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ADHD is a nosological condition linked to impaired brain maturity, especially at the frontal temporal level, which generates neurocognitive behavioral and social alterations. Previous research has shown that ADHD is associated with deficits in executive functions, including working memory, which is relevant for regulating behavior and social interactions. This study analyzed the capacity of working memory as an explanatory element of social skills in schoolchildren diagnosed with ADHD, through a structural equation model. A sample of N = 250, males, diagnosed with ADHD, in school, aged between 9 and 12 years old, was used. The sampling was probabilistic. The structural equation model was developed in RCran 4.3.2 software; the social skills variable (endogenous) was measured with 6 observable variables (interaction with authority figures, interaction with opposite sex, being in evidence, assertive expression of annoyance, displeasure, interaction with strangers and acting in public), using the CANSO-N24 questionnaire. The working memory variable (exogenous) was measured with 5 observable variables (digits, letters/numbers, color, word and word/color), and WISIV and Stroop tests were applied. The model converged after 73 iterations; a p-value < 0.05 was obtained in the Chi-square statistic, resulting in a fitted model. A positive covariance (0.17) was obtained between working memory and social skills, and a P(>|z|) of 0.379, indicating a low significance. This study tested the theoretical prediction that better working memory would correlate with better social skills in schoolchildren diagnosed with ADHD. These results indicate that while there is a positive relationship, working memory is not the only factor explaining social skills in ADHD. This supports the theory that multiple factors, including other mental processes, genetic, and environmental influences, contribute to social skills development in ADHD. [ABSTRACT FROM AUTHOR]

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental clinical entity associated with a reduction in brain maturation mainly at the frontotemporal level, generating neurocognitive deficits. This disorder usually presents in a comorbid form. Our research aimed to identify the neurocognitive characteristics of ADHD (combined, hyperactive/impulsive and inattentive) of pure presentation or with comorbidity with oppositional defiant disorder, specific learning disorder or autism spectrum disorder. We used a sample of N=712 school children with a diagnosis of ADHD, aged between 6 and 13 years, using non-probability sampling. We built predictive models to establish probabilistic rules with different types of variables (total IQ, working memory index, perceptual reasoning index, processing speed index, phonological/semantic fluency, attention, visuoverbal memory, verbal memory, working memory, visual memory, visual perception, constructional praxias), we used the automatic learning technique of Decision Trees (DTCM) in Rcran 4.2.1 software, which allowed us to establish a clinical hierarchy. The data for the analysis were obtained from the results of the psychometric tests provided to the sample (WISC-IV, Verbal Fluency, TMT, Visuoverbal Memory Scale, Verbal Memory Curve, Wechsler Memory Scale, Rey/Osterrieth Complex Figure). We conclude that children with pure ADHD present poor performance on tasks assessing the working memory index and perceptual reasoning that are not explained by deficits in IQ. Deficits in working memory are generalizable to all presentations and comorbidities of ADHD. One of the main advantages of DTCM over other predictive machine learning techniques is the possibility of differentiating the hierarchy of importance of the dependent variables, in this case, allowing the identification of the most important variables in four different populations of children diagnosed with ADHD. [ABSTRACT FROM AUTHOR]