Individualized treatment response prediction of dialectical behavior therapy for borderline personality disorder using multimodal magnetic resonance imaging

One Year of Outpatient Dialectical Behavioral Therapy and Its Impact on Neuronal Correlates of Attachment Representation in Patients with Borderline Personality Disorder Using a Personalized fMRI Task

(1) Background: BPD is characterized by affect dysregulation, interpersonal problems, and disturbances in attachment, but neuroimaging studies investigating attachment representations in BPD are rare. No study has examined longitudinal neural changes associated with interventions targeting these impairments. (2) Methods: We aimed to address this gap by performing a longitudinal neuroimaging study on n = 26 patients with BPD treated with Dialectic Behavioral Therapy (DBT) and n = 26 matched healthy controls (HCs; post intervention point: n = 18 BPD and n = 23 HCs). For functional imaging, we applied an attachment paradigm presenting attachment related scenes represented in drawings paired with related neutral or personalized sentences from one's own attachment narratives. In a prior cross-sectional investigation, we identified increased fMRI-activation in the human attachment network, in areas related to fear response and the conflict monitoring network in BPD patients. These were especially evident for scenes from the context of loneliness (monadic pictures paired with individual narrative sentences). Here, we tested whether these correlates of attachment representation show a near-to-normal development over one year of DBT intervention. In addition, we were interested in possible associations between fMRI-activation in these regions-of-interest (ROI) and clinical scores. (3) Results: Patients improved clinically, showing decreased symptoms of borderline personality organization (BPI) and increased self-directedness (Temperament and Character Inventory, TCI) over treatment. fMRI-activation was increased in the anterior medial cingulate cortex (aMCC) and left amygdala in BPD patients at baseline which was absent after intervention. When investigating associations between scores (BPI, TCI) and functional activation, we found significant effects in the bilateral amygdala. In contrast, aMCC activation at baseline was negatively associated with treatment outcome, indicating less effective treatment effects for those with higher aMCC activation at baseline. (4) Conclusions: Monadic attachment scenes with personalized sentences presented in an fMRI setup are capable of identifying increased activation magnitude in BPD. After successful DBT treatment, these increased activations tend to normalize which could be interpreted as signs of a better capability to regulate intensive emotions in the context of "social pain" towards a more organized/secure attachment representation. Amygdala activation, however, indicates high correlations with pre-treatment scores; activation in the aMCC is predictive for treatment gain. Functional activation of the amygdala and the aMCC as a response to attachment scenes representing loneness at baseline might be relevant influencing factors for DBT-intervention outcomes.

[Evidence-based inpatient psychotherapy in borderline personality disorder]

BACKGROUND

Borderline personality disorder (BPD) is frequent (prevalence in Germany between 0.7% and 4.5%) [11] and is associated with a high level of psychological stress and frequent emergency inpatient admissions. The provision of disorder-specific outpatient psychotherapy is still insufficient also in Germany.

OBJECTIVE

This article provides an overview of the available data on the effectiveness of inpatient psychotherapy for BPD.

MATERIAL AND METHODS

A qualitative review on the effectiveness and therapy outcome predictors was conducted based on a literature search in PubMed.

RESULTS

Overall, very few randomized controlled trials are available; in contrast uncontrolled studies are predominant. Most evidence is available for dialectical behavior therapy (DBT) but other approaches, including psychodynamic procedures, have also been studied.

DISCUSSION

The currently available data suggest an efficacy of inpatient psychotherapy for BPD; however, randomized trials with larger samples and sufficient representation including male patients are largely lacking. There is also no substantial direct evidence for the superiority of inpatient compared to outpatient psychotherapy.

Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review

BACKGROUND

Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges.

OBJECTIVE

This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality.

METHODS

A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided.

RESULTS

A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126).

CONCLUSIONS

These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.

Improving treatment outcomes for borderline personality disorder: what can we learn from biomarker studies of psychotherapy?

PURPOSE OF REVIEW

Borderline personality disorder (BPD) is a severe and common psychiatric disorder and though evidence-based psychotherapies are effective, rates of treatment nonresponse are as high as 50%. Treatment studies may benefit from interdisciplinary approaches from neuroscience and genetics research that could generate novel insights into treatment mechanisms and tailoring interventions to the individual.

RECENT FINDINGS

We provide a timely update to the small but growing body of literature investigating neurobiological and epigenetic changes and using biomarkers to predict outcomes from evidence-based psychotherapies for BPD. Using a rapid review methodology, we identified eight new studies, updating our earlier 2018 systematic review. Across all studies, neuroimaging ( n  = 18) and genetics studies ( n  = 4) provide data from 735 participants diagnosed with BPD (mean sample size across studies = 33.4, range 2-115).

SUMMARY

We report further evidence for psychotherapy-related alterations of neural activation and connectivity in regions and networks relating to executive control, emotion regulation, and self/interpersonal functioning in BPD. Emerging evidence also shows epigenetic changes following treatment. Future large-scale multisite studies may help to delineate multilevel treatment targets to inform intervention design, selection, and monitoring for the individual patient via integration of knowledge generated through clinical, neuroscience, and genetics research.

Amygdala connectivity and aggression

Neurobiological models propose that reactive aggression is predicated on impairments in amygdala-prefrontal connectivity that subserves moral decision-making and emotion regulation. The amygdala is a key component within this neural network that modulates reactive aggression. We provide a review of amygdala dysfunctional brain networks leading to reactive aggressive behavior. We elaborate on key concepts, focusing on moral decision-making and emotion regulation in a developmental context, and brain network connectivity factors relating to amygdala (dys)function-factors which we suggest predispose to reactive aggression. We additionally discuss insights into the latest treatment interventions, providing the utilization of the scientific findings for practice.

Investigating predictors of treatment response in Dialectical Behavior Therapy for borderline personality disorder using LASSO regression

OBJECTIVE

Prior studies of Dialectical Behavior Therapy (DBT) for borderline personality disorder (BPD) have yielded heterogeneous findings on what factors differentiate individuals with or without sufficient treatment response, highlighting the need for further research.

METHOD

We investigated a sample of 105 individuals with BPD receiving a 6-month course of DBT. Participants were categorized as sufficient or insufficient responders using clinical and statistical change indices (based on emotion dysregulation, BPD symptom severity, utilization of DBT skills, and functional impairment). Sociodemographic, clinical severity, and treatment process factors were tested as potential predictors of treatment response using a machine learning approach (LASSO regression).

RESULTS

Two cross-validated LASSO regression models predicted treatment response (AUCs > .75). They suggested that higher homework completion rate, retention in treatment, and greater baseline severity were the most important predictors of DBT treatment response indicated by BPD symptom severity and utilization of DBT skills. Favorable effects of some aspects of therapeutic alliance during initial sessions were also found.

CONCLUSIONS

Future research may benefit from consolidating the criteria of treatment response, identifying clinically relevant variables, and testing the generalizability of findings to enhance knowledge of insufficient treatment response in DBT for BPD.

Amygdala response predicts clinical symptom reduction in patients with borderline personality disorder: A pilot fMRI study

Borderline personality disorder (BPD) is a prevalent, devastating, and heterogeneous psychiatric disorder. Treatment success is highly variable within this patient group. A cognitive neuroscientific approach to BPD might contribute to precision psychiatry by identifying neurocognitive factors that predict who will benefit from a specific treatment. Here, we build on observations that BPD is accompanied by the enhanced impact of the aversive effect on behavior and abnormal neural signaling in the amygdala. We assessed whether BPD is accompanied by abnormal aversive regulation of instrumental behavior and associated neural signaling, in a manner that is predictive of symptom reduction after therapy. We tested a clinical sample of 15 female patients with BPD, awaiting dialectical behavior therapy (DBT), and 16 matched healthy controls using fMRI and an aversive Pavlovian-to-instrumental transfer (PIT) task that assesses how instrumental behaviors are influenced by aversive Pavlovian stimuli. Patients were assessed 1 year after the start of DBT to quantify changes in BPD symptom severity. At baseline, behavioral aversive PIT and associated neural signaling did not differ between groups. However, the BOLD signal in the amygdala measured during aversive PIT was associated with symptom reduction at 1-year follow-up: higher PIT-related aversive amygdala signaling before treatment was associated with reduced clinical improvement at follow-up. Thus, within the evaluated group of BPD patients, the BOLD signal in the amygdala before treatment was related to clinical symptom reduction 1 year after the start of treatment. The results suggest that less PIT-related responsiveness of the amygdala increases the chances of treatment success. We note that the relatively small sample size is a limitation of this study and that replication is warranted.

Machine learning detects predictors of symptom severity and impulsivity after dialectical behavior therapy skills training group in borderline personality disorder

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 skills training group. 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, 34/613 significant predictors for severity and 17/613 for impulsivity were identified. Using machine learning to identify the most important variables before starting DBT could be fundamental for personalized treatment and disease prognosis.

A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults

Artificial intelligence techniques were explored to assess the ability to anticipate self-harming behaviour in the mental health context using a database collected by an app previously designed to record the emotional states and activities of a group of subjects exhibiting self-harm. Specifically, the Leave-One-Subject-Out technique was used to train classification trees with a maximum of five splits. The results show an accuracy of 84.78%, a sensitivity of 64.64% and a specificity of 85.53%. In addition, positive and negative predictive values were also obtained, with results of 14.48% and 98.47%, respectively. These results are in line with those reported in previous work using a multilevel mixed-effect regression analysis. The combination of apps and AI techniques is a powerful way to improve the tools to accompany and support the care and treatment of patients with this type of behaviour. These studies also guide the improvement of apps on the user side, simplifying and collecting more meaningful data, and on the therapist side, progressing in pathology treatments. Traditional therapy involves observing and reconstructing what had happened before episodes once they have occurred. This new generation of tools will make it possible to monitor the pathology more closely and to act preventively.

Repetitive transcranial magnetic stimulation in patients with borderline personality disorder: A systematic review

The literature on the application of repetitive transcranial magnetic stimulation (rTMS) in Borderline Personality Disorder (BPD) is unclear, even though its neuromodulatory effects on underlying neural circuitry involved in BPD symptoms suggest that it could be a potential treatment option. We sought to review the evidence on rTMS as a treatment option in BPD. PubMed (for Medline database), Google Scholar, and Scopus were systematically searched following the PRISMA guidelines for studies of any design examining the application of the rTMS treatment in adult patients with precise and primary diagnosis of BPD written in the English language. The systematic review has been registered on PROSPERO (CRD42020215927). Forty one records were screened, and eight fulfilled inclusion criteria (total of 63 patients). The existing studies suggest that rTMS is a well-tolerated treatment in patients with BPD. Double-blind randomized controlled studies are necessary to help elucidate the effects of rTMS in the different symptoms in BPD and establish efficacy and the best cortical targets and stimulation protocols. Longitudinal studies that combine evidenced based psychotherapy with rTMS may be a future line of investigation that could potentially improve outcomes for this population.

Functional MRI findings in personality disorders: A review

Personality disorders (PDs) have a prevalence of approximately 10% in the United States, translating to over 30 million people affected in just one country. The true prevalence of these disorders may be even higher, as the paucity of objective diagnostic criteria could be leading to underdiagnosis. Because little is known about the underlying neuropathologies of these disorders, patients are diagnosed using subjective criteria and treated nonspecifically. To better understand the neural aberrancies responsible for these patients' symptoms, a review of functional MRI literature was performed. The findings reveal that each PD is characterized by a unique set of activation changes corresponding to individual structures or specific neural networks. While unique patterns of neural activity are distinguishable within each PD, aberrations of the limbic/paralimbic structures and default mode network are noted across several of them. In addition to identifying valuable activation patterns, this review reveals a void in research pertaining to paranoid, schizoid, histrionic, narcissistic, and dependent PDs. By delineating patterns in PD neuropathology, we can more effectively direct future research efforts toward enhancing objective diagnostic techniques and developing targeted treatment modalities. Furthermore, understanding why patients are manifesting certain symptoms can advance clinical awareness and improve patient outcomes.

Looking at the bigger picture: Cortical volume, thickness and surface area characteristics in borderline personality disorder with and without posttraumatic stress disorder

Borderline personality disorder (BPD) is a severe psychiatric disorder accompanied by multiple comorbidities. Neuroimaging studies have identified structural abnormalities in BPD with most findings pointing to gray matter volume reductions in the fronto-limbic network, although results remain inconsistent. Similar alterations were found in posttraumatic stress disorder (PTSD), a common comorbidity of BPD. Only a small number of studies have investigated structural differences in BPD patients regarding comorbid PTSD specifically and studies conducting additional surface analyses are scarce. We investigated structural differences in women with BPD with and without PTSD and non-patient controls. Automated voxel-based and region-based volumetric analyses were applied. Additionally, four surface-based measures were analyzed: cortical thickness, gyrification index, fractal dimension, and sulcus depth. Analyses did not identify cortical volume alterations in the fronto-limbic network. Instead, hypergyrification was detected in the right superior parietal cortex in BPD patients compared to non-patient controls. No distinction was revealed between BPD patients with and without PTSD. These findings underline the importance of a holistic investigation examining volumetric and surface measures as these might enhance the understanding of structural alterations in BPD.

Parsing variability in borderline personality disorder: a meta-analysis of neuroimaging studies

Though a plethora of functional magnetic resonance imaging (fMRI) studies explored the neurobiological underpinnings of borderline personality disorder (BPD), findings across different tasks were divergent. We conducted a systematic review and activation likelihood estimation (ALE) meta-analysis on the fMRI studies conducted in BPD patients compared to healthy controls (HC). We systematically searched PubMed and PsychINFO from inception until July 9th 2020 using combinations of database-specific terms like 'fMRI', 'Neuroimaging', 'borderline'. Eligible studies employed task-based fMRI of the brain in participants of any age diagnosed with BPD compared to HC, during any behavioral task and providing a direct contrast between the groups. From 762 entries, we inspected 92 reports full-texts and included 52 studies (describing 54 experiments). Across all experiments, the HC > BPD and BPD > HC meta-analyses did not yield any cluster of significant convergence of differences. Analyses restricted to studies of emotion processing revealed two significant clusters of activation in the bilateral hippocampal/amygdala complex and anterior cingulate for the BPD > HC meta-analysis. Fail-safe N and single study sensitivity analysis suggested significant findings were not robust. For the subgroup of emotional processing experiments, on a restricted number of experiments providing results for each group separately, another meta-analysis method (difference of convergence) showed a significant cluster in the insula/inferior frontal gyrus for the HC > BPD contrast. No consistent pattern of alteration in brain activity for BPD was evidenced suggesting substantial heterogeneity of processes and populations studied. A pattern of amygdala dysfunction emerged across emotion processing tasks, indicating a potential pathophysiological mechanism that could be transdiagnostic.

[Neurobiological principles of borderline personality disorder: integration into the ICD-11 model of personality disorders]

BACKGROUND

Borderline personality disorder (BPD) is a severe mental disorder associated with far-reaching impairments in the self and interpersonal functioning. The broad database has contributed to the fact that BPD remains the only categorical personality diagnosis in ICD-11, even if coupled to the determination of the severity of impairments. Nevertheless, we need to deal with a dimensional conceptualization of personality disorders-which is also supported by neuroscientific findings-at the latest in 2022 when the ICD-11 comes into effect .

OBJECTIVE

This narrative review provides an overview of neuroscientific findings regarding impairments in self and interpersonal functioning in patients with BPD.

RESULTS

Alterations in the medial prefrontal cortex, temporoparietal junction and precuneus mediate deficits in self-referential thought processes and the mentalization of emotions and intentions of others. Enhanced connectivity between the amygdala and midline structures is associated with hypermentalization. At the same time, elevated insula activation seems to underlie the strengthened nonreflective parts of feelings of other people. Frontolimbic alterations are transdiagnostically associated with deficient emotional regulation and negative affectivity and alterations in reward and cognitive control regions are related to impulsivity.

CONCLUSION

Neuroscientific findings help to have a better understanding of the underlying mechanisms of central functional impairments in BPD and can support the transition to ICD-11 as well as the implementation of new interventions.

Neural correlates of treatment effect and prediction of treatment outcome in patients with PTSD and comorbid personality disorder: study design

BACKGROUND

Neural alterations related to treatment outcome in patients with both post-traumatic stress disorder (PTSD) and comorbid personality disorder are unknown. Here we describe the protocol for a neuroimaging study of treatment of patients with PTSD and comorbid borderline (BPD) or cluster C (CPD) personality disorder traits. Our specific aims are to 1) investigate treatment-induced neural alterations, 2) predict treatment outcome using structural and functional magnetic resonance imaging (MRI) and 3) study neural alterations associated with BPD and CPD in PTSD patients. We hypothesize that 1) all treatment conditions are associated with normalization of limbic and prefrontal brain activity and hyperconnectivity in resting-state brain networks, with additional normalization of task-related activation in emotion regulation brain areas in the patients who receive trauma-focused therapy and personality disorder treatment; 2) Baseline task-related activation, together with structural brain measures and clinical variables predict treatment outcome; 3) dysfunction in task-related activation and resting-state connectivity of emotion regulation areas is comparable in PTSD patients with BPD or CPD, with a hypoconnected central executive network in patients with PTSD+BPD.

METHODS

We aim to include pre- and post-treatment 3 T-MRI scans in 40 patients with PTSD and (sub) clinical comorbid BPD or CPD. With an expected attrition rate of 50%, at least 80 patients will be scanned before treatment. MRI scans for 30 matched healthy controls will additionally be acquired. Patients with PTSD and BPD were randomized to either EMDR-only or EMDR combined with Dialectical Behaviour Therapy. Patients with PTSD and CPD were randomized to Imaginary Rescripting (ImRs) or to ImRs combined with Schema Focused Therapy. The scan protocol consists of a T1-weighted structural scan, resting state fMRI, task-based fMRI during an emotional face task and multi-shell diffusion weighted images. For data analysis, multivariate mixed-models, regression analyses and machine learning models will be used.

DISCUSSION

This study is one of the first to use neuroimaging measures to predict and better understand treatment response in patients with PTSD and comorbid personality disorders. A heterogeneous, naturalistic sample will be included, ensuring generalizability to a broad group of treatment seeking PTSD patients.

TRIAL REGISTRATION

Clinical Trials, NCT03833453 & NCT03833531 . Retrospectively registered, February 2019.

Dissociation in Borderline Personality Disorder: Recent Experimental, Neurobiological Studies, and Implications for Future Research and Treatment

PURPOSE OF REVIEW

The aim of this review article is to give an overview over recent experimental neurobiological research on dissociation in borderline personality disorder (BPD), in order to inform clinicians and to stimulate further research. First, we introduce basic definitions and models that conceptualize dissociation from a transdiagnostic perspective. Then, we discuss recent findings in BPD.

RECENT FINDINGS

Stress-related dissociation is a key symptom of BPD, closely linked to other core domains of the disorder (emotion dysregulation, identity disturbances, and interpersonal disturbances). The understanding of neurobiological correlates of dissociation across different psychiatric disorders (e.g., dissociative disorders, post-traumatic stress disorder) is steadily increasing. At the same time, studies explicitly focusing on dissociation in BPD are still scarce. There is evidence for adverse effects of dissociation on affective-cognitive functioning (e.g., interference inhibition), body perception, and psychotherapeutic treatment response in BPD. On the neural level, increased activity in frontal regions (e.g., inferior frontal gyrus) and temporal areas (e.g., inferior and superior temporal gyrus) during symptom provocation tasks and during resting state was observed, although findings are still diverse and need to be replicated. Conceptual differences and methodological differences in study designs and sample characteristics (e.g., comorbidities, trauma history) hinder a straightforward interpretation and comparison of studies. Given the potentially detrimental impact of dissociation in BPD, more research on the topic is strongly needed to deepen the understanding of this complex clinical condition.

Analyzing brain structural differences among undergraduates with different grades of self-esteem using multiple anatomical brain network

Background

Self-esteem is the individual evaluation of oneself. People with high self-esteem grade have mental health and can bravely cope with the threats from the environment. With the development of neuroimaging techniques, researches on cognitive neural mechanisms of self-esteem are increased. Existing methods based on brain morphometry and single-layer brain network cannot characterize the subtle structural differences related to self-esteem.

Method

To solve this issue, we proposed a multiple anatomical brain network based on multi-resolution region of interest (ROI) template to study the brain structural connections of self-esteem. The multiple anatomical brain network consists of ROI features and hierarchal brain network features that are extracted from structural MRI. For each layer, we calculated the correlation relationship between pairs of ROIs. In order to solve the high-dimensional problem caused by the large amount of network features, feature selection methods (t-test, mRMR, and SVM-RFE) are adopted to reduce the number of features while retaining discriminative information to the maximum extent. Multi-kernel SVM is employed to integrate the various types of features by appropriate weight coefficient.

Result

The experimental results show that the proposed method can improve classification accuracy to 97.26% compared with single-layer brain network.

Conclusions

The proposed method provides a new perspective for the analysis of brain structural differences of self-esteem, which also has potential guiding significance in other researches involved brain cognitive activity and brain disease diagnosis.

Neural Changes in Borderline Personality Disorder After Dialectical Behavior Therapy-A Review

The biological component of the biosocial theory of emotion regulation stipulates that borderline personality disorder (BPD) arises from biological vulnerabilities to heightened emotional reactivity. Comprehensive reviews have consistently implicated abnormalities in the amygdala, anterior cingulate cortex, and hippocampus in the neurobiology of BPD. While Dialectical Behavior Therapy (DBT) is the leading evidence-based psychotherapy for the treatment of BPD, there remains a paucity of literature examining changes in the neurobiology of BPD following DBT treatment. Nine studies were identified that examined neurobiological changes in BPD after the completion of DBT. Results indicated that there was significant deactivation of amygdala activity as well as the anterior cingulate cortex in patients with BPD after DBT treatment. As well, several studies found after DBT treatment, BPD patients had a decreased activity in the inferior frontal gyrus in response to arousing stimuli and increased activity in response to inhibitory control. Future research on the neurobiological change after DBT treatment can help clarify biological mechanisms of change in BPD.

Clinical Efficacy and Cost-Effectiveness of Imagery Rescripting Only Compared to Imagery Rescripting and Schema Therapy in Adult Patients With PTSD and Comorbid Cluster C Personality Disorder: Study Design of a Randomized Controlled Trial

Background: Posttraumatic stress disorder (PTSD) is a serious and relatively common mental disorder causing a high burden of suffering. Whereas evidence-based treatments are available, dropout and non-response rates remain high. PTSD and Cluster C personality disorders (avoidant, dependent or obsessive-compulsive personality disorder; CPD) are highly comorbid and there is evidence for suboptimal treatment effects in this subgroup of patients. An integrated PTSD and CPD treatment may be needed to increase treatment efficacy. However, no studies directly comparing the efficacy of regular PTSD treatment and treatment tailored to PTSD and comorbid CPD are available. Whether integrated treatment is more effective than treatment focused on PTSD alone is important, since (1) no evidence-based guideline for PTSD and comorbid CPD treatment exists, and (2) treatment approaches to CPD are costly and time consuming. Present study design describes a randomized controlled trial (RCT) directly comparing trauma focused treatment with integrated trauma focused and personality focused treatment. Methods: An RCT with two parallel groups design will be used to compare the clinical efficacy and cost-effectiveness of "standalone" imagery rescripting (n = 63) with integrated imagery rescripting and schema therapy (n = 63). This trial is part of a larger research project on PTSD and personality disorders. Predictors, mediators and outcome variables are measured at regular intervals over the course of 18 months. The main outcome is PTSD severity at 12 months. Additionally, machine-learning techniques will be used to predict treatment outcome using biopsychosocial variables. Discussion: This study protocol outlines the first RCT aimed at directly comparing the clinical efficacy and cost-effectiveness of imagery rescripting and integrated imagery rescripting and schema therapy for treatment seeking adult patients with PTSD and comorbid cluster C personality pathology. Additionally, biopsychosocial variables will be used to predict treatment outcome. As such, the trial adds to the development of an empirically informed and individualized treatment indication process. Clinical Trial registration: ClinicalTrials.gov, NCT03833531.

Präzisionspsychiatrie und der Beitrag von Brain Imaging und anderen Biomarkern

'Precision Psychiatry' as the psychiatric variant of 'Precision Medicine' aims to provide high-level diagnosis and treatment based on robust biomarkers and tailored to the individual clinical, neurobiological, and genetic constitution of the patient. The specific peculiarity of psychiatry, in which disease entities are normatively defined based on clinical experience and are also significantly influenced by contemporary history, society and philosophy, has so far made the search for valid and reliable psychobiological connections difficult. Nevertheless, considerable progress has now been made in all areas of psychiatric research, made possible above all by the critical review and renewal of previous concepts of disease and psychopathology, the increased orientation towards neurobiology and genetics, and in particular the use of machine learning methods. Notably, modern machine learning methods make it possible to integrate high-dimensional and multimodal data sets and generate models which provide new psychobiological insights and offer the possibility of individualized, biomarker-driven single-subject prediction of diagnosis, therapy response and prognosis. The aim of the present review is therefore to introduce the concept of 'Precision Psychiatry' to the interested reader, to concisely present modern, machine learning methods required for this, and to clearly present the current state and future of biomarker-based 'precision psychiatry'.

A scoping review of machine learning in psychotherapy research

Machine learning (ML) offers robust statistical and probabilistic techniques that can help to make sense of large amounts of data. This scoping review paper aims to broadly explore the nature of research activity using ML in the context of psychological talk therapies, highlighting the scope of current methods and considerations for clinical practice and directions for future research. Using a systematic search methodology, fifty-one studies were identified. A narrative synthesis indicates two types of studies, those who developed and tested an ML model (k=44), and those who reported on the feasibility of a particular treatment tool that uses an ML algorithm (k=7). Most model development studies used supervised learning techniques to classify or predict labeled treatment process or outcome data, whereas others used unsupervised techniques to identify clusters in the unlabeled patient or treatment data. Overall, the current applications of ML in psychotherapy research demonstrated a range of possible benefits for indications of treatment process, adherence, therapist skills and treatment response prediction, as well as ways to accelerate research through automated behavioral or linguistic process coding. Given the novelty and potential of this research field, these proof-of-concept studies are encouraging, however, do not necessarily translate to improved clinical practice (yet).

Daily Self-Monitoring of Symptoms and Skills Learning in Patients With Borderline Personality Disorder Through a Mobile Phone App: Protocol for a Pragmatic Randomized Controlled Trial

Background

Patient self-monitoring via mobile phones during psychotherapy can enhance and provide an overview of psychotherapeutic progress by graphically displaying current and previous symptom scores, providing feedback to the patient, delivering psychoeducative material, and providing timely data to the therapist or treatment team.

Objective

This study will aim to assess the effects of using a mobile phone to self-monitor symptoms and acquire coping skills instead of using pen and paper during psychotherapy in patients with borderline personality disorder (BPD). Dialectical behavior therapy will be performed to treat BPD. The primary outcome is the mean time needed to learn coping skills directed at emotion regulation; the secondary outcome is changes in the BPD symptom score as measured by the Zanarini Rating Scale for Borderline Personality Disorder.

Methods

This study is a pragmatic, multicenter randomized controlled trial. Participants were recruited through five public general psychiatric outpatient treatment facilities in Denmark. Patients are randomly assigned, on a 1:1 basis, to either the mobile phone condition (using the Monsenso mDiary mobile app) or pen-and-paper condition. Patients will complete several self-report questionnaires on symptom severity; assessments by trained raters on BPD severity will be performed as well. Survival analysis with a shared frailty model will be used to assess the primary outcome.

Results

Recruitment began in June 2017 and was completed in February 2019 after 80 participants were recruited. The study ended in February 2020. It is expected that the benefits of mobile phone–based self-report compared to the pen-and-paper method will be demonstrated for skill learning speed and registration compliance. To our knowledge, this is the first trial exploring the impact of cloud-based mobile registration in BPD treatment.

Conclusions

This trial will report on the effectiveness of mobile phone–based self-monitoring during psychiatric treatment. It has the potential to contribute to evidence-based clinical practice since apps are already in use clinically.

Trial Registration

ClinicalTrials.gov NCT03191565; https://clinicaltrials.gov/ct2/show/NCT03191565

International Registered Report Identifier (IRRID)

DERR1-10.2196/17737

Technology-Based Psychosocial Interventions for People with Borderline Personality Disorder: A Scoping Review of the Literature

Evidence-based psychosocial interventions for borderline personality disorder (BPD) still face multiple challenges regarding treatment accessibility, adherence, duration, and economic costs. Over the last decade, technology has addressed these concerns from different disciplines. The current scoping review aimed to delineate novel and ongoing clinical research on technology-based psychosocial interventions for patients with BPD. Online databases (PubMed, Cochrane Library, EMBASE, Web of Science, PsycInfo, and Google Scholar) were searched up to June 2020. Technology-based psychosocial treatments included innovative communication (eHealth) and computational (e.g., artificial intelligence), computing (e.g., computer-based), or medical (e.g., functional magnetic resonance imaging [fMRI]) software. Clinical research encompassed any testing stage (e.g., feasibility, efficacy). Fifteen studies met the inclusion criteria. The main findings were the following: almost two-thirds of the studies (9/15) tested software explicitly conceived as adjunctive interventions to conventional therapy; nearly half of the studies (7/15) were referred to as dialectical behavior therapy-based software; most studies (13/15) were focused on the initial stage of the clinical research cycle (feasibility/acceptance/usability testing), reporting good results at this point; more than one-third of the studies (6/15) tested mobile apps; there is emerging evidence for Internet-based interventions and real-time fMRI biofeedback but only little evidence for mHealth interventions, virtual and augmented reality, and computer-based interventions; there was no computational technology-based clinical research; and there was no satisfaction/preference, security/safety, or efficiency testing for any software. Taken together, the results suggest that there is a growing but still incipient amount of technology-based psychosocial interventions for BPD supported by some kind of clinical evidence. The limitations and directions for future research are discussed.

Individualized treatment response prediction of dialectical behavior therapy for borderline personality disorder using multimodal magnetic resonance imaging

INTRODUCTION

Individualized treatment prediction is crucial for the development and selection of personalized psychiatric interventions. Here, we use random forest classification via pretreatment clinical and demographical (CD), functional, and structural magnetic resonance imaging (MRI) data from patients with borderline personality disorder (BPD) to predict individual treatment response.

METHODS

Before dialectical behavior therapy (DBT), 31 female patients underwent functional (three different emotion regulation tasks) and structural MRI. DBT response was predicted using CD and MRI data in previously identified anatomical regions, which have been reported to be multimodally affected in BPD.

RESULTS

Amygdala and parahippocampus activation during a cognitive reappraisal task (in contrasts displaying neural activation for emotional challenge and for regulation), along with severity measures of BPD psychopathology and gray matter volume of the amygdala, provided best predictive power with neuronal hyperractivities in nonresponders. All models, except one model using CD data solely, achieved significantly better accuracy (>70.25%) than a simple all-respond model, with sensitivity and specificity of >0.7 and >0.7, as well as positive and negative likelihood ratios of >2.74 and <0.36 each. Surprisingly, a model combining all data modalities only reached rank five of seven. Among the functional tasks, only the activation elicited by a cognitive reappraisal paradigm yielded sufficient predictive power to enter the final models.

CONCLUSION

This proof of principle study shows that it is possible to achieve good predictions of psychotherapy outcome to find the most valid predictors among numerous variables via using a random forest classification approach.

Structural and Functional Abnormities of Amygdala and Prefrontal Cortex in Major Depressive Disorder With Suicide Attempts

Finding neural features of suicide attempts (SA) in major depressive disorder (MDD) may be helpful in preventing suicidal behavior. The ventral and medial prefrontal cortex (PFC), as well as the amygdala form a circuit implicated in emotion regulation and the pathogenesis of MDD. The aim of this study was to identify whether patients with MDD who had a history of SA show structural and functional connectivity abnormalities in the amygdala and PFC relative to MDD patients without a history of SA. We measured gray matter volume in the amygdala and PFC and amygdala-PFC functional connectivity using structural and functional magnetic resonance imaging (MRI) in 158 participants [38 MDD patients with a history of SA, 60 MDD patients without a history of SA, and 60 healthy control (HC)]. MDD patients with a history of SA had decreased gray matter volume in the right and left amygdala (F = 30.270, P = 0.000), ventral/medial/dorsal PFC (F = 15.349, P = 0.000), and diminished functional connectivity between the bilateral amygdala and ventral and medial PFC regions (F = 22.467, P = 0.000), compared with individuals who had MDD without a history of SA, and the HC group. These findings provide evidence that the amygdala and PFC may be closely related to the pathogenesis of suicidal behavior in MDD and implicate the amygdala-ventral/medial PFC circuit as a potential target for suicide intervention.