Common and distinct structural network abnormalities in major depressive disorder and borderline personality disorder

    Common and distinct structural network abnormalities in major depressive disorder and borderline personality disorder Malte S. Depping, Nadine D. Wolf, Nenad Vasic, Fabio Sambataro, Philipp A. Thomann, R. Christian Wolf PII: DOI: Reference:

S0278-5846(15)30043-9 doi: 10.1016/j.pnpbp.2015.09.007 PNP 8836

To appear in:

Progress in Neuropsychopharmacology & Biological Psychiatry

Received date: Revised date: Accepted date:

1 July 2015 31 August 2015 12 September 2015

Please cite this article as: Depping Malte S., Wolf Nadine D., Vasic Nenad, Sambataro Fabio, Thomann Philipp A., Wolf R. Christian, Common and distinct structural network abnormalities in major depressive disorder and borderline personality disorder, Progress in Neuropsychopharmacology & Biological Psychiatry (2015), doi: 10.1016/j.pnpbp.2015.09.007

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ACCEPTED MANUSCRIPT Common and distinct structural network abnormalities in Major Depressive

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Disorder and Borderline Personality Disorder

Malte S. Depping1#, Nadine D. Wolf2#, Nenad Vasic3, Fabio Sambataro4, Philipp A. Thomann1, R.

Center of Psychosocial Medicine, Department of General Psychiatry, University of Heidelberg,

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1

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Christian Wolf1,2*

Germany

Department of Psychiatry, Psychotherapy and Psychosomatics, Saarland University, Homburg,

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2

Germany

Department for Forensic Psychiatry and Psychotherapy at the District Hospital Günzburg, Ulm

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3

Department of Experimental and Clinical Medical Sciences (DISM), University of Udine, Italy

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4

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University, Germany

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* Corresponding author:

Robert Christian Wolf, MD Department

of

Psychiatry,

Psychotherapy

and

Saarland University Kirrberger Str. 1, 66421 Homburg, Germany E-mail: [email protected] Tel: +49 - 6841/16 24305; Fax: +49 - 6841/16 24270

#

M.S.D. and N.D.W. contributed equally to this work.

Short title: Structural networks in MDD and BPD

Psychosomatics

ACCEPTED MANUSCRIPT 1.

Introduction

Structural neuroimaging studies in major depressive disorder (MDD) converge on a pattern of

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aberrant brain volume encompassing anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), hippocampus, and striatum (Koolschijn et al. 2009, Bora et al. 2012, Du et al.

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2012, Lai 2013). Although these gray matter volume (GMV) changes have been substantiated on a meta-analytical level of evidence, their specificity to a diagnosis of MDD remains imprecise: As these findings rely on neuroimaging studies comparing patients with a single psychiatric diagnosis potential neurobiological abnormalities shared across common

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to healthy individuals,

psychopathology are underappreciated by this approach (Goodkind et al. 2015). Indeed, only very

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few studies have so far attempted to isolate the specific brain alterations of phenotypically related diagnoses by comparing diagnostic categories with each other (Kempton et al. 2011). The

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intriguing issue of limited associations between current psychiatric nosology and neurobiological findings yet emphasizes the need for such transdiagnostic research to discern shared and distinct

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neural substrates across psychiatric diagnoses (Cuthbert 2014).

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It is noteworthy that that vast majority of structural findings in MDD have been derived from univariate statistical approaches, such as voxel-based morphometry (VBM). VBM is a now widely

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employed structural data analysis method which is particularly powerful in detecting regionally specific volume loss. However, structural variation in one brain region is likely to affect multiple brain areas, even if the lesion occurs in a remote location. Investigating associations between multiple brain regions can therefore allow understanding how altered inter-relationships between regions can contribute to a disorder or to specific symptom expression (Xu et al. 2009, Kasparek et al. 2010). To address this issue, we applied a novel multivariate statistical technique for structural MRI data, i.e. “source-based morphometry” [SBM] (Xu et al. 2009), to a structural imaging data set of MDD patients, healthy controls and patients with BPD that previously underwent conventional univariate VBM analyses (Depping et al. 2015). Similar to VBM, SBM does not rely on a priori definition of regions of interest and allows an automated, userindependent investigation of brain structure. Unlike VBM, SBM includes Independent Component Analysis (ICA) to extract spatially independent patterns that occur in structural images. Thus,

ACCEPTED MANUSCRIPT SBM takes into account interrelationships between voxels to identify naturally grouped patterns of structural variation between populations, i.e. “structural networks”. As multivariate statistical approach, SBM can result in less-noisy networks of interest (Xu et al. 2009). Moreover, in SBM

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statistical analyses are based on component values (independent component [IC] loadings), significantly reducing the number of multiple comparisons. The application of SBM to patients with

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mental disorders (e.g. schizophrenia) has been shown to be successful in identifying distinct patterns of structural change which were not detected by VBM (Xu et al. 2009, Kasparek et al.

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2010).

To address the specificity of brain structural findings for a diagnosis of MDD, we included a

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second patient group, i.e. individuals with borderline personality disorder (BPD). BPD is characterized by emotional dysregulation, impulsivity and interpersonal sensitivity (Goodman et al. 2010). Affective shifts in individuals with BPD are considered to be more

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reactive and transient as opposed to a temporally more stable phenotype in MDD that

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includes mood bias towards negative emotions, impaired reward function, impaired executive function and neurovegetative signs (Hasler et al. 2004). Although MDD and BPD

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are conceptualized as distinct disorders, they essentially converge on aspects of negative emotionality and emotion regulation deficits (Skodol et al. 2002, Chanen et al. 2007,

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Cheavens and Heiy 2011). In this regard, it is noteworthy that MDD has also been associated with the emotional trait of affective instability (Thompson et al. 2011), while in BPD – in an analogous manner – more enduring symptoms such as chronic dysphoria may be present as well (Zanarini et al. 2007). The phenotypical commonalities of MDD and BPD, their high rate of comorbidity of up to 50% (Zanarini et al. 1998, Koenigsberg et al. 1999), and their treatment-relevant interactions in case of comorbid presentation (Gunderson et al. 2014) have been interpreted as evidence for shared neurobiological mechanisms (Koenigsberg et al. 1999, Goodman et al. 2010). To minimize clinical and neurobiological heterogeneity, we included only BPD patients without comorbid posttraumatic stress disorder (PTSD), attention-deficit/hyperactivity disorder (ADHD), bipolar disorder and schizophrenia-spectrum disorders (see e.g. (Mauchnik and Schmahl 2010).

ACCEPTED MANUSCRIPT Based on the available meta-analytical evidence for GMV alterations in MDD and BPD, we predicted that MDD would be predominantly characterized by lower anterior cingulate and lateral prefrontal cortical volume, whereas in BPD we expected predominant amygdala

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volume loss. Based on the available structural literature in both disorders, we further expected that shared neural substrates across both diagnoses would include volume

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decrease of the hippocampal formation. In addition, we explored associations between brain volume and clinical measures within and between diagnostic groups. In particular, we expected transdiagnostic associations between depressive symptoms and a structural

Methods

2.1

Participants

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2.

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network including medial temporal lobe regions.

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We analyzed data from 22 female patients with MDD, 17 female patients with BPD and 22 female controls (Table 1), as reported previously (Depping et al. 2015). Individuals with a past history or

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the presence of any medical or neurological disorders, presence of drug or alcohol abuse, a history of head trauma with loss of consciousness and learning disabilities were excluded.

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Additional exclusion criteria for MDD patients were any lifetime or comorbid Axis I and Axis II disorders according to DSM-IV-TR criteria. For the BPD group, additional exclusion criteria were lifetime diagnoses of schizophrenia, bipolar disorder, ADHD and alcohol and illicit drug abuse within 6 months prior to study participation. Moreover, only BPD patients who did not have a past history of or met current criteria for PTSD as a further attempt to reduce clinical heterogeneity (Mauchnik and Schmahl 2010). Axis I disorders in the BPD cohort included lifetime major depressive disorder (n=5), past drug and alcohol abuse (n=6), current mild to moderate depressive symptoms (n=8), eating disorders (n=4) and anxiety disorder (n=1). MDD and BPD patients were on a stable drug regime for at least 2 weeks prior to scanning. 14 out of 22 MDD patients and 17 out of 17 BPD patients were on psychotropic drug treatment at the time of the scan (medication details are given in Table 1, Supplementary data). The healthy control group

ACCEPTED MANUSCRIPT consisted of 22 unmedicated right-handed female participants matched for age and education. Participants with a neurological or a psychiatric disorder according to DSM-IV-TR criteria, a positive family history for neurological and psychiatric disorders, or current drug treatment (except

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birth control pills) were excluded. All patients and controls completed the Beck Depression Inventory (BDI) (Beck et al. 1961) and were complementary rated by means of the 21-item HAMD

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(Hamilton 1960). Overall BPD symptoms in the BPD patient group were assessed using the BSL23 (Bohus et al. 2009). Self-reported impulsivity in BPD patients and controls was assessed using the German version of the Barratt Impulsiveness Scale (BIS) (Patton et al. 1995). The study was

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approved by the local Research Ethics Committee (University of Ulm, Germany), and written informed consent according to the Declaration of Helsinki was obtained from all participants

2.2

MRI data acquisition

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following a complete description of the study.

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High-resolution structural data were acquired using a 3 T Magnetom ALLEGRA (Siemens, Erlangen, Germany) head MRI system. The MRI parameters of the three-dimensional

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magnetization-prepared rapid gradient-echo (3D-MPRAGE) sequence were as follows: TE = 3.93 ms; TR = 2080 ms; TI = 1100 ms; FOV = 256 mm; slice plane = axial; slice thickness = 1 mm;

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resolution = 1.0×1.0×1.0 mm3; number of slices = 256.

2.3

MRI data analysis

First, a conventional voxel-based morphometry (VBM) analysis was computed using Christian Gaser’s VBM toolbox (http://dbm.neuro.uni-jena.de/vbm8/) running within the Statistical Parametric Mapping software package version 8 (SPM8; http://www.fil.ion.ucl.ac.uk/spm). During the data segmentation step, individual T1 images were spatially normalised and segmented into grey and white matter and cerebrospinal fluid. This procedure was followed by partial volume estimation (Tohka et al. 2004), data denoising (Manjon et al. 2010), application of Markov Random Fields (Rajapakse et al. 1997) and Diffeomorphic Anatomic Registration through Exponentiated Lie (DARTEL) algebra normalization (Ashburner 2007). After data preprocessing,

ACCEPTED MANUSCRIPT normalized grey matter volume (GMV) images were smoothed using an 8 mm Full Width at Half Maximum (FWHM) Gaussian kernel. Subsequently, using the individual GMV a spatial independent component analysis was computed. We used the SBM algorithm (see (Xu et al.

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2009) for further methodological details) as implemented in the “Group ICA for fMRI Toolbox” [GIFT; http://mialab.mrn.org/software/gift]. We used an Infomax algorithm to estimate twenty

consistency,

we

used

the

ICASSO

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independent components (Canessa et al. 2013). To increase component reliability and algorithm

(Himberg

et

al.

2004);

http://research.ics.aalto.fi/ica/icasso/) and repeated the ICA estimation 250 times with

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bootstrapping and permutation. Components were clustered and robustness of ICA estimation was quantified using a quality index (Iq) ranging from 0 to 1, reflecting the difference between

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intra- and extracluster similarity (Himberg et al. 2004). All estimated components were associated with an Iq >0.9 indicating a highly stable decomposition (Allen et al. 2011). Using ICA, each GMV

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image was converted into a one-dimensional vector which was arrayed into one 34-row subject/by-segment data matrix. Next, this matrix was decomposed into one mixing and one

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source matrix: the mixing matrix represents the relationship between participants and

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components, and the source matrix represents the relationship between components and brain voxels. Subsequent between-group comparisons were performed using mixing matrix indices, i.e.

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loading parameters representing the contribution of every GMV component to the 61 participants. Component selection and post-hoc between-group comparisons were based on ANOVA models on every column of the mixing matrices. The ANOVA models included a group factor representing controls, MDD and BPD patients. As nominal level of significance a level of p<0.05 (uncorrected) was defined. Fisher’s least significant difference (LSD) tests were used post-hoc (p<0.05). ANOVA calculations and post-hoc-testing were performed offline using the Statistica software package (Version 10). For component visualization the source matrix was reshaped back to a three-dimensional image (i.e. the same dimension as the input images), scaled to unit standard deviations (Z maps) and thresholded at Z>3.5. Maps from components exhibiting significant differences between the groups (see below) were overlaid onto a Montreal Neurological Institute (MNI) normalized

ACCEPTED MANUSCRIPT anatomical template. Anatomical denominations and stereotaxic coordinates were obtained from clusters above a threshold of Z=3.5 by linking the SBM output to the Talairach Daemon data base (http://www.talairach.org/daemon.html). Amygdala coordinates were confirmed using the

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Automatic Anatomical Labelling (AAL) Atlas (Tzourio-Mazoyer et al. 2002). To determine sensitivity and specificity for a diagnosis of MDD vs. BPD, component loadings from networks

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exhibiting a significant group effect (see below) were extracted. These values were process using a web-based clinical calculator (http://vassarstats.net/clin1.html).

For the patient groups, exploratory correlation analyses between brain volume values and

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clinically relevant variables were calculated. Using the Prism software package (Version 6.00,

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GraphPad Software Inc., La Jolla, USA, www.graphpad.com), correlations were performed between network-specific component loadings (see below) and BDI, HAMD, BSL-23, BIS, the number of depressive episodes, and the disease duration in MDD. As nominal level of

Results

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significance a threshold of p<0.05 (uncorrected) was defined.

Three GMV components were identified which exhibited a significant group effect; see Figure 1

coordinates.

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and Tables 2 a-c for hemispheric laterality, anatomical denominations, Z-scores and stereotaxic

The first component (“frontostriatal network”) predominantly comprised bilateral anterior and lateral prefrontal cortical areas (including the dorsolateral prefrontal cortex, DLPFC) together with bilateral striatal regions (F(2, 58)=4.99, p=0.010). Post-hoc tests revealed that the expression of this pattern was significantly lower in MDD patients compared to both controls (p=0.003) and BPD patients (p=0.038). There were no significant differences between controls and BPD patients (p=0.47). The second component (“medial temporal network”) included medial temporal lobe structures (hippocampus, parahippocampus, amygdala) and medial frontal areas (F(2, 58)=8.81, p=0.0005).

ACCEPTED MANUSCRIPT Post-hoc tests revealed that the expression of this structural pattern was significantly lower in BPD patients compared to both controls (p=0.002) and MDD patients (p=0.0002). There were no significant differences between controls and MDD patients (p=0.44).

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The third component (“cingulate network”) comprised the anterior cingulate cortex together with

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dorsolateral prefrontal regions (F(2, 58)=3.65, p=0.032). Post-hoc tests revealed that the expression of this structural pattern was significantly lower in both MDD (p=0.02) and BPD (p=0.031) patients compared to controls. There were no significant differences between MDD and BPD patients (p=0.97). To test for confounding effects of age, multiple regression analyses were

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computed. For each structural network showing a significant effect of group, component loadings

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were defined as dependent variable and age was included as predictor. A nominal significance threshold of p<0.05 was chosen. In all analyses, the effect of gender was not significant. Sensitivity and specificity for a diagnosis of MDD were as follows: “frontostriatal network”: 0.59

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(95% confidence interval, CI: 0.37-0.71) and 0.82 (95% CI: 0.55-0.95). “Medial temporal network”:

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0.41 (95% CI: 0.21-0.63) and 0.94 (95% CI: 0.69-0.99). “Cingulate network”: 0.41 (95% CI: 0.210.63) and 0.76 (95% CI: 0.49-0.92).

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In the MDD group, a negative association between “cingulate network” loadings and HAMD scores was found (r=-0.45, p=0.035), see Supplementary Fig. 1. In the BPD group, “medial

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temporal network” loadings were negatively correlated with BSL and BIS total scores (r=-0.61, p=0.009 and r=-0.51; p=0.036), see Supplementary Fig. 2. Across the entire patient sample (MDD and BPD), a negative correlation was found between “medial temporal network” loadings and BDI scores (r=-0.37, p=0.021). There were no significant associations between component loadings and other clinical variables, such as the number of depressive episodes or disease duration in MDD.

4.

Discussion

In this study we used structural MRI and a multivariate statistical technique to investigate structural network patterns and the magnitude of their expression in female patients with MDD

ACCEPTED MANUSCRIPT compared to healthy control participants and female patients with BPD. Three main findings emerged: First, a “cingulate network” showed reduced volume in both MDD and BPD compared to HC, whereas it did not significantly differ between MDD and BPD. In addition, in MDD patients,

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the expression strength of this structural pattern was negatively correlated with the extent of depressive symptoms. Second, a “frontostriatal network” showed reduced volume in MDD both

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when compared to HC and BPD, respectively. Third, a “medial temporal network” displayed reduced volume in BPD compared to HC and compared to MDD. In patients with BPD, structural network strength was negatively correlated with the extent of BPD-specific symptoms.

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The three identified networks are in good accordance with structural brain changes previously

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identified in meta-analyses of structural MRI studies conducted in individuals with MDD (Bora et al. 2012, Du et al. 2012, Lai 2013) and BPD (Nunes et al. 2009, Ruocco et al. 2012). First, it is noteworthy that MDD and BPD were equally characterized by volume reduction of a “cingulate

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network”, i.e. reduced volume of this structural pattern did not differentiate between the two disorders. This expands findings from our previous VBM study of this sample of MDD and BPD

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individuals, where we suggested that ACC volumes in BPD might be of intermediate extent

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between ACC volumes in MDD and in HC (Depping et al. 2015). VBM studies in MDD have reported ACC volume reduction as most consistent feature (Bora et al. 2012, Du et al. 2012, Lai

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2013), while in BPD, neuroimaging research explicitly investigating ACC volume is relatively scarce. For BPD, currently available meta-analyses of structural MRI investigations have restricted their focus to other regions of interest, such as the amygdala and the hippocampus (Nunes et al. 2009, Hall et al. 2010, Ruocco et al. 2012). The dearth of both research and evidence on ACC volume change in BPD is surprising given the ACC's pivotal contribution to overarching aspects of cognitive and affective regulation (Sheth et al. 2012, Shenhav et al. 2013). Here, we found that while ACC volume loss – i.e. reduced “cingulate network” expression – was not specific for MDD when compared to BPD, abnormal ACC volume was only associated with diagnosis-specific symptoms in the MDD patient group. This is in line with previous research supporting evidence that ACC dysfunction is crucial for symptom expression in MDD. Congruently, ACC volume depletion has been associated with multiple clinical and functional

ACCEPTED MANUSCRIPT facets of MDD, such as symptom burden, illness duration or diminished ability to down-regulate negative emotions (Chen et al. 2007, Yucel et al. 2008, Mak et al. 2009). Furthermore, both ACC and dorsolateral prefrontal cortex have been suggested to form part of a more complex functional

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network of dysregulated fronto-cortical and fronto-subcortical circuitries associated with aberrant emotional processing in MDD (Pizzagalli 2011, Diener et al. 2012).

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In contrast, “cingulate network” volume was not related to clinical symptoms in BPD. It is possible that at least at the level of brain structure cingulate network abnormalities in BPD do not substantially mediate BPD symptoms. At the level of neural activity, however, it is possible that

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deficient functional coupling including the ACC and its subcortical connections are more closely

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associated with BPD symptom expression (Kamphausen et al. 2013, Scherpiet et al. 2014). At the same time, it could be that cortical regions other than the ACC are more closely associated with BPD core symptoms. For example, it has been suggested that impulsivity in BPD is

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specifically mediated by orbitofrontal cortex (OFC) dysfunction (Wolf et al. 2012). In line with this, our study found that impulsivity in individuals with BPD was associated with volume reduction of a

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„medial temporal network“, and this structural pattern also included regions within the OFC. At

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present, research linking volume abnormalities in MDD or BPD to different levels of neural activity are scarce (Vasic et al. 2015), and it remains open at this stage of research whether volume loss

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predicts functional change in these disorders. Second, we found that frontostriatal volume in MDD was significantly reduced compared to both controls and BPD patients. Striatal dysfunction is among the most replicated findings in functional MRI (fMRI) studies probing reward processing in MDD. Evidence for striatal volume reduction in MDD is supported by one meta-analysis of VBM studies (Bora et al. 2012) and one meta-analysis of structural neuroimaging data primarily employing a region-of-interest approach (Koolschijn et al. 2009), while two other structural meta-analyses could not demonstrate striatal volume loss in MDD (Du et al. 2012, Lai 2013). Dysfunction of striatal regions is considered as a critical neural basis of blunted reward encoding and reward-related learning in depressed subjects (Pizzagalli 2014). Such features of dysfunctional reward processing have been shown to underlie an anhedonic phenotype (Vrieze et al. 2013), which constitutes a core depressive

ACCEPTED MANUSCRIPT symptom dimension and has been proposed as promising endophenotype in MDD (Hasler et al. 2004). The “frontostriatal network” also included the DLPFC. DLPFC dysregulation has been consistently related to executive dysfunction in depressed subjects, i.e. impaired working memory

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and diminished cognitive flexibility (Vasic et al. 2007, Clark et al. 2009, Leh et al. 2010). While we could not establish significant relationships between “frontostriatal network” volume and

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global clinical measures, such as BDI or HAMD, it is possible that this structural component may be more closely related to distinct measures of cognitive deficits, which are well documented in MDD (Bora et al. 2013), or to distinct measures of anhedonia. The

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lack of neuropsychological data and the lack of psychometric measures of hedonic

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capacity however prevent us from drawing strong conclusions at this stage. While our previous VBM investigation of this clinical sample had not demonstrated volume depletion of striatum or DLPFC in MDD compared to HC or BPD (Depping et al. 2015), in

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this study, GMV loss of the “frontostriatal network” – as detected by SBM – yielded a high specificity in differentiating MDD from BPD. From a clinical perspective, it appears well

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plausible that a neural pattern pertaining to the brain reward system and to the brain

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cognitive system may be more specifically impaired in MDD compared to BPD. Future research employing multimodal neuroimaging protocols will have to address how

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abnormal striatal or DLPFC volume may relate to different levels of neural activity and distinct clinical symptoms, such as anhedonia or cognitive deficits, and whether such effects may be diagnosis-specific. Third, BPD was characterized by volume reduction of a “medial temporal network” including amygdala, hippocampus, parahippocampus, and medial frontal regions compared to HC and MDD. Importantly, lower network volumes were associated with both the extent of overall BPD symptoms, as well as with impulsivity scores. Robust evidence has suggested amygdala volume decrease as a biological hallmark of BPD. Congruently, three meta-analyses of structural MRI investigations comparing BPD to HC have reported significant volume reductions in limbic and paralimbic regions, most prominently in amygdala and hippocampus (Nunes et al. 2009, deAlmeida et al. 2012, Ruocco et al. 2012), and these alterations were found unrelated to comorbid

ACCEPTED MANUSCRIPT MDD, PTSD or substance use disorders (de-Almeida et al. 2012, Ruocco et al. 2012). Adding to this, our above-mentioned VBM study of BPD and MDD brain structure suggested that significant GMV differences between the two diagnostic entities appear to be restricted to the amygdala

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(Depping et al. 2015). The present findings essentially validate and expand our previous findings. The finding of decreased expression of a structural pattern encompassing medial temporal

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structures in BPD compared to both HC and MDD reinforces the specificity of reduced amygdala and (para-)hippocampal volume for a diagnosis of BPD. In addition, the significant correlation between the expression of this structural network and quantitative measures of BPD

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symptomatology adds plausibility to a model of BPD pathophysiology that centers around

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amygdala dysfunction.

Evidence for dissociable neural contributions to emotion dysregulation in BPD compared to MDD also comes from functional neuroimaging studies. A recent meta-analysis of fMRI studies probing

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emotional processing in MDD demonstrated that the perceptual bias of emotional stimuli in depressed individuals appears to be reflected in abnormal levels of amygdala activity, i.e.

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increased amygdala activity in MDD was detected during processing of negative emotional

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stimuli, while the amygdala was less active in MDD during processing of positive stimuli (Groenewold et al. 2013). In BPD, likewise based upon meta-analytical evidence, the long-

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standing notion of hyperresponsivity of the amygdala to negatively valenced stimuli (Herpertz et al. 2001, Donegan et al. 2003) has more recently been challenged in favor of a model in which processing of negative emotions in BPD is associated with reduced activation of the amygdala, potentially signifying a diminished neural capacity for emotion regulation (Ruocco et al. 2013). Although these recent data suggest different patterns of abnormal brain activity in MDD and BPD, it has to be noted that this evidence comes from studies individually comparing MDD or BPD with HC, and not from explicit direct comparisons between the two diagnostic groups. Also, it is unclear at present to what extent regionally abnormal brain volume may predict aberrant neural activity levels in patients, and whether these effects are diagnosis-specific. Potential limitations of this study include the limited patient sample size and treatmentheterogeneity in both clinical samples. Both psychopharmacotherapy and psychotherapy could

ACCEPTED MANUSCRIPT potentially have confounding effects on brain structure (Hamilton et al. 2008). We also focused on female patients only to avoid potentially confounding effects of gender (Soloff et al. 2008, Lorenzetti et al. 2009). Thus, the findings of our study need to be confirmed in medication-

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free and/or psychotherapy-naïve samples including both female and male patients. We also opted to include BPD patients without specific comorbidities such as PTSD, BD or ADHD, again

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to reduce confounds imposed by phenotypic heterogeneity (Niedtfeld et al. 2013). In this respect, we are aware that our findings may not fully apply to BPD samples with other comorbid disorders, especially those presenting with PTSD. Eventually, investigating brain structure across diagnostic

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categories provides a starting point for characterizing neural phenotypes within and between disorders. Also, this study's multivariate statistical approach provides evidence for multiple

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aberrant structural networks in MDD and BPD and suggests that these while these disorders present with distinct structural abnormalities, they also substantially share structural deficits. At

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the same time, we are aware that inferring brain activity dynamics from brain structure is clearly limited. We are also aware that sensitivity analyses yielded low to moderate sensitivity of

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structural network expression for a diagnosis of MDD. Integrating functional with

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structural data may substantially increase diagnostic sensitivity and specificity, and potentially warrant the application of multimodal neuroimaging techniques in clinical

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practice. Eventually, for advanced multivariate structural data analysis techniques, incorporating graph models of the brain – such as network-based statistics (Zalesky et al. 2010) – may be of complementary value in future studies. In conclusion, we have shown that MDD and BPD patients display impaired integrity of several structural networks which are differentially linked to disorder-specific symptoms. In MDD vs. BPD, respectively, both common and distinct abnormal structural network patterns can be found. MDD was characterized by volume reduction of a “frontostriatal network” compared to both controls and BPD patients. BPD exhibited significant volume reduction within a “medial temporal network” compared to controls and MDD patients, and overall BPD symptoms and impulsivity measures were significantly associated with this structural pattern. Decreased “cingulate network” volume was found in both MDD and BPD, suggesting a common neurobiological substrate. Future

ACCEPTED MANUSCRIPT studies detailing the specificity of altered brain volume in MDD or BPD will particularly benefit from longitudinal study designs, from combined functional and structural neuroimaging protocols, and from disentangling possible medication confounds at both the cross-sectional and the

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longitudinal level of evidence.

References

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ACCEPTED MANUSCRIPT Figure legends Figure 1 Structural networks (top) and the magnitude of their expression (bottom) in patients with MDD,

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patients with BPD and healthy controls.

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Top: Shown are the three GMV components which significantly differed between controls and patients as a result of an ANOVA, p<0.05, FDR-corrected. The components were thresholded at Z>3.5 and rendered onto the anatomical template implemented in GIFT. The color bars indicate

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Z-values.

Bottom: GVM component loadings (means and standard error) plotted for patients with MDD,

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patients with BPD, and controls (HC). * indicates significant between group differences as

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revealed by post-hoc tests. n.s. = non significant.

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BPD (n=17)

HC (n=22)

mean

SD

mean

SD

mean

SD

p-value

age (years)

33.5

8.9

28.6

7.3

11.2

0.29a

DOI (years)

5.5

4.7

n.a.

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MDD (n=22)

2.7

1.9

2.0

HAMD

28.4

4.7

15.2

BDI

28.7

8.9

33.6

BSL

n.a.

BIS

n.a.

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n.a.

n.a. 0.23 b

1.6 5.7

0.7

1.4

<0.0001a

9.7

1.9

2.4

<0.0001a

57.6

19.8

n.a.

71.3

9.3

55.7

7.3

<0.0000b

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episodes

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no. of depressive

31.4

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Table 1. Demographics and clinical characteristics. MDD: major depressive disorder; BPD: borderline personality disorder; HC: healthy controls; TIV: total intracranial volume (sum of gray

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matter, white matter and cerebrospinal fluid); a: ANOVA; b: t-test; n.a.: not available; HAMD: Hamilton Depression Rating Scale; BDI: Beck Depression Inventory; BSL: Borderline Symptom List; BIS:

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Barratt Impulsiveness Scale.

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Brodmann Area

Middle Frontal Gyrus

L Z-score/coordinates (x,y,z)

R Z-score/coordinates (x,y,z)

volume (cc) L/R

8, 9, 10

4.8 (-31, 31, 30)

9.4 (36, 31, 29)

3.6/5.6

6.1 (25, 9, -2)

2.0/2.0

9

3.7 (-39, 21, 35)

Lentiform Nucleus

8.8 (-24, 4, 1)

Precentral Gyrus Caudate

7.3 (-12, 16, 1) 10

5.6 (-19, 59, 5)

Postcentral Gyrus

4.3 (-37, -61, 33)

Precuneus

4.0 (-12, -53, 52)

Brodmann Area

Middle Temporal Gyrus

19, 20, 21, 37

Parahippocampal Gyrus

28, 34, 35

Amygdala* Fusiform Gyrus

20, 37

Superior Temporal Gyrus

39

Inferior Temporal Gyrus

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Cingulate network Brain region

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Brodmann Area

Inferior Parietal Lobule

0/0.5

5.3 (36, -77, 9)

0/0.5

4.3 (39, -56, 36)

0.5/0.5 0.5/0

8.5 (-56, -44, -9)

8.5 (59, -36, -11)

3.1/5.6

6.6 (-28, -17, -17)

7.6 (25, -11, -18)

1.5/5.1

(-21, -4, -21)

(24, -8, -12)

6.7 (-42, -33, -16)

7.0 (42, -16, -24)

6.1 (-39, -15, -28)

L Z-score/coordinates (x,y,z)

1.0/1.5

6.4 (49, -53, 10)

0/1.5

5.8 (25, -4, -21)

1.0/0.5

5.8 (61, -21, -17)

0/1.5

6.0 (42, -70, 30) 4.4 (10, 48, -9)

0/0.5 0/0.5

R Z-score/coordinates (x,y,z)

volume (cc) L/R

24, 32

5.2 (-3, 30, 14)

6.4 (4, 31, 11)

2.6/3.1

8

6.8 (-24, 7, 44)

4.6 (27, -4, 44)

1.0/2.0

5.9 (-55, -19, 24)

6.4 (56, -16, 27)

0.5/0.5

5.5 (-13, -61, 31)

Middle Temporal Gyrus

40

Inferior Frontal Gyrus

1.5/0 5.3 (58, -45, -7)

0/1.5

5.1 (-42, -39, 38)

3.5 (43, -36, 39)

0.5/0.5

4.1 (-46, 32, 1)

4.4 (37, 7, 30)

0.5/1.0

4.6 (49, -43, 15)

0/0.5

3.9 (6, 17, 27)

0/0.5 0.5/0

Superior Temporal Gyrus Cingulate Gyrus Declive

3.6/2.0

volume (cc) L/R

20

Angular Gyrus Medial Frontal Gyrus

Precuneus

2.0/2.0

R Z-score/coordinates (x,y,z)

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Uncus

Postcentral Gyrus

6.8 (15, 16, 5)

6.1 (34, 38, 31)

L Z-score/coordinates (x,y,z)

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Medial temporal network Brain region

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Angular Gyrus

Middle Frontal Gyrus

0.5/1.5

5.4 (55, -18, 26)

Middle Occipital Gyrus

Anterior Cingulate

7.4 (37, 25, 35)

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Superior Frontal Gyrus

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Frontostriatal network Brain region

19

24 3.8 (-13, -70, -22)

Table 2. Structural networks showing gray matter volume differences between controls and patients with MDD and BPD (see also “Results” section for further details). For the networks shown in Figure 1, voxels >Z=3.5 were converted from MNI to Talairach coordinates and coupled with the Talairach Daemon database to provide anatomical labels. Maximum Z-values and stereotaxic coordinates (x, y, z) are provided for each hemisphere (left = L, right = R). The volume of voxels in each area is provided in cubic centimeters (cc). *Amygdala labels and coordinates were validated using the AAL.

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Figure 1

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ACCEPTED MANUSCRIPT Research Highlights

We employ a novel data analysis strategy to compare brain volume in MDD, BPD and controls. In MDD, striatal network volume is reduced compared to BPD.

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In BPD, medial temporal network volume is diminished compared to MDD.

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MDD and BPD are equally characterized by reduced cingulate network volume.

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Abnormal network structure is differentially linked to disorder-specific symptoms.

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