Abstract TP145: Evolution of Language Network Plasticity Post Stroke Based on Graph Theory

Abstract

Introduction: Modeling the human brain as a large-scale network has been shown to provide systematic understanding of reorganizational evolvement secondary to stroke. To investigate these changes in the language network, we applied graph theory methods to resting-state fMRI data acquired from left hemisphere stroke patients at 2 time points and also compared them with healthy controls.Materials & Methods: Eyes-closed resting-state fMRI scans were collected along with a T-1 weighted anatomical scan from 12 left hemisphere stroke patients (11 right-handed) at two time points (visit 1 (V1) at one week and visit 2 (V2) approximately 6 months from stroke onset) and 34 age-gender matched healthy controls with 3T GE scanner. 23 defined language regions were used to derive Pearson correlation coefficient matrices from each subject’s R-S fMRI using AFNI. By using minimum spanning tree (to guarantee connectedness) combined with proportional thresholding on these matrices, Binary undirected connection matrices for each subject was obtained and it then were used to study networks’ measurements. We evaluated changes at the whole-network level, individual regions level using graph theoretic measures, and functional connectivity(FC) level using the Network Based Statistic approach. Results: At the whole-network level, Global efficiency was significantly increased in Patients’ V2 compared with patients’ V1 at different thresholds which is an indication of more efficient communication in network after recovery. At the regional level, sum of strengths over all right hemisphere regions increased significantly in patients’ V2 compared with V1 due to increased FC in this hemisphere post stroke and local efficiency in the right cerebellum increased significantly(p<0.001) in V2 compare to V1. At FC level, there were significant proportions of newly formed inter-hemispheric connections such as between the right cerebellum and key left hemisphere language areas. Conclusion: Results showed that during recovery process of patients with stroke in left hemisphere, significant neuroplastic changes lead to strengthening of connections mostly in the right hemisphere to increase functional efficiency of the language network.

How can graph theory inform the dual-stream model of speech processing? a resting-state fMRI study of post-stroke aphasia

“…In the area of degenerative brain disease, hub regions have been shown to be more vulnerable (Achard et al, 2006;Albert, Jeong, & Barabási, 2000;Bassett & Bullmore, 2006), in that damage to or disruptions of hub regions are typically more correlated with more severe neurocognitive impairments than damage or disruption to non-hub regions of a network (He et al, 2008;Roger et al, 2019;Stam et al, 2009). However, to our knowledge no previous studies have examined the functional network connectivity of the dual-stream model using graph theory, and only a few studies have investigated stroke-induced language impairments using graph theory (Bohland, Kapse, & Kiran, 2014;Duncan & Small, 2016;Mazrooyisebdani, A. Nair, Garcia-ramos, & Prabhakaran, 2018). These previous studies found some network level properties were changed and related to the deficiency or recovery of post-stroke aphasia.…”