JOURNAL ARTICLE

The Utility of a Novel Neuropsychological Measurement to Analyze Event-Related Attentional Behaviors among Young Children with Attention Deficit Hyperactivity Disorder—a Pilot Study.

  • Published In: Archives of Clinical Neuropsychology, 2025, v. 40, n. 1. P. 33 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Chen, I-Chun; Zheng, Yu-Qi; Zhao, Hui-Xuan; Lin, Li-Chen; Chen, Yun-Ju; Chang, Meng-Han; Ko, Li-Wei 3 of 3

Abstract

This article focuses on evaluating a novel neuropsychological measurement system, the ΣCOG, designed to assess attention-related behaviors in older preschool children with and without attention deficit hyperactivity disorder (ADHD). The ΣCOG, which incorporates trial-based event markers and a game-like interface, was compared with the established Conners' Kiddie Continuous Performance Test, Second Edition (K-CPT 2), in a sample of 33 children (14 diagnosed with ADHD). Results showed that the ΣCOG’s omission and response time scores correlated with clinical behavioral scales and aligned with key K-CPT 2 measures such as commissions and reaction time, while also enabling detailed within-task event-related behavioral analysis. The study suggests that the ΣCOG is clinically feasible and may serve as an alternative or complementary tool to the K-CPT 2, particularly by providing granular event-related data useful for individualized assessment and treatment planning in preschool ADHD.

Additional Information

  • Source:Archives of Clinical Neuropsychology. 2025/02, Vol. 40, Issue 1, p33
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2025
  • ISSN:0887-6177
  • DOI:10.1093/arclin/acae055
  • Accession Number:182905865
  • Copyright Statement:Copyright of Archives of Clinical Neuropsychology is the property of Oxford University Press / USA 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.