341 Structural and Functional Insights Into Topological Patterns of Networks in Patients With Advanced Parkinson's Disease. Issue Volume 65:Issue CN(2018)Supplement 1 (16th August 2018)

• Record Type: Journal Article
• Authors: Muller, Jennifer; Li, Lucy; Thalheimer, Sara; Silverman, Mackenzie; Alizadeh, Mahdi; Liang, Tsao-Wei; Layton, Kelly A; Kremens, Daniel; Romo, Victor M; Mohamed, Feroze; Wu, Chengyuan
• Is Part Of: Neurosurgery. Volume 65:Issue CN(2018)Supplement 1
• Page Start: 136
• Page End: 137
• Publication Date: 2018-08-16
• Subjects: Nervous system -- Surgery -- Periodicals; 617.48005
• DOI: 10.1093/neuros/nyy303.341
• Languages: English
• ISSNs: 0148-396X

Abstract

Abstract: INTRODUCTION: Parkinson's disease (PD) is a neurodegenerative disorder which results in motor impairment. Deep brain stimulation (DBS) is a treatment option for reducing motor symptoms in patients with PD. It is hypothesized that motor performance in PD reflects adjustments in a network of interacting neural circuits that contribute to motor control. In particular, increased putamen-cerebellar connectivity has been thought as a reflection of compensatory mechanisms in PD. Functional and diffusion tensor imaging (DTI) techniques may reveal insights into neural network changes in PD. Here, we aimed to investigate how changes in functional and structural connectivity may relate to patient response to DBS, through the examination of large-scale brain network changes. METHODS: Ten patients with advanced PD were included in this study. DTI and resting-state scans were acquired for structural and functional connectivity analysis. A total of 120 brain regions were registered to both structural and functional scans. Structural tracts from DTI and synchronization of functional signal were used to create 2D correlation matrices representing the connection strength between different regions. Network analysis was performed to compute both global and local graph theory metrics, which were correlated with UPDRS-III improvement for each structure. RESULTS: Combined structural and functional graph theory metrics highlighted 32 structures to be significantly correlated (mean P = .027) with UPDRS-III improvement. Many of these structures have been previously described as major hubs in PD (precuneus, postcentral, etc). Connections to the cerebellum and precuneus were found to be significantly correlated with UPDRS-III improvement across several metrics for both structural and functional connectivity. CONCLUSION: In this work, we combined DTI, fMRI, and graph theory analysis to evaluate improvement with DBS. We identified several imaging biomarkers that are robust predictors for UPDRS-III improvement. This work warrants investigation into the compensatory effect of the cerebellum and precuneus as well as other potential biomarkers for identifying DBS candidates.