CNS neuroscience & therapeutics · 2026
Geometric Microstructural Characteristics of White Matter Differentiate Patients With Facial Dyskinesias and Palsy.
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Zhu Hua, Yang Aocai, Luan Jixin, Xu Manxi, Liu Bing, Lv Kuan, Hu Pianpian, Shmuel Amir, Zhu Haogang, Yuan Zhen, Shu Ni, Cheng Jian, Ma Guolin
Аннотация
PURPOSE: The heterogeneous and homogeneous clinical manifestations of peripheral facial palsy (FP), hemifacial spasm (HFS), and Meige's syndrome (MS) complicate the differentiation of diagnoses for these facial motor diseases. To comprehensively investigate the white matter microstructural characteristics in patients with facial dyskinesias and palsy using geometric and integrity metrics in DTI.
MATERIAL AND METHODS: In this prospective study conducted from September 2020 to January 2022, patients with FP, HFS, and MS, as well as sex-matched healthy control subjects, underwent 3.0 T MRI. Geometric metrics (i.e., splay, bend, twist, and total distortion) based on "Director Field Analysis" and fractional anisotropy (FA) and mean diffusivity (MD) were calculated from DTI data. Cross-sectional tract-based spatial statistics were performed among FP, HFS, MS patients, and healthy controls. The correlation between disease severity and DTI metrics was evaluated. Additionally, the geometric microstructural properties combining FA and MD were used to classify FP, HFS, and MS patients using machine learning methods.
RESULTS: Geometric metrics and FA/MD were widely altered across white matter in FP and HFS patients compared with healthy controls. However, in MS patients only DFA metrics were significantly altered. FA and DFA values strongly correlated with the severity of facial movement disorder in FP patients. Combing conventional FA/MD value with DFA metrics enabled the diagnostic differentiation of FP and HFS from MS.
CONCLUSION: Our findings demonstrated that the geometric microstructural information of white matter fibers could provide novel insight into the underlying pathological changes in facial dyskinesias and palsy.
MATERIAL AND METHODS: In this prospective study conducted from September 2020 to January 2022, patients with FP, HFS, and MS, as well as sex-matched healthy control subjects, underwent 3.0 T MRI. Geometric metrics (i.e., splay, bend, twist, and total distortion) based on "Director Field Analysis" and fractional anisotropy (FA) and mean diffusivity (MD) were calculated from DTI data. Cross-sectional tract-based spatial statistics were performed among FP, HFS, MS patients, and healthy controls. The correlation between disease severity and DTI metrics was evaluated. Additionally, the geometric microstructural properties combining FA and MD were used to classify FP, HFS, and MS patients using machine learning methods.
RESULTS: Geometric metrics and FA/MD were widely altered across white matter in FP and HFS patients compared with healthy controls. However, in MS patients only DFA metrics were significantly altered. FA and DFA values strongly correlated with the severity of facial movement disorder in FP patients. Combing conventional FA/MD value with DFA metrics enabled the diagnostic differentiation of FP and HFS from MS.
CONCLUSION: Our findings demonstrated that the geometric microstructural information of white matter fibers could provide novel insight into the underlying pathological changes in facial dyskinesias and palsy.