The neurophysiological basis of interindividual variability (IIV) in ADHD

项目总结 任务表现的个体内变异性(IIV)增加是注意力缺陷/多动的强烈症状 精神障碍(ADHD)。然而，尽管理论框架表明低效的神经处理是一种潜在的 IIV的原因，证据是有限的，因为IIV通常使用行为的汇总统计数据来衡量 结果，如响应时间变异性(RTV)。这样的措施不仅受到稀疏时间的限制 采样，但它们也聚合时间，忽略潜在的动态，并限制了特异性，因此 IIV在ADHD中的翻译价值。在这个项目中，我们提出了神经生理测量的神经 处理效率可以从EEG的振荡和信号特性连续导出，以跟踪 自上而下信号的贡献(低频功率)、网络效率(低频振荡小世界 指标)和网络交互稳定性(信号复杂性)。这些措施可以作为一种连续的、在- 神经处理效率的受试者神经生理学指数，以及一个联系总结统计得出的指数 来自反应时间和潜在的网络动态。 为了验证这一想法，我们重新访问了三个现有的数据集(n=514)，其中包括儿童和成人，包括有无数据 ADHD，包含脑电和同时进行的脑电和功能磁共振成像，在持续注意力任务期间收集。在每一个中 数据集，我们基于EEG信号计算神经效率的连续测量，以在目标1中区分 注意缺陷多动障碍患者不同的神经生理学特征和IIV机制之间的关系以及检验这些指标是否可以预测 表现结果和症状的个体差异。在目标2中，我们还测试了神经 效率测量预测，在受试者内部，核心注意网络和 以前与ADHD有关的那些--即额顶、默认模式、腹侧/背侧注意、视觉 和额纹状体。本工作的目标是建立ADHD患者IIV的神经生理学基础，以及 因此，对IIV的假定临床靶点进行讲解，区分当前的IIV理论，以及验证 基于脑电的神经效率作为潜在网络动态的有效中间指标。

PROJECT SUMMARY Increased intraindividual variability (IIV) in task performance is a robust symptom of attention deficit/hyperactivity disorder (ADHD). However, while theoretical frameworks suggest inefficient neural processing as a potential cause of IIV, evidence is limited because IIV is typically measured using summary statistics of behavioral outcomes, such as response time variability (RTV). Not only are such measures limited by sparse temporal sampling, but they also aggregate time, ignoring underlying dynamics, and limiting the specificity and thus the translational value of IIV in ADHD. In this project, we propose that neurophysiological measures of neural processing efficiency can be continuously derived from the oscillatory and signal properties of EEG, to track the contribution of top-down signals (low-frequency power), network efficiency (low-frequency oscillatory small-world index) and network interaction stability (signal complexity). These measures can be used as a continuous, within- subject neurophysiological index of neural processing efficiency, and one that bridges summary statistics derived from reaction times and underlying network dynamics. To test this idea, we revisit three existing datasets (n=514) that include children and adults, with and without ADHD, and that contain EEG and concurrent EEG and fMRI, collected during sustained attention tasks. In each dataset, we compute continuous measures of neural efficiency based on EEG signals to, in Aim 1, differentiate between alternate neurophysiological profiles and mechanisms of IIV in ADHD and test if these predict performance outcomes and individual differences in symptoms. In Aim 2, we additionally test if neural efficiency measures predict, within-subject, aberrant interactions between core attention networks and those previously associated with ADHD – namely fronto-parietal, default-mode, ventral/dorsal attention, visual and fronto-striatal. The goal of the present work is to establish the neurophysiological basis of IIV in ADHD, and thus speak to putative clinical targets of IIV, differentiate between current theories of IIV, as well as to validate EEG-based neural efficiency as an effective intermediate indicator of underlying network dynamics.