Autism spectrum disorder in ICD-11-a critical reflection of its possible impact on clinical practice and research.

This perspective article compares and contrasts the conceptualization of Autism Spectrum Disorder (ASD) in ICD-11 and DSM-5. By guiding the user through the ICD-11 text, it is argued that, in contrast to DSM-5, ICD-11 allows a high variety in symptom combinations, which results in an operationalization of ASD that is in favor of an extreme diverse picture, yet possibly at the expense of precision, including unforeseeable effects on clinical practice, care, and research. The clinical utility is questionable as this conceptualization can hardly be differentiated from other mental disorders and autism-like traits. It moves away from an observable, behavioral, and neurodevelopmental disorder to a disorder of inner experience that can hardly be measured objectively. It contains many vague and subjective concepts that lead to non-falsifiable diagnoses. This bears a large danger of false positive diagnoses, of further increased prevalence rates, limitations of access to ASD-specific services and of increasing the non-specificity of treatments. For research, the hypothesis is that the specificity of ASD will be reduced and this will additional increase the already high heterogeneity with the effect that replication of studies will be hampered. This could limit our understanding of etiology and biological pathways of ASD and bears the risk that precision medicine, i.e., a targeted approach for individual treatment strategies based on precise diagnostic markers, is more far from becoming reality. Thus, a more precise, quantitative description and more objective measurement of symptoms are suggested that define the clinical ASD phenotype. Identification of core ASD subtypes/endophenotypes and a precise description of symptoms is the necessary next step to advance diagnostic classification systems. Therefore, employing a more finely grained, objective, clinical symptom characterization which is more relatable to neurobehavioral concepts is of central significance.

Culturally sensitive stepped care for adolescent refugees: efficacy and cost-utility of a multicentric randomized controlled trial.

Adolescent refugees and asylum seekers (ARAS) are highly vulnerable to mental health problems. Stepped care models (SCM) and culturally sensitive therapies offer promising treatment approaches to effectively provide necessary medical and psychological support. To our knowledge, we were the first to investigate whether a culturally sensitive SCM will reduce symptoms of depression and PTSD in ARAS more effectively and efficiently than treatment as usual (TAU). We conducted a multicentric, randomized, controlled and rater-blinded trial across Germany with ARAS between the ages of 14 to 21 years. Participants (N = 158) were stratified by their level of depressive symptom severity and then equally randomized to either SCM or TAU. Depending on their severity level, SCM participants were allocated to tailored interventions. Symptom changes were assessed for depression (PHQ) and PTSD (CATS) at four time points, with the primary end point at post-intervention after 12 weeks. Based on an intention-to-treat sample, we used a linear mixed model approach for the main statistical analyses. Further evaluations included cost-utility analyses, sensitivity analyses, follow-up-analyses, response and remission rates and subgroup analysis. We found a significant reduction of PHQ (d = 0.52) and CATS (d = 0.27) scores in both groups. However, there was no significant difference between SCM and TAU. Cost-utility analyses indicated that SCM generated greater cost-utility when measured as quality-adjusted life years compared to TAU. Subgroup analysis revealed different effects for the SCM interventions depending on the outcome measure. Although culturally sensitive, SCMs did not prove to be more effective in symptom change and represent a more cost-effective treatment alternative for mentally burdened ARAS. Our research contributes to the optimization of clinical productivity and the improvement of therapeutic care for ARAS. Disorder-specific interventions should be further investigated.

Modeling face recognition in the predictive coding framework: A combined computational modeling and functional imaging study.

The learning of new facial identities and the recognition of familiar faces are crucial processes for social interactions. Recently, a combined computational modeling and functional magnetic resonance imaging (fMRI) study used predictive coding as a biologically plausible framework to model face identity learning and to relate specific model parameters with brain activity (Apps and Tsakiris, Nat Commun 4, 2698, 2013). On the one hand, it was shown that behavioral responses on a two-option face recognition task could be predicted by the level of contextual and facial familiarity in a computational model derived from predictive-coding principles. On the other hand, brain activity in specific brain regions was associated with these parameters. More specifically, brain activity in the superior temporal sulcus (STS) varied with contextual familiarity, whereas activity in the fusiform face area (FFA) covaried with the prediction error parameter that updated facial familiarity. Literature combining fMRI assessments and computational modeling in humans still needs to be expanded. Furthermore, prior results are largely not replicated. The present study was, therefore, specifically set up to replicate these previous findings. Our results support the original findings in two critical aspects. First, on a group level, the behavioral responses were modeled best by the same computational model reported by the original authors. Second, we showed that estimates of these model parameters covary with brain activity in specific, face-sensitive brain regions. Our results thus provide further evidence that the functional properties of the face perception network conform to central principles of predictive coding. However, our study yielded diverging findings on specific computational model parameters reflected in brain activity. On the one hand, we did not find any evidence of a computational involvement of the STS. On the other hand, our results showed that activity in the right FFA was associated with multiple computational model parameters. Our data do not provide evidence for functional segregation between particular face-sensitive brain regions, as previously proposed.

The associations of Positive and Negative Valence Systems, Cognitive Systems and Social Processes on disease severity in anxiety and depressive disorders.

Background: Anxiety and depressive disorders share common features of mood dysfunctions. This has stimulated interest in transdiagnostic dimensional research as proposed by the Research Domain Criteria (RDoC) approach by the National Institute of Mental Health (NIMH) aiming to improve the understanding of underlying disease mechanisms. The purpose of this study was to investigate the processing of RDoC domains in relation to disease severity in order to identify latent disorder-specific as well as transdiagnostic indicators of disease severity in patients with anxiety and depressive disorders. Methods: Within the German research network for mental disorders, 895 participants (n = 476 female, n = 602 anxiety disorder, n = 257 depressive disorder) were recruited for the Phenotypic, Diagnostic and Clinical Domain Assessment Network Germany (PD-CAN) and included in this cross-sectional study. We performed incremental regression models to investigate the association of four RDoC domains on disease severity in patients with affective disorders: Positive (PVS) and Negative Valance System (NVS), Cognitive Systems (CS) and Social Processes (SP). Results: The results confirmed a transdiagnostic relationship for all four domains, as we found significant main effects on disease severity within domain-specific models (PVS: ß = -0.35; NVS: ß = 0.39; CS: ß = -0.12; SP: ß = -0.32). We also found three significant interaction effects with main diagnosis showing a disease-specific association. Limitations: The cross-sectional study design prevents causal conclusions. Further limitations include possible outliers and heteroskedasticity in all regression models which we appropriately controlled for. Conclusion: Our key results show that symptom burden in anxiety and depressive disorders is associated with latent RDoC indicators in transdiagnostic and disease-specific ways.

Empowerment group therapy for refugees with affective disorders: results of a multicenter randomized controlled trial.

BACKGROUND: Against the background of missing culturally sensitive mental health care services for refugees, we developed a group intervention (Empowerment) for refugees at level 3 within the stratified Stepped and Collaborative Care Model of the project Mental Health in Refugees and Asylum Seekers (MEHIRA). We aim to evaluate the effectiveness of the Empowerment group intervention with its focus on psychoeducation, stress management, and emotion regulation strategies in a culturally sensitive context for refugees with affective disorders compared to treatment-as-usual (TAU). METHOD: At level 3 of the MEHIRA project, 149 refugees and asylum seekers with clinically relevant depressive symptoms were randomized to the Empowerment group intervention or TAU. Treatment comprised 16 therapy sessions conducted over 12 weeks. Effects were measured with the Patient Health Questionnaire-9 (PHQ-9) and the Montgomery-Åsberg Depression Rating Scale (MÅDRS). Further scales included assessed emotional distress, self-efficacy, resilience, and quality of life. RESULTS: Intention-to-treat analyses show significant cross-level interactions on both self-rated depressive symptoms (PHQ-9; F(1,147) = 13.32, p < 0.001) and clinician-rated depressive symptoms (MÅDRS; F(1,147) = 6.91, p = 0.01), indicating an improvement in depressive symptoms from baseline to post-intervention in the treatment group compared to the control group. The effect sizes for both scales were moderate (d = 0.68, 95% CI 0.21-1.15 for PHQ-9 and d = 0.51, 95% CI 0.04-0.99 for MÅDRS). CONCLUSION: In the MEHIRA project comparing an SCCM approach versus TAU, the Empowerment group intervention at level 3 showed effectiveness for refugees with moderately severe depressive symptoms.

Shared sorrow, shared costs: cost-effectiveness analysis of the Empowerment group therapy approach to treat affective disorders in refugee populations.

BACKGROUND: Refugees and asylum seekers (RAS) in Germany need tailored and resource-oriented mental healthcare interventions. AIMS: To evaluate the cost-effectiveness of group psychotherapy for RAS with moderate depressive symptoms. METHOD: This is a post hoc cost-effectiveness analysis of Empowerment group psychotherapy that was embedded in a stratified stepped and collaborative care model (SCCM) from the multicentre randomised controlled MEHIRA trial. One hundred and forty-nine participants were randomly assigned to SCCM or treatment as usual (TAU) and underwent Empowerment (i.e. level 3 of the SCCM for adults) or TAU. Effects were measured with the nine-item Patient Health Questionnaire (PHQ-9) and quality adjusted life-years (QALY) post-intervention. Health service and intervention costs were measured. Incremental cost-effectiveness ratios (ICER) were estimated and net monetary benefit (NMB) regressions with 95% confidence intervals were performed. Cost-effectiveness was ascertained for different values of willingness to pay (WTP) using cost-effectiveness acceptability curves for probable scenarios. Trial registration number: NCT03109028 on ClinicalTrials.gov. RESULTS: Health service use costs were significantly lower for Empowerment than TAU after 1 year. Intervention costs were on average €409.6. Empowerment led to a significant change in PHQ-9 scores but not QALY. Bootstrapped mean ICER indicated cost-effectiveness according to PHQ-9 and varied considerably for QALY in the base case. NMB for a unit reduction in PHQ-9 score at WTP of €0 was €354.3 (€978.5 to -€269.9). Results were confirmed for different scenarios and varying WTP thresholds. CONCLUSIONS: The Empowerment intervention was cost-effective in refugees with moderate depressive symptoms regarding the clinical outcome and led to a reduction in direct healthcare consumption. Concerning QALYs, there was a lack of confidence that Empowerment differed from TAU.

Review of neurodevelopmental disorders in patients with HNF1B gene variations.

This review investigates the association between neurodevelopmental disorders (NDD) and variations of the gene HNF1B. Heterozygous intragenetic mutations or heterozygous gene deletions (17q12 microdeletion syndrome) of HNF1B are the cause of a multi-system developmental disorder, termed renal cysts and diabetes syndrome (RCAD). Several studies suggest that in general, patients with genetic variation of HNF1B have an elevated risk for additional neurodevelopmental disorders, especially autism spectrum disorder (ASD) but a comprehensive assessment is yet missing. This review provides an overview including all available studies of patients with HNF1B mutation or deletion with comorbid NDD with respect to the prevalence of NDDs and in how they differ between patients with an intragenic mutation or 17q12 microdeletion. A total of 31 studies was identified, comprising 695 patients with variations in HNF1B, (17q12 microdeletion N = 416, mutation N = 279). Main results include that NDDs are present in both groups (17q12 microdeletion 25.2% vs. mutation 6.8%, respectively) but that patients with 17q12 microdeletions presented more frequently with any NDDs and especially with learning difficulties compared to patients with a mutation of HNF1B. The observed prevalence of NDDs in patients with HNF1B variations seems to be higher than in the general population, but the validity of the estimated prevalence must be deemed insufficient. This review shows that systematical research of NDDs in patients with HNF1B mutations or deletions is lacking. Further studies regarding neuropsychological characteristics of both groups are needed. NDDs might be a concomitant of HFN1B-related disease and should be considered in clinical routine and scientific reports.

[Pathological Demand Avoidance: Current State of Research and Critical Discussion]. / Pathological Demand Avoidance ­ aktueller Forschungsstand und kritische Diskussion.

Pathological Demand Avoidance: Current State of Research and Critical Discussion Abstract: Pathological demand avoidance (PDA) describes children who obsessively avoid any demand to a clinically relevant extent and is presently the subject of controversial discussion. Their behavior may be interpreted as an attempt to reduce anxiety by establishing security and predictability through rigid control of the environment as well as the demands and expectations of others. The symptoms are described in the context of autism spectrum disorder. This article reviews the current state of research and discusses the questionable validity of pathological demand avoidance as an independent diagnostic entity. It also addresses the impact of the behavior profile on development and treatment. This paper concludes that PDA is not a diagnostic entity nor a subtype of autism; rather, it is a behavior profile that can be associated with adverse illness progression and unfavorable outcomes. PDA is one feature in a complex model. We must consider not only the patient's characteristics but also those of the caregiver and their psychopathology. The reactions of the interaction partners as well as the treatment decisions play a key role play for the affected individuals. Substantial research is needed concerning the occurrence of the behavior profile PDA in diverse disorders, treatment options, and treatment responses.

Social and Nonsocial Autism Symptom Domains in Children and Adolescents with Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder: Insights into Their Symptomatological Interplay.

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.

Using machine learning to improve diagnostic assessment of ASD in the light of specific differential and co-occurring diagnoses.

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.

Mapping Research Domain Criteria using a transdiagnostic mini-RDoC assessment in mental disorders: a confirmatory factor analysis.

This study aimed to build on the relationship of well-established self-report and behavioral assessments to the latent constructs positive (PVS) and negative valence systems (NVS), cognitive systems (CS), and social processes (SP) of the Research Domain Criteria (RDoC) framework in a large transnosological population which cuts across DSM/ICD-10 disorder criteria categories. One thousand four hundred and thirty one participants (42.1% suffering from anxiety/fear-related, 18.2% from depressive, 7.9% from schizophrenia spectrum, 7.5% from bipolar, 3.4% from autism spectrum, 2.2% from other disorders, 18.4% healthy controls, and 0.2% with no diagnosis specified) recruited in studies within the German research network for mental disorders for the Phenotypic, Diagnostic and Clinical Domain Assessment Network Germany (PD-CAN) were examined with a Mini-RDoC-Assessment including behavioral and self-report measures. The respective data was analyzed with confirmatory factor analysis (CFA) to delineate the underlying latent RDoC-structure. A revised four-factor model reflecting the core domains positive and negative valence systems as well as cognitive systems and social processes showed a good fit across this sample and showed significantly better fit compared to a one factor solution. The connections between the domains PVS, NVS and SP could be substantiated, indicating a universal latent structure spanning across known nosological entities. This study is the first to give an impression on the latent structure and intercorrelations between four core Research Domain Criteria in a transnosological sample. We emphasize the possibility of using already existing and well validated self-report and behavioral measurements to capture aspects of the latent structure informed by the RDoC matrix.

Predicting treatment outcomes of the Empowerment group intervention for refugees with affective disorders: Findings from the MEHIRA project.

BACKGROUND: Research on outcome predictors in the field of transcultural treatment for refugees and asylum seekers (RAS) is scarce. We aimed to evaluate predictors of outcome of a group intervention (Empowerment) for RAS with affective disorders which was incorporated at level three of the stratified stepped-care model within the Mental Health in Refugees and Asylum Seekers (MEHIRA) project. METHODS: A predictor analysis was performed at level three of the MEHIRA project, where 149 refugees with moderate depressive symptoms were treated either with Empowerment or Treatment-as-usual (TAU). Outcome measures were depression severity as assessed by patient-rated Patient Health Questionnaire 9 (PHQ-9) and clinician-rated Montgomery Asberg Depression Rating Scale (MADRS). Regression models with change scores (T1-T0) of PHQ-9 and MADRS as dependent variables were fit. Predictor selection was a mixed-method approach combining testing of literature-based hypotheses and explorative hypothesis-generating analyses of multiple baseline variables. RESULTS: Intention-to-treat (ITT) analyses revealed significant linear relationships between change in PHQ-9 and baseline depression severity (ß = -0.35, t = -3.27, p = .002) and perceived self-efficacy (ß = -0.24, t = -2.26, p = .027) in the treatment (verum) condition. MADRS change scores were predicted by baseline depression severity (ß = -0.71, t = -8.65, p < .001) in the treatment (verum) condition. LIMITATIONS: Due to small cell numbers, single predictors could not be evaluated reliably. CONCLUSIONS: Severity of depression and self-efficacy at baseline were predictors of symptom improvement at level three (Empowerment) of the MEHIRA project. Comorbidity and trauma indicators did not predict outcomes in the treatment (verum) condition, pointing towards broad applicability of the Empowerment intervention in refugee populations.

Conceptualization of the latent structure of autism: further evidence and discussion of dimensional and hybrid models.

Autism spectrum disorder (ASD) might be conceptualized as an essentially dimensional, categorical, or hybrid model. Yet, current empirical studies are inconclusive and the latent structure of ASD has explicitly been examined only in a few studies. The aim of our study was to identify and discuss the latent model structure of behavioral symptoms related to ASD and to address the question of whether categories and/or dimensions best represent ASD symptoms. We included data of 2920 participants (1-72 years of age), evaluated with the Autism Diagnostic Observation Schedule (Modules 1-4). We applied latent class analysis, confirmatory factor analysis, and factor mixture modeling and evaluated the model fit by a combination of criteria. Based on the model selection criteria, the model fits, the interpretability as well as the clinical utility we conclude that the hybrid model serves best for conceptualization and assessment of ASD symptoms. It is both grounded in empirical evidence and in clinical usefulness, is in line with the current classification system (DSM-5) and has the potential of being more specific than the dimensional approach (decreasing false positive diagnoses).

A data driven machine learning approach to differentiate between autism spectrum disorder and attention-deficit/hyperactivity disorder based on the best-practice diagnostic instruments for autism.

Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are two frequently co-occurring neurodevelopmental conditions that share certain symptomatology, including social difficulties. This presents practitioners with challenging (differential) diagnostic considerations, particularly in clinically more complex cases with co-occurring ASD and ADHD. Therefore, the primary aim of the current study was to apply a data-driven machine learning approach (support vector machine) to determine whether and which items from the best-practice clinical instruments for diagnosing ASD (ADOS, ADI-R) would best differentiate between four groups of individuals referred to specialized ASD clinics (i.e., ASD, ADHD, ASD + ADHD, ND = no diagnosis). We found that a subset of five features from both ADOS (clinical observation) and ADI-R (parental interview) reliably differentiated between ASD groups (ASD & ASD + ADHD) and non-ASD groups (ADHD & ND), and these features corresponded to the social-communication but also restrictive and repetitive behavior domains. In conclusion, the results of the current study support the idea that detecting ASD in individuals with suspected signs of the diagnosis, including those with co-occurring ADHD, is possible with considerably fewer items relative to the original ADOS/2 and ADI-R algorithms (i.e., 92% item reduction) while preserving relatively high diagnostic accuracy. Clinical implications and study limitations are discussed.

Assessment of Reward-Related Brain Function After a Single Dose of Oxytocin in Autism: A Randomized Controlled Trial.

Background: Autism spectrum disorder (ASD) is characterized by difficulties in social communication and interaction, which have been related to atypical neural processing of rewards, especially in the social domain. As intranasal oxytocin has been shown to modulate activation of the brain's reward circuit, oxytocin might ameliorate the processing of social rewards in ASD and thus improve social difficulties. Methods: In this randomized, double-blind, placebo-controlled, crossover functional magnetic resonance imaging study, we examined effects of a 24-IU dose of intranasal oxytocin on reward-related brain function in 37 men with ASD without intellectual impairment and 37 age- and IQ-matched control participants. Participants performed an incentive delay task that allows the investigation of neural activity associated with the anticipation and receipt of monetary and social rewards. Results: Nonsignificant tests suggested that oxytocin did not influence neural processes related to the anticipation of social or monetary rewards in either group. Complementary Bayesian analyses indicated moderate evidence for a null model, relative to an alternative model. Our results were inconclusive regarding possible oxytocin effects on amygdala responsiveness to social rewards during reward consumption. There were no significant differences in reward-related brain function between the two groups under placebo. Conclusions: Our results do not support the hypothesis that intranasal oxytocin generally enhances activation of reward-related neural circuits in men with and without ASD.

Effectiveness and cost-effectiveness for the treatment of depressive symptoms in refugees and asylum seekers: A multi-centred randomized controlled trial.

Background: Current evidence points towards a high prevalence of psychological distress in refugee populations, contrasting with a scarcity of resources and amplified by linguistic, institutional, financial, and cultural barriers. The objective of the study is to investigate the overall effectiveness and cost-effectiveness of a Stepped Care and Collaborative Model (SCCM) at reducing depressive symptoms in refugees, compared with the overall routine care practices within Germany's mental healthcare system (treatment-as-usual, TAU). Methods: A multicentre, clinician-blinded, randomised, controlled trial was conducted across seven university sites in Germany. Asylum seekers and refugees with relevant depressive symptoms with a Patient Health Questionnaires score of ≥ 5 and a Refugee Health Screener score of ≥ 12. Participants were randomly allocated to one of two treatment arms (SCCM or TAU) for an intervention period of three months between April 2018 and March 2020. In the SCCM, participants were allocated to interventions tailored to their symptom severity, including watchful waiting, peer-to-peer- or smartphone intervention, psychological group therapies or mental health expert treatment. The primary endpoint was defined as the change in depressive symptoms (Patient Health Questionnaire-9, PHQ-9) after 12 weeks. The secondary outcome was the change in Montgomery Åsberg Depression Rating Scale (MADRS) from baseline to post-intervention. Findings: The intention-to-treat sample included 584 participants who were randomized to the SCCM (n= 294) or TAU (n=290). Using a mixed-effects general linear model with time, and the interaction of time by randomisation group as fixed effects and study site as random effect, we found significant effects for time (p < .001) and time by group interaction (p < .05) for intention-to-treat and per-protocol analysis. Estimated marginal means of the PHQ-9 scores after 12 weeks were significantly lower in SCCM than in TAU (for intention-to-treat: PHQ-9 mean difference at T1 1.30, 95% CI 1.12 to 1.48, p < .001; Cohen's d=.23; baseline-adjusted PHQ-9 mean difference at T1 0.57, 95% CI 0.40 to 0.74, p < .001). Cost-effectiveness and net monetary benefit analyses provided evidence of cost-effectiveness for the primary outcome and quality-adjusted life years. Robustness of results were confirmed by sensitivity analyses. Interpretation: The SSCM resulted in a more effective and cost-effective reduction of depressive symptoms compared with TAU. Findings suggest a suitable model to provide mental health services in circumstances where resources are limited, particularly in the context of forced migration and pandemics. Funding: This project is funded by the Innovationsfond and German Ministry of Health [grant number 01VSF16061]. The present trial is registered under Clinical-Trials.gov under the registration number: NCT03109028. https://clinicaltrials.gov/ct2/show/NCT03109028.

Corrigendum: Abilities and Disabilities-Applying Machine Learning to Disentangle the Role of Intelligence in Diagnosing Autism Spectrum Disorders.

[This corrects the article DOI: 10.3389/fpsyt.2022.826043.].

Autism Spectrum Disorder and IQ - A Complex Interplay.

Autism spectrum disorder (ASD) is characterized as a very heterogeneous child-onset disorder, whose heterogeneity is partly determined by differences in intelligence quotient (IQ). Older epidemiological studies suggested that the IQ-related spectrum tends to be skewed to the left, i.e., a larger proportion of individuals with ASD have below average intelligence, while only few individuals with ASD may have an IQ above average. This picture changed over time with broadening the spectrum view. Within the present perspective article, we discuss discrepancies in IQ profiles between epidemiological and clinical studies and identify potential underlying aspects, for example, the influence of external factors such as sample biases or differences in availability of autism health services. Additionally, we discuss the validity and reciprocal influences of ASD diagnostics and IQ measurement. We put the impact of these factors for diagnostic as well as care and support situations of patients into perspective and want to encourage further research to contribute to the conceptualization of "autism" more comprehensively including the IQ as well as to examine broader (life) circumstances, interacting factors and diagnostic requirements of given diagnoses in childhood as compared to adulthood.

Phenotypic differences between female and male individuals with suspicion of autism spectrum disorder.

BACKGROUND: Although autism spectrum disorder (ASD) is a common developmental disorder, our knowledge about a behavioral and neurobiological female phenotype is still scarce. As the conceptualization and understanding of ASD are mainly based on the investigation of male individuals, females with ASD may not be adequately identified by routine clinical diagnostics. The present machine learning approach aimed to identify diagnostic information from the Autism Diagnostic Observation Schedule (ADOS) that discriminates best between ASD and non-ASD in females and males. METHODS: Random forests (RF) were used to discover patterns of symptoms in diagnostic data from the ADOS (modules 3 and 4) in 1057 participants with ASD (18.1% female) and 1230 participants with non-ASD (17.9% % female). Predictive performances of reduced feature models were explored and compared between females and males without intellectual disabilities. RESULTS: Reduced feature models relied on considerably fewer features from the ADOS in females compared to males, while still yielding similar classification performance (e.g., sensitivity, specificity). LIMITATIONS: As in previous studies, the current sample of females with ASD is smaller than the male sample and thus, females may still be underrepresented, limiting the statistical power to detect small to moderate effects. CONCLUSION: Our results do not suggest the need for new or altered diagnostic algorithms for females with ASD. Although we identified some phenotypic differences between females and males, the existing diagnostic tools seem to sufficiently capture the core autistic features in both groups.

Abilities and Disabilities-Applying Machine Learning to Disentangle the Role of Intelligence in Diagnosing Autism Spectrum Disorders.

Objective: Although autism spectrum disorder (ASD) is a relatively common, well-known but heterogeneous neuropsychiatric disorder, specific knowledge about characteristics of this heterogeneity is scarce. There is consensus that IQ contributes to this heterogeneity as well as complicates diagnostics and treatment planning. In this study, we assessed the accuracy of the Autism Diagnostic Observation Schedule (ADOS/2) in the whole and IQ-defined subsamples, and analyzed if the ADOS/2 accuracy may be increased by the application of machine learning (ML) algorithms that processed additional information including the IQ level. Methods: The study included 1,084 individuals: 440 individuals with ASD (with a mean IQ level of 3.3 ± 1.5) and 644 individuals without ASD (with a mean IQ level of 3.2 ± 1.2). We applied and analyzed Random Forest (RF) and Decision Tree (DT) to the ADOS/2 data, compared their accuracy to ADOS/2 cutoff algorithms, and examined most relevant items to distinguish between ASD and Non-ASD. In sum, we included 49 individual features, independently of the applied ADOS module. Results: In DT analyses, we observed that for the decision ASD/Non-ASD, solely one to four items are sufficient to differentiate between groups with high accuracy. In addition, in sub-cohorts of individuals with (a) below (IQ level ≥4)/ID and (b) above average intelligence (IQ level ≤ 2), the ADOS/2 cutoff showed reduced accuracy. This reduced accuracy results in (a) a three times higher risk of false-positive diagnoses or (b) a 1.7 higher risk for false-negative diagnoses; both errors could be significantly decreased by the application of the alternative ML algorithms. Conclusions: Using ML algorithms showed that a small set of ADOS/2 items could help clinicians to more accurately detect ASD in clinical practice across all IQ levels and to increase diagnostic accuracy especially in individuals with below and above average IQ level.