Jacobo SITT| Latest developments in Neurophysiology of States of Consciousness: From Mechanistic Principles to Novel Diagnostic and Therapeutic Tools
INS Seminar Room, Campus Timone, Red Wing, 5th Floor.
Abstract :Uncovering the neural mechanisms that allow conscious access to information is a major challenge of neuroscience. An incomplete list of still open
questions include, What are the necessary brain computational properties to permit access to a stream of conscious contents? What is the relationship between conscious perception, self-awareness, and multisensory processing of bodily signals? How these processes change when the brain transitions to an ‘unconscious’ state (like sleep, anesthesia or pathological conditions)? Can we externally trigger state-of-consciousness (SOC) transitions using
stimulation? In this presentation, I will present my work focus on these relevant scientific and clinical questions.
I will present our latest developments including different pre-clinical and clinical experimental models (brain-injuries and/or anesthesia),
neuroimaging methods (EEG, fMRI or brain/body interactions) and stimulation techniques (tES, auditory/somatosensory/visual stimulation).
Overall I will try to demonstrate that the integration of multimodal neural information provides critical information to characterize the state-of-consciousness in physiological and pathological conditions and might help to predict novel optimal therapeutic strategies.
Bio:
Medical Doctor (psychiatrist) with multidisciplinary training (Ph.D. in Physics of Dynamical Systems). Associate professor (CR1 researcher) INSERM (French National Institute of Health and Medical Research). Research focus in testing the causal role of neural markers of consciousness using different experimental models (brain-injuries, sleep and anesthesia), neuroimaging methods (M/EEG, sEEG, fMRI) and stimulation techniques (tES, auditory stimulation). Expert in neuroimaging signal analysis, mathematical modeling, nonlinear dynamics, complexity science, information theory, and machine learning.