Maiella Michele, Benedetti Martina, Alaimo Di Loro Pierfrancesco, Maruotti Antonello
Department of Behavioural and Clinical Neurology, Santa Lucia Foundation IRCCS, Rome, Italy.
Department of Neuroscience and Rehabilitation, University of Ferrara and Center for Translational Neurophysiology of Speech and Communication (CTNSC), Italian Institute of Technology (IIT), Ferrara, Italy.
Dyslexia. 2025 Nov;31(4):e70013. doi: 10.1002/dys.70013.
The multi-deficit framework employed to identify dyslexia requires statistical learning-based models to account for the complex interplay of cognitive skills. Traditional methods often rely on simplistic statistical techniques, which may fail to capture the heterogeneity inherent in dyslexia. This study introduces a model-based clustering framework, employing finite mixtures of contaminated Gaussian distributions, to better understand and classify dyslexia. Using data from a cohort of 122 children in Poland, including 51 diagnosed with dyslexia, we explore the effectiveness of this method in distinguishing between dyslexic and control groups. Our approach integrates variable selection techniques to identify clinically relevant cognitive skills while addressing issues of outliers and redundant variables. Results demonstrate the superiority of multivariate finite mixture models, achieving high accuracy in clustering and revealing the importance of specific variables such as Reading, Phonology, and Rapid Automatized Naming. This study emphasises the value of the multiple-deficit model and robust statistical techniques in advancing the diagnosis and understanding of dyslexia.
用于识别诵读困难的多缺陷框架需要基于统计学习的模型来解释认知技能之间复杂的相互作用。传统方法通常依赖于简单的统计技术，这可能无法捕捉诵读困难所固有的异质性。本研究引入了一个基于模型的聚类框架，采用受污染高斯分布的有限混合模型，以更好地理解和分类诵读困难。利用来自波兰122名儿童队列的数据，其中包括51名被诊断为诵读困难的儿童，我们探讨了该方法在区分诵读困难组和对照组方面的有效性。我们的方法整合了变量选择技术，以识别临床相关的认知技能，同时解决异常值和冗余变量的问题。结果表明多变量有限混合模型的优越性，在聚类中实现了高精度，并揭示了阅读、语音学和快速自动命名等特定变量的重要性。本研究强调了多缺陷模型和稳健统计技术在推进诵读困难诊断和理解方面的价值。
