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.

中文翻译：

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儿童执行功能相关行为问题的数据驱动亚型

客观的

执行功能（EF）是认知技能，对于调节行为和实现目标很重要。执行功能缺陷在上学挣扎的儿童中很常见，并与多种神经发育障碍有关。但是，即使在诊断类别内，儿童之间也存在相当大的异质性。这项研究采取了一种数据驱动的方法，以识别具有EF相关困难的常见特征的不同儿童群，然后确定区分这些数据驱动组的大脑组织模式。

方法

样本包括442名儿童，这些儿童被卫生和教育专业人员识别为在注意力，学习和/或记忆方面有困难的儿童。我们应用了社区聚类（一种数据驱动的聚类算法），在EF相关行为困难的常用评分量表Conners 3调查表上按相似性对儿童进行分组。然后，我们调查了使用结构连接组学方法与偏最小二乘分析相结合，在白质连通性上是否可以区分由算法确定的组。

结果

数据驱动的聚类产生了以下症状之一的3个不同组的儿童：（1）注意力不集中和多动/冲动增加，EF差；（2）学习问题；或（3）攻击行为和同伴关系问题。这些组与前额叶和前扣带回皮质的白质连通性的个体差异显着相关。

结论

总而言之，数据驱动的EF相关行为困难分类确定了稳定的儿童群体，很好地说明了个体之间的差异，并与潜在的神经生物学底物紧密结合。