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Resting-state connectivity predicts patient-specific effects of deep brain stimulation for Parkinson's disease

By Xiaoyu Chen, Chencheng Zhang, Yuxin Li, Pei Huang, Qian Lv, Wenwen Yu, Shengdi Chen, Bomin Sun, Zheng Wang

Posted 15 Oct 2017
bioRxiv DOI: 10.1101/203406

Neural circuit-based guidance for optimizing patient screening, target selection and parameter tuning for deep brain stimulation (DBS) remains limited. To this end, we propose a functional brain connectome-based modeling approach that simulates network-spreading effects of stimulating different brain regions and quantifies rectification of abnormal network topology in silico. We validate these analyses by predicting nuclei in basal-ganglia circuits as top-ranked targets for 43 local patients with Parkinson's disease and 90 patients from public database. However, individual connectome-based predictions demonstrate that globus pallidus and subthalamic nucleus (STN) constituted as the best choice for 21.1% and 19.5% of patients, respectively. Notably, the priority rank of STN significantly correlated with motor symptom severity in the local cohort. By introducing whole-brain network diffusion dynamics, these findings unfold a new dimension of brain connectomics and underscore the importance of neural network modeling for personalized DBS therapy, which warrants experimental investigation to validate its clinical utility.

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