Prognostic Role of Hemispherical Functional Connectivity in Stroke: A Study via Graph Theory Versus Coherence of Electroencephalography Rhythms

The objective of the present study is to explore whether acute stroke may result in changes in brain network architecture by electroencephalography functional coupling analysis and graph theory.Ninety acute stroke patients and 110 healthy subjects were enrolled in different clinical centers in Rome, Italy, starting from 2013, and for each one electroencephalographies were recorded within <15 days from stroke onset. All patients were clinically evaluated through National Institutes of Health Stroke Scale, Barthel Index, and Action Research Arm Test in the acute stage and during the follow-up. Functional connectivity was assessed using Total Coherence and Small World (SW) by comparing the affected and the unaffected hemisphere between groups (Stroke versus Healthy). Correlations between connectivity and poststroke recovery scores have been carried out.In stroke patients, network hemispheric asymmetry, in terms of Total Coherence, was mainly detected in the affected hemisphere with lower values in Delta, Theta, Alpha1, and Alpha2 (P=0.000001), whereas the unaffected hemisphere showed lower Total Coherence only in Delta and Theta (P=0.000001). SW revealed a significant difference only in the affected hemisphere in all electroencephalography bands (lower SW in Delta (P=0.000003), Theta (P=0.000003), Alpha1 (P=0.000203), and Alpha2 (P=0.028) and higher SW in Beta2 (P=0.000002) and Gamma (P=0.000002)). We also found significant correlations between SW and improvement in National Institutes of Health Stroke Scale (Theta SW: r=-0.2808), Barthel Index (Delta SW: r=0.3692; Theta SW: r=0.3844, Beta2 SW: r=-0.3589; Gamma SW: r=-04948), and Action Research Arm Test (Beta2 SW: r=-0.4274; Gamma SW: r=-0.4370).These findings demonstrated changes in global functional connectivity and in the balance of network segregation and integration induced by acute stroke. The findings on the correlations between clinical outcome(s) and poststroke network architecture indicate the possibility to identify a predictive index of recovery useful to address and personalize the rehabilitation program.