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Impact of Additional Occipital Involvement in Parkinson’s Disease With Posterior Cortical Hypoperfusion
Chan Wook Park, Su Hong Kim, Phil Hyu Lee, Yun Joong Kim, Young H. Sohn, Yong Jeong, Seok Jong Chung
J Mov Disord. 2026;19(1):58-66.   Published online November 7, 2025
DOI: https://doi.org/10.14802/jmd.25231
  • 1,331 View
  • 47 Download
AbstractAbstract PDFSupplementary Material
Objective
This study aims to investigate the clinical relevance of occipital hypoperfusion in patients with Parkinson’s disease (PD) with respect to clinical phenotype and the risk of dementia conversion.
Methods
We enrolled 349 patients with newly diagnosed PD and 48 healthy controls who underwent dual-phase 18F-N-(3-fluoropropyl)-2β-carboxymethoxy-3β-(4-iodophenyl) nortropane (18F-FP-CIT) positron emission tomography (PET). Patients with PD were classified into three groups based on posterior cortical perfusion patterns on early-phase 18F-FP-CIT PET images: PD with preserved posterior cortical perfusion (n=186), PD with parieto-temporal hypoperfusion (n=84), and PD with parieto-temporo-occipital hypoperfusion (n=79). Baseline clinical features and dementia conversion risk were compared across PD groups.
Results
Patients with preserved posterior cortical perfusion were younger than those in the other PD groups. Compared with the other groups, the parieto-temporo-occipital hypoperfusion group tended to have lower Cross-Cultural Smell Identification Test scores, a higher prevalence of rapid eye movement sleep behavior disorder, higher Unified PD Rating Scale motor scores, and more severe reductions in striatal dopamine transporter availability. The risk of dementia conversion was lower in patients with preserved posterior cortical perfusion than in those with posterior cortical hypoperfusion. However, the risk of dementia conversion did not differ between the parieto-temporal and parieto-temporo-occipital hypoperfusion groups.
Conclusion
Additional occipital hypoperfusion was not associated with an imminent risk of dementia conversion in patients with PD with posterior cortical hypoperfusion. Nonetheless, occipital involvement may serve as an indicator of the diffuse malignant subtype of PD.
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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
  • 7,542 View
  • 188 Download
  • 13 Web of Science
  • 14 Crossref
AbstractAbstract PDFSupplementary 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  
  • Cognitive and motor reserve in Parkinson's disease: Two sides of the same coin?
    Isabella Anzuino, Sonia Di Tella, Alice Tondinelli, Clio Scopetani Testa, Martina Petracca, Maria Rita Lo Monaco, Anna Rita Bentivoglio, Maria Caterina Silveri
    Journal of Neuropsychology.2026;[Epub]     CrossRef
  • Is motor reserve associated with a rapid progression of Parkinson disease?
    Myung Jun Lee, Kyoungjune Pak, Jae-Hyeok Lee, Seok Jong Chung, Min Seok Baek
    Journal of Parkinson’s Disease.2025; 15(3): 552.     CrossRef
  • Dopamine Transporter Imaging as Objective Monitoring Biomarker in Parkinson's Disease
    Verena Dzialas, Gérard N. Bischof, Kathrin Möllenhoff, Alexander Drzezga, Thilo van Eimeren
    Annals of Neurology.2025; 98(1): 120.     CrossRef
  • Risk factors and predictors for Lewy body dementia: a systematic review
    Ahalya Ratnavel, Francesca R. Dino, Celina Jiang, Sarah Azmy, Kathryn A. Wyman-Chick, Ece Bayram
    npj Dementia.2025;[Epub]     CrossRef
  • Quantifying motor–cognitive reserve using a novel multi-modal stress test
    Tal Kozlovski, Inbal Maidan, Eran Gazit, Amgad Droby, Avner Thaler, Jeffrey M Hausdorff, Nir Giladi, Yoav Benjamini, Anat Mirelman
    Brain Communications.2025;[Epub]     CrossRef
  • Hippocampal Perfusion Affects Motor and Cognitive Functions in Parkinson Disease: An Early Phase 18F‐FP‐CIT Positron Emission Tomography Study
    Min Young Chun, Seok Jong Chung, Su Hong Kim, Chan Wook Park, Seong Ho Jeong, Hye Sun Lee, Phil Hyu Lee, Young H. Sohn, Yong Jeong, Yun Joong Kim
    Annals of Neurology.2024; 95(2): 388.     CrossRef
  • Imaging Procedure and Clinical Studies of [18F]FP-CIT PET
    Changhwan Sung, Seung Jun Oh, Jae Seung Kim
    Nuclear Medicine and Molecular Imaging.2024; 58(4): 185.     CrossRef
  • Influence of cognitive reserve on cognitive and motor function in α-synucleinopathies: A systematic review and multilevel meta-analysis
    Isaac Saywell, Lauren Foreman, Brittany Child, Alexander L. Phillips-Hughes, Lyndsey Collins-Praino, Irina Baetu
    Neuroscience & Biobehavioral Reviews.2024; 161: 105672.     CrossRef
  • Structural underpinnings and long-term effects of resilience in Parkinson’s disease
    Verena Dzialas, Merle C. Hoenig, Stéphane Prange, Gérard N. Bischof, Alexander Drzezga, Thilo van Eimeren
    npj Parkinson's Disease.2024;[Epub]     CrossRef
  • Considering the response in addition to the challenge – a narrative review in appraisal of a motor reserve framework
    Daniel Zeller, Shawn Hiew, Thorsten Odorfer, Carine Nguemeni
    Aging.2024; 16(6): 5772.     CrossRef
  • Defining the concept of reserve in the motor domain: a systematic review
    Andreina Giustiniani, Angelo Quartarone
    Frontiers in Neuroscience.2024;[Epub]     CrossRef
  • The association of motor reserve and clinical progression in Parkinson’s disease
    Xueqin Bai, Shiwei Zhang, Qiuyue Li, Tao Guo, Xiaojun Guan, Andan Qian, Shuangli Chen, Ronghui Zhou, Yitong Cheng, Haoxin Chen, Zhaoke Gou, Chenglong Xie, Zhen Wang, Minming Zhang, Xiangwu Zheng, Meihao Wang
    NeuroImage: Clinical.2024; 44: 103704.     CrossRef
  • Occipital hypoperfusion and motor reserve in Parkinson’s disease: an early-phase 18F-FP-CIT PET study
    Yeo Jun Yoon, Su Hong Kim, Seong Ho Jeong, Chan Wook Park, Hye Sun Lee, Phil Hyu Lee, Yun Joong Kim, Young H. Sohn, Yong Jeong, Seok Jong Chung
    npj Parkinson's Disease.2024;[Epub]     CrossRef
  • 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.2022; 13(1): 39.     CrossRef
Article image
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
  • 15,220 View
  • 249 Download
  • 12 Web of Science
  • 11 Crossref
AbstractAbstract 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  
  • Neuroimaging of the monoaminergic system in Parkinson's disease: A narrative review
    Yavuz Samanci, Sonny Tan, Yasin Temel, Ali Jahanshahi
    NeuroImage: Reports.2026; 6(2): 100345.     CrossRef
  • Local-to-remote brain function alterations in patients with thyroid-associated ophthalmopathy: a resting-state functional magnetic resonance imaging (fMRI) study
    C. Yan, M. Zou, D. Wu, Y. Chen, Y. Cheng, Y. Li, S. Hong, Z. Yang, H. Zhu
    Clinical Radiology.2025; 91: 107108.     CrossRef
  • 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

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