The 'Hub Disruption Index', a reliable index sensitive to the brain networks reorganization. A study of the contralesional hemisphere in stroke

• Main Authors: Maite Termenon, Sophie Achard, Assia Jaillard, Chantal Delon-Martin
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2016-08-01
• Series: Frontiers in Computational Neuroscience
• Subjects: Stroke; Reliability; ICC; resting state fMRI; intra-hemispheric connectivity; graph theory analysis
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fncom.2016.00084/full

Stroke, resulting in focal structural damage, induces changes in brain function at both local and global levels. Following stroke, cerebral networks present structural and functional reorganization to compensate for the dysfunctioning provoked by the lesion itself and its remote effects. As some recent studies underlined the role of the contralesional hemisphere during recovery, we studied its role %of the contralesional hemispherein the reorganization of brain function of stroke patients using resting state fMRI and graph theory. We explored this reorganization using the 'hub disruption index' (kappa), a global index sensitive to the reorganization of nodes within the graph. For a given graph metric, kappa of a subject corresponds to the slope of the linear regression model between the mean local network measures of a reference group, and the difference between that reference and the subject under study. In order to translate the use of kappa in clinical context, a prerequisite to achieve meaningful results is to investigate the reliability of this index. In a preliminary part, we studied the reliability of kappa by computing the intraclass correlation coefficient in a cohort of 100 subjects from the Human Connectome Project. Then, we measured intra-hemispheric kappa index in the contralesional hemisphere of 20 subacute stroke patients compared to 20 age-matched healthy controls. Finally, due to the small number of patients, we tested the robustness of our results repeating the experiment 1000 times by bootstrapping on the Human Connectome Project database. Statistical analysis showed a significant reduction of kappa for the contralesional hemisphere of right stroke patients compared to healthy controls. Similar results were observed for the right contralesional hemisphere of left stroke patients. We showed that kappa, is more reliable than global graph metrics and more sensitive to detect differences between groups of patients as compared to healthy controls. Using new graph metrics as kappa allows us to show that stroke induces a network-wide pattern of reorganization in the contralesional hemisphere whatever the side of the lesion. Graph modeling combined with measure of reorganization at the level of large-scale networks can become a useful tool in clinic.