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. 2020 Mar 25;10:5429. doi: 10.1038/s41598-020-61572-4

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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The effect of DBS on pro- vs. anti-saccade latencies. Blue circles show the change in latencies for pro- and anti-saccade in each individual participant. The dark line is the fitter linear model to the data. (Top-left) There is a strong positive correlation between the change in the pro-saccade latencies in the both pro-saccade and the anti-saccade tasks ( $r = 0.6$ , $p < 0.001$ ). (Top-right) The changes in the pro-saccade latency and the anti-saccade latency caused by DBS are not correlated ( $r = 0.07$ , $p = 0.12$ ). (Bottom) A schematic of the oculomotor network that involves the basal ganglia and relevant projections. Excitatory (green) and inhibitory (red) projections are shown. SNr and GPi, the two output nuclei of the basal ganglia, project to SC and thalamus (Th). STN DBS can influence saccades through either the GPi-Th-Frontal Cortex pathway, or the SNr-SC pathway. Lower brainstem, cerebellar and extra-frontal cortical oculomotor pathways are not illustrated.