Correlates of comorbid anxiety and externalizing disorders in childhood obsessive compulsive disorder

The present study examines the influence of diagnostic comorbidity on the demographic, psychiatric, and functional status of youth with a primary diagnosis of obsessive compulsive disorder (OCD). Two hundred and fifteen children (ages 5–17) referred to a university-based OCD specialty clinic were compared based on DSM-IV diagnostic profile: OCD without comorbid anxiety or externalizing disorder, OCD plus anxiety disorder, and OCD plus externalizing disorder. No age or gender differences were found across groups. Higher OCD severity was found for the OCD + ANX group, while the OCD + EXT group reported greater functional impairment than the other two groups. Lower family cohesion was reported by the OCD + EXT group compared to the OCD group and the OCD + ANX group reported higher family conflict compared to the OCD + EXT group. The OCD + ANX group had significantly lower rates of tic disorders while rates of depressive disorders did not differ among the three groups. The presence of comorbid anxiety and externalizing psychopathology are associated with greater symptom severity and functional and family impairment and underscores the importance of a better understanding of the relationship of OCD characteristics and associated disorders. Results and clinical implications are further discussed

Evidence-Based Assessment of Child Obsessive Compulsive Disorder: Recommendations for Clinical Practice and Treatment Research

Obsessive-compulsive disorder (OCD) presents heterogeneously and can be difficult to assess in youth. This review focuses on research-supported assessment approaches for OCD in childhood. Content areas include pre-visit screening, diagnostic establishment, differential diagnosis, assessment of comorbid psychiatric conditions, tracking symptom severity, determining psychosocial functioning, and evaluating clinical improvement. Throughout this review, similarities and differences between assessment approaches geared towards clinical and research settings are discussed

The Pediatric Obsessive-Compulsive Disorder Treatment Study II: rationale, design and methods

This paper presents the rationale, design, and methods of the Pediatric Obsessive-Compulsive Disorder Treatment Study II (POTS II), which investigates two different cognitive-behavior therapy (CBT) augmentation approaches in children and adolescents who have experienced a partial response to pharmacotherapy with a serotonin reuptake inhibitor for OCD. The two CBT approaches test a "single doctor" versus "dual doctor" model of service delivery. A specific goal was to develop and test an easily disseminated protocol whereby child psychiatrists would provide instructions in core CBT procedures recommended for pediatric OCD (e.g., hierarchy development, in vivo exposure homework) during routine medical management of OCD (I-CBT). The conventional "dual doctor" CBT protocol consists of 14 visits over 12 weeks involving: (1) psychoeducation, (2), cognitive training, (3) mapping OCD, and (4) exposure with response prevention (EX/RP). I-CBT is a 7-session version of CBT that does not include imaginal exposure or therapist-assisted EX/RP. In this study, we compared 12 weeks of medication management (MM) provided by a study psychiatrist (MM only) with two types of CBT augmentation: (1) the dual doctor model (MM+CBT); and (2) the single doctor model (MM+I-CBT). The design balanced elements of an efficacy study (e.g., random assignment, independent ratings) with effectiveness research aims (e.g., differences in specific SRI medications, dosages, treatment providers). The study is wrapping up recruitment of 140 youth ages 7–17 with a primary diagnosis of OCD. Independent evaluators (IEs) rated participants at weeks 0,4,8, and 12 during acute treatment and at 3,6, and 12 month follow-up visits

Bayesian supervised machine learning classification of neural networks with pathological perturbations

Objective. Extraction of temporal features of neuronal activity from electrophysiological data can be used for accurate classification of neural networks in healthy and pathologically perturbed conditions. In this study, we provide an extensive approach for the classification of human in vitro neural networks with and without an underlying pathology, from electrophysiological recordings obtained using a microelectrode array (MEA) platform. Approach. We developed a Dirichlet mixture (DM) Point Process statistical model able to extract temporal features related to neurons. We then applied a machine learning algorithm to discriminate between healthy control and pathologically perturbed in vitro neural networks. Main Results. We found a high degree of separability between the classes using DM point process features (p-value <0.001 for all the features, paired t-test), which reaches 93.10 of accuracy (92.37 of ROC AUC) with the Random Forest classifier. In particular, results show a higher latency in firing for pathologically perturbed neurons (43 ± 16 ms versus 67 ± 31 ms, μIG feature distribution). Significance. Our approach has been successful in extracting temporal features related to the neurons' behaviour, as well as distinguishing healthy from pathologically perturbed networks, including classification of responses to a transient induced perturbation