- Potential Link Between Cognition and Motor Reserve in Patients With Parkinson’s Disease
-
Seok Jong Chung, Yae Ji Kim, Yun Joong Kim, Hye Sun Lee, Mijin Yun, Phil Hyu Lee, Yong Jeong, Young H. Sohn
-
J Mov Disord. 2022;15(3):249-257. Published online September 7, 2022
-
DOI: https://doi.org/10.14802/jmd.22063
-
-
2,181
View
-
137
Download
-
1
Citations
-
Abstract
PDF Supplementary Material
- Objective
To investigate whether there is a link between cognitive function and motor reserve (i.e., individual capacity to cope with nigrostriatal dopamine depletion) in patients with newly diagnosed Parkinson’s disease (PD).
Methods A total of 163 patients with drug-naïve PD who underwent 18F-FP-CIT PET, brain MRI, and a detailed neuropsychological test were enrolled. We estimated individual motor reserve based on initial motor deficits and striatal dopamine depletion using a residual model. We performed correlation analyses between motor reserve estimates and cognitive composite scores. Diffusion connectometry analysis was performed to map the white matter fiber tracts, of which fractional anisotropy (FA) values were well correlated with motor reserve estimates. Additionally, Cox regression analysis was used to assess the effect of initial motor reserve on the risk of dementia conversion.
Results The motor reserve estimate was positively correlated with the composite score of the verbal memory function domain (γ = 0.246) and with the years of education (γ = 0.251). Connectometry analysis showed that FA values in the left fornix were positively correlated with the motor reserve estimate, while no fiber tracts were negatively correlated with the motor reserve estimate. Cox regression analysis demonstrated that higher motor reserve estimates tended to be associated with a lower risk of dementia conversion (hazard ratio, 0.781; 95% confidence interval, 0.576–1.058).
Conclusion The present study demonstrated that the motor reserve estimate was well correlated with verbal memory function and with white matter integrity in the left fornix, suggesting a possible link between cognition and motor reserve in patients with PD.
-
Citations
Citations to this article as recorded by 
- Extra-Basal Ganglia Brain Structures Are Related to Motor Reserve in Parkinson’s Disease
Jinyoung Youn, Ji Hye Won, Mansu Kim, Junmo Kwon, Seung Hwan Moon, Minkyeong Kim, Jong Hyun Ahn, Jun Kyu Mun, Hyunjin Park, Jin Whan Cho Journal of Parkinson's Disease.2023; 13(1): 39. CrossRef
- Alteration in the Local and Global Functional Connectivity of Resting State Networks in Parkinson’s Disease
-
Maryam Ghahremani, Jaejun Yoo, Sun Ju Chung, Kwangsun Yoo, Jong C. Ye, Yong Jeong
-
J Mov Disord. 2018;11(1):13-23. Published online January 23, 2018
-
DOI: https://doi.org/10.14802/jmd.17061
-
-
10,096
View
-
237
Download
-
9
Citations
-
Abstract
PDF
- Objective
Parkinson’s disease (PD) is a neurodegenerative disorder that mainly leads to the impairment of patients’ motor function, as well as of cognition, as it progresses. This study tried to investigate the impact of PD on the resting state functional connectivity of the default mode network (DMN), as well as of the entire brain.
Methods
Sixty patients with PD were included and compared to 60 matched normal control (NC) subjects. For the local connectivity analysis, the resting state fMRI data were analyzed by seed-based correlation analyses, and then a novel persistent homology analysis was implemented to examine the connectivity from a global perspective.
Results
The functional connectivity of the DMN was decreased in the PD group compared to the NC, with a stronger difference in the medial prefrontal cortex. Moreover, the results of the persistent homology analysis indicated that the PD group had a more locally connected and less globally connected network compared to the NC.
Conclusion
Our findings suggest that the DMN is altered in PD, and persistent homology analysis, as a useful measure of the topological characteristics of the networks from a broader perspective, was able to identify changes in the large-scale functional organization of the patients’ brain.
-
Citations
Citations to this article as recorded by 
- Topological disruption of high‐order functional networks in cognitively preserved Parkinson's disease
Song'an Shang, Siying Zhu, Jingtao Wu, Yao Xu, Lanlan Chen, Weiqiang Dou, Xindao Yin, Yu‐Chen Chen, Dejuan Shen, Jing Ye CNS Neuroscience & Therapeutics.2023; 29(2): 566. CrossRef - IABC: A Toolbox for Intelligent Analysis of Brain Connectivity
Yuhui Du, Yanshu Kong, Xingyu He Neuroinformatics.2023; 21(2): 303. CrossRef - Topological data analysis in biomedicine: A review
Yara Skaf, Reinhard Laubenbacher Journal of Biomedical Informatics.2022; 130: 104082. CrossRef - Altered Long- and Short-Range Functional Connectivity Density in Patients With Thyroid-Associated Ophthalmopathy: A Resting-State fMRI Study
Wen-Hao Jiang, Huan-Huan Chen, Wen Chen, Qian Wu, Lu Chen, Jiang Zhou, Xiao-Quan Xu, Hao Hu, Fei-Yun Wu Frontiers in Neurology.2022;[Epub] CrossRef - Modulations of static and dynamic functional connectivity among brain networks by electroacupuncture in post-stroke aphasia
Minjie Xu, Ying Gao, Hua Zhang, Binlong Zhang, Tianli Lyu, Zhongjian Tan, Changming Li, Xiaolin Li, Xing Huang, Qiao Kong, Juan Xiao, Georg S. Kranz, Shuren Li, Jingling Chang Frontiers in Neurology.2022;[Epub] CrossRef - Multi-dimensional persistent feature analysis identifies connectivity patterns of resting-state brain networks in Alzheimer’s disease
Jin Li, Chenyuan Bian, Haoran Luo, Dandan Chen, Luolong Cao, Hong Liang Journal of Neural Engineering.2021; 18(1): 016012. CrossRef - Characterizing resting‐state networks in Parkinson’s disease: A multi‐aspect functional connectivity study
Mahdieh Ghasemi, Ali Foroutannia, Abbas Babajani‐Feremi Brain and Behavior.2021;[Epub] CrossRef - The role of the medial prefrontal cortex in cognition, ageing and dementia
Dan D Jobson, Yoshiki Hase, Andrew N Clarkson, Rajesh N Kalaria Brain Communications.2021;[Epub] CrossRef - Image Target Recognition Model of Multi- Channel Structure Convolutional Neural Network Training Automatic Encoder
Sen Zhang, Qiuyun Cheng, Dengxi Chen, Haijun Zhang IEEE Access.2020; 8: 113090. CrossRef
|