Classification of brain states using time-dependent structure of dynamics

What is it about?

Brain states emerge through continuously evolving dynamics of brain networks. The usual way of modelling these dynamics is by using stationary systems: there is one structure (attractor) which is responsible of the brain dynamics. We adopt a different approach by characterising the brain activity through a landscape of informational structures (IS) changing in time. We use a model transformation procedure to produce these structures and look at several properties related to how the different brain networks interact not in the observed resting-state fMRI signal but in the information structure underlying it. These properties provide measures strongly related with relevant characteristics of conscious activity, such as metastability, information integration or synchronisation. The distribution of IS measures is studied for healthy controls (HC) and two groups of post-comatose patients with disorders of consciousness (DOC): minimally conscious state (MCS) and unresponsive wakefulness syndrome (UWS). Based on IS measures, machine learners classifiers identify the state of consciousness with an outstanding discrimination (precision of 95.6% por HC/DOC and 86.6% for MCS/UWS).

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Photo by Robina Weermeijer on Unsplash

Photo by Robina Weermeijer on Unsplash

Why is it important?

By modelling the brain as a dynamical system, we observe that the structure of the global attractor is related to states of consciousness. We have observed that measures of cooperation (related to integration) among different brain networks are important for characterising conscious states.

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