scholarly journals Disrupted morphological grey matter networks in early-stage Parkinson’s disease

2021 ◽  
Author(s):  
Xueling Suo ◽  
Du Lei ◽  
Nannan Li ◽  
Wenbin Li ◽  
Graham J. Kemp ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Severity ◽  
Grey Matter ◽  
Early Stage ◽  
Clustering Coefficient ◽  
Subject Classification ◽  
Single Subject ◽  
Topological Organization

AbstractWhile previous structural-covariance studies have an advanced understanding of brain alterations in Parkinson's disease (PD), brain–behavior relationships have not been examined at the individual level. This study investigated the topological organization of grey matter (GM) networks, their relation to disease severity, and their potential imaging diagnostic value in PD. Fifty-four early-stage PD patients and 54 healthy controls (HC) underwent structural T1-weighted magnetic resonance imaging. GM networks were constructed by estimating interregional similarity in the distributions of regional GM volume using the Kullback–Leibler divergence measure. Results were analyzed using graph theory and network-based statistics (NBS), and the relationship to disease severity was assessed. Exploratory support vector machine analyses were conducted to discriminate PD patients from HC and different motor subtypes. Compared with HC, GM networks in PD showed a higher clustering coefficient (P = 0.014) and local efficiency (P = 0.014). Locally, nodal centralities in PD were lower in postcentral gyrus and temporal-occipital regions, and higher in right superior frontal gyrus and left putamen. NBS analysis revealed decreased morphological connections in the sensorimotor and default mode networks and increased connections in the salience and frontoparietal networks in PD. Connection matrices and graph-based metrics allowed single-subject classification of PD and HC with significant accuracy of 73.1 and 72.7%, respectively, while graph-based metrics allowed single-subject classification of tremor-dominant and akinetic–rigid motor subtypes with significant accuracy of 67.0%. The topological organization of GM networks was disrupted in early-stage PD in a way that suggests greater segregation of information processing. There is potential for application to early imaging diagnosis.

Empirical Wavelet Transform Based Features for Classification of Parkinson’s Disease Severity

2017 ◽  
Vol 42 (2) ◽  
Author(s):  
Qi Wei Oung ◽  
Hariharan Muthusamy ◽  
Shafriza Nisha Basah ◽  
Hoileong Lee ◽  
Vikneswaran Vijean
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Wavelet Transform ◽  
Disease Severity ◽  
Empirical Wavelet Transform

scholarly journals Screening Patients with Early Stage Parkinson’s Disease Using a Machine Learning Technique: Measuring the Amount of Iron in the Basal Ganglia

2020 ◽  
Vol 10 (23) ◽  
pp. 8732
Author(s):  
Seon Lee ◽  
Se-Hong Oh ◽  
Sun-Won Park ◽  
Chaewon Shin ◽  
Jeehun Kim ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Grey Matter ◽  
Early Stage ◽  
Support Vector ◽  
Motor Symptoms ◽  
Model Based ◽  
Svm Model ◽  
Deep Grey Matter ◽  
Non Motor Symptoms

The purpose of this study was to determine whether a support vector machine (SVM) model based on quantitative susceptibility mapping (QSM) can be used to differentiate iron accumulation in the deep grey matter of early Parkinson’s disease (PD) patients from healthy controls (HC) and Non-Motor Symptoms Scale (NMSS) scores in early PD patients. QSM values on magnetic resonance imaging (MRI) were obtained for 24 early PD patients and 27 age-matched HCs. The mean QSM values in deep grey matter areas were used to construct SVM and logistic regression (LR) models to differentiate between early PD patients and HCs. Additional SVM and LR models were constructed to differentiate between low and high NMSS scores groups. A paired t-test was used to assess the classification results. For the differentiation between early PD patients and HCs, SVM had an accuracy of 0.79 ± 0.07, and LR had an accuracy of 0.73 ± 0.03 (p = 0.027). SVM for NMSS classification had a fairly high accuracy of 0.79 ± 0.03, while LR had 0.76 ± 0.04. An SVM model based on QSM offers competitive accuracy for screening early PD patients and evaluates non-motor symptoms, which may offer clinicians the ability to assess the progression of motor symptoms in the patient population.

Classification of early stage Parkinson's disease in EEG and tremor time-frequency features space

2016 ◽  
Vol 22 ◽  
pp. e164 ◽  
Author(s):  
Olga Sushkova ◽  
Yuri Obukhov ◽  
Ivan Kershner ◽  
Alexey Karabanov ◽  
Alexandra Gabova
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Stage ◽  
Time Frequency ◽  
Frequency Features

scholarly journals Delayed orthostatic hypotension in Parkinson’s disease

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Sang-Won Yoo ◽  
Joong-Seok Kim ◽  
Ji-Yeon Yoo ◽  
Eunkyeong Yun ◽  
Uicheul Yoon ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Orthostatic Hypotension ◽  
Disease Severity ◽  
Cortical Thickness ◽  
Early Stage ◽  
Brain Magnetic Resonance Imaging ◽  
Clinical Scales ◽  
Drug Naïve

AbstractOrthostatic hypotension (OH) is relatively common in the early stage of Parkinson’s disease (PD). It is divided into delayed OH and classical OH. Classical OH in PD has been investigated widely, however, the clinical implications of delayed OH in PD have seldom been studied. The purpose of this study is to characterize delayed OH in PD. A total of 285 patients with early drug-naïve PD were enrolled and divided into three groups according to orthostatic change: no-OH, delayed OH, and classical OH. The disease severity in terms of motor, non-motor, and cognitive functions was assessed. The cortical thickness of 82 patients was analyzed with brain magnetic resonance imaging. The differences among groups and linear tendency in the order of no-OH, delayed OH, and classical OH were investigated. Seventy-seven patients were re-evaluated. Initial and follow-up evaluations were explored to discern any temporal effects of orthostasis on disease severity. Sixty-four (22.5%) patients were defined as having delayed OH and 117 (41.1%) had classical OH. Between-group comparisons revealed that classical OH had the worst outcomes in motor, non-motor, cognitive, and cortical thickness, compared to the other groups. No-OH and delayed OH did not differ significantly. Linear trends across the pre-ordered OH subtypes found that clinical parameters worsened along with the orthostatic challenge. Clinical scales deteriorated and the linear gradient was maintained during the follow-up period. This study suggests that delayed OH is a mild form of classical OH in PD. PD with delayed OH has milder disease severity and progression.

scholarly journals Reduced habit-driven errors in Parkinson’s Disease

2018 ◽  
Author(s):  
Colin Bannard ◽  
Mariana Leriche ◽  
Oliver Bandmann ◽  
Christopher Brown ◽  
Elisa Ferracane ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Diagnosis ◽  
Early Stage ◽  
Healthy Controls ◽  
Error Type ◽  
Simple Motor ◽  
Classification Of Error

Parkinson’s Disease can be understand as a disorder of motor habits. A prediction of this theory is that early stage Parkinson’s patients will display fewer errors caused by interference from previously over-learned behaviours. We test this prediction in the domain of skilled typing, where actions are easy to record and errors easy to identify. We describe a method for categorising errors as simple motor errors or habit-driven errors. We test Spanish and English participants with and without Parkinson’s, and show that indeed patients make fewer habit errors than healthy controls, and, further, that classification of error type increases the accuracy of discriminating between patients and healthy controls. As well as being a validation of a theory-led prediction, these results offer promise for automated, enhanced and early diagnosis of Parkinson’s Disease.

scholarly journals Parkinson disease classification: a comparative analysis on classification techniques

2020 ◽  
Vol 18 (3) ◽  
pp. 1351
Author(s):  
Nazri Mohd Nawi ◽  
Mokhairi Makhtar ◽  
Zehan Afizah Afip ◽  
Mohd Zaki Salikon
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Decision Tree ◽  
Neurological Disorders ◽  
Early Stage ◽  
Current Trend ◽  
Disease Classification ◽  
High Demand

Parkinson’s disease (PD) among Alzheimer’s and epilepsy are one of the most common neurological disorders which appreciably affect not only live of patients but also their households. According to the current trend of aging social behaviour, it is expected to see a rise of Parkinson’s disease. Even though there is no cure for PD, a proper medication at the early stage can help significantly in alleviating the symptoms. Since, the traditional method for identifying PD is rather invasive, expansive and complicated for self-use, there is a high demand for using classification method on PD detection. This paper compares the performance of Neural Network and decision tree for classifying and discriminating healthy people for people with Parkinson’s disease (PD) by distinguishing dysphonia. The simulation results demonstrate that Neural Network outperformed decision tree by giving accurate results with 87% accuracy as compared to decision tree with only 84% accuracy in determining the classification of healthy and people with Parkinson’s.

scholarly journals Topological Abnormalities of Functional Brain Network in Early-Stage Parkinson’s Disease Patients With Mild Cognitive Impairment

2020 ◽  
Vol 14 ◽  
Author(s):  
Xiangbin Chen ◽  
Mengting Liu ◽  
Zhibing Wu ◽  
Hao Cheng
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Information Exchange ◽  
Early Stage ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Functional Brain ◽  
Functional Brain Network

Recent studies have demonstrated structural and functional alterations in Parkinson’s disease (PD) with mild cognitive impairment (MCI). However, the topological patterns of functional brain networks in newly diagnosed PD patients with MCI are unclear so far. In this study, we used functional magnetic resonance imaging (fMRI) and graph theory approaches to explore the functional brain network in 45 PD patients with MCI (PD-MCI), 22 PD patients without MCI (PD-nMCI), and 18 healthy controls (HC). We found that the PD-MCI, PD-nMCI, and HC groups exhibited a small-world architecture in the functional brain network. However, early-stage PD-MCI patients had decreased clustering coefficient, increased characteristic path length, and changed nodal centrality in the default mode network (DMN), control network (CN), somatomotor network (SMN), and visual network (VN), which might contribute to factors for MCI symptoms in PD patients. Our results demonstrated that PD-MCI patients were associated with disrupted topological organization in the functional network, thus providing a topological network insight into the role of information exchange in the underlying development of MCI symptoms in PD patients.

Subcortical grey matter changes in untreated, early stage Parkinson's disease without dementia

2014 ◽  
Vol 20 (6) ◽  
pp. 622-626 ◽  
Author(s):  
Hye Mi Lee ◽  
Kyum-Yil Kwon ◽  
Min-Jik Kim ◽  
Ji-Wan Jang ◽  
Sang-il Suh ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Grey Matter ◽  
Early Stage
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