Project 5 The causal role of neocortical beta events in human sensory perception

项目 5 新皮质β事件在人类感官知觉中的因果作用

• 批准号: 10246478
• 负责人: STEPHANIE Ruggiano JONES
• 金额: $ 39.93万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10246478
• 关键词: Address; Area; Attention; Beta Rhythm; Biophysics; Brain; Cues; Data; Detection; Disease; Electroencephalography; Event; Fingers; Future; Human; Intervention; Knowledge; Location; Measures; Modeling; Motor; Nervous System Physiology; Neuraxis; Output; Parkinson Disease; Pattern; Perception; Performance; Pharmacology; Physiologic pulse; Probability; Protocols documentation; Randomized; Reporting; Role; Sensory; Signal Transduction; Somatosensory Cortex; Tactile; Techniques; Testing; Transcranial magnetic stimulation; design; distraction; encephalography; improved functioning; models and simulation; neocortical; neural model; novel; recruit; relating to nervous system; tactile stimulation

Beta rhythms (15-29 Hz) are one of the most dominant brain signals measured non-invasively in humans with magento- and electro-encephalography (MEG/EEG). They are strong predictors of perception and motor performance, and disrupted in disease states, such as Parkinson’s Disease. Yet, beta’s causal role in function is still unknown. In this proposal, we will combine human EEG, transcranial magnetic stimulation (TMS) and biophysically principled neural modeling to investigate a direct causal relationship between beta and perception and to define novel TMS paradigms that optimally impact perception. Our prior studies have shown that prestimulus beta activity measured with MEG in human primary somatosensory cortex (SI) “inhibits” tactile detection, such that the higher the averaged prestimulus beta power the less likely the subject detects a threshold level tap to the finger. Further, averaged beta power increases in non-attended regions, presumably as a means to filter distracting information to facilitate perception (Jones et al J. Neurosci. 2010). More recently, we reported that that high power beta activity emerges as brief “events” (<150ms) in unaveraged data, with a dominant peak lasting one beta period ~40-60ms (Sherman et al PNAS 2016). Preliminary data suggests such beta events are intermittent and that the rate of beta events underlies beta’s shift with attention and impact on perception. Prestimulus beta event rates decrease in attended, and increase in non-attended somatotopic regions (areas of distraction), corresponding with a higher probability of detection. We predict detection of tactile stimulation in an attended location will be inhibited when tactile stimulation is delivered after high spontaneous EEG beta event rates in the corresponding somatotopic region, and enhanced when delivered after high beta event rates in the non-attended somatotopic region (area of distraction) (Aim 1). We further predict TMS protocols that mimic endogenous beta event patterns will impact perception more effectively than non-TMS conditions and standard functionally “inhibitory” TMS protocols (Aim 2). Computational neural modeling specifically designed by our group to simulate macro-scale EEG signals will be used to intepret circuit mechanisms underlying observed data (Aim 3). !

Beta 节律（15-29 Hz）是在人类中以非侵入性方式测量的最主要的大脑信号之一 磁磁图和脑电图（MEG/EEG）。它们是感知和运动的有力预测因子 表现，并在帕金森病等疾病状态下受到干扰。然而，贝塔在功能中的因果作用 仍然未知。在这个提案中，我们将结合人类脑电图、经颅磁刺激（TMS）和 基于生物物理学原理的神经模型，用于研究 β 和感知之间的直接因果关系 并定义能够最佳地影响感知的新型 TMS 范例。 我们之前的研究表明，用 MEG 测量人类原发性刺激前 β 活性 体感皮层（SI）“抑制”触觉检测，因此平均刺激前β功率越高 受试者检测到手指敲击阈值水平的可能性越小。此外，平均贝塔功率增加 无人值守的区域，大概是作为过滤分散注意力的信息以促进感知的一种手段（Jones 等 神经科学杂志。 2010）。最近，我们报道称，高功率贝塔活动以短暂的“事件”形式出现 (<150ms) 在非平均数据中，主峰持续一个 beta 周期 ~40-60ms (Sherman 等人 PNAS 2016）。初步数据表明，此类 β 事件是间歇性的，并且 β 事件的发生率是其发生的基础。 贝塔随着注意力的变化和对感知的影响而变化。刺激前β事件发生率下降，并且 无人照管的躯体区域（注意力分散区域）的增加，相应于更高的概率 检测。 我们预测，当触觉刺激被控制时，在有人值守的位置对触觉刺激的检测将会受到抑制。 在相应躯体区域的高自发脑电图β事件发生率后交付，并且 当在无人照管的躯体区域（区域）的高β事件发生率后交付时增强 分散注意力）（目标 1）。我们进一步预测模仿内源性 beta 事件模式的 TMS 协议将影响 比非 TMS 条件和标准功能“抑制”TMS 协议更有效的感知（目标 2）。我们小组专门设计的用于模拟宏观脑电图信号的计算神经模型将 用于解释观察到的数据背后的电路机制（目标 3）。 ！