Resting-state functional connectivity of neurotransmitter producing sites in female patients with borderline personality disorder

BackgroundStudies in borderline personality disorder (BPD) have consistently revealed abnormalities in fronto-limbic brain regions during emotional, somatosensory and cognitive challenges. Here we investigated changes in resting-state functional connectivity (RSFC) of three fronto-limbic core regions of specific importance to BPD.MethodFunctional magnetic resonance imaging data were acquired in 20 unmedicated female BPD patients and 17 healthy controls (HC, matched for age, sex and education) during rest. The amygdala, and the dorsal and ventral anterior cingulate cortex (ACC) were defined as seeds to investigate RSFC patterns of a medial temporal lobe network, the salience network and default mode network. The Dissociation Experience Scale (DES), a measure of trait dissociation, was additionally used as a predictor of RSFC with these seed regions.ResultsCompared with HC, BPD patients showed a trend towards increased RSFC between the amygdala and the insula, orbitofrontal cortex and putamen. Compared with controls, patients furthermore exhibited diminished negative RSFC between the dorsal ACC and posterior cingulate cortex, a core region of the default mode network, and regions of the dorsomedial prefrontal cortex. Last, increased negative RSFC between the ventral ACC and medial occipital regions was observed in BPD patients. DES scores were correlated with amygdala connectivity with the dorsolateral prefrontal cortex and fusiform gyrus.ConclusionsOur findings suggest alterations in resting-state networks associated with processing of negative emotions, encoding of salient events, and self-referential processing in individuals with BPD compared with HC. These results shed more light on the role of abnormal brain connectivity in BPD.

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Multiscale classification reveals a multivariate functional connectivity marker for borderline personality disorder

10.31219/osf.io/gdqru ◽

2021 ◽

Author(s):

Juha M. Lahnakoski ◽

Tobias Nolte ◽

Alec Solway ◽

Iris Vilares ◽

Andreas Hula ◽

...

Keyword(s):

Borderline Personality Disorder ◽

Functional Connectivity ◽

Personality Disorder ◽

Cross Validation ◽

Brain Connectivity ◽

Borderline Personality ◽

Small Sample ◽

Self Report ◽

Validation Dataset ◽

Resting State Functional Connectivity

BackgroundFunctional connectivity measures have garnered interest as possible biomarkers of psychiatric disorders including borderline personality disorder (BPD). However, small sample sizes and lack of within-study replications have led to divergent findings with no clear spatial foci. Therefore, we adopted an exploratory full-brain approach in the current study to evaluate which combinations of regions are most consistently predictive of BPD diagnosis.MethodsWe studied fMRI resting state functional connectivity in matched subsamples of 116 BPD and 72 control individuals defined by three grouping strategies: 1) referral diagnosis, 2) clinical diagnostic interview excluding patients no longer filling diagnostic criteria or controls scoring above threshold in a screening questionnaire and 3) self-reported symptom severity. We predicted BPD status using classifiers with repeated cross-validation based on multiscale functional connectivity within and between regions of interest (ROIs) covering the whole brain— global ROI-based network, seed-based ROI-connectivity, functional consistency and voxel-to-voxel connectivity within and between ROIs. Finally, we evaluated the generalizability of the classification in the left-out portion of non-matched data.ResultsFull-brain connectivity allowed successful classification (~70%) of BPD patients vs. control individuals in matched inner cross-validation. The classification remained significant when applied to unmatched out-of-sample data, but accuracies were lower (~61–70%) than in fully matched samples. The over-estimation of inner cross-validation accuracy was exacerbated by univariate regression of nuisance variables, particularly in smaller samples. Highest seed-based accuracies were in a similar range to global accuracies (~70–75%), but spatially more specific. In the seed-based classification, the regions implicated most often included midline, temporal and somatomotor regions. Highest accuracies were achieved with the clinical interview followed by referral diagnosis group definition. Self-report results remained at chance level. The accuracies were affected by an interaction of medications and global signal and univariate nuisance regression. Pairwise correlations, local consistencies and fine-scale connectivity matrices were not significantly predictive of BPD after multiple comparison corrections, but weak local effects coincided with the most discriminative ROIs in the classification. ConclusionsOur multivariate results indicate that complex global functional connectivity differences are moderately predictive of BPD despite heterogeneity of the patient population. However, univariate nuisance regression applied to full cross-validation dataset can cause inflation of accuracies compared with left-out test data.

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