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Correction to: Molecular Psychiatry, published online 2 May 2023 In this article Honami Arai, Irene Bollettini, Rosa Calvo Escalona, Ana Coelho, Federica Colombo, Leila Darwich, Martine Fontaine, Toshikazu Ikuta, Jonathan C. Ipser, Asier Juaneda-Seguí, Hitomi Kitagawa, Gerd Kvale, Mafalda Machado-Sousa, Astrid Morer, Takashi Nakamae, Jin Narumoto, Joseph O’Neill, Sho Okawa, Eva Real, Veit Roessner, Joao R. Sato, Cinto Segalàs, Roseli G. Shavitt, Dick J. Veltman, Kei Yamada were missing from the author list indexed under the ENIGMA-OCD Working Group. Additionally, there was an error regarding Tokiko Yoshida’s name, where the first name and last name were written in the wrong order. The original article has been corrected.

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohens d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohens d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.

Autism spectrum disorder shares many symptoms with other mental health disorders, and comorbid disorders such as mood and anxiety disorders are common, making the diagnostic process challenging. We aimed to explore the diagnostic accuracy of two standard autism spectrum disorder diagnostic instruments and to identify those behavioral items that best differentiate between autism spectrum disorder and mood and anxiety disorder in a naturalistic sample of patients utilizing autism spectrum disorder specialist services. The study included data of 847 participants (5-65 years of age, n = 586 with autism spectrum disorder, n = 261 with mood and anxiety disorder) all evaluated with the Autism Diagnostic Observation Schedule in the context of the diagnostic process. Data of the Autism Diagnostic Interview--Revised were available for 428 participants (5-51 years of age, n = 367 with autism spectrum disorder, n = 61 with mood and anxiety disorder). By means of binominal logistic regressions and an ensemble feature selection, we identified a subset of items that best differentiated between autism spectrum disorder and mood and anxiety disorder. Overall, the results indicate that a combination of communicational deficits and unusual and/or inappropriate social overtures differentiates autism spectrum disorder and mood and anxiety disorder. Aspects of social cognition are also relevant. Limitations of the current study and implications for research and practice are discussed.

International studies show disadvantages for adults with autism spectrum disorder (ASD) in the labor market. Data about their participation in the German labor market are scarce. The aim of this study was to examine the integration of adults with ASD in the German labor market in terms of education, employment and type of occupation by means of a cross-sectional-study, using a postal questionnaire. Findings show above average levels of education for adults with ASD compared to the general population of Germany and simultaneously, below average rates of employment and high rates of financial dependency. That indicates a poor integration of adults with ASD in the German labor market and emphasizes the need for vocational support policies for adults with ASD.

Autism spectrum disorders (ASD) are associated with high services use, but European data on costs are scarce. Utilisation and annual costs of 385 individuals with ASD (aged 4-67 years; 18.2% females; 37.4% IQ < 85) from German outpatient clinics were assessed. Average annual costs per person were 3287 EUR, with psychiatric inpatient care (19.8%), pharmacotherapy (11.1%), and occupational therapy (11.1%) being the largest cost components. Females incurred higher costs than males (4864 EUR vs. 2936 EUR). In a regression model, female sex (Cost Ratio: 1.65), lower IQ (1.90), and Asperger syndrome (1.54) were associated with higher costs. In conclusion, ASD-related health costs are comparable to those of schizophrenia, thus underlining its public health relevance. Higher costs in females demand further research.

Regardless of their limited evidence and potential adverse effects, use of complementary and alternative medicine is common in children with autism spectrum disorder. Nevertheless, data on complementary and alternative medicine use in children with autism spectrum disorder in Germany are lacking. Therefore, a questionnaire survey on the use of complementary and alternative medicine was distributed to parents of children with autism spectrum disorder from three academic autism spectrum disorder outpatient clinics in Germany. Of 211 respondents, 46% stated that their child currently used or had ever used some form of complementary and alternative medicine in their life. The complementary and alternative medicine modalities most frequently used were manipulative and body-based methods (e.g. craniosacral therapy). And 18% of caregivers expressed willingness to try complementary and alternative medicine treatments for their child with autism spectrum disorder in the future, with mind-body interventions predominating. Health professionals should be aware of the considerable complementary and alternative medicine use prevalence among children with autism spectrum disorder and offer parents information about its effectiveness and potential side effects.

This is the fourth international preparatory study designed to develop International Classification of Functioning, Disability and Health (ICF, and Children and Youth version, ICF-CY) Core Sets for Autism Spectrum Disorder (ASD). Examine functioning of individuals diagnosed with ASD as documented by the ICF-CY in a variety of clinical settings. A cross-sectional study was conducted, involving 11 units from 10 countries. Clinical investigators assessed functioning of 122 individuals with ASD using the ICF-CY checklist. In total, 139 ICF-CY categories were identified: 64 activities and participation, 40 body functions and 35 environmental factors. The study results reinforce the heterogeneity of ASD, as evidenced by the many functional and contextual domains impacting on ASD from a clinical perspective.

Introduction: Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) share overlapping symptomatology, particularly with regard to social impairments (including peer relationship difficulties), and they frequently co-occur. However, the nature of their co-occurrence remains unclear. Therefore, the current study aimed to examine the nature of the transdiagnostic link between ASD and ADHD from a symptomatological point of view measured with the Autism Diagnostic Observation Schedule (ADOS Module 3) and the Autism Diagnostic Interview-Revised (ADI-R). Methods: We analyzed the social and nonsocial ASD symptom domain scores from both diagnostic instruments in 4 clinically referred groups (i.e., ASD, ADHD, ASD + ADHD, and no psychiatric diagnosis) without other co-occurring mental disorders using a two-by-two full-factorial MANOVA design with the factors ASD (yes/no) and ADHD (yes/no). Results: We found no ASD by ADHD interaction effects across all symptom domain scores of ADOS and ADI-R, except for ADOS imagination/creativity. There were only main effects of the factor ASD but no main effects of ADHD. Follow-up contrasts showed that exclusively, ASD had an impact on the measured symptomatology in case of co-occurring ASD + ADHD. Conclusion: Overall, the results support an additive model of the symptomatology across areas of communication, social interaction, and stereotyped behaviors and restricted interests in case of the co-occurrence of ASD and ADHD when assessed with ADOS/ADI-R. Thus, one can assume that the phenotypic overlap of ASD + ADHD may be less complicated than suspected – at least with regard to ASD symptomatology – and that in the presence of ADHD, ASD symptomatology is generally well measurable with best-practice diagnostic instruments. [ABSTRACT FROM AUTHOR]

Background: Diagnostic assessment of ASD requires substantial clinical experience and is particularly difficult in the context of other disorders with behavioral symptoms in the domain of social interaction and communication. Observation measures such as the Autism Diagnostic Observation Schedule (ADOS) do not take into account such co‐occurring disorders. Method: We used a well‐characterized clinical sample of individuals (n = 1,251) that had received detailed outpatient evaluation for the presence of an ASD diagnosis (n = 481) and covered a range of additional overlapping diagnoses, including anxiety‐related disorders (ANX, n = 122), ADHD (n = 439), and conduct disorder (CD, n = 194). We focused on ADOS module 3, covering the age range with particular high prevalence of such differential diagnoses. We used machine learning (ML) and trained random forest models on ADOS single item scores to predict a clinical best‐estimate diagnosis of ASD in the context of these differential diagnoses (ASD vs. ANX, ASD vs. ADHD, ASD vs. CD), in the context of co‐occurring ADHD, and an unspecific model using all available data. We employed nested cross‐validation for an unbiased estimate of classification performance and made available a Webapp to showcase the results and feasibility for translation into clinical practice. Results: We obtained very good overall sensitivity (0.89–0.94) and specificity (0.87–0.89). In particular for individuals with less severe symptoms, our models showed increases of up to 35% in sensitivity or specificity. Furthermore, we analyzed item importance profiles of the ANX, ADHD, and CD models in comparison with the unspecific model revealing distinct patterns of importance for specific ADOS items with respect to differential diagnoses. Conclusions: ML‐based diagnostic classification may improve clinical decisions by utilizing the full range of information from detailed diagnostic observation instruments such as the ADOS. Importantly, this strategy might be of particular relevance for older children with less severe symptoms for whom the diagnostic decision is often particularly difficult. [ABSTRACT FROM AUTHOR]