Grants and Contributions:

Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023)

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.