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.

鉴于与 ADHD 相关的早期行为问题的负面轨迹，早期诊断被认为是实现干预和治疗的关键。为此，目前的调查采用机器学习来评估父母/教师评分、执行功能 (EF) 的行为和神经测量在预测由 162 名幼儿（4-7 岁，平均5.55 岁，82.6% 西班牙裔/拉丁裔）。在目标测量中，教师对 EF 的评分是对 ADHD 最具预测性的。虽然对神经测量的更广泛评估，例如弥散加权成像，可能会提供更多信息，因为它们与 ADHD 相关的潜在认知缺陷有关，但目前的研究表明，在研究中获得的皮质解剖学测量，以及经常在常规评估中获得的 EF 认知测量值，在区分典型发育儿童和诊断患有 ADHD 的儿童方面几乎没有增量价值。重要的是要注意，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在学术和社交领域的功能障碍的重要性，将为他们在 ADHD 中的贡献提供更多的见解。