EEG-based functional connectivity to analyze motor recovery after stroke: A pilot study. (March 2019) - Eldeeb, Safaa
Abstract: In this paper, we introduce electroencephalography (EEG)- PDC based network connectivity average mean degrees (E-PDC) measure to analyze the interhemispheric interaction between the left and right motor cortices after stroke. E-PDC uses a graph and partial directed coherence (PDC) approach to quantify the directional functional connectivity between the motor cortices, which is not only altered after stroke but also is one of the important mechanisms linked with poor recovery of hand function. The brain activity between the two motor cortices is calculated via PDC and is used to form a graph. The PDC based network connectivity average mean degree of connectivity defined over this graph is defined as the E-PDC, which quantifies the directional connectivity between the two motor cortices. We preliminarily validated the novel E-PDC measure with three individuals with stroke, where one individual received a non-invasive brain stimulation (NIBS) intervention and the other two received sham-NIBS intervention. Unlike the two individuals who received sham-NIBS, the individual who received the NIBS intervention showed improvement in E-PDC after intervention, which strongly correlated with improvement in hand function after intervention (Fugl Meyer Upper Extremity Subscale and grip strength). This implies that the introduced E-PDC measure quantifies the interactions between the motor cortices and could be used to elucidate the underlying mechanism in restoring hand function after stroke.