Modeling vulnerability and intervention targets in the Borderline Personality Disorder system: A network analysis of in silico and in vivo interventions.

Modeling psychopathology as a complex dynamic system represents Borderline Personality Disorder (BPD) as a constellation of symptoms (e.g., nodes) that feedback and self-sustain each other shaping a network structure. Through in silico interventions, we simulated the evolution of the BPD system by manipulating: 1) the connectivity strength between nodes (i.e., vulnerability), 2) the external disturbances (i.e., stress) and 3) the predisposition of symptoms to manifest. Similarly, using network analysis we evaluated the effect of an in vivo group psychotherapy to detect the symptoms modified by the intervention. We found that a network with greater connectivity strength between nodes (more vulnerable) showed a higher number of activated symptoms than networks with less strength connectivity. We also found that increases in stress affected more vulnerable networks compared to less vulnerable ones, while decreases in stress revealed a hysteresis effect in the most strongly connected networks. The in silico intervention to symptom alleviation revealed the relevance of nodes related to difficulty in anger regulation, nodes which were also detected as impacted by the in vivo intervention. The complex systems methodology is an alternative to the common cause model with which research has approached the BPD phenomenon.

Positive Mental Health Scale (PMHS) in Parents of Children with Cancer: A Psychometric Evaluation Using Item Response Theory.

Mental health is currently a public health issue worldwide. However, evidence is lacking regarding the validity of the instruments used to measure and assess positive mental health in specific populations. The objective of this study was to evaluate the psychometric properties of the PMHS using IRT. A cross-sectional retrospective study with non-probabilistic convenience sampling was conducted with 623 parents of children undergoing cancer treatment at the National Institute of Health in Mexico City. The participants responded to a battery of tests, including a sociodemographic questionnaire, the PMHS, Measurement Scale of Resilience, Beck Depression Inventory, Inventory of Quality of Life, Beck Anxiety Inventory, an interview regarding caregiver burden, and the World Health Organization Well-Being Index. PMHS responses were analyzed using Samejima's graded response model. The PMHS findings indicated that the IRT-based graded response model validated the single latent trait model. The scale scores were independent of depression, anxiety, well-being, caregiver burden, quality of life, and resilience. The PMHS scores were associated with low subjective well-being. The PMHS findings reveal that from an IRT-based perspective, this scale is unidimensional and is a valid, reliable, and culturally relevant instrument for assessing positive mental health in parents of children with chronic diseases.

Social closeness modulates brain dynamics during trust anticipation.

Anticipation of trust from someone with high social closeness is expected. However, if there is uncertainty in the interaction because a person is a stranger or because he has distrusted us on another occasion, we need to keep track of his behavior and intentions. Using functional Magnetic Resonance Imaging (fMRI) we wanted to find the brain regions related to trust anticipation from partners who differ in their level of social closeness. We designed an experiment in which 30 participants played an adapted trust game with three trustors: A computer, a stranger, and a real friend. We covertly manipulated their decisions in the game, so they trusted 75% of the trials and distrusted in remaining trials. Using a psychophysiological interaction analysis, we found increases in functional coupling between the anterior insula (AIns) and intra parietal sulcus (IPS) during trust anticipation between a high versus low social closeness partner. Also, the right parietal cortex was coupled with the fusiform gyrus (FG) and the inferior/middle temporal gyrus during trust anticipation of a friend versus a stranger. These results suggest that brain regions involved in encoding the intentions of others are recruited during trust anticipation from a friend compared to a stranger.

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.

Resilience measurement scale in family caregivers of children with cancer: Multidimensional item response theory modeling.

Background: Currently, information about the psychometric properties of the Resilience Measurement Scale (RESI-M) in family caregivers of children with cancer according to item response theory (IRT) is not available; this information could complement and confirm the findings available from classical test theory (CTT). The objective of this study was to test the five-factor structure of the RESI-M using a full information confirmatory multidimensional IRT graded response model and to estimate the multidimensional item-level parameters of discrimination (MDISC) and difficulty (MDIFF) from the RESI-M scale to investigate its construct validity and level of measurement error. Methods: An observational study was carried out, which included a sample of 633 primary caregivers of children with cancer, who were recruited through nonprobabilistic sampling. The caregivers responded to a battery of tests that included a sociodemographic variables questionnaire, the RESI-M, and measures of depression, quality of life, anxiety, and caregiver burden to explore convergent and divergent validity. Results: The main findings confirmed a five-factor structure of the RESI-M scale, with RMSEA = 0.078 (95% CI: 0.075, 0.080), TLI = 0.90, and CFI = 0.91. The estimation of the MDISC and MDIFF parameters indicated different values for each item, showing that all the items contribute differentially to the measurement of the dimensions of resilience. Conclusion: That regardless of the measurement approach (IRT or CTT), the five-factor model of the RESI-M is valid at the theoretical, empirical, and methodological levels.

Facial and Vocal Expressions During Clinical Interviews Suggest an Emotional Modulation Paradox in Borderline Personality Disorder: An Explorative Study.

Videotape recordings obtained during an initial and conventional psychiatric interview were used to assess possible emotional differences in facial expressions and acoustic parameters of the voice between Borderline Personality Disorder (BPD) female patients and matched controls. The incidence of seven basic emotion expressions, emotional valence, heart rate, and vocal frequency (f0), and intensity (dB) of the discourse adjectives and interjections were determined through the application of computational software to the visual (FaceReader) and sound (PRAAT) tracks of the videotape recordings. The extensive data obtained were analyzed by three statistical strategies: linear multilevel modeling, correlation matrices, and exploratory network analysis. In comparison with healthy controls, BPD patients express a third less sadness and show a higher number of positive correlations (14 vs. 8) and a cluster of related nodes among the prosodic parameters and the facial expressions of anger, disgust, and contempt. In contrast, control subjects showed negative or null correlations between such facial expressions and prosodic parameters. It seems feasible that BPD patients restrain the facial expression of specific emotions in an attempt to achieve social acceptance. Moreover, the confluence of prosodic and facial expressions of negative emotions reflects a sympathetic activation which is opposed to the social engagement system. Such BPD imbalance reflects an emotional alteration and a dysfunctional behavioral strategy that may constitute a useful biobehavioral indicator of the severity and clinical course of the disorder. This face/voice/heart rate emotional expression assessment (EMEX) may be used in the search for reliable biobehavioral correlates of other psychopathological conditions.

Lunar periodic modulation of the circadian activity rhythm in a wild North Colombian night monkey (Aotus griseimembra).

South American night monkeys (genus Aotus) are the only nocturnal simian primates. Early activity recordings in North Colombian A. griseimembra monkeys kept under semi-natural conditions and extensive chronobiological studies carried out in laboratory settings revealed a strictly nocturnal behavior and strong activity enhancing (disinhibiting) effects of moonlight or corresponding luminosities during the dark time. To check whether the results from captive individuals correspond to the behavior of wild monkeys, we carried out long-term activity recordings of a wild female A. griseimembra in a tropical rainforest near San Juan de Carare, Northern Colombia. Our data from about 150 days of continuous activity records with an "Actiwatch Mini" (CamNtech, UK) accelerometer-data logger device, confirmed: (1) strictly nocturnal behavior, (2) a pronounced bimodal activity pattern with prominent peaks during dusk and dawn, and (3) a lunar periodic modulation (masking) of the night monkey's circadian activity rhythm due to distinct activity inhibiting effects of the absence of moonlight throughout the night. The results from this wild-living tropical night monkey are consistent with those from captive conspecifics studied decades earlier.

Identifying cognitive deficits in cocaine dependence using standard tests and machine learning.

There is a growing need to address the variability in detecting cognitive deficits with standard tests in cocaine dependence (CD). The aim of the current study was to identify cognitive deficits by means of Machine Learning (ML) algorithms: Generalized Linear Model (Glm), Random forest (Rf) and Elastic Net (GlmNet), to allow more effective categorization of CD and Non-dependent controls (NDC and to address common methodological problems. For our validation, we used two independent datasets, the first consisted of 87 participants (53 CD and 34 NDC) and the second of 40 participants (20 CD and 20 NDC). All participants were evaluated with neuropsychological tests that included 40 variables assessing cognitive domains. Using results from the cognitive evaluation, the three ML algorithms were trained in the first dataset and tested on the second to classify participants into CD and NDC. While the three algorithms had a receiver operating curve (ROC) performance over 50%, the GlmNet was superior in both the training (ROC = 0.71) and testing datasets (ROC = 0.85) compared to Rf and Glm. Furthermore, GlmNet was capable of identifying the eight main predictors of group assignment (CD or NCD) from all the cognitive domains assessed. Specific variables from each cognitive test resulted in robust predictors for accurate classification of new cases, such as those from cognitive flexibility and inhibition domains. These findings provide evidence of the effectiveness of ML as an approach to highlight relevant sections of standard cognitive tests in CD, and for the identification of generalizable cognitive markers.