• stroke
• middle cerebral artery occlusion
• analisi nonlineare di serie temporali
• lfp
• ictus
• local field potential
• nonlinear time series analysis

Stroke is one of the most frequent causes of death after ischaemic heart disease. One extremely promising line of research in this context is devoted to so-called local field potentials (LFP). LFP signals emerge as a result of the overall electrical activity produced by a volume of nervous tissue close to the detecting microelectrode. One of the great advantages of these signals consists in their higher signal-to-noise ratio, as compared with other commonly used electrophysiological measurements. Yet the difficulty in interpreting LFPs represents a challenge for both theoretical and experimental research. The present work makes use of both linear and nonlinear time series analysis methods to investigate possible changes in LFP features in the stroke-affected brain. In particular, we test statistical differences in the level of interhemispheric coupling between mice subjected to middle cerebral artery occlusion (MCAO) and healthy ones. In addition, complexity measures based on dynamical systems and information theory are also analysed along with their capacity to distinguish an injured brain from a healthy one. This should turn out helpful in the investigation of an even more important problem, namely, the possibility of segregating subgroups among stroke patients based on some particular features of the signal. A relationship between those features and clinical parameters would then lead to significant improvements in both quality and accuracy of rehabilitation therapies.