Is there any incremental benefit to conducting neuroimaging and neurocognitive assessments in the diagnosis of ADHD in young children? A machine learning investigation.
Is there any incremental benefit to conducting neuroimaging and neurocognitive assessments in the diagnosis of ADHD in young children? A machine learning investigation.
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DOI：
10.1016/j.dcn.2021.100966
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发表时间：
2021-06
影响因子：
4.7
通讯作者：
Dick AS
中科院分区：
文献类型：
--
作者：
Öztekin I;Finlayson MA;Graziano PA;Dick AS
鉴于与ADHD相关的早期行为问题的负面轨迹，早期诊断被认为是进行干预和治疗的关键。为此，目前的调查采用机器学习来评估父母/教师评级，执行功能（EF）的行为和神经测量在预测由162名幼儿（年龄4-7岁，平均年龄5.55，82.6%西班牙裔/拉丁裔）组成的样本中的ADHD的相对预测价值。在目标措施中，教师评价的EF是最有预测力的ADHD。虽然更广泛的评估神经措施，如弥散加权成像，可能会提供更多的信息，因为它们涉及到与ADHD相关的潜在认知缺陷，目前的研究表明，在研究中获得的皮质解剖措施，以及在常规评估中经常获得的EF认知措施，在区分典型发育儿童与诊断为ADHD的儿童方面几乎没有增量价值。值得注意的是，Brief中的一些EF问题与ADHD症状之间的重叠可能会增强这种效果。因此，未来的研究评估这些措施在预测儿童的功能障碍在学术和社会领域的重要性，将提供额外的洞察力，他们在ADHD的贡献作用。
Given the negative trajectories of early behavior problems associated with ADHD, early diagnosis is considered critical to enable intervention and treatment. To this end, the current investigation employed machine learning to evaluate the relative predictive value of parent/teacher ratings, behavioral and neural measures of executive function (EF) in predicting ADHD in a sample consisting of 162 young children (ages 4–7, mean age 5.55, 82.6 % Hispanic/Latino). Among the target measures, teacher ratings of EF were the most predictive of ADHD. While a more extensive evaluation of neural measures, such as diffusion-weighted imaging, may provide more information as they relate to the underlying cognitive deficits associated with ADHD, the current study indicates that measures of cortical anatomy obtained in research studies, as well cognitive measures of EF often obtained in routine assessments, have little incremental value in differentiating typically developing children from those diagnosed with ADHD. It is important to note that the overlap between some of the EF questions in the BRIEF, and the ADHD symptoms could be enhancing this effect. Thus, future research evaluating the importance of such measures in predicting children’s functional impairment in academic and social areas would provide additional insight into their contributing role in ADHD.
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