Borderline personality disorder (BPD) is characterized by a high prevalence of comorbid psychiatric disorders, including major depression (MD). The aim of this study was to examine whether a co-occurrence of MD is associated with structural changes in the amygdala of BPD patients.
Twenty-five right-handed, female patients with BPD and 25 matched healthy control subjects were examined. Diagnoses of BPD and MD were made according to DSM IV. Depressive symptomatology was determined with the Hamilton Depression Scale (HAMD). Magnetic resonance imaging scans were performed with 1.5 T Magnetom Vision (Siemens, Erlangen, Germany). The software program "BRAINS" was applied for brain volumetry and segmentation. The amygdala was delineated as "region of interest."
Comparison of amygdala volumes between the whole group of BPD patients and control subjects revealed no significant difference. Amygdala volumes in both hemispheres were significantly larger in BPD patients with MD compared with those without MD. There was a significant correlation in BPD patients between left amygdala volume and depressive symptoms as measured by HAMD.
Correlation of amygdala volume with depression in BPD patients might indicate a causal relationship. Future studies should clarify whether amygdala enlargement is a risk factor for MD in BPD patients or a consequence of the affective disorder.
"Therefore, they might be an additional brain region implicated in affect regulation and aggression. BPD patients' aggression has also been reported to be negatively associated with gray matter volume in the hippocampus (Sala et al., 2011; Zetzsche et al., 2007), but not in the amygdala (Zetzsche et al., 2006). Comparing male antisocial offenders with BPD to male antisocial offenders with high psychopathic traits, our group found specific structural abnormalities: Male BPD offenders showed smaller volume in brain regions involved in affect regulation (orbitofrontal and ventromedial prefrontal cortex), whereas psychopathic offenders showed less volume in cortical midline areas (dorsomedial prefrontal cortex and precuneus) that are involved in self-referential emotion processing and self-reflection and could thus mirror psychopathic callousness and poor moral judgment (Bertsch, Grothe, et al., 2013). "
[Show abstract][Hide abstract] ABSTRACT: This article proposes a multidimensional model of aggression in borderline personality disorder (BPD) from the perspective of the biobehavioral dimensions of affective dysregulation, impulsivity, threat hypersensitivity, and empathic functioning. It summarizes data from studies that investigated these biobehavioral dimensions using self-reports, behavioral tasks, neuroimaging, neurochemistry as well as psychophysiology, and identifies the following alterations: (a) affective dysregulation associated with prefrontal-limbic imbalance, enhanced heart rate reactivity, skin conductance, and startle response; (b) impulsivity also associated with prefrontal-limbic imbalance, central serotonergic dysfunction, more electroencephalographic slow wave activity, and reduced P300 amplitude in a 2-tone discrimination task; (c) threat hypersensitivity associated with enhanced perception of anger in ambiguous facial expressions, greater speed and number of reflexive eye movements to angry eyes (shown to be compensated by exogenous oxytocin), enhanced P100 amplitude in response to blends of happy versus angry facial expressions, and prefrontal-limbic imbalance; (d) reduced cognitive empathy associated with reduced activity in the superior temporal sulcus/gyrus and preliminary findings of lower oxytocinergic and higher vasopressinergic activity; and (e) reduced self-other differentiation associated with greater emotional simulation and hyperactivation of the somatosensory cortex. These biobehavioral dimensions can be nicely linked to conceptual terms of the alternative Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) model of BPD, and thus to a multidimensional rather than a traditional categorical approach. (PsycINFO Database Record
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"Most studies on brain volume in BPD used manual tracing methods –, –, , thereby following an a priori region-of-interest approach that allows for precise detection of small volume differences. To our knowledge, there are only six studies available that report whole-brain results on GMV in BPD using voxel-based morphometry (VBM), which is a technique to conduct voxel-wise comparisons of and gray matter concentration (GMC) between groups of subjects, searching for structural differences within the whole brain . "
[Show abstract][Hide abstract] ABSTRACT: Patients with Borderline Personality Disorder (BPD) showed reduced volume of amygdala and hippocampus, but similar findings are evident in Posttraumatic Stress Disorder (PTSD). Applying voxel-based morphometry (VBM) in a larger cohort of patients with BPD, we sought to extend earlier findings of volume abnormalities in limbic regions and to evaluate the influence of co-occurring PTSD in BPD patients. We used voxel-based morphometry to study gray matter volume (GMV) in 60 healthy controls (HC) and 60 patients with BPD. Subgroup analyses on 53 patients concerning the role of co-occurring PTSD were conducted. Additionally, regression analyses were calculated to assess the relation between borderline symptom severity as well as dissociative experiences and GMV. Differences in local GMV between patients with BPD and HC were observed in the amygdale and hippocampus as well as in the fusiform and cingulate gyrus. Co-occurring PTSD was accompanied by increased GMV in the superior temporal gyrus and dorsolateral prefrontal cortex. Independent of co-occurring PTSD, severity of BPD symptoms predicted smaller GMV in the amygdala and dorsal ACC. Dissociation was positively related to GMV in the middle temporal gyrus. We could replicate earlier findings of diminished limbic GMV in patients with BPD and additionally show that patients with co-morbid PTSD feature increased GMV in prefrontal regions associated with cognitive control.
PLoS ONE 06/2013; 8(6):e65824. DOI:10.1371/journal.pone.0065824 · 3.23 Impact Factor
"Several quantitative methods are available to analyze neuroimaging data. The development of voxel-based-morphometry (Ashburner and Friston, 2000), cortical surface modeling (Fischl et al., 1999), and deep-structures volumetry (Bigler et al., 1997; Appenzeller et al., 2005; Zetzsche et al., 2006)started a remarkable series of innovation. At the same time, functional magnetic resonance imaging (fMRI) based on BOLD signal (Ogawa et al., 1990) has become widely used in Neuroscience research. "
[Show abstract][Hide abstract] ABSTRACT: Pattern recognition methods have demonstrated to be suitable analyses tools to handle the high dimensionality of neuroimaging data. However, most studies combining neuroimaging with pattern recognition methods focus on two-class classification problems, usually aiming to discriminate patients under a specific condition (e.g., Alzheimer's disease) from healthy controls. In this perspective paper we highlight the potential of the one-class support vector machines (OC-SVM) as an unsupervised or exploratory approach that can be used to create normative rules in a multivariate sense. In contrast with the standard SVM that finds an optimal boundary separating two classes (discriminating boundary), the OC-SVM finds the boundary enclosing a specific class (characteristic boundary). If the OC-SVM is trained with patterns of healthy control subjects, the distance to the boundary can be interpreted as an abnormality score. This score might allow quantification of symptom severity or provide insights about subgroups of patients. We provide an intuitive description of basic concepts in one-class classification, the foundations of OC-SVM, current applications, and discuss how this tool can bring new insights to neuroimaging studies.
Frontiers in Neuroscience 12/2012; 6:178. DOI:10.3389/fnins.2012.00178 · 3.66 Impact Factor
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