White matter changes in drug-naïve Parkinson's disease patients with impulse control & probable REM sleep behavior disorders

2021 ◽  
pp. 120032
Author(s):  
Mahsa Dolatshahi ◽  
Amir Ashraf-Ganjouei ◽  
I-Wei Wu ◽  
Yu Zhang ◽  
Mohammad Hadi Aarabi ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
White Matter ◽  
Rem Sleep ◽  
Behavior Disorders ◽  
Impulse Control ◽  
White Matter Changes ◽  
Sleep Behavior ◽  
Drug Naïve

scholarly journals White Matter Tract Alterations in Drug-Naïve Parkinson’s Disease Patients With Impulse Control Disorders

2018 ◽  
Vol 9 ◽  
Author(s):  
Mahtab Mojtahed Zadeh ◽  
Amir Ashraf-Ganjouei ◽  
Farzaneh Ghazi Sherbaf ◽  
Maryam Haghshomar ◽  
Mohammad Hadi Aarabi
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
White Matter ◽  
Impulse Control ◽  
Impulse Control Disorders ◽  
White Matter Tract ◽  
Drug Naïve

Melatonin in correction of REM-sleep behavior disorders in Parkinson’s disease

2015 ◽  
Vol 115 (6. Vyp. 2) ◽  
pp. 40 ◽  
Author(s):  
Е. А. Lyashenko ◽  
O. S. Levin ◽  
M. G. Poluektov
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rem Sleep ◽  
Behavior Disorders ◽  
Sleep Behavior

scholarly journals Nonmotor Symptoms and Cognitive Decline in de novo Parkinson’s Disease

2014 ◽  
Vol 41 (5) ◽  
pp. 597-602 ◽  
Author(s):  
Kyum-Yil Kwon ◽  
Sung Hoon Kang ◽  
Minjik Kim ◽  
Hye Mi Lee ◽  
Ji Wan Jang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rem Sleep ◽  
Behavior Disorders ◽  
Odds Ratio ◽  
Depression Scale ◽  
Nonmotor Symptoms ◽  
Sleep Behavior ◽  
Dementia Risk ◽  
State Examination

AbstractBackgroundCognitive impairments are common in Parkinson’s disease (PD). Despite its clinical importance, the development of dementia is still difficult to predict. In this study, we investigated the possible associations between non-motor symptoms and the risk of developing dementia within a 2-year observation period in PD.MethodsA total of 80 patients with PD participated in this study. Nonmotor symptoms (the Nonmotor Symptoms Questionnaire), PD status (Unified Parkinson’s Disease Rating Scale), depression (Geriatric d Depression Scale or Montgomery-Asberg Depression Scale), stereopsis and severity of nonmotor symptoms (Non-motor symptoms scale) were assessed. Global cognitive function (Mini-Mental State Examination) were evaluated at baseline and 2 years later.ResultsPresence of depression, vivid dreaming, REM sleep behavior disorders, hyposmia, abnormal stereopsis, non-smoking and postural instability/ gait disturbance phenotype were associated with a significantly more rapid decline of Mini-Mental State Examination. Logistic regression analyses demonstrated that depression (odds ratio=13.895), abnormal stereopsis (odds ratio=10.729), vivid dreaming (odds ratio=4.16), REM sleep behavior disorders (odds ratio=5.353) and hyposmia (odds ratio=4.911) were significant independent predictors of dementia risk within 2 years. Postural instability/ gait disturbance phenotype and age >62 years were also independent predictors of dementia risk (odd ratio=38.333, odds ratio=10.625).ConclusionWe suggest that depression, vivid dreaming, REM sleep behavior disorders, hyposmia and abnormal stereopsis are closely associated with cognitive decline, and that presence of these nonmotor symptoms predict the subsequent development of Parkinson’s disease dementia.

REM sleep behavior disorder is associated to increased risk of impulse control symptoms in patients with Parkinson’s disease

2013 ◽  
Vol 14 ◽  
pp. e31-e32
Author(s):  
M. Fantini ◽  
M. Laura ◽  
Z. Maurizio ◽  
C. Alessandro ◽  
L. Leonardo ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rem Sleep ◽  
Impulse Control ◽  
Rem Sleep Behavior Disorder ◽  
Behavior Disorder ◽  
Sleep Behavior ◽  
Increased Risk

scholarly journals Application of Machine Learning Technique to Distinguish Parkinson’s Disease Dementia and Alzheimer’s Dementia: Predictive Power of Parkinson’s Disease-Related Non-Motor Symptoms and Neuropsychological Profile

2020 ◽  
Vol 10 (2) ◽  
pp. 31 ◽  
Author(s):  
Haewon Byeon
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rem Sleep ◽  
Behavior Disorders ◽  
Predictive Power ◽  
Screening Tests ◽  
Motor Symptoms ◽  
Sleep Behavior ◽  
Non Motor Symptoms

In order to develop a predictive model that can distinguish Parkinson’s disease dementia (PDD) from other dementia types, such as Alzheimer’s dementia (AD), it is necessary to evaluate and identify the predictive accuracy of the cognitive profile while considering the non-motor symptoms, such as depression and rapid eye movement (REM) sleep behavior disorders. This study compared Parkinson’s disease (PD)’s non-motor symptoms and the diagnostic predictive power of cognitive profiles that distinguish AD and PD using machine learning. This study analyzed 118 patients with AD and 110 patients with PDD, and all subjects were 60 years or older. In order to develop the PDD prediction model, the dataset was divided into training data (70%) and test data (30%). The prediction accuracy of the model was calculated by the recognition rate. The results of this study show that Parkinson-related non-motor symptoms, such as REM sleep behavior disorders, and cognitive screening tests, such as Korean version of Montreal Cognitive Assessment, were highly accurate factors for predicting PDD. It is required to develop customized screening tests that can detect PDD in the early stage based on these results. Furthermore, it is believed that including biomarkers such as brain images or cerebrospinal fluid as input variables will be more useful for developing PDD prediction models in the future.

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