Functional reorganization of brain networks after stroke

The brain is a complex system that can be described as a network consisting of nodes (brain regions) and links (connections). This network undergoes functional reorganisations after a stroke and in cognitive decline. Therefore, we assume that the post-stroke cognitive deficit is related to the disruption of the brain network caused by the stroke. This thesis aims to find possible relationships between the network and cognitive parameters in post-stroke patients. I evaluated the resting-state closed-eye electroencephalographic (EEG) data and the neuropsychological tests for 45 post-stroke patients. An automatic modular pipeline was developed to process the EEG recordings and estimate network parameters. The algorithm includes removal of the cardiac and ocular artefacts based on the independent component analysis. The functional connectivity was estimated based on coherence, imaginary coherence and the weighted phase lag index in three frequency bands, theta, alpha and beta. In cooperation with a neuropsychologist, we proposed a parametrisation of the neuropsychological tests and converted them into cognitive parameters. Finally, I used the Kendall rank correlation coefficient to correlate the network and cognitive parameters. The thesis showed some interesting trends in the theta and beta bands for imaginary coherence. However, statistical evidence for those trends was rather weak when I accounted for the repeated testing. Consequently, I cannot state there is a relationship between the cognitive and network parameters in poststroke patients.