Individual cortical connectivity changes after stroke: a resampling approach to enable statistical assessment at single-subject level.

One of the main limitations commonly encountered when dealing with the estimation of brain connectivity is the difficulty to perform a statistical assessment of significant changes in brain networks at a single-subject level. This is mainly due to the lack of information about the distribution of the connectivity estimators at different conditions. While group analysis is commonly adopted to perform a statistical comparison between conditions, it may impose major limitations when dealing with the heterogeneity expressed by a given clinical condition in patients. This holds true particularly for stroke when seeking for quantitative measurements of the efficacy of any rehabilitative intervention promoting recovery of function. The need is then evident of an assessment which may account for individual pathological network configuration associated with different level of patients' response to treatment; such network configuration is highly related to the effect that a given brain lesion has on neural networks. In this study we propose a resampling-based approach to the assessment of statistically significant changes in cortical connectivity networks at a single subject level. First, we provide the results of a simulation study testing the performances of the proposed approach under different conditions. Then, to show the sensitivity of the method, we describe its application to electroencephalographic (EEG) data recorded from two post-stroke patients who showed different clinical recovery after a rehabilitative intervention.