The posttraumatic stress disorder diagnosis in preschool- and elementary school-age children exposed to motor vehicle accidents

Objective: Increasingly, children are being diagnosed with psychiatric disorders, including preschool-age children. These diagnoses in young children raise questions pertaining to 1) how diagnostic algorithms for individual disorders should be modified for young age groups, 2) how psychopathology is best detected at an early stage, and 3) how to make use of multiple informants. The authors examined these issues in a prospective longitudinal assessment of preschool- and elementary school-age children who were exposed to a traumatic event. Method: Participants were 114 children (age range: 2-10 years) who had experienced a motor vehicle accident. Parents and older children (age range: 7-10 years) completed structured interviews 2-4 weeks (initial assessment) and 6 months (6-month follow-up) after the traumatic event. A recently proposed alternative symptom algorithm for diagnosing posttraumatic stress disorder (PTSD) was utilized and compared with the standard DSM-IV algorithms for diagnosing PTSD and acute stress disorder. Results: At the 2- to 4-week assessment, 11.5% of the children met conditions for a diagnosis of PTSD based on the alternative algorithm criteria per parent report, and 13.9% met criteria for this diagnosis at the 6-month follow-up. These percentages were much higher than those for DSM-IV diagnoses of acute stress disorder and PTSD. Among 7- to 10-year-old subjects, the use of combined parent- and child-reported symptoms to derive a diagnosis resulted in an increased number of children in this age group who were identified with psychiatric illness relative to the use of parent report alone. Agreement between parent and child on symptoms for 1) a diagnosis of PTSD based on the alternative algorithm criteria and 2) diagnoses of DSM-IV acute stress disorder and PTSD in this age group was poor. Among 2- to 6-year-old subjects, the alternative algorithm PTSD diagnosis per parent report was a more sensitive predictor of later onset psychopathology relative to a diagnosis of DSM-IV acute stress disorder or PTSD per parent report. However, among 7- to 10-year-old subjects, a combined symptom report (from both parent and child) was optimal in predicting posttraumatic psychopathology. Conclusions: These findings support the use of the proposed alternative algorithm for assessing PTSD in young children and suggest that the diagnosis of PTSD based on the alternative algorithm criteria is stable from the acute phase onward. When both parent- and child-reported symptoms are utilized for the assessment of PTSD among 7- to 10-year-old children, the alternative algorithm and DSM-IV criteria have broad comparable validity. However, in the absence of child-reported symptoms, the alternative algorithm criteria per parent report appears to be an optimal diagnostic measure of PTSD among children in this age group, relative to the standard DSM-IV algorithm for diagnosing the disorder

Using Twitter to learn about the autism community

Considering the raising socio-economic burden of autism spectrum disorder (ASD), timely and evidence-driven public policy decision making and communication of the latest guidelines pertaining to the treatment and management of the disorder is crucial. Yet evidence suggests that policy makers and medical practitioners do not always have a good understanding of the practices and relevant beliefs of ASD-afflicted individuals' carers who often follow questionable recommendations and adopt advice poorly supported by scientific data. The key goal of the present work is to explore the idea that Twitter, as a highly popular platform for information exchange, could be used as a data-mining source to learn about the population affected by ASD -- their behaviour, concerns, needs etc. To this end, using a large data set of over 11 million harvested tweets as the basis for our investigation, we describe a series of experiments which examine a range of linguistic and semantic aspects of messages posted by individuals interested in ASD. Our findings, the first of their nature in the published scientific literature, strongly motivate additional research on this topic and present a methodological basis for further work.Comment: Social Network Analysis and Mining, 201

Why study movement variability in autism?

Autism has been defined as a disorder of social cognition, interaction and communication where ritualistic, repetitive behaviors are commonly observed. But how should we understand the behavioral and cognitive differences that have been the main focus of so much autism research? Can high-level cognitive processes and behaviors be identified as the core issues people with autism face, or do these characteristics perhaps often rather reflect individual attempts to cope with underlying physiological issues? Much research presented in this volume will point to the latter possibility, i.e. that people on the autism spectrum cope with issues at much lower physiological levels pertaining not only to Central Nervous Systems (CNS) function, but also to peripheral and autonomic systems (PNS, ANS) (Torres, Brincker, et al. 2013). The question that we pursue in this chapter is what might be fruitful ways of gaining objective measures of the large-scale systemic and heterogeneous effects of early atypical neurodevelopment; how to track their evolution over time and how to identify critical changes along the continuum of human development and aging. We suggest that the study of movement variability—very broadly conceived as including all minute fluctuations in bodily rhythms and their rates of change over time (coined micro-movements (Figure 1A-B) (Torres, Brincker, et al. 2013))—offers a uniquely valuable and entirely objectively quantifiable lens to better assess, understand and track not only autism but cognitive development and degeneration in general. This chapter presents the rationale firstly behind this focus on micro-movements and secondly behind the choice of specific kinds of data collection and statistical metrics as tools of analysis (Figure 1C). In brief the proposal is that the micro-movements (defined in Part I – Chapter 1), obtained using various time scales applied to different physiological data-types (Figure 1), contain information about layered influences and temporal adaptations, transformations and integrations across anatomically semi-independent subsystems that crosstalk and interact. Further, the notion of sensorimotor re-afference is used to highlight the fact that these layered micro-motions are sensed and that this sensory feedback plays a crucial role in the generation and control of movements in the first place. In other words, the measurements of various motoric and rhythmic variations provide an access point not only to the “motor systems”, but also access to much broader central and peripheral sensorimotor and regulatory systems. Lastly, we posit that this new lens can also be used to capture influences from systems of multiple entry points or collaborative control and regulation, such as those that emerge during dyadic social interactions

From “Oh, OK” to “Ah, yes” to “Aha!”: Hyper-systemizing and the rewards of insight\ud

Hyper-systemizers are individuals displaying an unusually strong bias toward systemizing, i.e. toward explaining events and solving problems by appeal to mechanisms that do not involve intentions or agency. Hyper-systemizing in combination with deficit mentalizing ability typically presents clinically as an autistic spectrum disorder; however, the development of hyper-systemizing in combination with normal-range mentalizing ability is not well characterized. Based on a review and synthesis of clinical, observational, experimental, and neurofunctional studies, it is hypothesized that repeated episodes of insightful problem solving by systemizing result in attentional and motivational sensitization toward further systemizing via progressive and chronic deactivation of the default network. This hypothesis is distinguished from alternatives, and its correlational and causal implications are discussed. Predictions of the default-deactivation model accessible to survey-based instruments, standard cognitive measures and neurofunctional methods are outlined, and evidence pertaining to them considered

EEG analytics for early detection of autism spectrum disorder: a data-driven approach

Autism spectrum disorder (ASD) is a complex and heterogeneous disorder, diagnosed on the basis of behavioral symptoms during the second year of life or later. Finding scalable biomarkers for early detection is challenging because of the variability in presentation of the disorder and the need for simple measurements that could be implemented routinely during well-baby checkups. EEG is a relatively easy-to-use, low cost brain measurement tool that is being increasingly explored as a potential clinical tool for monitoring atypical brain development. EEG measurements were collected from 99 infants with an older sibling diagnosed with ASD, and 89 low risk controls, beginning at 3 months of age and continuing until 36 months of age. Nonlinear features were computed from EEG signals and used as input to statistical learning methods. Prediction of the clinical diagnostic outcome of ASD or not ASD was highly accurate when using EEG measurements from as early as 3 months of age. Specificity, sensitivity and PPV were high, exceeding 95% at some ages. Prediction of ADOS calibrated severity scores for all infants in the study using only EEG data taken as early as 3 months of age was strongly correlated with the actual measured scores. This suggests that useful digital biomarkers might be extracted from EEG measurements.This research was supported by National Institute of Mental Health (NIMH) grant R21 MH 093753 (to WJB), National Institute on Deafness and Other Communication Disorders (NIDCD) grant R21 DC08647 (to HTF), NIDCD grant R01 DC 10290 (to HTF and CAN) and a grant from the Simons Foundation (to CAN, HTF, and WJB). We are especially grateful to the staff and students who worked on the study and to the families who participated. (R21 MH 093753 - National Institute of Mental Health (NIMH); R21 DC08647 - National Institute on Deafness and Other Communication Disorders (NIDCD); R01 DC 10290 - NIDCD; Simons Foundation)Published versio