Frontiers in aging neuroscience · 2026
Multimodal data-driven eye-movement subtypes and their cerebral glucose metabolic patterns in Parkinson's disease.
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Zhang Yifan, Zhang Wenli, Li Guoyang, Huang Jing, Zou Huahui, Zhang Xucheng, Wang Xiangcheng, Luo Xiaoguang
Аннотация
BACKGROUND: Previously reported Parkinson's disease (PD) subtyping schemes often show limited stability and cross-cohort generalizability.
OBJECTIVE: To derive data-driven oculomotor subtypes in PD using multi-task eye-movement assessment and to characterize their cerebral glucose metabolic patterns.
METHODS: We administered a non-invasive multi-task eye-movement battery to 122 patients with PD and 69 healthy controls. Multidimensional oculomotor features were analyzed using unsupervised k-means clustering to identify PD subtypes. In a PD subset undergoing 18F-fluorodeoxyglucose positron emission tomography (FDG-PET; n = 30), regional cerebral glucose metabolism was quantified to compare metabolic profiles between subtypes.
RESULTS: Clustering identified two PD subtypes: an oculomotor-efficient subtype (PD-E) and an oculomotor-inefficient subtype (PD-I). The subtypes differed across multiple oculomotor parameters, with antisaccade (AS) metrics showing the most prominent divergence. Compared with PD-I, PD-E showed higher FDG uptake in frontotemporal cortices. Metabolic differences were directionally concordant with groupwise patterns in cognitive measures.
CONCLUSION: Integrating eye-movement digital phenotypes with FDG-PET metabolism may provide complementary information for cognitive-domain profiling and assessment in PD. Longitudinal studies and independent cohort validation are needed to confirm stability and clinical translatability.
OBJECTIVE: To derive data-driven oculomotor subtypes in PD using multi-task eye-movement assessment and to characterize their cerebral glucose metabolic patterns.
METHODS: We administered a non-invasive multi-task eye-movement battery to 122 patients with PD and 69 healthy controls. Multidimensional oculomotor features were analyzed using unsupervised k-means clustering to identify PD subtypes. In a PD subset undergoing 18F-fluorodeoxyglucose positron emission tomography (FDG-PET; n = 30), regional cerebral glucose metabolism was quantified to compare metabolic profiles between subtypes.
RESULTS: Clustering identified two PD subtypes: an oculomotor-efficient subtype (PD-E) and an oculomotor-inefficient subtype (PD-I). The subtypes differed across multiple oculomotor parameters, with antisaccade (AS) metrics showing the most prominent divergence. Compared with PD-I, PD-E showed higher FDG uptake in frontotemporal cortices. Metabolic differences were directionally concordant with groupwise patterns in cognitive measures.
CONCLUSION: Integrating eye-movement digital phenotypes with FDG-PET metabolism may provide complementary information for cognitive-domain profiling and assessment in PD. Longitudinal studies and independent cohort validation are needed to confirm stability and clinical translatability.