Abstract
To investigate brain network centrality and connectivity alterations in different Parkinson’s disease (PD) clinical subtypes using resting-state functional magnetic resonance imaging (RS-fMRI), and to explore the correlation between baseline connectivity changes and the clinical progression. Ninety-two PD patients were enrolled at baseline, alongside 38 age- and sex-matched healthy controls. Of these, 85 PD patients underwent longitudinal assessments with a mean of 2.75 ± 0.59 years. Two-step cluster analysis integrating comprehensive motor and non-motor manifestations was performed to define PD subtypes. Degree centrality (DC) and secondary seed-based functional connectivity (FC) were applied to identify brain network centrality and connectivity changes among groups. Regression analysis was used to explore the correlation between baseline connectivity changes and clinical progression. Cluster analysis identified two main PD subtypes: mild PD and moderate PD. Two different subtypes within the mild PD were further identified: mild motor-predominant PD and mild-diffuse PD. Accordingly, the disrupted DC and seed-based FC in the left inferior frontal orbital gyrus and left superior occipital gyrus were severe in moderate PD. The DC and seed-based FC alterations in the right gyrus rectus and right postcentral gyrus were more severe in mild-diffuse PD than in mild motor-predominant PD. Moreover, disrupted DC were associated with clinical manifestations at baseline in patients with PD and predicted motor aspects progression over time. Our study suggested that brain network centrality and connectivity changes were different among PD subtypes. RS-fMRI holds promise to provide an objective assessment of subtype-related connectivity changes and predict disease progression in PD.
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Origin data of the current study are available from the corresponding author on reasonable request.
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This work was supported by Science and Technology Planning Project of Guangzhou (No.202201000005); National Natural Science Foundation of China (No.82071419); Key Research and Development Program of Guangzhou (No.202206010086); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No.2022B1212010011); Research Project of Wuhan Health Care Commission (No.WX21A08); Research Project of Wuhan Health Care Commission (No.WZ22Q38).
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Conception and organization of the research project: Z.Z.C., Y.H.Z.; Collection of clinical data of participant: C.T.H., P.Z., X.H.L., W.L.H., P.Y.Z.; Statistical analysis: Z.Z.C., X.C., S.F.H.; Review and Critique of the statistical analysis: Y.H.Z.; Writing of the first draft: Z.Z.C.; Review and critique on the manuscript: P.Y.Z., M.F.C., L.J.W.; Final approval for submission: Y.H.Z.
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Chen, Z., He, C., Zhang, P. et al. Brain network centrality and connectivity are associated with clinical subtypes and disease progression in Parkinson’s disease. Brain Imaging and Behavior (2024). https://doi.org/10.1007/s11682-024-00862-1
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DOI: https://doi.org/10.1007/s11682-024-00862-1