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Assessing the Contribution of Measures of Attention and Executive Function to Diagnosis of ADHD or Autism.

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Title: Assessing the Contribution of Measures of Attention and Executive Function to Diagnosis of ADHD or Autism.
Authors: Harkness, Kelsey, Bray, Signe, Durber, Chelsea M., Dewey, Deborah, Murias, Kara
Source: Journal of Autism & Developmental Disorders. Apr2025, Vol. 55 Issue 4, p1353-1364. 12p.
Subjects: Diagnosis of autism, Attention-deficit hyperactivity disorder, Research funding, Executive function, Descriptive statistics, Attention, Asperger's syndrome, Regression analysis
Abstract: Attention and executive function (EF) dysregulation are common in a number of disorders including autism and attention-deficit/hyperactivity disorder (ADHD). Better understanding of the relationship between indirect and direct measures of attention and EF and common neurodevelopmental diagnoses may contribute to more efficient and effective diagnostic assessment in childhood. We obtained cognitive (NIH Toolbox, Little Man Task, Matrix Reasoning Task, and Rey Delayed Recall) and symptom (CBCL, and BPMT) assessment data from the Adolescent Brain and Cognitive Development (ABCD) database for three groups, autistic (N = 110), ADHD (N = 878), and control without autism or ADHD diagnoses (N = 9130) and used ridge regression to determine which attention and EF assessments were most strongly associated with autism or ADHD. More variance was accounted for in the model for the ADHD group (31%) compared to the autism group (2.7%). Finally, we ran odds ratios (using clinical cutoffs where available and 2 standard deviations below the mean when not) for each assessment measure, which generally demonstrated a greater significance within the indirect measures when compared to the direct measures. These results add to the growing literature of symptom variably across diagnostic groups allowing for better understanding of presentations in autism and ADHD and how best to assess diagnosis. It also highlights the increased difficulty in differentiating autism and controls when compared to ADHD and controls and the importance of indirect measures of attention and EF in this differentiation. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Autism & Developmental Disorders is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Assessing the Contribution of Measures of Attention and Executive Function to Diagnosis of ADHD or Autism.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Autism+%26+Developmental+Disorders%22">Journal of Autism & Developmental Disorders</searchLink>. Apr2025, Vol. 55 Issue 4, p1353-1364. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Diagnosis+of+autism%22">Diagnosis of autism</searchLink><br /><searchLink fieldCode="DE" term="%22Attention-deficit+hyperactivity+disorder%22">Attention-deficit hyperactivity disorder</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Executive+function%22">Executive function</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Attention%22">Attention</searchLink><br /><searchLink fieldCode="DE" term="%22Asperger's+syndrome%22">Asperger's syndrome</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink>
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  Data: Attention and executive function (EF) dysregulation are common in a number of disorders including autism and attention-deficit/hyperactivity disorder (ADHD). Better understanding of the relationship between indirect and direct measures of attention and EF and common neurodevelopmental diagnoses may contribute to more efficient and effective diagnostic assessment in childhood. We obtained cognitive (NIH Toolbox, Little Man Task, Matrix Reasoning Task, and Rey Delayed Recall) and symptom (CBCL, and BPMT) assessment data from the Adolescent Brain and Cognitive Development (ABCD) database for three groups, autistic (N = 110), ADHD (N = 878), and control without autism or ADHD diagnoses (N = 9130) and used ridge regression to determine which attention and EF assessments were most strongly associated with autism or ADHD. More variance was accounted for in the model for the ADHD group (31%) compared to the autism group (2.7%). Finally, we ran odds ratios (using clinical cutoffs where available and 2 standard deviations below the mean when not) for each assessment measure, which generally demonstrated a greater significance within the indirect measures when compared to the direct measures. These results add to the growing literature of symptom variably across diagnostic groups allowing for better understanding of presentations in autism and ADHD and how best to assess diagnosis. It also highlights the increased difficulty in differentiating autism and controls when compared to ADHD and controls and the importance of indirect measures of attention and EF in this differentiation. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Autism & Developmental Disorders is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Apr2025
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