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

项目摘要 α振荡，神经信号中的8- 12 Hz波动，在物种间是鲁棒的，与 注意力，并在多种疾病状态下受到影响，近年来已成为候选生物标志物 注意力系统功效的研究，特别是在注意力障碍如注意力缺陷多动障碍中， （ADHD）。然而，尽管α振荡被广泛接受为注意力读出信号， 关于α振荡的机制和解释仍然存在问题。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 严格的注意力，或者它们是否更密切地反映了其他机制，如局部处理动态， 全系统的紧张性警觉 为了解决这个问题，我们将同时记录人类参与者的EEG和fMRI，并采用因果关系。 模型，以识别视觉皮层的功能连接（3 T扫描）和层状激活（7 T扫描） 在不同的任务环境中进行阿尔法调制。我们的目标是测试（1）如果α增加反映皮质 抑制或紧张性警觉，（2）如果α降低反映皮层增强或局部信息 流动，以及，（3）如果阿尔法调制反映自上而下的信号，或者如果他们是一个被动的副产品， 皮质-皮质或丘脑-皮质驱动器。结果将直接影响α振荡的解释， ADHD和相关疾病中注意力系统功效的生物标志物，概述了一个可以靶向治疗的模型。 治疗或监测ADHD的注意力缺陷。通过解释现有发现的可变性，这项工作 还将区分阿尔法振荡的两个理论框架，并谈到最近的辩论， α振荡是自上而下信号的相关性还是产物。

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.