including demyelination, membrane permeability, axonal count and diameter, and crossing of fibers (Alexander, Lee, Lazar, & Field, Reference Alexander, Lee, Lazar and Field2007; Feldman, Yeatman, Lee, Barde, & Gaman-Bean, Reference Feldman, Yeatman, Lee, Barde and Gaman-Bean2010; Jones et al., Reference Jones, Knösche and Turner2013). When drawing conclusions about the nature of microstructural changes, additional consideration should also be given to the other DTI metrics, for which we observed an increase in RD, whereas AD and MD showed no differences.

A few limitations of this study should be noted. First, the results of this study are cross-sectional in nature, making it impossible to draw causal conclusions. Inferences about whether the observed effects are state-dependent or persistent differences should be drawn with caution. Comprehensively answering this question requires future longitudinal studies examining the same patients in varying mood states (Benedetti et al., Reference Benedetti, Yeh, Bellani, Radaelli, Nicoletti, Poletti and Brambilla2011b; Phillips, Reference Phillips2019). Second, the majority of patients were taking psychotropic medications, with differences between BD and MDD. Although all effects remained unchanged after accounting for current medication load, we cannot completely exclude the possibility that – especially past – medication use confounded the effects. However, in support of our findings, a normalizing effect has already been reported for lithium, counteracting and possibly even concealing changes in WM microstructure in BD (Favre et al., Reference Favre, Pauling, Stout, Hozer, Sarrazin, Abé and Houenou2019; Hafeman, Chang, Garrett, Sanders, & Phillips, Reference Hafeman, Chang, Garrett, Sanders and Phillips2012). This supports our results as we found significant effects even though some of the BD patients received lithium medication. Finally, although we controlled for the number of depressive episodes and hospitalizations, there are many more variables associated with disease course that may have an effect on microstructural WM changes (Favre et al., Reference Favre, Pauling, Stout, Hozer, Sarrazin, Abé and Houenou2019; Koshiyama et al., Reference Koshiyama, Fukunaga, Okada, Morita, Nemoto, Usui and Hashimoto2020; Repple et al., Reference Repple, Meinert, Grotegerd, Kugel, Redlich, Dohm and Dannlowski2017; Yang et al., Reference Yang, Li, Hu, Luo, Kuang, Lui and Gong2019).

Taken together, our results contribute to a deeper understanding of WM microstructure impairment in BD. To the best of our knowledge, no previous study has investigated differences between BD and MDD with detailed consideration of patients' current mood. Although interpretation should be made with caution due to the cross-sectional design of the study, our results support the existence of global microstructural WM disruptions in BD patients as compared to MDD patients, unaffected by current affective state and symptom severity. To further investigate the transient v. persistent nature of WM integrity impairments in BD, future studies using longitudinal designs are needed.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S0033291722001490.

Acknowledgements

This work is part of the German multicenter consortium ‘Neurobiology of Affective Disorders. A translational perspective on brain structure and function’, funded by the German Research Foundation (Deutsche Forschungsgemeinschaft DFG; Forschungsgruppe/Research Unit FOR2107). Principal investigators (PIs) with respective areas of responsibility in the FOR2107 consortium are: Work Package WP1, FOR2107/MACS cohort and brainimaging: Tilo Kircher (speaker FOR2107; DFG grant numbers KI 588/14-1, KI 588/14-2), Udo Dannlowski (co-speaker FOR2107; DA 1151/5-1, DA 1151/5-2), Axel Krug (KR 3822/5-1, KR 3822/7-2), Igor Nenadic (NE 2254/1-2), Carsten Konrad (KO 4291/3-1). WP2, animal phenotyping: Markus Wöhr (WO 1732/4-1, WO 1732/4-2), Rainer Schwarting (SCHW 559/14-1, SCHW 559/14-2). WP3, miRNA: Gerhard Schratt (SCHR 1136/3-1, 1136/3-2). WP4, immunology, mitochondriae: Judith Alferink (AL 1145/5-2), Carsten Culmsee (CU 43/9-1, CU 43/9-2), Holger Garn (GA 545/5-1, GA 545/7-2). WP5, genetics: Marcella Rietschel (RI 908/11-1, RI 908/11-2), Markus Nöthen (NO 246/10-1, NO 246/10-2), Stephanie Witt (WI 3439/3-1, WI 3439/3-2). WP6, multi-method data analytics: Andreas Jansen (JA 1890/7-1, JA 1890/7-2), Tim Hahn (HA 7070/2-2), Bertram Müller-Myhsok (MU1315/8-2), Astrid Dempfle (DE 1614/3-1, DE 1614/3-2). CP1, biobank: Petra Pfefferle (PF 784/1-1, PF 784/1-2), Harald Renz (RE 737/20-1, 737/20-2). CP2, administration. Tilo Kircher (KI 588/15-1, KI 588/17-1), Udo Dannlowski (DA 1151/6-1), Carsten Konrad (KO 4291/4-1). Data access and responsibility: All PIs take responsibility for the integrity of the respective study data and their components. All authors and coauthors had full access to all study data. Acknowledgements and members by Work Package 1: Henrike Bröhl, Katharina Brosch, Bruno Dietsche, Rozbeh Elahi, Jennifer Engelen, Sabine Fischer, Jessica Heinen, Svenja Klingel, Felicitas Meier, Tina Meller, Julia-Katharina Pfarr, Kai Ringwald, Torsten Sauder, Simon Schmitt, Frederike Stein, Annette Tittmar, Dilara Yüksel (Dept. of Psychiatry, Marburg University). Mechthild Wallnig, Rita Werner (Core-Facility Brainimaging, Marburg University). Carmen Schade-Brittinger, Maik Hahmann (Coordinating Centre for Clinical Trials, Marburg). Michael Putzke (Psychiatric Hospital, Friedberg). Rolf Speier, Lutz Lenhard (Psychiatric Hospital, Haina). Birgit Köhnlein (Psychiatric Practice, Marburg). Peter Wulf, Jürgen Kleebach, Achim Becker (Psychiatric Hospital Hephata, Schwalmstadt-Treysa). Ruth Bär (Care facility Bischoff, Neukirchen). Matthias Müller, Michael Franz, Siegfried Scharmann, Anja Haag, Kristina Spenner, Ulrich Ohlenschläger (Psychiatric Hospital Vitos, Marburg). Matthias Müller, Michael Franz, Bernd Kundermann (Psychiatric Hospital Vitos, Gießen). Christian Bürger, Katharina Dohm, Fanni Dzvonyar, Verena Enneking, Stella Fingas, Katharina Förster, Janik Goltermann, Dominik Grotegerd, Hannah Lemke, Susanne Meinert, Nils Opel, Ronny Redlich, Jonathan Repple, Katharina Thiel, Kordula Vorspohl, Bettina Walden, Lena Waltemate, Alexandra Winter, Dario Zaremba (Dept. of Psychiatry, University of Münster). Harald Kugel, Jochen Bauer, Walter Heindel, Birgit Vahrenkamp (Dept. of Clinical Radiology, University of Münster). Gereon Heuft, Gudrun Schneider (Dept. of Psychosomatics and Psychotherapy, University of Münster). Thomas Reker (LWL-Hospital Münster). Gisela Bartling (IPP Münster). Ulrike Buhlmann (Dept. of Clinical Psychology, University of Münster). We are deeply indebted to all participants of this study, the recruitment sites and their staff.

Financial support

This work was further funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) (TK, grant numbers KI 588/14-1, KI 588/14-2, KI 588/15-1, KI 588/17-1; UD, grant numbers DA 1151/5-1, DA 1151/5-2, DA 1151/6-1, SFB-TRR58, Projects C09 and Z02; AK, grant numbers KR 3822/5-1, KR 3822/7-2; IN, grant numbers NE 2254/1-2; AJ, grant numbers JA 1890/7-1, JA 1890/7-2; TH, grant number HA 7070/2-2); the Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Münster (UD, grant number Dan3/012/17; NO, grant number SEED 11/18); ‘Innovative Medizinische Forschung’ (IMF) of the medical faculty of Münster (RR, grant numbers RE111604, RE111722; JR, grant number RE221707; EJL, grant number LE121703; KD, grant number KO121806), and the Deanery of the Medical Faculty of the University of Münster.

Conflict of interest

Tilo Kircher received unrestricted educational grants from Servier, Janssen, Recordati, Aristo, Otsuka, neuraxpharm. This funding is not associated with the current work. On behalf of all other authors, the corresponding author states that there is no conflict of interest and nothing to disclose.

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Figure 0

Table 1. Demographic and clinical characteristics of the study sample

Figure 1

Table 2. Clinical characteristics of patient subgroups

Figure 2

Fig. 1. Main effect of diagnosis across bipolar disorder (BD), major depressive disorder (MDD), and healthy controls (HC). (a) Mean fractional anisotropy (FA) across HC, patients with MDD and patients with BD. The mean FA value was obtained from FA values of all the voxels that showed a significant main effect of diagnosis (ptfce-FWE < 0.05). Error bars represent 95% confidence intervals. p values were obtained from pairwise post hoc t-contrasts, asterisks indicate significant differences between groups. (b) Effect displayed on the FMRIB58 template. Highlighted areas represent voxels (using FSL's ‘fill’ command for better visualization), where a significant main effect of diagnosis on FA was detected (ptfce-FWE < 0.05). MNI coordinates for the section plane: x = −4, y = 5, z = 24.

Figure 3

Fig. 2. Reduced fractional anisotropy (FA) in patients with bipolar disorder (BD) compared to healthy controls (HC) and major depressive disorder (MDD). MNI coordinates for the section plane: x = −28, y = −17, z = 23. Highlighted areas represent voxels (using FSL's ‘fill’ command for better visualization), where significant differences between groups (ptfce-FWE < 0.05) were detected. (a) Reduced FA in patients with BD compared to HC. (b) Reduced FA in patients with BD compared to patients with MDD.

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