Data-Driven Subtyping of Executive Function Related Behavioral Problems in Children
Data-Driven Subtyping of Executive Function Related Behavioral Problems in Children
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DOI：
10.1016/j.jaac.2018.01.014
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发表时间：
2018-04-01
影响因子：
13.3
通讯作者：
Astle, Duncan E.
中科院分区：
文献类型：
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作者：
Bathelt, Joe;Holmes, Joni;Astle, Duncan E.
目的：执行功能（Executive functions，EF）是调节行为和实现目标的重要认知技能。执行功能缺陷在学校挣扎的儿童中很常见，并与多种神经发育障碍有关。然而，即使在诊断类别内，儿童之间也存在相当大的异质性。本研究采取了数据驱动的方法，以确定不同的集群的儿童与EF相关的困难的共同配置文件，然后确定模式的大脑组织，区分这些data-driven group.Method：该样本包括442名儿童确定的健康和教育专业人士有困难的注意力，学习，和/或记忆。我们应用社区聚类，数据驱动的聚类算法，分组儿童的相似性，常用的评级量表EF相关的行为困难，Conners 3问卷。然后，我们调查了是否可以区分的白色物质的连接使用结构连接组学方法结合偏最小二乘analysis.Results：数据驱动的聚类产生了3个不同的儿童群体的症状之一，以下：（1）升高的注意力不集中和多动/冲动，和EF差;（2）学习问题;或（3）攻击行为和同伴关系问题。这些群体与显着的个体间变异白色物质连接的前额叶和前扣带cortices.Conclusion：总之，数据驱动的EF相关的行为困难的分类确定稳定的儿童群体，提供了一个很好的帐户的个体间差异，并与潜在的神经生物学基板密切配合。
Objective: Executive functions (EF) are cognitive skills that are important for regulating behavior and for achieving goals. Executive function deficits are common in children who struggle in school and are associated with multiple neurodevelopmental disorders. However, there is also considerable heterogeneity across children, even within diagnostic categories. This study took a data-driven approach to identify distinct clusters of children with common profiles of EF-related difficulties, and then identified patterns of brain organization that distinguish these data-driven groups.Method: The sample consisted of 442 children identified by health and educational professionals as having difficulties in attention, learning, and/or memory. We applied community clustering, a data-driven clustering algorithm, to group children by similarities on a commonly used rating scale of EF-associated behavioral difficulties, the Conners 3 questionnaire. We then investigated whether the groups identified by the algorithm could be distinguished on white matter connectivity using a structural connectomics approach combined with partial least squares analysis.Results: The data-driven clustering yielded 3 distinct groups of children with symptoms of one of the following: (1) elevated inattention and hyperactivity/impulsivity, and poor EF; (2) learning problems; or (3) aggressive behavior and problems with peer relationships. These groups were associated with significant interindividual variation in white matter connectivity of the prefrontal and anterior cingulate cortices.Conclusion: In sum, data-driven classification of EF-related behavioral difficulties identified stable groups of children, provided a good account of interindividual differences, and aligned closely with underlying neurobiological substrates.