Borderline personality disorder classification based on brain network measures during emotion regulation

Abstract

Borderline Personality Disorder (BPD) is characterized by an increased emotional sensitivity and dysfunctional capacity to regulate emotions. While amygdala and prefrontal cortex interactions are regarded as the critical neural mechanisms underlying these problems, the empirical evidence hereof is inconsistent. In the current study, we aimed to systematically test different properties of brain connectivity and evaluate the predictive power to detect borderline personality disorder. Patients with borderline personality disorder (n = 51), cluster C personality disorder (n = 26) and non-patient controls (n = 44), performed an fMRI emotion regulation task. Brain network analyses focused on two properties of task-related connectivity: phasic refers to task-event dependent changes in connectivity, while tonic was defined as task-stable background connectivity. Three different network measures were estimated (strength, local efficiency, and participation coefficient) and entered as separate models in a nested cross-validated linear support vector machine classification analysis. Borderline personality disorder vs. non-patient controls classification showed a balanced accuracy of 55%, which was not significant under a permutation null-model, p = 0.23. Exploratory analyses did indicate that the tonic strength model was the highest performing model (balanced accuracy 62%), and the amygdala was one of the most important features. Despite being one of the largest data-sets in the field of BPD fMRI research, the sample size may have been limited for this type of classification analysis. The results and analytic procedures do provide starting points for future research, focusing on network measures of tonic connectivity, and potentially focusing on subgroups of BPD.

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

“…In the predictors domain, 187 of 555 models (33.7%; 95% CI, 29.9%- 37.6%) were rated with high ROB (Table 1). Defining predictors by knowing the outcome of these models was the unique source of the high ROB in this domain (ie, signaling question 2.2: were predictor assessments made without knowledge of outcome data?).…”

“…Defining predictors by knowing the outcome of these models was the unique source of the high ROB in this domain (ie, signaling question 2.2: were predictor assessments made without knowledge of outcome data?). In the outcome domain, high ROB was scored for 198 of 469 models (35.7%; 95% CI, 31.8%-39.7%) (Table 1). These models had a high ROB because the outcome knowledge of testing data sets was leaked into the predictors of the training set (ie, signaling question 3.3: were predictors excluded from the outcome definition?…”

“…High concern for clinical applicability in the participants domain was found for 2 of 555 models (0.4%; 95% CI, 0.00%-0.01%) . Furthermore, high concerns regarding clinical applicability were found for 199 of 555 models (35.9%; 95% CI, 31.9%-39.9%) in the predictors domain and for 217 of 555 models (39.1%; 95% CI, 35.0%-43.2%) in the outcome domain …”

“…One-hundred fifty of 555 models (27.0%; 95% CI, 23.3%-30.7%) were rated with poor reporting quality in the applications domain across 2 signaling questions . Lacking intrinsic reports or discussions of the clinical implications of these models was the main source of the poor rating in this domain (eg, signaling question: were use cases and target conditions discussed?).…”

Functional MRI findings in personality disorders: A review

“…This study showed no significant BPD‐specific findings. BPD‐tb‐20 146 assessed various predictive models for BPD using fMRI brain changes, but was unable to provide a model with an accuracy over 62%.…”