Ricerc@Sapienza

Neural activity underlying human brain produces electrical signals that we can measure and observe at different frequencies. Specific frequencies encode unique information about the cognitive mechanisms. Thus, classical investigations devoted to understanding brain functioning have focused the analysis on limited frequency bands in which the brain is supposed to work during the task under exam. However, this choice might not paint the whole picture, having a serious impact on the results, as far as it prevents to see brain activity as a whole. Moreover, many recent studies support the hypothesis that brain functioning is based on mechanisms of integration and segregation within and across different frequency domains. All these evidences require the development of a pipeline in which there is no need for aggregation or selection of information, but in which considering brain signals at different frequencies simultaneously. Our project addresses this issue by aiming to provide a novel framework for the analysis of the interaction between brain regions across frequencies. We will ground our framework on the latest advances in graph theory, building a multilayer network model in which each layer embodies the brain functional connectivity information carried by a single frequency band. Such multilayer network will be estimated from electroencephalographic signals collected from both healthy subjects and stroke patients during resting state and a tasks of motor imagination/execution. These networks will be then analyzed through indices based on node centrality and community detection. With this model we aim to provide a more complete and accurate description of brain functional connectivity to gain insights on brain functioning. Furthermore, we want to test if this description varies between healthy subjects and patients, as this difference could provide clinical relevant information.