Differences in Brain Network Mechanisms Between STN and GPi Deep Brain Stimulation in the Treatment of Craniocervical Dystonia

Anhui Provincial Hospital60 enrolled

Overview

Craniocervical dystonia, characterized by symptoms distributed in the craniofacial and/or cervical regions, is a type of focal or segmental dystonia and is the most common form of dystonia in adults. Deep brain stimulation (DBS) is a significant therapeutic approach for medically refractory craniocervical dystonia. The commonly utilized DBS targets are the Globus Pallidus internus (GPi) and the Subthalamic Nucleus (STN). Current research indicates no significant difference in efficacy between these two targets, although there are some differences in the onset time, stimulation voltage, and complications. Studies utilizing magnetic resonance imaging (MRI) to assess brain activity differences in patients with dystonia have found that patients exhibit increased activity and enhanced plasticity across a broad range of brain regions, including the brainstem, cortex, subcortical structures, and the basal ganglia, among others. Consequently, an increasing number of studies are classifying dystonia within the spectrum of brain network disorders. This study aims to recruit patients with craniocervical dystonia who meet the inclusion criteria, randomly assigning them into two groups of 30 patients each. One group will receive stimulation targeting the STN, and the other will target the GPi. Using functional MRI, researcher will conduct a dynamic brain network analysis to explore the differences in the brain network mechanisms underlying the treatment of craniocervical dystonia patients between the STN and GPi targets.

The objectives of this study are: 1) to explore the differences of brain network mechanisms between STN and GPi targets in craniocervical dystonia; 2. To explore the effects of DBS on dysarthsia and dysphagia in patients with craniocervical dystonia and the differences in efficacy, quality of life and side effects between GPi and STN targets in the treatment of craniocervical dystonia; 3. To explore the differences in the electrophysiological signals of nuclei collected during DBS surgery and their application in programming.

Study Type

INTERVENTIONAL

Allocation

RANDOMIZED

Purpose

TREATMENT

Masking

Eligibility

Sex: ALLMin age: 18 YearsMax age: 75 Years

Medical Language ↔ Plain English

Inclusion Criteria: 1. Age 18-75 years 2. Meeting the diagnostic criteria for primary craniocervical dystonia (including patients with cranial, cervical, or unilateral extremity dystonia) 3. Disease duration ≥1 year 4. Normal cognitive function 5. The subject himself or his legal representative can sign the informed consent form Exclusion Criteria: 1. Patients with other dystonia who did not meet the inclusion criteria 2. Diagnosed with other neuropsychiatric diseases (Alzheimer's disease, amyotrophic lateral sclerosis, Parkinson's disease, etc.) 3. Previous history of craniocerebral surgery 4. Major depression or anxiety 5. The presence of neurosurgical contraindications such as cerebral infarction, hydrocephalus, cerebral atrophy, and sequelae of cerebrovascular disease 6. Contraindications to CT/MRI scanning (e.g. Claustrophobia) 7. women known to be pregnant or lactating, or who had a positive pregnancy test before randomization 8. Presence of contraindications to general anesthesia (such as severe arrhythmia, severe anemia, abnormal liver and kidney function, etc.) 9. Expected survival less than 12 months 10. has participated in other interventional clinical studies that may have affected the outcome assessment 11. other circumstances considered by the investigator to be inappropriate for participation in the study or likely to pose a significant risk to the patient

Outcomes

Primary Outcomes

resting-state functional magnetic resonance imaging data

First, all participants underwent a standardized rs-fMRI scan for data collection of resting-state BOLD signals. Secondly, data preprocessing was carried out, including head movement correction, non-brain tissue removal, temporal layer correction, spatial normalization, filtering, and removal of physiological noise. Then, the brain network was constructed based on the pre-processed data and the structural features of the brain network were identified. Finally, statistical analysis was used to compare the differences in brain network characteristics between the two groups.