Diagnosing selective mutism: a critical review of measures for clinical practice and research

Selective mutism (SM) is an anxiety disorder (prevalence 1–2%), characterized by the consistent absence of speaking in specific situations (e.g., in school), while adequately speaking in other situations (e.g., at home). SM can have a debilitating impact on the psychosocial and academic functioning in childhood. The use of psychometrically sound and cross-culturally valid instruments is urgently needed.The aim of this paper is to identify and review the available assessment instruments for screening or diagnosing the core SM symptomatology. We conducted a systematic search in 6 databases. We identified 1469 studies from the last decade and investigated the measures having been used in a diagnostic assessment of SM. Studies were included if original data on the assessment or treatment of SM were reported. It was found that 38% of published studies on SM reporting original data did not report the use of any standardized or objective measure to investigate the core symptomatology. The results showed that many different questionnaires, interviews and observational instruments were used, many of these only once. The Selective Mutism Questionnaire (SMQ), Anxiety Disorders Interview Schedule (ADIS) and School Speech Questionnaire (SSQ) were used most often. Psychometric data on these instruments are emerging. Beyond these commonly used instruments, more recent developed instruments, such as the Frankfurt Scale of SM (FSSM) and the Teacher Telephone Interview for SM (TTI-SM), are described, as well as several interesting observational measures. The strengths and weaknesses of the instruments are discussed and recommendations are made for their use in clinical practice and research.

Recreational Psychedelic Users Frequently Encounter Complete Mystical Experiences: Trip Content and Implications for Wellbeing

A growing proportion of the population is engaging in recreational psychedelic use. Psychedelics are uniquely capable of reliably occasioning mystical experiences in ordinary humans without contemplative or religious backgrounds. While clinical research has made efforts to characterize psychedelic experiences, comparably little is understood about how humans naturalistically engage with psychedelics. The present study employs a mixed-methods approach to examine the content and implications of psychedelic and mystical experiences, occurring outside of laboratory settings. We use text mining analyses to arrive at a qualitative description of psychedelic experiential content by abstracting from over two-thousand written reports of first-person psychedelic experiences. Following up, we conducted quantitative analyses on psychometric data from a large survey (N = 1424) to reveal associations between psychedelic use practices, complete mystical experiences, and psychological wellbeing. Topic-modelling and sentiment analyses present a bottom-up description of human interactions with psychedelic compounds and the content of such experiences. Psychometric results suggest psychedelic users encounter complete mystical experiences in high proportions, dependent on factors such as drug type and dose-response effects. Furthermore, a salient association was established between diverse metrics of wellbeing and those with complete mystical experiences. Our results paint a new picture of the growing relationships between humans and psychedelic experiences in the real-world use context. Ordinary humans appear to encounter complete mystical experiences via recreational psychedelic use, and such experiences are strongly associated with improved psychological wellbeing.

The Consciousness State of Traditional Nidrâ Yoga/Modern Yoga Nidra: Phenomenological Characterization and Preliminary Insights from an EEG Study

Nidrâ yoga is an ancient yogic practice capable of inducing altered states of consciousness characterized by deep relaxation, strong concentration, acute self-awareness, and joy. In modern contemplative neuroscience language, it is known by the name yoga nidra, and few studies have investigated its phenomenological and psychophysiological effects. Six healthy volunteers (four females aged 31–74) performed 12 yoga nidra sessions guided by an expert during a 6-day retreat. Each session consisted of 10 minutes in a resting state (baseline) followed by 2 hours of yoga nidra. Psychometric data regarding dissociative experiences (Clinician Administered Dissociative States Scale) and the state of consciousness (Phenomenology of Consciousness Inventory) were collected after baseline and yoga nidra, while high-density EEG was recorded during the entire session. During nidra sessions, no sleep hallmarks (i.e., K-complexes and sleep spindles) were detected by the EEG in any subject. Psychometric data we re analyzed using a Wilcoxon signed-rank test corrected with the false discovery rate approach for multiple comparisons. Compared to baseline, yoga nidra practice was related to: (1) increased dissociative effects (p = 0.022); (2) perception of being in an altered state of consciousness (p = 0.026); (3) alterations in perceived body image (p = 0.022); (4) increased “meaningfulness” attributed to the experience (p = 0.026); (5) reduced rational thinking (p = 0.029); and (6) reduced volitional thought control (p = 0.026). First-person experience is discussed in relation to descriptive EEG power spectral density analysis, which was performed in one subject because of severe EEG artifacts in the other recordings; that subject showed, compared to baseline: (1) early increase of alpha and beta power, followed by a progressive widespread reduction; (2) widespread early increase of theta power, followed by a progressive reduction; and (3) widespread increase of gamma power in the latest stages. The present preliminary results enrich the knowledge of yoga nidra, elucidating its phenomenology and suggesting some psychophysiological correlates that future studies may address.

Time-Lagged Prediction of Food Craving With Qualitative Distinct Predictor Types: An Application of BISCWIT

Food craving (FC) peaks are highly context-dependent and variable. Accurate prediction of FC might help preventing disadvantageous eating behavior. Here, we examine whether data from 2 weeks of ecological momentary assessment (EMA) questionnaires on stress and emotions (active EMA, aEMA) alongside temporal features and smartphone sensor data (passive EMA, pEMA) are able to predict FCs ~2.5 h into the future in N = 46 individuals. A logistic prediction approach with feature dimension reduction via Best Item Scale that is Cross-Validated, Weighted, Informative and Transparent (BISCWIT) was performed. While overall prediction accuracy was acceptable, passive sensing data alone was equally predictive to psychometric data. The frequency of which single predictors were considered for a model was rather balanced, indicating that aEMA and pEMA models were fully idiosyncratic.

A machine learning approach for the factorization of psychometric data with application to the Delis Kaplan Executive Function System

While a replicability crisis has shaken psychological sciences, the replicability of multivariate approaches for psychometric data factorization has received little attention. In particular, Exploratory Factor Analysis (EFA) is frequently promoted as the gold standard in psychological sciences. However, the application of EFA to executive functioning, a core concept in psychology and cognitive neuroscience, has led to divergent conceptual models. This heterogeneity severely limits the generalizability and replicability of findings. To tackle this issue, in this study, we propose to capitalize on a machine learning approach, OPNMF (Orthonormal Projective Non-Negative Factorization), and leverage internal cross-validation to promote generalizability to an independent dataset. We examined its application on the scores of 334 adults at the Delis–Kaplan Executive Function System (D-KEFS), while comparing to standard EFA and Principal Component Analysis (PCA). We further evaluated the replicability of the derived factorization across specific gender and age subsamples. Overall, OPNMF and PCA both converge towards a two-factor model as the best data-fit model. The derived factorization suggests a division between low-level and high-level executive functioning measures, a model further supported in subsamples. In contrast, EFA, highlighted a five-factor model which reflects the segregation of the D-KEFS battery into its main tasks while still clustering higher-level tasks together. However, this model was poorly supported in the subsamples. Thus, the parsimonious two-factors model revealed by OPNMF encompasses the more complex factorization yielded by EFA while enjoying higher generalizability. Hence, OPNMF provides a conceptually meaningful, technically robust, and generalizable factorization for psychometric tools.

Adult ADHD Self-Report Scale Screener Ratings in College Students: Concurrent Validity and Test–Retest Reliability

The Adult ADHD Self-Report Scale screener is commonly used to determine if further evaluation of ADHD is required. However, despite use in college settings, the ASRS was not developed for this population, and limited relevant psychometric data exist. In this study, 190 college students completed the ASRS screener as well as a lengthier measure of the 18 DSM symptoms of ADHD. A subgroup ( n = 141) completed the ASRS twice, with at least 1 week in between measures. Concurrent validity of ASRS scores was substantial ( r = 0.73 with DSM inattentive symptoms and r = 0.57 with DSM hyperactive/impulsive symptoms) and test–retest reliability was adequate ( r = 0.69 with an average interval of 42 days). However, the sensitivity of the screener was only moderate (66%), and approximately one-fifth of the students changed screening status (positive vs. negative) across the time interval. Implications for future research and practice are discussed.