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  • Correlation of Volume of Tissue Activation Distance from Probabilistic White Matter Fiber Tractography Associated with Treatment Efficacy in Patients with Parkinson’s Disease Selected for Deep Brain S

    Final Number:
    4117

    Authors:
    Jennifer Muller; John Pearce; Mahdi Alizadeh; Lauren Kozlowski; Feroze Mohamed; Ashwini Dayal Sharan; Chengyuan Wu

    Study Design:
    Laboratory Investigation

    Subject Category:

    Meeting: Congress of Neurological Surgeons 2017 Annual Meeting - Late Breaking Science

    Introduction: High frequency deep brain stimulation (DBS) of the globus pallidus (GPi) or subthalamic nucleus (STN) has been shown to be clinically effective in relieving symptoms associated with Parkinson’s Disease (PD). [6] Precision in location of electrodes within the target region is correlated with improved motor enhancement. [4][9] Calculation of the volume of tissue activated (VTA) of a DBS contact can be used to visualize the probable effect of stimulation. [3] Diffusion tensor imaging (DTI) may provide 3D visualization of neuronal pathways. [1] Here, we examine the correlation between proximity of VTA to the area of highest track density (TD) of DTI-derived tracts, with the percent improvement in postoperative UPDRS score.

    Methods: Five patients (4 GPI DBS, 1 STN DBS) with PD were included in this study. All subjects underwent preoperative MRI examinations and DTI images were acquired. Track density maps were generated from probabilistic tractography using FSL. Postoperative images were registered to DTI space and DBS-Electrodes were reconstructed using Lead-DBS software (http://www.lead-dbs.org). [4] VTA was calculated using the Maedler 2012 FE model. [6] Euclidean distance, VTA volume, and dice coefficient were calculated.

    Results: Three out of four patients with GPi DBS responded well to treatment with one patient having 0% improvement. The patient with STN stimulation had 10% improvement. Overall, GPi-DBS patients had more favorable outcomes and smaller Euclidean distances from the area of highest track density in either the left or right hemisphere for both the stimulated contact position and VTA distance. Additionally, more favorable outcomes showed a greater amount of overlap between the VTA and TD map as calculated by the dice coefficient.

    Conclusions: Our study suggests that both proximity of VTA to center of TD and degree of overlap is correlated with successful patient outcome. Further study including a larger patient cohort is planned to strengthen correlation.

    Patient Care: A high correlation between proximity of VTA to the area of highest track density may reveal enhanced targeting methods to improve patient outcomes for deep brain stimulation.

    Learning Objectives: By the conclusion of this session, participants should be able to: 1. Appreciate the significance of accurately revealing white matter architecture, hyperdirect, and direct pathways using probabilistic tractography for pre-surgical planning 2. Analyze the correlation of proximity of VTA to the area of highest track density 3. Investigate the improvement of precision for the location of electrodes within a target region for DBS surgery

    References: [1] Anderson WS, Lenz FA. Surgery Insight: deep brain stimulation for movement disorders. Nature Clinical Practice Neurology. 2006;2(6):310-320. doi:10.1038/ncpneuro0193. [2] Assaf, Y., & Pasternak, O. (2008). Diffusion tensor imaging (DTI)-based white matter mapping in brain research: a review. Journal of molecular neuroscience, 34(1), 51-61. [3] Butson CR, Maks CB, Mcintyre CC. Sources and effects of electrode impedance during deep brain stimulation. Clinical Neurophysiology. 2006;117(2):447-454. doi:10.1016/j.clinph.2005.10.007. [4] Frankemolle, A. M., Wu, J., Noecker, A. M., Voelcker-Rehage, C., Ho, J. C., Vitek, J. L., ... & Alberts, J. L. (2010). Reversing cognitive–motor impairments in Parkinson’s disease patients using a computational modelling approach to deep brain stimulation programming. Brain, 133(3), 746-761. [5] Horn A, Kühn AA. Lead-DBS: A toolbox for deep brain stimulation electrode localizations and visualizations. NeuroImage. 2015;107:127-135. doi:10.1016/j.neuroimage.2014.12.002. [6] Madler B, Coenen VA. Explaining Clinical Effects of Deep Brain Stimulation through Simplified Target-Specific Modeling of the Volume of Activated Tissue. American Journal of Neuroradiology. 2012;33(6):1072-1080. doi:10.3174/ajnr.a2906. [7] Sharma, V., Naik, K., Buetefisch, C., Triche, S., Willie, J., Boulis, N., ... & DeLong, M. (2016). Clinical Outcomes Of Deep Brain Stimulation Placement Using Intraoperative MRI for Parkinson Disease (P3. 359). Neurology, 86(16 Supplement), P3-3 [8] Simon SL, Douglas P, Baltuch GH, Jaggi JL. Error Analysis of MRI and Leksell Stereotactic Frame Target Localization in Deep Brain Stimulation Surgery. Stereotactic and Functional Neurosurgery. 2005;83(1):1-5. doi:10.1159/000083861. [9] Welter M-L, Schupbach M, Czernecki V, et al. Optimal target localization for subthalamic stimulation in patients with Parkinson disease. Neurology. 2014;82(15):1352-1361. doi:10.1212/wnl.0000000000000315.

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