Executive function (EF) and emotion regulation (ER) have been proposed as transdiagnostic factors that contribute to the socio-emotional and behavioural difficulties observed in neurodevelopmental disorders, particularly autism and attention deficit/hyperactivity disorder (ADHD). Investigating potential differences in the association between EF and ER in autism, ADHD and autism/ADHD co-occurrence could be an important avenue to inform possible differential diagnosis. In this study, we present a systematic review examining the associations between EF and ER in autism, ADHD, and autism/ADHD. PubMed, Web of Science, PsycInfo, and Scopus datasets were searched for empirical articles, published between January 2013 and October 2024. Twenty-two articles were included. Of these, four analysed the relationship between EF and ER in autism, 16 in ADHD and two in autism/ADHD, demonstrating a clear focus on the study of these dimensions in ADHD over the past decade. Although age (i.e., children versus adults) and methodological (i.e., task-based versus report-based measures) differences may contribute to the variability of findings, the overall evidence suggests an association between EF difficulties and emotional dysregulation across conditions. Further research comparing autism, ADHD and autism/ADHD individuals is needed to draw clearer conclusions about how the association between EF and ER differs across these neurodevelopmental disorders, to inform more accurate diagnosis.

Background: Machine learning modeling can provide valuable support in different areas of mental health, because it enables to make rapid predictions and therefore support the decision making, based on valuable data. However, few studies have applied this method to predict symptoms’ worsening, based on sociodemographic, contextual, and clinical data. Thus, we applied machine learning techniques to identify predictors of symptomatologic changes in a Spanish cohort of OCD patients during the initial phase of the COVID-19 pandemic.Methods: 127 OCD patients were assessed using the Yale–Brown Obsessive-Compulsive Scale (Y-BOCS) and a structured clinical interview during the COVID-19 pandemic. Machine learning models for classification (LDA and SVM) and regression (linear regression and SVR) were constructed to predict each symptom based on patient’s sociodemographic, clinical and contextual information.Results: A Y-BOCS score prediction model was generated with 100% reliability at a score threshold of ± 6. Reliability of 100% was reached for obsessions and/or compulsions related to COVID-19. Symptoms of anxiety and depression were predicted with less reliability (correlation R of 0.58 and 0.68, respectively). The suicidal thoughts are predicted with a sensitivity of 79% and specificity of 88%. The best results are achieved by SVM and SVR.Conclusion: Our findings reveal that sociodemographic and clinical data can be used to predict changes in OCD symptomatology. Machine learning may be valuable tool for helping clinicians to rapidly identify patients at higher risk and therefore provide optimized care, especially in future pandemics. However, further validation of these models is required to ensure greater reliability of the algorithms for clinical implementation to specific objectives of interest.

Obsessive Compulsive Disorder is associated with affected executive functioning, including memory, cognitive flexibility, and organizational strategies. As it was reported in previous studies, patients with preserved executive functions respond better to pharmacological treatment, whilst others need to keep trying different pharmacological strategies. In this work we used machine learning techniques to predict pharmacological response (OCD patients' symptomatology reduction) based on executive functioning and clinical variables. Among those variables we used anxiety, depression and obsessive-compulsive symptoms scores by applying State-Trait Anxiety Inventory, Hamilton Depression Rating Scale and Yale-Brown Obsessive Compulsive Scale respectively, whilst Rey-Osterrieth Complex Figure Test was used to assess organisation skills and non-verbal memory; Digits' subtests from Wechsler Adult Intelligence Scale-IV were used to assess short-term memory and working memory; and Raven's Progressive Matrices were applied to assess problem solving and abstract reasoning. As a result of our analyses, we created a reliable algorithm that predicts Y-BOCS score after 12 weeks based on patients' clinical characteristics (sex at birth, age, pharmacological strategy, depressive and obsessive-compulsive symptoms, years passed since diagnostic and Raven progressive matrices score) and Digits' scores. A high correlation (0.846) was achieved in predicted and true values. The present study proves the viability to predict if a patient would respond or not to a certain pharmacological strategy with high reliability based on sociodemographics, clinical variables and cognitive functions as short-term memory and working memory. These results are promising to develop future prediction models to help clinical decision making.