Differences in the intra-cerebellar connections and graph theoretical measures between Parkinson's disease and multiple system atrophy

Sako, Wataru; Abe, Takashi; Furukawa, Takahiro; Oki, Ryosuke; Haji, Shotaro; Murakami, Naotoshi; Izumi, Yuishin; Harada, Masafumi; Kaji, Ryuji

doi:10.1016/j.jns.2019.03.022

Cited by 13 publications

(6 citation statements)

References 32 publications

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“…The cerebellum‐cortical circuit was severely “disconnected” in the MSA‐P patients, and the basal ganglia‐cortical circuit was significantly “disconnected” in the IPD patients. This specific damage pattern has not been comprehensively reported in earlier studies (Baggio et al, 2019 ; Sako et al, 2019 ). More crucially, we discovered evidence that rs‐fMRI functional connectome measures have strong discriminative ability in early IPD and MSA‐P, implying that the functional connectome could be employed as imaging indicators for diagnosis.…”

Section: Discussionmentioning

confidence: 60%

“…Zheng et al ( 2020 ) revealed aberrant nodal metrics in MSA patients in several prefrontal areas, cerebellar hemispheres and vermis areas. However, Sako et al ( 2019 ) found no significant differences in graph theory metrics between groups in a limited sample (11 MSA, 23 IPD, and 11 HCs). Finally, because graph theory analyses of rs‐fMRI studies of MSA are rare, sample sizes have been inadequate, and previous studies have not used the dual syndrome hypothesis to analyse subtypes of MSA, our results are difficult to compare with earlier investigations.…”

Section: Discussionmentioning

confidence: 97%

“…Zheng et al ( 2020 ) investigated the functional network topology of 24 MSA patients and 20 HCs and identified nodal attribute abnormalities mainly in the dorsolateral prefrontal cortex and cerebellum. However, Sako et al ( 2019 ) recruited 23 IPD patients, 11 MSA patients, and 11 HCs and concluded that neither IPD nor MSA patients had significant alterations in global‐ or nodal‐level metrics. Recent research by N. Wang et al ( 2019 ) and Baggio et al ( 2019 ) found that MSA patients displayed lower FC strength between the cerebellar dentate nucleus (DN) and DMN regions than IPD patients or HCs.…”

Section: Introductionmentioning

confidence: 99%

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Functional connectome automatically differentiates multiple system atrophy (parkinsonian type) from idiopathic Parkinson's disease at early stages

Chen

Cui

Wang

et al. 2023

Human Brain Mapping

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Differentiating the parkinsonian variant of multiple system atrophy (MSA‐P) from idiopathic Parkinson's disease (IPD) is challenging, especially in the early stages. This study aimed to investigate differences and similarities in the brain functional connectomes of IPD and MSA‐P patients and use machine learning methods to explore the diagnostic utility of these features. Resting‐state fMRI data were acquired from 88 healthy controls, 76 MSA‐P patients, and 53 IPD patients using a 3.0 T scanner. The whole‐brain functional connectome was constructed by thresholding the Pearson correlation matrices of 116 regions, and topological properties were evaluated through graph theory approaches. Connectome measurements were used as features in machine learning models (random forest [RF]/logistic regression [LR]/support vector machine) to distinguish IPD and MSA‐P patients. Regarding graph metrics, early IPD and MSA‐P patients shared network topological properties. Both patient groups showed functional connectivity disruptions within the cerebellum‐basal ganglia‐cortical network, but these disconnections were mainly in the cortico‐thalamo‐cerebellar circuits in MSA‐P patients and the basal ganglia‐thalamo‐cortical circuits in IPD patients. Among the connectome parameters, t tests combined with the RF method identified 15 features, from which the LR classifier achieved the best diagnostic performance on the validation set (accuracy = 92.31%, sensitivity = 90.91%, specificity = 93.33%, area under the receiver operating characteristic curve = 0.89). MSA‐P and IPD patients show similar whole‐brain network topological alterations. MSA‐P primarily affects cerebellar nodes, and IPD primarily affects basal ganglia nodes; both conditions disrupt the cerebellum‐basal ganglia‐cortical network. Moreover, functional connectome parameters showed outstanding value in the differential diagnosis of early MSA‐P and IPD.

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Section: Discussionmentioning

confidence: 60%

Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Functional connectome automatically differentiates multiple system atrophy (parkinsonian type) from idiopathic Parkinson's disease at early stages

Chen

Cui

Wang

et al. 2023

Human Brain Mapping

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“…At the node and connection level, PD patients exhibited reduced lengths of node centralities and connectivity, mainly not only in the temporal‐occipital regions, but also in the sensorimotor regions 28 . No significant differences in intransitivity, characteristic path length, and degree were observed in the motor network related to the basal ganglia and cerebellum in PD patients, nor in the related network‐level values compared with normal controls 29 . Levodopa modulates the global and local efficiency measures of small‐world topology in PD patients 30 .…”

Section: Introductionmentioning

confidence: 84%

Effect of deep brain stimulation on brain network and white matter integrity in Parkinson's disease

Huang

Chen²,

Wu³

et al. 2021

CNS Neurosci Ther

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Aims The effects of subthalamic nucleus (STN)‐deep brain stimulation (DBS) on brain topological metrics, functional connectivity (FC), and white matter integrity were studied in levodopa‐treated Parkinson’s disease (PD) patients before and after DBS. Methods Clinical assessment, resting‐state functional MRI (rs‐fMRI), and diffusion tensor imaging (DTI) were performed pre‐ and post‐DBS in 15 PD patients, using a within‐subject design. The rs‐fMRI identified brain network topological metric and FC changes using graph‐theory‐ and seed‐based methods. White matter integrity was determined by DTI and tract‐based spatial statistics. Results Unified Parkinson's Disease Rating Scale III (UPDRS‐ III) scores were significantly improved by 35.3% (p < 0.01) after DBS in PD patients, compared with pre‐DBS patients without medication. Post‐DBS PD patients showed a significant decrease in the graph‐theory‐based degree and cost in the middle temporal gyrus and temporo‐occipital part‐Right. Changes in FC were seen in four brain regions, and a decrease in white matter integrity was seen in the left anterior corona radiata. The topological metrics changes were correlated with Beck Depression Inventory II (BDI‐II) and the FC changes with UPDRS‐III scores. Conclusion STN‐DBS modulated graph‐theoretical metrics, FC, and white matter integrity. Brain connectivity changes observed with multi‐modal imaging were also associated with postoperative clinical improvement. These findings suggest that the effects of STN‐DBS are caused by brain network alterations.

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“…Previous studies reported the disrupted default mode network (DMN), striatal-thalamo-cortical network, cerebello-thalamo-cortical network, sensorimotor network and visual associated network in MSA patients ( You et al, 2011 ; Wang et al, 2017 ; Yao et al, 2017 ; Ren et al, 2019 ; Zheng et al, 2019 ). Furthermore, researchers detected altered network topology and graph theory attributes by resting state graph theoretical analysis ( Morisi et al, 2018 ; Sako et al, 2019 ). Previous studies provided evidence for the hypothesis that MSA may be caused by the disconnection syndrome, indicating that the accumulated alpha-synuclein may destroy several specific networks, and result in the movement disorder and other clinical symptoms ( Rosskopf et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Altered Brain Volume, Microstructure Metrics and Functional Connectivity Features in Multiple System Atrophy

Zheng

et al. 2022

Front. Aging Neurosci.

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In order to deeply understand the specific patterns of volume, microstructure, and functional changes in Multiple System Atrophy patients with cerebellar ataxia syndrome (MSA-c), we perform the current study by simultaneously applying structural (T1-weighted imaging), Diffusion tensor imaging (DTI), functional (BOLD fMRI) and extended Network-Based Statistics (extended-NBS) analysis. Twenty-nine MSA-c type patients and twenty-seven healthy controls (HCs) were involved in this study. First, we analyzed the whole brain changes of volume, microstructure, and functional connectivity (FC) in MSA-c patients. Then, we explored the correlations between significant multimodal MRI features and the total Unified Multiple System Atrophy Rating Scale (UMSARS) scores. Finally, we searched for sensitive imaging biomarkers for the diagnosis of MSA-c using support vector machine (SVM) classifier. Results showed significant grey matter atrophy in cerebellum and white matter microstructural abnormalities in cerebellum, left fusiform gyrus, right precentral gyrus and lingual gyrus. Extended-NBS analysis found two significant different connected components, featuring altered functional connectivity related to left and right cerebellar sub-regions, respectively. Moreover, the reduced fiber bundle counts at right Cerebellum_3 (Cbe3) and decreased fractional anisotropy (FA) values at bilateral Cbe9 were negatively associated with total UMSARS scores. Finally, the significant features at left Cbe9, Cbe1, and Cbe7b were found to be useful as sensitive biomarkers to differentiate MSA-c from HCs according to the SVM analysis. These findings advanced our understanding of the neural pathophysiological mechanisms of MSA from the perspective of multimodal neuroimaging.

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Differences in the intra-cerebellar connections and graph theoretical measures between Parkinson's disease and multiple system atrophy

Cited by 13 publications

References 32 publications

Functional connectome automatically differentiates multiple system atrophy (parkinsonian type) from idiopathic Parkinson's disease at early stages

Functional connectome automatically differentiates multiple system atrophy (parkinsonian type) from idiopathic Parkinson's disease at early stages

Effect of deep brain stimulation on brain network and white matter integrity in Parkinson's disease

Altered Brain Volume, Microstructure Metrics and Functional Connectivity Features in Multiple System Atrophy

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