The neurophysiological basis of interindividual variability (IIV) in ADHD

项目摘要 任务表现的个体内变异性（IIV）增加是注意力缺陷/多动的一个明显症状 ADHD（ADHD）。然而，虽然理论框架表明，低效的神经处理是一种潜在的 由于IIV的原因，证据是有限的，因为IIV通常是使用行为的汇总统计来衡量的。 响应时间变异性（RTV）。这种措施不仅受到稀疏时间的限制， 采样，但他们也聚集时间，忽略了潜在的动态，并限制了特异性，从而 IIV在ADHD中的翻译价值在这个项目中，我们提出，神经生理学措施的神经 处理效率可以连续地从EEG的振荡和信号特性中导出，以跟踪EEG信号。 自上而下信号的贡献（低频功率）、网络效率（低频振荡小世界 指数）和网络交互稳定性（信号复杂度）。这些措施可以作为一个连续的，在- 神经生理学指标是神经处理效率的一个指标，并通过汇总统计得出 从反应时间和潜在的网络动力学。 为了验证这个想法，我们重新访问了三个现有的数据集（n=514），包括儿童和成人，有和没有 ADHD，包含EEG和并发EEG和fMRI，在持续注意力任务期间收集。在每个 数据集，我们根据EEG信号计算神经效率的连续度量，以在目标1中区分 在ADHD中交替的神经生理学特征和IIV机制之间，并测试这些是否预测 表现结果和症状的个体差异。在目标2中，我们还测试了神经网络 效率指标预测，受试者内，核心注意力网络之间的异常互动， 那些以前与ADHD相关的-即额顶叶，默认模式，腹侧/背侧注意，视觉 和额纹状体目前工作的目标是建立ADHD中IIV的神经生理学基础， 因此，对IIV的假定临床靶点发言，区分IIV的当前理论，以及验证 基于EEG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.