Using micro-cognition biomarkers of neurosystem dysfunction to redefine ADHD subtypes: A scalable digital path to diagnosis based on brain function.
Using micro-cognition biomarkers of neurosystem dysfunction to redefine ADHD subtypes: A scalable digital path to diagnosis based on brain function.
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使用神经系统功能障碍的微观认知生物标志物重新定义 ADHD 亚型：基于大脑功能的可扩展数字诊断路径。
DOI：
10.1016/j.psychres.2023.115348
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发表时间：
2023
影响因子：
11.3
通讯作者：
Kish,Ryan
中科院分区：
文献类型：
--
作者：
Wexler,BruceE;Kish,Ryan
基于症状的诊断与基础神经病理学不一致，混淆了个体患者的新治疗开发和治疗选择。使用数字神经治疗（DNT）期间获得的神经系统功能障碍的高精度微认知生物标志物，我们描述了具有不同神经病理学的ADHD儿童亚组。K均值聚类应用于6 - 9名6-9岁ADHD儿童，使用来自Go/NoGo测试的表现变量，针对58名典型发育（TD）儿童进行标准化，确定了四个亚组，这些亚组经过验证，并通过从DNT期间的数千个响应中提取的微认知生物标志物进一步表征。这些集群在ADHD的象征性特征上有所不同。第4组表现出反应抑制差和注意力不一致。簇3仅显示不良反应抑制，而其他两个均未显示。集群2显示更快，更一致的反应，更高的检测简单的目标和更好的工作记忆比TD儿童，但显着的性能递减时，需要跟踪多个目标或忽略干扰。第1组显示出更大的能力，识别抽象类别的成员，而不是自然的类别，儿童通过与环境的物理互动学习，而第4组则相反。细粒度，低成本，非侵入性和可扩展的数字微认知生物标志物可以识别具有相同的基于神经病理学的诊断但具有不同神经病理学的患者。
Symptom-based diagnosis does not align with underlying neruropathology, confounding new treatment development and treatment selection for individual patients. Using high precision micro-cognition biomarkers of neurosystem dysfunction acquired during digital neurotherapy (DNT), we characterized subgroups of ADHD children with different neuropathology. K-means clustering applied to 69 children 6–9 years old with ADHD using performance variables from a Go/NoGo test normalized against 58 typically developing (TD) children identified four subgroups that were validated and further characterized by micro-cognition biomarkers extracted from thousands of responses during the DNT. The clusters differed on emblematic features of ADHD. Cluster 4 showed poor response inhibition and inconsistent attention. Cluster 3 showed only poor response inhibition and the other two showed neither. Cluster 2 showed faster and more consistent responses, higher detection of simple targets and better working memory than TD children but marked performance decrements when required to track multiple targets or ignore distractors. Cluster 1 showed much greater ability recognizing members of abstract categories rather than natural categories that children learn through physical interaction with the environment while Cluster 4 was the opposite. Fine-grained, low-cost, noninvasive, and scalable digital micro-cognition biomarkers can identify patients with the same symptom-based diagnosis but differing neuropathology.