scholarly journals Machine learning detects predictors of clinical change after Dialectical Behavior Therapy in Borderline Personality Disorder

2021 ◽  
Author(s):  
Said Enrique Jiménez ◽  
Diego Angeles-Valdez ◽  
Andres Rodríguez Delgado ◽  
Ana Fresán ◽  
Edgar Miranda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Borderline Personality Disorder ◽  
Personality Disorder ◽  
Dialectical Behavior Therapy ◽  
Cross Validation ◽  
Behavioral Therapy ◽  
Borderline Personality ◽  
Self Report ◽  
Clinical Change ◽  
Fold Cross Validation

Only 50% of the patients with Borderline Personality Disorder (BPD) respond to psychotherapies, such as Dialectical Behavioral Therapy (DBT), this might be increased by identifying baseline predictors of clinical change. We use machine learning to detect clinical features that could predict improvement/worsening for severity and impulsivity of BPD after DBT treatment. To predict illness severity, we analyzed data from 125 patients with BPD divided into 17 DBT psychotherapy groups, and for impulsiveness, we analyzed 89 patients distributed into 12 DBT groups. All patients were evaluated at baseline using widely self-report tests; ~70% of the sample were randomly selected and two machine learning models (lasso and Random forest [Rf]) were trained using 10-fold cross-validation and compared to predict the post-treatment response. Models’ generalization was assessed in ~30% of the remaining sample. Relevant variables for DBT (i.e. the mindfulness ability “non-judging”, or “non-planning” impulsiveness) measured at baseline, were robust predictors of clinical change after six months of weekly DBT sessions. Using 10-fold cross-validation, the Rf model had significantly lower prediction error than lasso for the BPD severity variable, Mean Absolute Error (MAE) lasso - Rf = 1.55 (95% CI, 0.63-2.48) as well as for impulsivity, MAE lasso - Rf = 1.97 (95% CI, 0.57 - 3.35). According to Rf and the permutations method, 34/614 significant predictors for severity and 17/614 for impulsivity were identified. Using machine learning to identify the most important clinical characteristics before starting DBT could be fundamental for personalized treatment and disease prognosis.

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

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.

Emotion-Oriented Coping Style Predicts Self-Harm in Response to Acute Psychiatric Hospitalization

Crisis ◽  
2020 ◽  
pp. 1-7
Author(s):  
Jacqueline M. Frei ◽  
Vladimir Sazhin ◽  
Melissa Fick ◽  
Keong Yap
Keyword(s):  
Borderline Personality Disorder ◽  
Personality Disorder ◽  
Psychiatric Hospitalization ◽  
Coping Styles ◽  
Psychiatric Inpatients ◽  
Borderline Personality ◽  
Self Report ◽  
Therapeutic Interventions ◽  
Coping Behaviors ◽  
Self Harm

Abstract. Psychiatric hospitalization can cause significant distress for patients. Research has shown that to cope with the stress, patients sometimes resort to self-harm. Given the paucity of research on self-harm among psychiatric inpatients, a better understanding of transdiagnostic processes as predictors of self-harm during psychiatric hospitalization is needed. The current study examined whether coping styles predicted self-harm after controlling for commonly associated factors, such as age, gender, and borderline personality disorder. Participants were 72 patients (mean age = 39.32 years, SD = 12.29, 64% male) admitted for inpatient treatment at a public psychiatric hospital in Sydney, Australia. Participants completed self-report measures of coping styles and ward-specific coping behaviors, including self-harm, in relation to coping with the stress of acute hospitalization. Results showed that younger age, diagnosis of borderline personality disorder, and higher emotion-oriented coping were associated with self-harm. After controlling for age and borderline personality disorder, higher levels of emotion-oriented coping were found to be a significant predictor of self-harm. Findings were partially consistent with hypotheses; emotion-oriented but not avoidance-oriented coping significantly predicted self-harm. This finding may help to identify and provide psychiatric inpatients who are at risk of self-harm with appropriate therapeutic interventions.

scholarly journals Dyadic Emotion Regulation in Women with Borderline Personality Disorder

2021 ◽  
Author(s):  
Annemarie Miano ◽  
Sven Barnow ◽  
Stina Wagner ◽  
Stefan Roepke ◽  
Isabel Dziobek
Keyword(s):  
Emotion Regulation ◽  
Borderline Personality Disorder ◽  
Personality Disorder ◽  
Positive Emotions ◽  
Male Partner ◽  
Borderline Personality ◽  
Self Report ◽  
Negative Behaviors ◽  
Support Seeking ◽  
Threatening Situation

Abstract Background Emotion regulation (ER) and interpersonal dysfunction constitute key features of borderline personality disorder (BPD). Here, we tested if females with BPD show impairments in dyadic ER, that is in their support seeking and creation of closeness. We investigated if women with BPD might over-rely on their male partner by excessive support seeking and establishing of closeness, during conversations with personally and relationship-threatening topics. Methods Thirty couples in which the women were diagnosed with BPD and 34 healthy control (HC) couples were videotaped while discussing neutral, personally threatening, and relationship-threatening topics. Support seeking was rated by three independent raters, using a naïve observer method. The creation of closeness was rated using a continuous video-rating. Perceived emotions were assessed using self-report after each conversation. Results Women with BPD engaged in more support seeking than HC women, especially in more negative behaviors to elicit support, they created less closeness to their partner than HCs and reported a greater decline of positive emotions in both threatening conditions. Women with BPD displayed more fluctuations than controls between creating closeness and distance in the personally-threatening situation. They reported a larger increase in negative emotions after the relationship threatening conversation compared to female HC. Conclusions The present study indicates an increased demand of dyadic ER in BPD. Increased negative support seeking and less creation of closeness to the partner might reflect ineffective strategies to actually receive support from the partner.

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