Impaired Interhemispheric Synchrony in Parkinson’s Disease with Fatigue

Yuan, Yongsheng; Ji, Min; Gan, Caiting; Sun, Huimin; Wang, Lina; Zhang, Kezhong

doi:10.3390/jpm12060884

Cited by 2 publications

(3 citation statements)

References 43 publications

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“…Similarly, a magnetoencephalography study found that physical fatigue was associated with the left sensorimotor area, 37 which supported our conjecture. Furthermore, our previous study also demonstrated that interhemispheric functional incoordination might participate in the pathogenic mechanism of fatigue in PD, suggesting fatigue correlated with brain lateralization 38 . Unfortunately, we did not find differences in the structure of fatigued PD patients.…”

Section: Discussioncontrasting

confidence: 65%

“…Furthermore, our previous study also demonstrated that interhemispheric functional incoordination might participate in the pathogenic mechanism of fatigue in PD, suggesting fatigue correlated with brain lateralization. 38 Unfortunately, we did not find differences in the structure of fatigued PD patients. Moreover, there is no clear and consistent evidence of functional or pathologic localization of lateralization in PD patients with fatigue.…”

Section: Discussionmentioning

confidence: 59%

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Aberrant voxel‐based degree centrality and functional connectivity in Parkinson's disease patients with fatigue

et al. 2023

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Aims The study aimed to investigate alterations in the inherent connectivity pattern of global functional networks in Parkinson's disease (PD) patients with fatigue. Methods Eighteen PD patients with fatigue (PD‐F), 20 PD patients without fatigue (PD‐NF), and 23 healthy controls (HCs) were recruited and analyzed by the voxel‐wise degree centrality (DC) and the seed‐based functional connectivity (FC) analysis. Meanwhile, the surface‐based morphometry (SBM) analysis was also commanded to explore the structural alternations among groups. Results PD‐F patients displayed reduced DC values in the left postcentral gyrus relative to PD‐NF and HCs groups, while increased DC values in the bilateral precuneus compared to HCs. Simultaneously, altered DC value in the left postcentral gyrus negatively corresponded to the mean fatigue severity scale (FSS) in PD‐F patients. Additionally, the receiver operating characteristic (ROC) curves uncovered that the reduced DC value of the left postcentral gyrus could discriminate PD‐F from PD‐NF and HCs groups. Our FC analysis further revealed that altered FC was located predominantly in the sensorimotor network in the PD‐F group. Moreover, we discovered no statistically significant differences between the three groups concerning cortical thickness. Conclusion Our findings indicated that the altered functional connectivity in the sensorimotor network centering on the left postcentral gyrus and the bilateral precuneus might be the potential pathogenesis of PD with fatigue.

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Section: Discussioncontrasting

confidence: 65%

Section: Discussionmentioning

confidence: 59%

Aberrant voxel‐based degree centrality and functional connectivity in Parkinson's disease patients with fatigue

et al. 2023

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“…For instance, utilizing MRI data to complete missing PET data [ 204 ], creating CT scans from MRIs [ 205 , 206 , 207 ], cycle-consistent generative adversarial networks (GAN) [ 208 ], and feature-consistent GAN [ 207 ]. As an alternative, deep designs that include handling procedures for missing data can be applied [ 209 , 210 , 211 , 212 , 213 ]. Additionally, data augmentation might be useful in this context to increase the dataset and address unbalanced classes.…”

Section: Discussion: Challenges and Recommendationsmentioning

confidence: 99%

A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson’s Disease: Past Studies and Future Perspectives

et al. 2022

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According to the World Health Organization (WHO), Parkinson’s disease (PD) is a neurodegenerative disease of the brain that causes motor symptoms including slower movement, rigidity, tremor, and imbalance in addition to other problems like Alzheimer’s disease (AD), psychiatric problems, insomnia, anxiety, and sensory abnormalities. Techniques including artificial intelligence (AI), machine learning (ML), and deep learning (DL) have been established for the classification of PD and normal controls (NC) with similar therapeutic appearances in order to address these problems and improve the diagnostic procedure for PD. In this article, we examine a literature survey of research articles published up to September 2022 in order to present an in-depth analysis ofthe use of datasets, various modalities, experimental setups, and architectures that have been applied in the diagnosis of subjective disease. This analysis includes a total of 217 research publications with a list of the various datasets, methodologies, and features. These findings suggest that ML/DL methods and novel biomarkers hold promising results for application in medical decision-making, leading to a more methodical and thorough detection of PD. Finally, we highlight the challenges and provide appropriate recommendations on selecting approaches that might be used for subgrouping and connection analysis with structural magnetic resonance imaging (sMRI), DaTSCAN, and single-photon emission computerized tomography (SPECT) data for future Parkinson’s research.

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Impaired Interhemispheric Synchrony in Parkinson’s Disease with Fatigue

Cited by 2 publications

References 43 publications

Aberrant voxel‐based degree centrality and functional connectivity in Parkinson's disease patients with fatigue

Aberrant voxel‐based degree centrality and functional connectivity in Parkinson's disease patients with fatigue

A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson’s Disease: Past Studies and Future Perspectives

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