Dataset Information

ADHD symptoms and learning behaviors in children with ASD without intellectual disability. A mediation analysis of executive functions.

ABSTRACT: In spite of its importance for education, the relationship between learning behaviors (LB), attention deficit hyperactivity disorder symptoms (ADHD) and executive functioning (EF) in children with autism spectrum disorder (ASD) has hardly been explored. The first objective of the present study was to compare children with ASD without intellectual disability and children with typical development (TD) on ADHD symptoms and learning behaviors: Motivation/competence, attitude toward learning, persistence on the task, and strategy/flexibility. The second objective was to analyze the mediator role of behavioral regulation and metacognition components of EF between ADHD symptoms and learning behaviors in children with ASD. Participants were 89 children between 7 and 11 years old, 52 with ASD and 37 with TD, matched on age and intelligence. Their teachers filled out questionnaires assessing executive functioning as well as learning behaviors. Parents and teachers reported on inattention and hyperactivity/impulsivity behaviors. Compared to children with TD, children with ASD presented significantly more ADHD symptoms and poorer learning behaviors. In addition, there were significant mediation effects of the behavioral regulation index (BRI) and metacognition index (MI) of EF, indicating that both are part of the route through which ADHD symptoms impact to learning behaviors of children with ASD.

SUBMITTER: Rosello B

PROVIDER: S-EPMC6241124 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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ADHD symptoms and learning behaviors in children with ASD without intellectual disability. A mediation analysis of executive functions.

Rosello Belen B Berenguer Carmen C Baixauli Inmaculada I Colomer Carla C Miranda Ana A

PloS one 20181114 11

In spite of its importance for education, the relationship between learning behaviors (LB), attention deficit hyperactivity disorder symptoms (ADHD) and executive functioning (EF) in children with autism spectrum disorder (ASD) has hardly been explored. The first objective of the present study was to compare children with ASD without intellectual disability and children with typical development (TD) on ADHD symptoms and learning behaviors: Motivation/competence, attitude toward learning, persist ...[more]

PMID: 30427902

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Project description:Maternal autoantibody-related ASD (MAR ASD) is a subtype of autism in which pathogenic maternal autoantibodies (IgG) cross the placenta, access the developing brain, and cause neurodevelopmental alterations and behaviors associated with autism in the exposed offspring. We previously reported maternal IgG response to eight proteins (CRMP1, CRMP2, GDA LDHA, LDHB, NSE, STIP1, and YBOX) and that reactivity to nine specific combinations of these proteins (MAR ASD patterns) was predictive of ASD risk. The aim of the current study was to validate the previously identified MAR ASD patterns (CRMP1 + GDA, CRMP1 + CRMP2, NSE + STIP1, CRMP2 + STIP1, LDHA + YBOX, LDHB + YBOX, GDA + YBOX, STIP1 + YBOX, and CRMP1 + STIP1) and their accuracy in predicting ASD risk in a prospective cohort employing maternal samples collected prior to parturition. We used prenatal plasma from mothers of autistic children with or without co-occurring intellectual disability (ASD = 540), intellectual disability without autism (ID = 184) and general population controls (GP = 420) collected by the Early Markers for Autism (EMA) study. We found reactivity to one or more of the nine previously identified MAR ASD patterns in 10% of the ASD group compared with 4% of the ID group and 1% of the GP controls (ASD vs GP: Odds Ratio (OR) = 7.81, 95% Confidence Interval (CI) 3.32 to 22.43; ASD vs ID: OR = 2.77, 95% CI (1.19-7.47)) demonstrating that the MAR ASD patterns are strongly associated with the ASD group and could be used to assess ASD risk prior to symptom onset. The pattern most strongly associated with ASD was CRMP1 + CRMP2 and increased the odds for an ASD diagnosis 16-fold (3.32 to >999.99). In addition, we found that several of these specific MAR ASD patterns were strongly associated with ASD with intellectual disability (ASD + ID) and others associated with ASD without ID (ASD-no ID). Prenatal screening for these MAR patterns may lead to earlier identification of ASD and facilitate access to the appropriate early intervention services based on each child's needs.

Project description:BackgroundThose with autism spectrum disorder (ASD) and/or attention-deficit-hyperactivity disorder (ADHD) exhibit symptoms of hyperactivity and inattention, causing significant hardships for families and society. A potential mechanism involved in these conditions is atypical executive function (EF). Inconsistent findings highlight that EF features may be shared or distinct across ADHD and ASD. With ADHD and ASD each also being heterogeneous, we hypothesized that there may be nested subgroups across disorders with shared or unique underlying mechanisms.MethodsParticipants (N = 130) included adolescents aged 7-16 with ASD (n = 64) and ADHD (n = 66). Typically developing (TD) participants (n = 28) were included for a comparative secondary sub-group analysis. Parents completed the K-SADS and youth completed an extended battery of executive and other cognitive measures. A two stage hybrid machine learning tool called functional random forest (FRF) was applied as a classification approach and then subsequently to subgroup identification. We input 43 EF variables to the classification step, a supervised random forest procedure in which the features estimated either hyperactive or inattentive ADHD symptoms per model. The FRF then produced proximity matrices and identified optimal subgroups via the infomap algorithm (a type of community detection derived from graph theory). Resting state functional connectivity MRI (rs-fMRI) was used to evaluate the neurobiological validity of the resulting subgroups.ResultsBoth hyperactive (Mean absolute error (MAE) = 0.72, Null model MAE = 0.8826, (t(58) = -4.9, p < .001) and inattentive (MAE = 0.7, Null model MAE = 0.85, t(58) = -4.4, p < .001) symptoms were predicted better than chance by the EF features selected. Subgroup identification was robust (Hyperactive: Q = 0.2356, p < .001; Inattentive: Q = 0.2350, p < .001). Two subgroups representing severe and mild symptomology were identified for each symptom domain. Neuroimaging data revealed that the subgroups and TD participants significantly differed within and between multiple functional brain networks, but no consistent "severity" patterns of over or under connectivity were observed between subgroups and TD.ConclusionThe FRF estimated hyperactive/inattentive symptoms and identified 2 distinct subgroups per model, revealing distinct neurocognitive profiles of Severe and Mild EF performance per model. Differences in functional connectivity between subgroups did not appear to follow a severity pattern based on symptom expression, suggesting a more complex mechanistic interaction that cannot be attributed to symptom presentation alone.

Dataset Information

ADHD symptoms and learning behaviors in children with ASD without intellectual disability. A mediation analysis of executive functions.

Publications

ADHD symptoms and learning behaviors in children with ASD without intellectual disability. A mediation analysis of executive functions.

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