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Updated: Aug 1, 2026

Analysis of Gene Expression Changes in the Rat Hippocampus After Deep Brain Stimulation of the Anterior Thalamic Nucleus
Published on: March 8, 2015
Subthalamic nucleus deep brain stimulation for cranial-cervical dystonia: optimal stimulation sites and networks
Hutao Xie1, Jiansong Huang1, Ningfei Li2
1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Background And Objectives:
Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for medically refractory cranial-cervical dystonia (CCD or Meige syndrome). However, clinical responses vary substantially across individuals, likely due to differences in electrode placement and modulation of target neural circuits.
Methods:
We retrospectively analyzed 51 patients with CCD treated with STN-DBS at a single center. Pre- and postoperative imaging was used to reconstruct electrode locations and model patient-specific electric fields. We then performed (i) voxel-wise sweet spot mapping to identify optimal stimulation sites, (ii) fiber filtering using normative tractography to determine white matter pathways associated with clinical improvement, and (iii) network mapping based on resting-state fMRI to identify functional connectivity patterns predictive of DBS response.
Results:
Voxel-wise correlation analysis revealed that the optimal stimulation localized to the STN motor subregion (R = 0.52, p < 0.001). Normative structural connectivity analysis showed that symptom improvement correlated strongly with modulation of fibers projecting to the cranial and cervical regions of sensorimotor cortex (R = 0.52, p < 0.001) and sensorimotor-associated basal ganglia pathways (R = 0.62, p < 0.001). Functional network mapping further revealed connectivity to the sensorimotor cortex as significantly associated with clinical improvement (R = 0.43, p = 0.002).
Conclusion:
These findings inform refinement of STN targeting strategies in DBS for CCD. The involvement of cranial and cervical sensorimotor regions highlights the importance of symptom-based dystonia classification for individualized neuromodulation approaches.
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