An emotion recognition subtyping approach to studying the heterogeneity and comorbidity of autism spectrum disorders and attention-deficit/hyperactivity disorder

BackgroundEmotion recognition dysfunction has been reported in both autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). This suggests that emotion recognition is a cross-disorder trait that may be utilised to understand the heterogeneous psychopathology of ASD and ADHD. We aimed to identify emotion recognition subtypes and to examine their relation with quantitative and diagnostic measures of ASD and ADHD to gain further insight into disorder comorbidity and heterogeneity.MethodsFactor mixture modelling was used on speed and accuracy measures of auditory and visual emotion recognition tasks. These were administered to children and adolescents with ASD (N=89), comorbid ASD+ADHD (N=64), their unaffected siblings (N=122), ADHD (N=111), their unaffected siblings (N=69), and controls (N=220). Identified classes were compared on diagnostic and quantitative symptom measures.ResultsA four-class solution was revealed, with the following emotion recognition abilities: (1) average visual, impulsive auditory; (2) average-strong visual and auditory; (3) impulsive/imprecise visual, average auditory; (4) weak visual and auditory. The weakest performing class (4) contained the highest percentage of patients (66.07%) and the lowest percentage controls (10.09%), scoring the highest on ASD/ADHD measures. The best performing class (2) demonstrated the opposite: 48.98% patients, 15.26% controls with relatively low scores on ASD/ADHD measures.ConclusionsSubgroups of youths can be identified that differ both in quantitative and qualitative aspects of emotion recognition abilities. Weak emotion recognition abilities across sensory domains are linked to an increased risk for ASD as well as ADHD, although emotion recognition impairments alone are neither necessary nor sufficient parts of these disorders

Improved Diagnostic Validity of the ADOS Revised Algorithms: A Replication Study in an Independent Sample

Recently, Gotham et al. (2007) proposed revised algorithms for the Autism Diagnostic Observation Schedule (ADOS) with improved diagnostic validity. The aim of the current study was to replicate predictive validity, factor structure, and correlations with age and verbal and nonverbal IQ of the ADOS revised algorithms for Modules 1 and 2 in a large independent Dutch sample (N = 532). Results showed that the improvement of diagnostic validity was most apparent for autism, except in very young or low functioning children. Results for other autism spectrum disorders were less consistent. Overall, these findings support the use of the more homogeneous revised algorithms, with the use of similar items across developmental cells making it easier to compare ADOS scores within and between individuals

Prognostic association of cardiac anxiety with new cardiac events and mortality following myocardial infarction

BACKGROUND: General anxiety and depressive symptoms following a myocardial infarction are associated with a worse cardiac prognosis. However, the contribution of specific aspects of anxiety within this context remains unclear. AIMS: To evaluate the independent prognostic association of cardiac anxiety with cardiac outcome after myocardial infarction. METHOD: We administered the Cardiac Anxiety Questionnaire (CAQ) during hospital admission (baseline, n = 193) and 4 months (n = 147/193) after discharge. CAQ subscale scores reflect fear, attention, avoidance and safety-seeking behaviour. Study end-point was a major adverse cardiac event (MACE): readmission for ischemic cardiac disease or all-cause mortality. In Cox regression analysis, we adjusted for age, cardiac disease severity and depressive symptoms. RESULTS: The CAQ sum score at baseline and at 4 months significantly predicted a MACE (HRbaseline = 1.59, 95% CI 1.04-2.43; HR4-months = 1.77, 95% CI 1.04-3.02) with a mean follow-up of 4.2 (s.d. = 2.0) years and 4.3 (s.d. = 1.7) years respectively. Analyses of subscale scores revealed that this effect was particularly driven by avoidance (HRbaseline = 1.23, 95% CI 0.99-1.53; HR4-months = 1.77, 95% CI 1.04-1.83). CONCLUSIONS: Cardiac anxiety, particularly anxiety-related avoidance of exercise, is an important prognostic factor for a MACE in patients after myocardial infarction, independent of cardiac disease severity and depressive symptoms

Measuring adverse drug effects on multimorbity using tractable Bayesian networks

Managing patients with multimorbidity often results in polypharmacy: the prescription of multiple drugs. However, the long-term effects of specific combinations of drugs and diseases are typically unknown. In particular, drugs prescribed for one condition may result in adverse effects for the other. To investigate which types of drugs may affect the further progression of multimorbidity, we query models of diseases and prescriptions that are learned from primary care data. State-of-the-art tractable Bayesian network representations, on which such complex queries can be computed efficiently, are employed for these large medical networks. Our results confirm that prescriptions may lead to unintended negative consequences in further development of multimorbidity in cardiovascular diseases. Moreover, a drug treatment for one disease group may affect diseases of another group.status: publishe

Exploring disease interactions using Markov networks

Network medicine is an emerging paradigm for studying the co-occurrence between diseases. While diseases are often interlinked through complex patterns, most of the existing work in this area has focused on studying pairwise relationships between diseases. In this paper, we use a state-of-the-art Markov network learning method to learn interactions between musculoskeletal disorders and cardiovascular diseases and compare this to pairwise approaches. Our experimental results confirm that the sophisticated structure learner produces more accurate models, which can help reveal interesting patterns in the co-occurrence of diseases.status: publishe

Dissociative Symptoms are Highly Prevalent in Adults with Narcolepsy Type 1

Introduction: The core symptoms of narcolepsy such as excessive daytime sleepiness and cataplexy are well known. However, there is mounting evidence for a much broader symptom spectrum, including psychiatric symptoms. Disordered sleep has previously been linked with dissociative symptoms, which may imply that patients with narcolepsy are more prone to develop such symptoms. Objectives: To investigate the frequency of dissociative symptoms in adult patients with narcolepsy type 1 compared to population controls. Methods: In a retrospective case control study, sixty adult patients fulfilling the criteria for narcolepsy type 1 and 120 matched population control subjects received a structured interview using the Schedules for Clinical Assessment in Neuropsychiatry (SCAN) to assess dissociative symptoms and disorders. Results: A majority of narcolepsy patients reported dissociative symptoms, and even fulfilled the DSM-IV-TR criteria of a dissociative disorder (62% vs 1% in controls, p < .001). Most frequently reported symptoms were "dissociative amnesia" (37% vs 1%, p < .001) and "dissociative disorder of voluntary movement" (32% vs 1%, p < .001). Conclusion: Dissociative symptoms are strikingly prevalent in adult patients with narcolepsy type 1. Although a formal diagnosis of dissociation disorder should not be made as the symptoms can be explained by narcolepsy as an underlying condition, the findings do illustrate the extent and severity of the dissociative symptoms. As for the pathophysiological mechanism, there may be symptom overlap between narcolepsy and dissociation disorder. However, there may also be a more direct link between disrupted sleep and dissociative symptoms. In either case, the high frequency of occurrence of dissociative symptoms should result in an active inquiry by doctors, to improve therapeutic management and guidance

Understanding disease processes by partitioned dynamic Bayesian networks

For many clinical problems in patients the underlying pathophysiological process changes in the course of time as a result of medical interventions. In model building for such problems, the typical scarcity of data in a clinical setting has been often compensated by utilizing time homogeneous models, such as dynamic Bayesian networks. As a consequence, the specificities of the underlying process are lost in the obtained models. In the current work, we propose the new concept of partitioned dynamic Bayesian networks to capture distribution regime changes, i.e. time non-homogeneity, benefiting from an intuitive and compact representation with the solid theoretical foundation of Bayesian network models. In order to balance specificity and simplicity in real-world scenarios, we propose a heuristic algorithm to search and learn these non-homogeneous models taking into account a preference for less complex models. An extensive set of experiments were ran, in which simulating experiments show that the heuristic algorithm was capable of constructing well-suited solutions, in terms of goodness of fit and statistical distance to the original distributions, in consonance with the underlying processes that generated data, whether it was homogeneous or non-homogeneous. Finally, a study case on psychotic depression was conducted using non-homogeneous models learned by the heuristic, leading to insightful answers for clinically relevant questions concerning the dynamics of this mental disorder. (C) 2016 Elsevier Inc. All rights reserved