title: The "Hub Disruption Index," a Reliable Index Sensitive to the Brain Networks Reorganization. A Study of the Contralesional Hemisphere in Stroke.
abstract: 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 in the reorganization of brain function of stroke patients using resting state fMRI and graph theory. We explored this reorganization using the "hub disruption index" (κ), a global index sensitive to the reorganization of nodes within the graph. For a given graph metric, κ 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 κ 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 κ by computing the intraclass correlation coefficient in a cohort of 100 subjects from the Human Connectome Project. Then, we measured intra-hemispheric κ 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 κ 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 κ, 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 κ 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.
authors: Grenoble Institut des Neurosciences, Université Grenoble AlpesGrenoble, France; Institut National de la Santé et de la Recherche Médicale, U1216Grenoble, France.
authors: Maite
authors: M
authors: Termenon
authors: GIPSA-Lab, Université Grenoble AlpesGrenoble, France; GIPSA-Lab, Centre National de la Recherche ScientifiqueGrenoble, France.
authors: Sophie
authors: S
authors: Achard
authors: Centre Hospitalier Universitaire (CHU) de GrenobleGrenoble, France; Pole Recherche, Centre Hospitalier Universitaire (CHU) GrenobleGrenoble, France; IRMaGe, Institut National de la Santé et de la Recherche Médicale US17 Centre National de la Recherche Scientifique UMS 3552Grenoble, France.
authors: Assia
authors: A
authors: Jaillard
authors: Grenoble Institut des Neurosciences, Université Grenoble AlpesGrenoble, France; Institut National de la Santé et de la Recherche Médicale, U1216Grenoble, France.
authors: Chantal
authors: C
authors: Delon-Martin
keywords: ICC; contralesional hemisphere; graph theory; hub disruption index; intra-hemispheric connectivity; reliability; resting state fMRI; stroke