Adolescents with borderline personality disorder show a higher response to stress but a lack of self-perception: Evidence through affective computing.

Stress reactivity is a complex phenomenon associated with multiple and multimodal expressions and functions. Herein, we hypothesized that compared with healthy controls (HCs), adolescents with borderline personality disorder (BPD) would exhibit a stronger response to stressors and a deficit in self-perception of stress due to their lack of insight. Twenty adolescents with BPD and 20 matched HCs performed a socially evaluated mental arithmetic test to induce stress. We assessed self- and heteroperception using both human ratings and affective computing-based methods for the automatic extraction of 39 behavioral features (2D + 3D video recording) and 62 physiological features (Nexus-10 recording). Predictions were made using machine learning. In addition, salivary cortisol was measured. Human ratings showed that adolescents with BPD experienced more stress than HCs. Human ratings and automated machine learning indicated opposite results regarding self- and heteroperceived stress in adolescents with BPD compared to HCs. Adolescents with BPD had higher levels of heteroperceived stress than self-perceived stress. Similarly, affective computing achieved better classification for heteroperceived stress. HCs had an opposite profile; they had higher levels of self-perceived stress, and affective computing reached a better classification for self-perceived stress. We conclude that adolescents with BPD are more sensitive to stress and show a lack of self-perception (or insight). In terms of clinical implications, our affective computing measures may help distinguish hetero- vs. self-perceptions of stress in natural settings and may offer external feedback during therapeutic interaction.

Distinguish self- and hetero-perceived stress through behavioral imaging and physiological features.

Stress reactivity is a complex phenomenon associated to multiple and multimodal expressions. Response to stressors has an obvious survival function and may be seen as an internal regulation to adapt to threat or danger. The intensity of this internal response can be assessed as the self-perception of the stress response. In species with social organization, this response also serves a communicative function, so-called hetero-perception. Our study presents multimodal stress detection assessment - a new methodology combining behavioral imaging and physiological monitoring for analyzing stress from these two perspectives. The system is based on automatic extraction of 39 behavioral (2D+3D video recording) and 62 physiological (Nexus-10 recording) features during a socially evaluated mental arithmetic test. The analysis with machine learning techniques for automatic classification using Support Vector Machine (SVM) show that self-perception and hetero-perception of social stress are both close but different phenomena: self-perception was significantly correlated with hetero-perception but significantly differed from it. Also, assessing stress with SVM through multimodality gave excellent classification results (F1 score values: 0.9±0.012 for hetero-perception and 0.87±0.021 for self-perception). In the best selected feature subsets, we found some common behavioral and physiological features that allow classification of both self- and hetero-perceived stress. However, we also found the contributing features for automatic classifications had opposite distributions: self-perception classification was mainly based on physiological features and hetero-perception was mainly based on behavioral features.