Spectral graph theory to investigate topological and dynamic properties of EEG-based brain networks: an application to post-stroke patients.

Brain networks represent one of the most fascinating biophysics contributions to modern medicine. Nowadays clinical practice largely benefits from network theory to characterize both physiological and pathological brain networks. However, to date the analysis of such systems still relies on classic approaches, thus limiting the amount of information extracted from those networks. As to overcome such a limitation, this work investigates the behavior of brain networks combinig combining both spectral graph theory and random walk analysis. Specifically, the algebraic connectivity and the relaxation time have been extracted from functional resting state brain networks estimated from a population of subacute post-stroke subjects. Results have been correlated with the Upper Extremity Fugl-Mayer Assessment (UEFMA) score of each subject as to assess whether a dependence exists between network's organization and motor impairment in the upper limb.