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

这项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。对大脑产生的电活动的直接测量提供了反映大脑活动的时间分辨信号，即无论是在认知任务中还是在休息状态下，神经群体的同步。睡眠也是大脑产生和处理信息的一个特别重要的时期，为了在即将到来的清醒时间更有效率，大脑对相互连接的突触网络进行可塑性重组。事实上，人类在休息状态或睡眠中的自发大脑活动的特征是最具挑战性的科学问题之一。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(脑电图法，即在头皮上测量的电势)和较新的脑磁图记录(磁脑图法，即测量头部周围的磁场)。对于大多数电生理信号，EM记录是一个复杂的持续时间短和窄带振荡(节律活动)的混合体，具有更多的长程和宽带的动态过程，而没有特征的时间尺度(心律失常活动)。通常的频谱分析主要集中在节律成分上，即通常被归类为频带的振荡(增量：0.1-4赫兹；θ：4-8赫兹；阿尔法：8-16赫兹；贝塔：16-32赫兹；伽马：&gt；32赫兹)。虽然心律失常的成分通常是由幂规律过程和自相似过程模拟的，没有关于大脑活动的功能解释，但最近的工作证明了这种反映神经活动和神经群体之间的非线性相互作用的自发心律失常信号与认知相关。然而，这种依赖于唯一谱指数的模型不能完全考虑真实数据的复杂性。基于多重分形分析的最新进展，主要目的是验证新的先进信号分析方法和数值工具，这些方法和数值工具提供新的和稳健的频谱描述符，阐明EM神经生理信号的节律和心律失常成分之间的耦合，并在大脑中定位与这些成分相关的特定神经过程。具体的(但不受限制的)应用领域将是对健康和老龄化人群中基于动态神经处理工具的睡眠质量和完整性的定量评估。

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.