Emotions and worries during 1.5 years of the COVID-19 pandemic - how adults with and without mental health conditions coped with the crisis.

BACKGROUND: During the COVID 19 pandemic, there were social restrictions with severe mental stress for a long time. Most studies on mental health consequences of the pandemic focused primarily on the beginning of the pandemic. The present study on families of patients or study participants of a child and adolescent psychiatry aimed to examine long-term profiles of emotions and worries in adults with and without mental health condition (mhc) during the first 1.5 years of the COVID-19 pandemic. METHODS: We surveyed emotions and worries of 128 adults with (n = 32) and without (n = 96) pre-pandemic mhc over a 1.5-year study period from spring 2020 until summer/autumn 2021. Emotions and worries were captured at four time points: [i] pre-pandemic, [ii] spring 2020 (first lockdown was implemented), [iii] December 2020 (hard lockdown at Christmas time) and [iv] summer/autumn 2021 (considerable ease of regulations); [i] pre-pandemic and [iii] December 2020 were measured retrospectively). First, we run non-parametric tests to compare emotions and worries between adults with and without pre-pandemic mhc at the four time points. Next, we conducted latent profile analysis to identify subgroups from the total sample who share similar trajectories of emotions and worries. Finally, a logistic regression analysis was run to examine whether socio-demographic and psycho-social factors were related to identified trajectories of emotions and worries. RESULTS: Adults without pre-pandemic mhc reported a strong worsening of emotions and worries at the beginning of the pandemic and a lower worsening during the course, while adults with pre-pandemic mhc reported a constant worsening of emotions and worries. The latent profile analysis revealed three profiles of adults who show either i) an adaption, ii) no adaption or iii) a continuous high condition. With increasing age, higher perceived stress and pre-pandemic mhc, the likelihood of an adaption was increased. CONCLUSION: The results of the present study suggested that adults (both with and without pre-pandemic mhc) coped the crisis with different strategies and that most of them returned to their initial, pre-pandemic levels of emotions and worries when social restrictions were considerably eased or stopped.

App-Based Mindfulness Meditation Training and an Audiobook Intervention Reduce Symptom Severity but Do Not Modify Backward Inhibition in Adolescent Obsessive-Compulsive Disorder: Evidence from an EEG Study.

(1) Background: 1-2% of children and adolescents are affected by Obsessive-Compulsive Disorder (OCD). The rigid, repetitive features of OCD and an assumed disability to inhibit recent mental representations are assumed to have led to a paradoxical advantage in that the Backward Inhibition (BI) effect was recently found to be lower in adolescents with OCD as compared to healthy controls. It was hypothesized that app-based mindfulness meditation training could reduce the disability to inhibit recent mental representations and thus increase the BI-effect by adapting cognitive flexibility and inhibition abilities according to healthy controls. (2) Methods: 58 adolescents (10-19 years) with OCD were included in the final sample of this interviewer-blind, randomized controlled study. Participants were allocated to an intervention group (app-based mindfulness meditation training) or an (active) control group (app-based audiobook) for eight weeks. Symptom (CY-BOCS), behavioral (reaction times and mean accuracy), and neurophysiological changes (in EEG) of the BI-effect were analyzed in a pre-post design. (3) Results: The intervention and the control group showed an intervention effect (Reliable Change Index: 67%) with a significant symptom reduction. Contrary to the hypothesis, the BI-effect did not differ between pre vs. post app-based mindfulness meditation training. In addition, as expected the audiobook application showed no effects. Thus, we observed no intervention-specific differences with respect to behavioral (reaction times and mean accuracy) or with respect to neurophysiological (perceptual [P1], attentional [N1], conflict monitoring [N2] or updating and response selection [P3]) processes. However, in an exploratory approach, we revealed that the BI-effect decreased in participants who did not benefit from using an app, regardless of group. (4) Conclusions: Both listening to an app-based mindfulness meditation training and to an audiobook reduce symptom severity in adolescent OCD as measured by the CY-BOCS; however, they have no specific effect on BI. The extent of the baseline BI-effect might be considered as an intra-individual component to predict the benefit of both mindfulness meditation training and listening to an audiobook.

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.

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.

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.

How Do Adults with Autism Spectrum Disorder Participate in the Labor Market? A German Multi-center Survey.

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.

Subdimensions of social-communication behavior in autism-A replication study.

Introduction: In order to identify more refined dimensions of social-communication impairments in autism spectrum disorder (ASD) a previous study applied exploratory and confirmatory factor analyses to diagnostic algorithm scores of the autism diagnostic observation schedule (ADOS), Module 3. A three-factor model consisting of repetitive behaviors, impairments in 'Basic Social-Communication' and in 'Interaction quality' (IQ) was established and confirmed. The current study aimed to replicate this model in an independent sample. To advance our understanding of the latent structure of social communication deficits, previous work was complemented by a probabilistic approach. Methods: Participants (N = 1363) included verbally fluent children and young adults, diagnosed as ASD or non-ASD based on "gold standard" best-estimate clinical diagnosis. Confirmatory factor analysis examined the factor structure of algorithm items from the ADOS Module 3 and correlations with individual characteristics (cognitive abilities, age) were analyzed. Linear Regressions were used to test the contribution of each latent factor to the prediction of an ASD diagnosis. To tackle large inter-correlations of the latent factors, a Bayesian exploratory factor analysis (BEFA) was applied. Results: Results confirmed the previously reported observation of three latent dimensions in the ADOS algorithm reflecting 'Restricted, Repetitive Behaviors', 'Basic Social-Communication' behaviors and 'Interaction Quality'. All three dimensions contributed independently and additively to the prediction of an ASD diagnosis. Conclusion: By replicating previous findings in a large clinical sample our results contribute to further conceptualize the social-communication impairments in ASD as two dimensional.

Health Services Use and Costs in Individuals with Autism Spectrum Disorder in Germany: Results from a Survey in ASD Outpatient Clinics.

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.

Differentiation of autism spectrum disorder and mood or anxiety disorder.

LAY ABSTRACT: Symptoms of mood and anxiety disorders overlap with symptoms of autism spectrum disorder, making the diagnostic process challenging. This study found that a combination of communicational deficits and unusual and/or inappropriate social overtures facilitates differentiation between autism spectrum disorder and mood and anxiety disorders. Furthermore, the results confirm the essential need of a behavioral observation with the Autism Diagnostic Observation Schedule in combination with a full Autism Diagnostic Interview-Revised to support diagnostic decisions.

Distinguishing Multiple Coding Levels in Theta Band Activity During Working Memory Gating Processes.

Cognitive control and working memory (WM) processes are essential for goal-directed behaviour. Cognitive control and WM are probably based on overlapping neurophysiological mechanisms. For example, theta-band activity (TBA) plays an important role in both functions. For cognitive control processes, it is known that different aspects of information about stimulus content, motor processes and stimulus-response relationships are encoded simultaneously in the TBA. All this information is probably processed during WM gating processes and must be controlled during them. However, direct data for this are lacking. This question is investigated in this study by combining methods of EEG temporal signal decomposition, time-frequency decomposition and beamforming. We show that portions of stimulus-related information, motor response-related information and information related to the interaction between the stimulus and motor responses in the TBA are influenced in parallel and to a similar extent by WM gate opening and gate closing processes. Nevertheless, it is stimulus-related information in the theta signal in particular that modulates behavioural performance in WM-gating. The data suggest that the identified processes are implemented in specific neuroanatomical structures. In particular, the medial frontal cortex, temporal cortical regions and insular cortex are involved in these dynamics. The study shows that principles of information coding relevant to cognitive control processes are also crucial for understanding WM gating.

Is the Combination of ADOS and ADI-R Necessary to Classify ASD? Rethinking the "Gold Standard" in Diagnosing ASD.

Diagnosing autism spectrum disorder (ASD) requires extensive clinical expertise and training as well as a focus on differential diagnoses. The diagnostic process is particularly complex given symptom overlap with other mental disorders and high rates of co-occurring physical and mental health concerns. The aim of this study was to conduct a data-driven selection of the most relevant diagnostic information collected from a behavior observation and an anamnestic interview in two clinical samples of children/younger adolescents and adolescents/adults with suspected ASD. Via random forests, the present study discovered patterns of symptoms in the diagnostic data of 2310 participants (46% ASD, 54% non-ASD, age range 4-72 years) using data from the combined Autism Diagnostic Observation Schedule (ADOS) and Autism Diagnostic Interview-Revised (ADI-R) and ADOS data alone. Classifiers built on reduced subsets of diagnostic features yield satisfactory sensitivity and specificity values. For adolescents/adults specificity values were lower compared to those for children/younger adolescents. The models including ADOS and ADI-R data were mainly built on ADOS items and in the adolescent/adult sample the classifier including only ADOS items performed even better than the classifier including information from both instruments. Results suggest that reduced subsets of ADOS and ADI-R items may suffice to effectively differentiate ASD from other mental disorders. The imbalance of ADOS and ADI-R items included in the models leads to the assumption that, particularly in adolescents and adults, the ADI-R may play a lesser role than current behavior observations.

Ventral Striatal Activation During Reward Anticipation of Different Reward Probabilities in Adolescents and Adults.

Adolescence has been linked to an enhanced tolerance of uncertainty and risky behavior and is possibly connected to an increased response toward rewards. However, previous research has produced inconsistent findings. To investigate whether these findings are due to different reward probabilities used in the experimental design, we extended a monetary incentive delay (MID) task by including three different reward probabilities. Using functional magnetic resonance imaging, 25 healthy adolescents and 22 adults were studied during anticipation of rewards in the VS. Differently colored cue stimuli indicated either a monetary or verbal trial and symbolized different reward probabilities, to which the participants were blinded. Results demonstrated faster reaction times for lower reward probabilities (33%) in both age groups. Adolescents were slower through all conditions and had less activation on a neural level. Imaging results showed a three-way interaction between age group x condition x reward probability with differences in percent signal change between adolescents and adults for the high reward probabilities (66%, 88%) while adolescents demonstrated differences for the lowest (33%). Therefore, previous inconsistent findings could be due to different reward probabilities, which makes examining these crucial for a better understanding of adolescent and adult behavior.

Special educational support in children and adolescents with Autism Spectrum Disorder in Germany: Results from a parent survey.

OBJECTIVE: Children and adolescents with Autism Spectrum Disorder (ASD) often receive special educational support (SES). This study aimed to evaluate SES prevalence in children and adolescents with ASD in Germany. METHODS: A mail survey was distributed to the caregivers of 637 children and adolescents recruited at three German ASD outpatient clinics. RESULTS: Among the 211 respondents (response: 33.1 %), 82.5 % were provided with a special educational needs statement, and 63.9 % received special education, most of them attending a public special school (57.9 %). The most frequently indicated additional support was a classroom assistant (69.0 %), followed by smaller learning groups (31.7 %). Special education was less frequently provided to individuals with Asperger syndrome than to those with childhood or atypical autism (36.0 %, 76.1 %, and 63.4 %, respectively). Using logistic regression analysis, receiving special education was significantly associated with lower IQ (<85) (Odds Ratio (OR): 8.72; 95 % confidence interval (CI): 3.41-22.32) and younger age (≤11 years, OR: 2.87; 95 % CI: 1.11-7.38), but not with ASD symptom severity. CONCLUSIONS: The majority of children and adolescents with ASD received SES, indicating a satisfactory supply of such services in Germany. The finding that lower IQ but not ASD symptom severity predicted access to SES raises questions about the specificity of the used selection criteria.

The Underestimated Role of Refractive Error (Hyperopia, Myopia, and Astigmatism) and Strabismus in Children With ADHD.

Objective: To examine the association of refractive error (myopia, hyperopia, astigmatism) and strabismus with ADHD. Method: Based on data from the large, representative, epidemiological sample of the German Health Interview and Examination Survey for Children and Adolescents (KiGGS) study (N = 13,488), the associations of myopia, hyperopia, astigmatism, and strabismus with ADHD were examined, with and without consideration of other common ADHD risk factors. Results: In single logistic regression models, all examined forms of refractive error and strabismus showed an association with ADHD. After controlling for confounding variables, results remained stable and showed an increased risk for ADHD in children with hyperopia, astigmatism, and strabismus compared with the control group. Only the association between myopia and ADHD in children was not significant. Conclusion: Hyperopia, astigmatism, and strabismus seem to be independently associated with ADHD. Health care professionals in different medical fields should consider this association to adequately diagnose and treat affected children.

Siblings and Birth Order-Are They Important for the Occurrence of ADHD?

Objective: The associations of birth order, number of siblings, and ADHD was examined. Method: The analysis based on representative, epidemiological data from the German Health Interview and Examination Survey for Children and Adolescents (KiGGS) study (N = 13,488). Results: An increased risk for ADHD in firstborn versus youngest born children (odds ratio [OR] = 1.31, 95% confidence interval [CI] [1.09, 1.58]) and also versus children with no sibling (OR = 1.31, 95% CI [1.03, 1.68]) was revealed, while number of siblings was not associated with ADHD. Results remained stable after controlling for confounders. Conclusion: Firstborn children may receive simultaneously less parental resources and more responsibilities if younger siblings are born. This happens during the vulnerable developmental period of ADHD. In addition, due to higher levels of insecurity, parents are assumed to focus more on potential physical or psychological abnormities in their firstborn children. This may result in a diagnostic bias in firstborn children.