Christoforou Christoforos, Theodorou Maria, Fella Argyro, Papadopoulos Timothy C
Division of Computer Science, Mathematics and Science, St. John's University, New York, NY, United States.
Independent Researcher, New York, NY, United States.
Front Psychol. 2023 Jun 20;14:1076501. doi: 10.3389/fpsyg.2023.1076501. eCollection 2023.
Naming speed, behaviorally measured via the serial Rapid automatized naming (RAN) test, is one of the most examined underlying cognitive factors of reading development and reading difficulties (RD). However, the unconstrained-reading format of serial RAN has made it challenging for traditional EEG analysis methods to extract neural components for studying the neural underpinnings of naming speed. The present study aims to explore a novel approach to isolate neural components during the serial RAN task that are (a) informative of group differences between children with dyslexia (DYS) and chronological age controls (CAC), (b) improve the power of analysis, and (c) are suitable for deciphering the neural underpinnings of naming speed.
We propose a novel machine-learning-based algorithm that extracts spatiotemporal neural components during serial RAN, termed RAN-related neural-congruency components. We demonstrate our approach on EEG and eye-tracking recordings from 60 children (30 DYS and 30 CAC), under phonologically or visually similar, and dissimilar control tasks.
Results reveal significant differences in the RAN-related neural-congruency components between DYS and CAC groups in all four conditions.
Rapid automatized naming-related neural-congruency components capture the neural activity of cognitive processes associated with naming speed and are informative of group differences between children with dyslexia and typically developing children.
We propose the resulting RAN-related neural-components as a methodological framework to facilitate studying the neural underpinnings of naming speed and their association with reading performance and related difficulties.
通过连续快速自动命名（RAN）测试进行行为测量的命名速度，是阅读发展和阅读障碍（RD）研究中考察最多的潜在认知因素之一。然而，连续RAN的无约束阅读形式使得传统脑电图分析方法难以提取神经成分来研究命名速度的神经基础。本研究旨在探索一种新方法，以在连续RAN任务中分离出神经成分，这些成分应满足以下条件：（a）能够体现诵读困难儿童（DYS）与实际年龄匹配的对照组（CAC）之间的组间差异；（b）提高分析效能；（c）适合解读命名速度的神经基础。
我们提出一种基于机器学习的新算法，该算法可在连续RAN过程中提取时空神经成分，称为RAN相关神经一致性成分。我们在60名儿童（30名DYS和30名CAC）的脑电图和眼动追踪记录上展示了我们的方法，这些记录来自语音或视觉相似及不相似的对照任务。
结果显示，在所有四种条件下，DYS组和CAC组在RAN相关神经一致性成分上存在显著差异。
快速自动命名相关神经一致性成分捕捉了与命名速度相关的认知过程的神经活动，并能体现诵读困难儿童与正常发育儿童之间的组间差异。
我们提出所得的RAN相关神经成分作为一种方法框架，以促进对命名速度的神经基础及其与阅读表现和相关障碍的关联的研究。
