Understanding the neurocognitive mechanisms of alpha-band brain oscillations using concurrent EEG-fMRI recordings

项目概要 Alpha 振荡，即神经信号中 8-12Hz 的波动，在不同物种中都很稳健，与以下状态共变： 注意力，并受到多种疾病状态的影响，近年来已成为候选生物标志物 注意力系统的功效，特别是注意力障碍，例如注意力缺陷多动障碍 （多动症）。然而，尽管人们广泛接受阿尔法振荡作为注意力读出信号，但 关于阿尔法振荡的机制和解释仍然存在疑问。我们的目标 研究的目的是回答阿尔法振荡的神经认知机制是否是一个核心问题 严格注意还是它们是否更密切地反映其他机制，例如局部处理动态或 全系统的紧张警觉性。 为了解决这个问题，我们将同时记录人类参与者的脑电图和功能磁共振成像，并采用因果关系 模型，识别视觉皮层的功能连接（3T 扫描）和层流激活（7T 扫描） 在跨不同任务上下文的阿尔法调制期间。我们的目标是测试 (1) α 的增加是否反映了皮质 抑制或强直警觉性，(2) α 降低是否反映皮质增强或局部信息 流，以及，(3) α 调制是否反映自上而下的信号，或者它们是任一信号的无源副产品 皮质-皮质或丘脑-皮质驱动器。结果将直接影响对 α 振荡的解释 ADHD 及相关疾病中注意力系统功效的生物标志物，概述了一个可针对 ADHD 和相关疾病的模型 治疗或监测注意力缺陷多动症（ADHD）的注意力缺陷。通过考虑现有研究结果的可变性，这项工作 还将区分阿尔法振荡的两种理论框架，并讨论最近关于 阿尔法振荡是否是自上而下信号的相关性或产物。

PROJECT SUMMARY Alpha oscillations, 8-12Hz fluctuations in neural signals, are robust across species, covary with states of attention, and are impacted in multiple disorder states, and in recent years have become a candidate biomarker of attention system efficacy, particularly in disorders of attention such as attention deficit hyperactivity disorder (ADHD). Yet, despite this broad acceptance of alpha oscillations as an attention read-out signal, significant questions remain regarding the mechanisms and thus interpretation of alpha oscillations. The goal of our research is to answer the central question of whether the neurocognitive mechanisms of alpha oscillations are strictly attentional or whether they more closely reflect other mechanisms such as local processing dynamics or system-wide tonic alertness. To address this question, we will record EEG and fMRI concurrently in human participants, and employ causal models, to identify functional connectivity (3T scanning) and laminar activation (7T scanning) of visual cortex during alpha modulations across different task contexts. We aim to test (1) if alpha increases reflect cortical suppression or tonic alertness, (2) if alpha decreases reflect cortical enhancement or local information flow, and, (3) if alpha modulations reflect top-down signals or if they are a passive by-product of either cortico-cortical or thalamo-cortical drivers. The results will directly impact interpretation of alpha oscillations as a biomarker of attention-system efficacy in ADHD and related conditions, outlining a model that can be targeted in treatment or monitoring of attention deficits in ADHD. By accounting for variability in existing findings, this work will also differentiate between two theoretical frameworks of alpha oscillations and speak to recent debates on whether alpha oscillations are a correlate or a product of top-down signals.