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.

scholarly journals Parkinson’s disease and REM sleep behavior disorder result in increased non-motor symptoms

2014 ◽  
Vol 15 (8) ◽  
pp. 959-966 ◽  
Author(s):  
Ariel B. Neikrug ◽  
Julie A. Avanzino ◽  
Lianqi Liu ◽  
Jeanne E. Maglione ◽  
Loki Natarajan ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rem Sleep ◽  
Rem Sleep Behavior Disorder ◽  
Behavior Disorder ◽  
Motor Symptoms ◽  
Sleep Behavior ◽  
Non Motor Symptoms

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.

scholarly journals A Stage-Based Approach to Therapy in Parkinson’s Disease

Biomolecules ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. 388 ◽  
Author(s):  
Claudia Carrarini ◽  
Mirella Russo ◽  
Fedele Dono ◽  
Martina Di Pietro ◽  
Marianna G. Rispoli ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Neurodegenerative Disorder ◽  
Rem Sleep Behavior Disorder ◽  
Behavior Disorder ◽  
Motor Symptoms ◽  
Pharmacological Management ◽  
Sleep Behavior ◽  
Heavy Burden ◽  
Non Motor Symptoms

Parkinson’s disease (PD) is a neurodegenerative disorder that features progressive, disabling motor symptoms, such as bradykinesia, rigidity, and resting tremor. Nevertheless, some non-motor symptoms, including depression, REM sleep behavior disorder, and olfactive impairment, are even earlier features of PD. At later stages, apathy, impulse control disorder, neuropsychiatric disturbances, and cognitive impairment can present, and they often become a heavy burden for both patients and caregivers. Indeed, PD increasingly compromises activities of daily life, even though a high variability in clinical presentation can be observed among people affected. Nowadays, symptomatic drugs and non-pharmaceutical treatments represent the best therapeutic options to improve quality of life in PD patients. The aim of the present review is to provide a practical, stage-based guide to pharmacological management of both motor and non-motor symptoms of PD. Furthermore, warning about drug side effects, contraindications, as well as dosage and methods of administration, are highlighted here, to help the physician in yielding the best therapeutic strategies for each symptom and condition in patients with PD.

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