Studies of the mechanisms in the brain that allow complex neuronal activity to arise in a coordinated fashion have produced some of the most spectacular discoveries in neuroscience, and still promise huge potential understanding as novel technological and especially methodological tools are developped. Neurophysiology has allowed to understand small-scale networks of neurons, and the study of brain lesions has identified local brain areas specifically associated with a certain function. Non-invasive brain imaging techniques, such as electro- or magneto-encephalography (EEG and MEG) and functional magnetic resonance imaging (fMRI) have brought brain research an incredible amount of a novel type of data, that is, multivariate time series representing local dynamics at each of multiple sites or sources throughout the whole human brain while functioning. These data have already demonstrated that neurophysiological processes typically have long memory or fractal scaling properties at a univariate level of analysis and demonstrate complex network topological organization at a multivariate level of analysis using wavelet correlations. Compared to estimators employed previously, this wavelet correlation is well-behaved for long memory and nonstationary processes. These findings suggest that brain dynamics and networks may have important statistical properties in common with other, substantively diverse, complex systems that are currently the focus of exciting activity in the general field of statistical physics. This project is focusing on the study of consciousness disorders (a medical state that follows coma) for which understanding the disconnexion process of the brain is crucial to improve everyday management for these patients. Consciousness disorders is a major concern in public health. Due to the progress in intensive care, more and more patients will survive severe acute brain damage. Disorders of consciousness can be acute and reversible or they can be irreversible and permanent. In the context of consciousness disorders, the aim of this project is to characterize multivariate neurophysiological datasets to elucidate the biological basis for information processing and propragation in the human brain. Neuroimagery provides us with time series, associated with either voxels or sensors, which correspond to the information processed by a brain area in time. These data have brought to light the dynamic nature of the brain, which follows complex time patterns, a subject which has been at the center of neuroscience for a century; but so far, the space-time patterns have remained nearly unexplored. This is why the revolution of understanding that they can bring is yet to come, as what they reveal of the circulation of the information in the brain and its dynamics has not been yet successfully analysed. Participating in this novel understanding, this project will open up new directions to help clinicians for the diagnosis and to give better predictions concerning the possible recovery of these patients. This will also bring new opportunities to understand the spontaneous fluctuations in brain activity observed on resting state healthy volunteers and the nature of consciousness. More generally, after Geschwind's pioneer work in neurology and psychiatry, pathologies could be described in terms of disconnection syndromes. Thus, the approach described here might be extended to other pathologies such as multiple sclerosis, epilepsy, stroke, Alzheimer disease, schizophrenia. This project requires a very broad skillset, comprising statistical signal processing, complex network analysis and visualization, and systems neuroscience. Thus, the team is composed of experts in medical applications (S. Krémer), neurosciences (C. Delon-Martin), statistics (S. Achard and J.-F. Coeurjolly) and signal processing (V. Noblet).