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Diagnosing autism spectrum disorders (ASD) is a complicated, time-consuming process which is particularly challenging in older individuals. One of the most widely used behavioral diagnostic tools is the Autism Diagnostic Observation Schedule (ADOS). Previous work using machine learning techniques suggested that ASD detection in children can be achieved with substantially fewer items than the original ADOS. Here, we expand on this work with a specific focus on adolescents and adults as assessed with the ADOS Module 4. We used a machine learning algorithm (support vector machine) to examine whether ASD detection can be improved by identifying a subset of behavioral features from the ADOS Module 4 in a routine clinical sample of N = 673 high-functioning adolescents and adults with ASD (n = 385) and individuals with suspected ASD but other best-estimate or no psychiatric diagnoses (n = 288). We identified reduced subsets of 5 behavioral features for the whole sample as well as age subgroups (adolescents vs. adults) that showed good specificity and sensitivity and reached performance close to that of the existing ADOS algorithm and the full ADOS, with no significant differences in overall performance. These results may help to improve the complicated diagnostic process of ASD by encouraging future efforts to develop novel diagnostic instruments for ASD detection based on the identified constructs as well as aiding clinicians in the difficult question of differential diagnosis.

Background: Autism Spectrum Disorder (ASD) is a severe, lifelong neurodevelopmental disorder with early onset that places a heavy burden on affected individuals and their families. Due to the need for highly specialized health, educational and vocational services, ASD is a cost-intensive disorder, and strain on health care systems increases with increasing age of the affected individual., Methods: The ASD-Net will study Germany's largest cohort of patients with ASD over the lifespan. By combining methodological expertise from all levels of clinical research, the ASD-Net will follow a translational approach necessary to identify neurobiological pathways of different phenotypes and their appropriate identification and treatment. The work of the ASD-Net will be organized into three clusters concentrating on diagnostics, therapy and health economics. In the diagnostic cluster, data from a large, well-characterized sample (N = 2568) will be analyzed to improve the efficiency of diagnostic procedures. Pattern classification methods (machine learning) will be used to identify algorithms for screening purposes. In a second step, the developed algorithm will be tested in an independent sample. In the therapy cluster, we will unravel how an ASD-specific social skills training with concomitant oxytocin administration can modulate behavior through neurobiological pathways. For the first time, we will characterize long-term effects of a social skills training combined with oxytocin treatment on behavioral and neurobiological phenotypes. Also acute effects of oxytocin will be investigated to delineate general and specific effects of additional oxytocin treatment in order to develop biologically plausible models for symptoms and successful therapeutic interventions in ASD. Finally, in the health economics cluster, we will assess service utilization and ASD-related costs in order to identify potential needs and cost savings specifically tailored to Germany. The ASD-Net has been established as part of the German Research Network for Mental Disorders, funded by the BMBF (German Federal Ministry of Education and Research)., Discussion: The highly integrated structure of the ASD-Net guarantees sustained collaboration of clinicians and researchers to alleviate individual distress, harm, and social disability of patients with ASD and reduce costs to the German health care system., Trial Registration: Both clinical trials of the ASD-Net are registered in the German Clinical Trials Register: DRKS00008952 (registered on August 4, 2015) and DRKS00010053 (registered on April 8, 2016).

Background: Group-based psychotherapy in Autism Spectrum Disorder (ASD) has predominantly been studied in the United States by small studies in school-aged children without long-term follow-up. We report results of a large, confirmatory, multicentre randomized-controlled phase-III trial in children and adolescents studying the ASD specific, manualized group-based cognitive behavioural SOSTA-FRA approach., Methods: High-functioning ASD individuals aged 8-19 years old were randomized to 12 sessions SOSTA-FRA or treatment as usual. Primary outcomes were change in total raw score of the parent-rated Social Responsiveness Scale (pSRS) between baseline (T2) and end of intervention (T4), and between T2 and 3 months after end of intervention (T5)., Trial Registration: ISRCTN94863788., Results: Between 20/5/2010 and 14/2/2013, n = 320 ASD patients were screened, n = 228 patients were randomized, and N = 209 analysed. Mean pSRS difference between groups at T4 was -6.5 (95% CI -11.6 to - 1.4; p = .013), and at T5 -6.4 (-11.5 to -1.3, p = .015). Pre-treatment SRS and IQ were positively associated with stronger improvement at T4 and T5., Conclusions: Short-term ASD-specific add-on group-based psychotherapy has shown postintervention efficacy with regard to parent-rated social responsiveness predominantly in male high-functioning children and adolescents with ASD. Future studies should implement blinded standardized observational measures of peer-related social interaction., (© 2015 Association for Child and Adolescent Mental Health.)