Ricerc@Sapienza

A stroke in the brain is characterized by a deprivation of oxygen in some brain regions. When this happens in the motor cortex, one or more parts of the body result impaired. This causes a dramatic alteration on the brain network and a consequent process of reorganization of the different cerebral districts (brain plasticity). In order to restore the motor function, different rehabilitative treatments have been introduced. Among these, Brain Computer Interfaces (BCIs), have been aroused considerable interest in recent years due to the experimental evidences that have been demonstrated their effectiveness in the rehabilitative context. Many studies have shown how the restoring of motor function with BCI is related with the increase of connections between the two hemispheres. Thus, the mere study of changes in lesioned brain areas is not sufficient to describe the brain plasticity and it is necessary to use brain connectivity. Although scientific evidences support the use of brain connectivity to study the effects of stroke on brain network, monitoring the evolution of the connections between hemispheres, within (intra-session) and across (cross-session) different sessions, it is still a challenging issue. This is mainly due to methodological limitations of the brain connectivity estimators. In fact, in the intra-session case the number of data samples available for the estimation process is not adequate to provide an accurate estimation. Instead, in the cross-session case the accuracy and reliability of brain connectivity estimators have not been tested. The main objective of this project consist in the development, implementation and testing of an approach based on artificial neural network (ANN) that can overcomes these limitations. The final toolbox will be validated on 28 stroke patients involved in an rehabilitative intervention based on BCI technology. In conclusion, this project could provide an ANN based toolbox for monitoring the recovery of stroke patients.