Poster No:
1713
Submission Type:
Abstract Submission
Authors:
Hao Chen1, Ying Wang2, Kexue Deng2, Yingxing Zhang3
Institutions:
1The First Affiliated Hospital of USTC, Hefei, Hefei, 2The First Affiliated Hospital of USTC, Hefei, Anhui, 3Anhui Hospital Affiliated to Children’s Hospital of Fudan University/Anhui Children’s Hospital, Hefei, China
First Author:
Hao Chen
The First Affiliated Hospital of USTC
Hefei, Hefei
Co-Author(s):
Ying Wang
The First Affiliated Hospital of USTC
Hefei, Anhui
Kexue Deng
The First Affiliated Hospital of USTC
Hefei, Anhui
Yingxing Zhang
Anhui Hospital Affiliated to Children’s Hospital of Fudan University/Anhui Children’s Hospital
Hefei, China
Introduction:
Meige's syndrome is one of the focal dystonic movement disorders, mainly characterized by bilateral blepharospasm and symmetrical and irregular contraction of oral and facial muscles(Hirono et al. 2014). Hemifacial spasm (HFS) is another kind of dystonia disorder with different clinical manifestations, which is often characterized by a unilateral twitch of the face(Brimley and Sampath 2021). Microvascular decompression is a commonly used method for the radical treatment of HFS(Joo et al. 2022), but there is no ideal treatment for Meige. Investigation of the differences in the neural basis of the two forms of dystonia might give us the inspiration to understand these diseases, which might further help with improving the treatment. Based on previous findings on the neural basis of both diseases, we hypothesized changed role of the frontal regions in the motor network might be related to the etiology of Meige and the main difference between these two disorders. The present study aimed to estimate the differences in terms of functional connectivity between Meige's syndrome and the HFS.
Methods:
Nineteen unilateral HFS patients (57.26±4.17 years, 13 females) and 15 Meige's syndrome patients (58.67±6.89 years, 10 females) were recruited in this study. All participants were right-handed with no major psychiatric disorders or neurological illnesses.
All participants underwent MRI scanning on a GE 3.0T magnetic resonance scanner (GE, 750W). Resting-state MRI data with 242 frames were acquired with a T2*-weighted echo-planar imaging sequence (TE=30ms, TR=2000ms, FOV=240mm, matrix=64×64, flip angle=85°) with 33 axial slices (no gaps, voxel size: 3.6×3.6×3.6mm ³) covering the whole brain. Corresponding high-resolution T1-weighted three-dimensional gradient-echo (for stereotaxic transformation) images were also collected (TR=1900ms; TE=2.26ms; TI=900ms; 1mm isotropic voxel; 250mm field of view).
Resting-state fMRI data processing was conducted with AFNI (version AFNI_19.0.24, pre-compiled binary linux_ubuntu_16_64). To analyze the rsFC, we adopted the Anatomical Automatic Labeling (AAL) template, a brain atlas system including 90 regions of interest (ROIs) covering all the brain regions of cerebral cortex(Tzourio-Mazoyer et al. 2002). For each participant, the time-course of each ROI was extracted, and the corresponding degree centrality(Cd) and betweenness centrality(BC) of each ROI were computed.
BC refers to the ratio of the shortest path that passes through a point and connects the two points to the total number of shortest path lines between the two nodes in the network(Verbavatz and Barthelemy 2022). Cd is a simple count of the total number of connections linked to a vertex, which is the most direct measure to describe node centrality in network analysis(Telesford et al. 2011).
For either BC or Cd of all the 90 ROIs, we conducted a group t-test between Meige's patients and the HFS patients. And ROIs with p values were FDR corrected (with mafdr function embedded with Matlab R2022a,) and FDR less than 0.05 were considered as ROIs with significant between-group differences.
Results:
There was no significant difference in age (t=−0.69, ρ=0.49) or gender distribution between the HFS and the Meige's syndrome patients, as shown in Fig. 1.
As shown in Fig. 2, Meige's syndrome patients showed higher BC of the right superior frontal cortex and higher Cd of the left superior frontal cortex, the left superior medial frontal cortex, the right superior medial frontal cortex, and the left cerebellum cortex than the HFS patients (FDR correction with FDR value less than 0.05, whose uncorrected ρ< 0.005).
Conclusions:
Higher centrality of the frontal and cerebellum cortex might be the topological property difference between Meige's syndrome and HFS, which may help to preliminarily understand the mechanism of and abnormal functional connection in Meige's patients, and further explore better treatment.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s)
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling 1
Keywords:
FUNCTIONAL MRI
Movement Disorder
1|2Indicates the priority used for review

·Age and gender distribution of the participants.

·Higher betweenness and degree centrality in frontal and cerebellum cortex of Meige’s syndrome patients
Provide references using author date format
Brimley, CJ (2021) Hemifacial Spam: Endoscopic Assistance in Facial Nerve Decompression With Lateral Spread Response Corroboration: 2-Dimensional Operative Video. Operative Neurosurgery (Hagerstown, Md.), 20(2), E128. doi:10.1093/ons/opaa301.
Hirono, S (2014) Continuous intraoperative monitoring of abnormal muscle response in microvascular decompression for hemifacial spasm; a real-time navigator for complete relief. Neurosurgical Review, 37(2), 311–319; discussion 319-320. doi:10.1007/s10143-013-0507-5.
Joo, B-E (2022) Advances in Intraoperative Neurophysiology During Microvascular Decompression Surgery for Hemifacial Spasm. Journal of Clinical Neurology (Seoul, Korea), 18(4), 410–420. doi:10.3988/jcn.2022.18.4.410.
Telesford, QK (2011) The brain as a complex system: using network science as a tool for understanding the brain. Brain Connectivity, 1(4), 295–308. doi:10.1089/brain.2011.0055.
Tzourio-Mazoyer, N (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15(1), 273–289. doi:10.1006/nimg.2001.0978.
Verbavatz, V (2022) Betweenness centrality in dense spatial networks. Physical Review. E, 105(5–1), 054303. doi:10.1103/PhysRevE.105.054303.