Investigation of sensorimotor dysfunction in Parkinson disease by resting-state fMRI

Wang, Shuaiwen; Lei, Junqiang; Guo, Shunlin

doi:10.1016/j.neulet.2020.135512

Cited by 25 publications

(30 citation statements)

References 46 publications

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“…Neuroimaging studies discovered that cerebellum lobule VIII was predominantly involved in sensorimotor processes and connected with cerebral motor and sensory cortices ( Kipping et al, 2013 ). These results appeared to indicate that PD patients had abnormal sensorimotor functional circuits involving the cerebellum, as described previously ( Palmer et al, 2020 ; Wang S. et al, 2021 ). Nevertheless, the role of the sensorimotor network involving cerebellum in PD is still controversial.…”

Section: Discussionsupporting

confidence: 86%

“…Nevertheless, the role of the sensorimotor network involving cerebellum in PD is still controversial. Wang S. et al (2021) considered that the increased FC in the internal cerebellar networks and sensorimotor networks in PD might compensate for the motor control dysfunction to maintain the integrity of the sensorimotor network. Palmer et al (2020) proposed that persistent error detection and correction occurring in the cerebellum of PD patients induced the enhanced cerebello-cortical FC.…”

Section: Discussionmentioning

confidence: 99%

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Altered functional connectivity of cerebellar dentate nucleus in peak-dose dyskinesia in Parkinson’s disease

Zhang

Wang

Gan

et al. 2022

Front. Aging Neurosci.

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The cerebellum is associated with the emergence of levodopa-induced dyskinesia (LID) in Parkinson’s disease (PD), yet the neural mechanism remains obscure. Our aim was to ascertain the role of functional connectivity (FC) patterns of the cerebellar dentate nucleus (DN) in the pathogenesis of peak-dose dyskinesia in PD. Twenty-three peak-dose dyskinetic PD patients, 27 non-dyskinetic PD patients, and 36 healthy controls (HCs) were enrolled and underwent T1-weighted and resting-state functional magnetic resonance imaging (rs-fMRI) scans after dopaminergic medication intake. We selected left and right DN as the regions of interest and then employed voxel-wise FC analysis and voxel-based morphometry analysis (VBM). The correlations between the altered FC pattern and clinical scores were also examined. Finally, receiver operating characteristic (ROC) curve analysis was performed to assess the potential of DN FC measures as a feature of peak-dose dyskinesia in PD. Dyskinetic PD patients showed excessively increased FC between the left DN and right putamen compared with the non-dyskinetic. When compared with controls, dyskinetic PD patients mainly exhibited increased FC between left DN and bilateral putamen, left paracentral lobule, right postcentral gyrus, and supplementary motor area. Additionally, non-dyskinetic PD patients displayed increased FC between left DN and left precentral gyrus and right paracentral lobule compared with controls. Meanwhile, increased FC between DN (left/right) and ipsilateral cerebellum lobule VIII was observed in both PD subgroups. However, no corresponding alteration in gray matter volume (GMV) was found. Further, a positive correlation between the z-FC values of left DN-right putamen and the Unified Dyskinesia Rating Scale (UDysRS) was confirmed in dyskinetic PD patients. Notably, ROC curve analyses revealed that the z-FC values of left DN-right putamen could be a potential neuroimaging feature identifying dyskinetic PD patients. Our findings demonstrated that the excessively strengthened connectivity of DN-putamen might contribute to the pathophysiological mechanisms of peak-dose dyskinesia in PD.

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Section: Discussionsupporting

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Altered functional connectivity of cerebellar dentate nucleus in peak-dose dyskinesia in Parkinson’s disease

Zhang

Wang

Gan

et al. 2022

Front. Aging Neurosci.

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“…Studies on the human brain system from the perspective of complex networks, especially the topological structure formed by the interaction of various functional areas, may aid researchers to further investigate the mechanism of information processing and mental expression in the brain (30,31). Some studies have reported changes in the connectivity within the sensorimotor network and the interaction between the visual network and other brain modules in Parkinson's patients (32)(33)(34). In addition, significant differences of topological characteristics between PD and HC are reported in the default mode network and the occipital region (35,36).…”

Section: Introductionmentioning

confidence: 99%

Altered Degree Centrality of Brain Networks in Parkinson's Disease With Freezing of Gait: A Resting-State Functional MRI Study

Jin

Teng

et al. 2021

Front. Neurol.

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Freezing of gait (FOG) in Parkinson's disease (PD) leads to devastating consequences; however, little is known about its functional brain network. We explored the differences in degree centrality (DC) of functional networks among PD with FOG (PD FOG+), PD without FOG (PD FOG–), and healthy control (HC) groups. In all, 24 PD FOG+, 37 PD FOG–, and 22 HCs were recruited and their resting-state functional magnetic imaging images were acquired. The whole brain network was analyzed using graph theory analysis. DC was compared among groups using the two-sample t-test. The DC values of disrupted brain regions were correlated with the FOG Questionnaire (FOGQ) scores. Receiver operating characteristic curve analysis was performed. We found significant differences in DC among groups. Compared with HCs, PD FOG+ patients showed decreased DC in the middle frontal gyrus (MFG), superior temporal gyrus (STG), parahippocampal gyrus (PhG), inferior temporal gyrus (ITG), and middle temporal gyrus (MTG). Compared with HC, PD FOG– presented with decreased DC in the MFG, STG, PhG, and ITG. Compared with PD FOG–, PD FOG+ showed decreased DC in the MFG and ITG. A negative correlation existed between the DC of ITG and FOGQ scores; the DC in ITG could distinguish PD FOG+ from PD FOG– and HC. The calculated AUCs were 81.3, 89.5, and 77.7% for PD FOG+ vs. HC, PD FOG– vs. HC, and PD FOG+ vs. PD FOG–, respectively. In conclusion, decreased DC of ITG in PD FOG+ patients compared to PD FOG– patients and HCs may be a unique feature for PD FOG+ and can likely distinguish PD FOG+ from PD FOG– and HC groups.

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“…The SFG and PrG are important components of the sensorimotor network, which plays a central role in the preparation and execution of motor functions. Multiple previous studies have reported the sensorimotor network dysfunction in patients with PD ( Tuovinen et al, 2018 ; Rubbert et al, 2019 ; Cao et al, 2020 ; Chen et al, 2021 ; De Micco et al, 2021 ; Wang et al, 2021 ). Abnormal brain activation of SFG and PrG was also revealed in previous studies ( Lin et al, 2017 ; Peng et al, 2017 ; Cao et al, 2020 ; Guo et al, 2020 ; Tian et al, 2020 ; Pang et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Machine Learning for Detecting Parkinson’s Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis

Shi

Zhang

Wang

et al. 2022

Front. Aging Neurosci.

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Parkinson’s disease (PD) is one of the most common progressive degenerative diseases, and its diagnosis is challenging on clinical grounds. Clinically, effective and quantifiable biomarkers to detect PD are urgently needed. In our study, we analyzed data from two centers, the primary set was used to train the model, and the independent external validation set was used to validate our model. We applied amplitude of low-frequency fluctuation (ALFF)-based radiomics method to extract radiomics features (including first- and high-order features). Subsequently, t-test and least absolute shrinkage and selection operator (LASSO) were harnessed for feature selection and data dimensionality reduction, and grid search method and nested 10-fold cross-validation were applied to determine the optimal hyper-parameter λ of LASSO and evaluate the performance of the model, in which a support vector machine was used to construct the classification model to classify patients with PD and healthy controls (HCs). We found that our model achieved good performance [accuracy = 81.45% and area under the curve (AUC) = 0.850] in the primary set and good generalization in the external validation set (accuracy = 67.44% and AUC = 0.667). Most of the discriminative features were high-order radiomics features, and the identified brain regions were mainly located in the sensorimotor network and lateral parietal cortex. Our study indicated that our proposed method can effectively classify patients with PD and HCs, ALFF-based radiomics features that might be potential biomarkers of PD, and provided further support for the pathological mechanism of PD, that is, PD may be related to abnormal brain activity in the sensorimotor network and lateral parietal cortex.

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Investigation of sensorimotor dysfunction in Parkinson disease by resting-state fMRI

Cited by 25 publications

References 46 publications

Altered functional connectivity of cerebellar dentate nucleus in peak-dose dyskinesia in Parkinson’s disease

Altered functional connectivity of cerebellar dentate nucleus in peak-dose dyskinesia in Parkinson’s disease

Altered Degree Centrality of Brain Networks in Parkinson's Disease With Freezing of Gait: A Resting-State Functional MRI Study

Machine Learning for Detecting Parkinson’s Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis

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