Classifying consciousness states through an arrow of time inspired framework

Abstract

Sleep is a natural, recurring state of rest and unconsciousness in which the body and mind undergo a series of physiological and neurological changes. It is divided in 4 stages: REM, N1, N2 and N3, also called deep sleep, which is the lowest level of consciousness that a person undergoes without considering it an abnormal clinical stage. Traditionally, brain activity during sleep has been studied through electroencephalographic recordings. In this work we propose a framework to study N3 using fMRI data only. This work also had the objective of finding a new method to classify different levels of consciousness, given that the actual methods of disorders of consciousness diagnosis usually depend on biased interpretations of the observed reaction of the patient to certain stimuli. The Generative Connectivity of the Arrow of Time (GCAT), a thermodynamics inspired framework, has been used to obtain the generative effective connectivity (GEC) of the brain, which is a measure that includes structural and functional information. In the GCAT framework a wholebrain model is used to determine the causal mechanisms underlying changes in brain hierarchy. Using the GEC, it has been able to classify between wake and deep sleep in human subjects, with an accuracy of over 90%. Furthermore, relevant topological changes have been found between these two states, identifying the brain regions which could be responsible for the loss of consciousness in deep sleep. These results open the door towards expanding the knowledge about sleep stages as well as being the first steps towards achieving a systematic and objective method for disorders of consciousness’ diagnosis.