Rhythmic and arrhythmic brain activity: from advanced signal processing tools to new electromagnetic neuroimaging methods

节律和心律失常的大脑活动：从先进的信号处理工具到新的电磁神经成像方法

• 批准号: RGPIN-2017-05960
• 负责人: Lina, JeanMarc
• 金额: $ 1.75万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=679964
• 关键词: Rhythmic; arrhythmic; brain; activity; advanced

The main objective of this research program is to provide neuroscientists and neuro-clinicians with a new generation of signal processing and imaging tools based on the electromagnetic signals produced by the brain. Direct measurement of the electrical activity produced by the brain provides time-resolved signals which reflect the brain in action', i.e. the synchronization of neural populations, either during cognitive tasks or resting state. Sleep is also a particularly important period during which the brain generates and processes information, and undergoes plastic reorganization of the interconnected synaptic networking in order to be more efficient during the forthcoming awake time. As a matter of fact, the characterization of spontaneous brain activity during resting states or sleep in humans is among the most challenging scientific issues. Electromagnetic (EM) signals produced by the brain can be non invasively measured in two complementary ways: the usual EEG signals (Electroencephalography, i.e. electric potential measured on the scalp) and more recent MEG recordings (Magnetoencephalography, i.e. magnetic field measured around the head). As for most of electrophysiological signals, EM recordings are a complex mix of short lasting and narrow-banded oscillations (rhythmic activity), with more long-range and broadband dynamical processes without characteristic time scale (arrhythmic activity). Usual spectral analysis mostly focus on the rhythmic component, i.e. the oscillations that are usually categorized into frequency bands (delta:0.1-4 Hz; theta:4-8 Hz; alpha:8-16 Hz; beta:16-32 Hz; gamma: >32 Hz). Although the arrhythmic component has been generally modeled by power-law processes and self-similar processes without functional interpretation with respect to the brain activity, recent works has demonstrated the functional relevance of this spontaneous arrythmic signal that reflects neural activity and non linear interactions between neural populations with implication in cognition. However, this model that relies on a unique spectral exponent, cannot account for the full complexity of the real data. Based on the recent development in multifractal analysis, the main objective aims at validating new advanced signal analyses methods and numerical tools that offer new and robust spectral descriptors elucidating the coupling between rhythmic and arrhythmic components of EM neuro-physiological signals, and to localize in the brain specific neural processes associated to those components. The specific (but not restricted) field of application will be a quantitative assessment of the sleep quality and integrity based on dynamical neuro-processing tools in the healthy and aging population.

该研究计划的主要目标是为神经科学家和神经临床医生提供基于大脑产生的电磁信号的新一代信号处理和成像工具。由大脑产生的电活动的直接测量提供了时间分辨的信号，其反映了大脑在认知任务或休息状态期间的“活动”，即神经群的同步。睡眠也是一个特别重要的时期，在此期间，大脑产生和处理信息，并经历相互连接的突触网络的可塑性重组，以便在即将到来的清醒时间内更有效。事实上，表征人类在静息状态或睡眠期间的自发脑活动是最具挑战性的科学问题之一。由大脑产生的电磁（EM）信号可以通过两种互补的方式进行非侵入性测量：通常的EEG信号（脑电图，即在头皮上测量的电势）和最近的MEG记录（脑磁图，即在头部周围测量的磁场）。与大多数电生理信号一样，EM记录是持续时间短和窄带振荡（节律活动）的复杂混合，具有更长范围和宽带动力学过程，没有特征时间尺度（神经活动）。频谱分析主要集中在节奏分量上，即通常被分类为频带的振荡（δ：0.1-4 Hz; θ：4-8 Hz; α：8-16 Hz; β：16-32 Hz; γ：>32 Hz）。虽然心律失常成分通常由幂律过程和自相似过程建模，而没有关于大脑活动的功能解释，但最近的工作已经证明了这种自发心律失常信号的功能相关性，它反映了神经活动和神经群体之间的非线性相互作用，具有认知意义。然而，这种依赖于唯一谱指数的模型不能解释真实的数据的全部复杂性。基于多重分形分析的最新发展，主要目的是验证新的先进的信号分析方法和数值工具，提供新的和强大的频谱描述符阐明EM神经生理信号的节律和神经元成分之间的耦合，并定位在大脑中与这些成分相关的特定神经过程。具体（但不限于）应用领域将是基于健康和老龄化人群中的动态神经处理工具对睡眠质量和完整性进行定量评估。