Dynamic functional connectivity assesses the progression of Parkinson’s disease

Zhibao Li; Wei Chen; Xiaoyu Zeng; Jun Ni; Yuzhu Guo; Hua Zhang; Yang Li; Yina Ma; Fangang Meng

doi:10.59717/j.xinn-med.2023.100027

The Innovation Medicine > 2023 Vol. 1 > No. 2 > 100027

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Dynamic functional connectivity assesses the progression of Parkinson’s disease

1.
Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
2.
Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing 100070, China
3.
Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
4.
State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
5.
Department of Automation Sciences and Electrical Engineering, Beihang University, Beijing 100191, China
6.
Chinese Institute for Brain Research, Beijing 102206, China

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Corresponding authors: liyang@buaa.edu.cn (Y.L.); yma@bnu.edu.cn (Y.M.); mengfg@ccmu.edu.cn (F.M.)
Received Date: July 09, 2023

Accepted Date: September 09, 2023

Published Online: September 11, 2023

The Innovation Medicine 1(2), https://doi.org/10.59717/j.xinn-med.2023.100027 | Cite this article

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GRAPHICAL ABSTRACT

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PUBLIC SUMMARY

PUBLIC SUMMARY
1. Progression of Parkinson’s disease (PD) induces modulations in dynamic functional brain networks.
  
  Changes of dynamics functional brain network are linked to worsening PD symptoms.
  
  Dynamic brain network has potential as a biomarker for evaluating PD progression.

ABSTRACT

ABSTRACT

Parkinson’s disease (PD) induces functional connectivity (FC) changes during its course. However, the impact of PD progression on the temporal properties of FC remains ambiguous. In the current study, we aimed to uncover longitudinal shifts in dynamic FC (DFC) temporal properties of brain networks during PD progression, proposing a novel biomarker for PD progression evaluation. We conducted a longitudinal study on 45 PD patients from the Parkinson’s Progression Markers Initiative database. Patients underwent dual-timepoint neurological assessments and resting-state fMRI scans at baseline and 1-4 years of subsequent follow-up. The sliding-window technique and k-means clustering were employed to scrutinize DFC patterns of the entire brain network, including individual cortical subnetworks and subcortical nuclei (SN) at every timepoint. From this analysis, DFC analyses revealed two predominant states: a high-frequency sparse FC state and a low-frequency intense FC state. For the entire brain network, the mean dwell time (MDT) in the sparse FC state diminished with PD progression, and this decrease was closely tied to motor deterioration. Concerning cortical subnetworks and SN, MDTs in the sparse FC state reduced at the second timepoint in both visual (VN) and limbic networks (LN) linked with the SN. The MDT reduction in LN-SN positively correlated with cognitive decline, while the MDT reduction in VN-SN showed a strong link with motor degradation. These results emphasize that DFC might offer insights into the evolving brain dynamics in PD patients over the disease's course, underscoring its prospective utility as a progression biomarker.

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Li Z., Chen W., Zeng X., et al., (2023). Dynamic functional connectivity assesses the progression of Parkinson’s disease. The Innovation Medicine 1(2), 100027. https://doi.org/10.59717/j.xinn-med.2023.100027

Li Z., Chen W., Zeng X., et al., (2023). Dynamic functional connectivity assesses the progression of Parkinson’s disease. The Innovation Medicine 1(2), 100027. https://doi.org/10.59717/j.xinn-med.2023.100027

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Dynamic functional connectivity assesses the progression of Parkinson’s disease

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Dynamic functional connectivity assesses the progression of Parkinson’s disease

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