Delay discounting and response disinhibition under acute experimental stress in women with borderline personality disorder and adult attention deficit hyperactivity disorder.

BACKGROUND: Impulsivity is a core feature of borderline personality disorder (BPD) and attention deficit hyperactivity disorder (ADHD). In BPD, impulsive behavior primarily occurs under acute stress; impulse control deficits under non-stress conditions may be partly related to co-morbid ADHD. We aimed to investigate whether acute experimental stress has an impact on self-reported impulsivity, response inhibition (action withholding, action cancelation) and delay discounting in BPD compared to ADHD. METHOD: Thirty female BPD patients, 28 female ADHD patients (excluding patients with co-morbid BPD and ADHD), and 30 female healthy controls (HC) completed self-reports and behavioral measures of impulsivity (IMT, assessing action withholding; GoStop, measuring action cancelation, Delay Discounting Task) under baseline conditions and after an experimental stress induction (Mannheim Multicomponent Stress Test). RESULTS: Both patient groups reported higher impulsivity than HC, ADHD reported higher trait impulsivity than BPD. On the IMT, ADHD showed significant action-withholding deficits under both conditions, while BPD performed significantly worse than HC under stress. In BPD but not ADHD and HC, action-withholding deficits (IMT) were significantly increased under stress compared to baseline, while no group/stress effects were found for action cancelation (GoStop). Delay discounting was significantly more pronounced in BPD than in HC (no stress effect was found). CONCLUSIONS: In BPD, behavioral deficits in action withholding (but not in action cancelation) appear to be influenced by acute experimental stress. Delay discounting seems to be a general feature of BPD, independent of co-morbid ADHD and acute stress, possibly underlying typical expressions of behavioral impulsivity in the disorder.

Impact of stress on different components of impulsivity in borderline personality disorder.

BACKGROUND: Previous research on impulsivity in borderline personality disorder (BPD) has revealed inconsistent findings. Impulsive behaviour is often observed during states of emotional distress and might be exaggerated by current attention deficit hyperactivity disorder (ADHD) symptoms in individuals with BPD. We aimed to investigate different components of impulsivity dependent on stress induction controlling for self-reported ADHD symptoms in BPD. METHOD. A total of 31 unmedicated women with BPD and 30 healthy women (healthy controls; HCs), matched for age, education and intelligence, completed self-reports and behavioural tasks measuring response inhibition (go/stop task) and feedback-driven decision making (Iowa Gambling Task) under resting conditions and after experimental stress induction. ADHD symptoms were included as a covariate in the analyses of behavioural impulsivity. Additionally, self-reported emotion-regulation capacities were assessed. RESULTS: BPD patients reported higher impulsive traits than HCs. During stress conditions - compared with resting conditions - self-reported impulsivity was elevated in both groups. Patients with BPD reported higher state impulsivity under both conditions and a significantly stronger stress-dependent increase in state impulsivity. On the behavioural level, BPD patients showed significantly impaired performance on the go/stop task under stress conditions, even when considering ADHD symptoms as a covariate, but not under resting conditions. No group differences on the Iowa Gambling Task were observed. Correlations between impulsivity measures and emotion-regulation capacities were observed in BPD patients. CONCLUSIONS: Findings suggest a significant impact of stress on self-perceived state impulsivity and on response disinhibition (even when considering current ADHD symptoms) in females with BPD.

Artificial intelligence to predict clinical disability in patients with multiple sclerosis using FLAIR MRI.

PURPOSE: The purpose of this study was to create an algorithm that combines multiple machine-learning techniques to predict the expanded disability status scale (EDSS) score of patients with multiple sclerosis at two years solely based on age, sex and fluid attenuated inversion recovery (FLAIR) MRI data. MATERIALS AND METHODS: Our algorithm combined several complementary predictors: a pure deep learning predictor based on a convolutional neural network (CNN) that learns from the images, as well as classical machine-learning predictors based on random forest regressors and manifold learning trained using the location of lesion load with respect to white matter tracts. The aggregation of the predictors was done through a weighted average taking into account prediction errors for different EDSS ranges. The training dataset consisted of 971 multiple sclerosis patients from the "Observatoire français de la sclérose en plaques" (OFSEP) cohort with initial FLAIR MRI and corresponding EDSS score at two years. A test dataset (475 subjects) was provided without an EDSS score. Ten percent of the training dataset was used for validation. RESULTS: Our algorithm predicted EDSS score in patients with multiple sclerosis and achieved a MSE=2.2 with the validation dataset and a MSE=3 (mean EDSS error=1.7) with the test dataset. CONCLUSION: Our method predicts two-year clinical disability in patients with multiple sclerosis with a mean EDSS score error of 1.7, using FLAIR sequence and basic patient demographics. This supports the use of our model to predict EDSS score progression. These promising results should be further validated on an external validation cohort.