Visualizing perturbations of conscious states using whole-brain models combined with variational autoencoders

YONATAN SANZ PERL; ENZO TAGLIAZUCCHI; GUSTAVO DECO

Estocolmo

Workshop; Interpretable Brain Data (IBD) workshop; 2023

Applying external perturbations to a system to determine its properties is one of the oldest tricks in the natural sciences. In the context of neuroscience and neurology, perturbational approaches (e.g. combined TMS and EEG) show great promise to characterize brain states in terms of responsiveness and conscious awareness. Moreover, external and non-invasive perturbations are also rehearsed to modify the state of consciousness, as in the case of brain injured patients presenting disorders of consciousness. In spite of the obvious importance of perturbations to understand states of consciousness, we lack a general theory to predict what different stimulation protocols will do in terms of brain activity, cognition and behavior. In this talk, we will show how computational models combined with dimensionality reduction techniques from deep learning (variational autoencoders) are useful to visualize the effect of different perturbations applied to brain states such as sleep, anesthesia and patients suffering from disorders of consciousness.