scholarly journals Combined accelerometer and genetic analysis to differentiate essential tremor from Parkinson’s disease

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5308 ◽  
Author(s):  
Bhuvan Molparia ◽  
Brian N. Schrader ◽  
Eli Cohen ◽  
Jennifer L. Wagner ◽  
Sandeep R. Gupta ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Essential Tremor ◽  
Simple Procedure ◽  
Home Monitoring ◽  
Area Under The Curve ◽  
Community Setting ◽  
Diagnostic Procedures ◽  
Polygenic Risk Score ◽  
At Home

Essential tremor (ET) and Parkinson’s disease (PD) are among the most common adult-onset tremor disorders. Clinical and pathological studies suggest that misdiagnosis of PD for ET, and vice versa, occur in anywhere from 15% to 35% of cases. Complex diagnostic procedures, such as dopamine transporter imaging, can be powerful diagnostic aids but are lengthy and expensive procedures that are not widely available. Preliminary studies suggest that monitoring of tremor characteristics with consumer grade accelerometer devices could be a more accessible approach to the discrimination of PD from ET, but these studies have been performed in well-controlled clinical settings requiring multiple maneuvers and oversight from clinical or research staff, and thus may not be representative of at-home monitoring in the community setting. Therefore, we set out to determine whether discrimination of PD vs. ET diagnosis could be achieved by monitoring research subject movements at home using consumer grade devices, and whether discrimination could be improved with the addition of genetic profiling of the type that is readily available through direct-to-consumer genetic testing services. Forty subjects with PD and 27 patients with ET were genetically profiled and had their movements characterized three-times a day for two weeks through a simple procedure meant to induce rest tremors. We found that tremor characteristics could be used to predict diagnosis status (sensitivity = 76%, specificity = 65%, area under the curve (AUC) = 0.75), but that the addition of genetic risk information, via a PD polygenic risk score, did not improve discriminatory power (sensitivity = 80%, specificity = 65%, AUC = 0.73).

scholarly journals Assessing inertial measurement unit locations for freezing of gait detection and patient preference

2021 ◽  
Author(s):  
Johanna O’Day ◽  
Marissa Lee ◽  
Kirsten Seagers ◽  
Shannon Hoffman ◽  
Ava Jih-Schiff ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Patient Preference ◽  
Freezing Of Gait ◽  
Home Monitoring ◽  
Inertial Measurement ◽  
Technical Performance ◽  
Percent Time ◽  
Time Freezing ◽  
At Home

ABSTRACTBackgroundFreezing of gait, a common symptom of Parkinson’s disease, presents as sporadic episodes in which an individual’s feet suddenly feel stuck to the ground. Inertial measurement units (IMUs) promise to enable at-home monitoring and personalization of therapy, but there is a lack of consensus on the number and location of IMUs for detecting freezing of gait. The purpose of this study was to assess IMU sets in the context of both freezing of gait detection performance and patient preference.MethodsSixteen people with Parkinson’s disease were surveyed about sensor preferences. Raw IMU data from seven people with Parkinson’s disease, wearing up to eleven sensors, were used to train convolutional neural networks to detect freezing of gait. Models trained with data from different sensor sets were assessed for technical performance; a best technical set and minimal IMU set were identified. Clinical utility was assessed by comparing model- and human-rater-determined percent time freezing and number of freezing events.ResultsThe best technical set consisted of three IMUs (lumbar and both ankles, AUROC = 0.83), all of which were rated highly wearable. The minimal IMU set consisted of a single ankle IMU (AUROC = 0.80). Correlations between these models and human raters were good to excellent for percent time freezing (ICC = 0.93, 0.89) and number of freezing events (ICC = 0.95, 0.86), for the best technical set and minimal IMU set, respectively.ConclusionsSeveral IMU sets consisting of three IMUs or fewer were highly rated for both technical performance and wearability, and more IMUs did not necessarily perform better in FOG detection. We openly share our data and software to further the adoption of a general, open-source model that uses raw signals and a standard sensor set for at-home monitoring of freezing of gait.

scholarly journals Excess of singleton loss-of-function variants in Parkinson’s disease contributes to genetic risk

2020 ◽  
Vol 57 (9) ◽  
pp. 617-623 ◽  
Author(s):  
Dheeraj Reddy Bobbili ◽  
Peter Banda ◽  
Rejko Krüger ◽  
Patrick May
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rare Variant ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Classification Model ◽  
Polygenic Risk Score ◽  
Disease Prediction ◽  
Common Variants ◽  
Loss Of Function

BackgroundParkinson’s disease (PD) is a neurodegenerative disorder with complex genetic architecture. Besides rare mutations in high-risk genes related to monogenic familial forms of PD, multiple variants associated with sporadic PD were discovered via association studies.MethodsWe studied the whole-exome sequencing data of 340 PD cases and 146 ethnically matched controls from the Parkinson’s Progression Markers Initiative (PPMI) and performed burden analysis for different rare variant classes. Disease prediction models were built based on clinical, non-clinical and genetic features, including both common and rare variants, and two machine learning methods.ResultsWe observed a significant exome-wide burden of singleton loss-of-function variants (corrected p=0.037). Overall, no exome-wide burden of rare amino acid changing variants was detected. Finally, we built a disease prediction model combining singleton loss-of-function variants, a polygenic risk score based on common variants, and family history of PD as features and reached an area under the curve of 0.703 (95% CI 0.698 to 0.708). By incorporating a rare variant feature, our model increased the performance of the state-of-the-art classification model for the PPMI dataset, which reached an area under the curve of 0.639 based on common variants alone.ConclusionThe main finding of this study is to highlight the contribution of singleton loss-of-function variants to the complex genetics of PD and that disease risk prediction models combining singleton and common variants can improve models built solely on common variants.

scholarly journals Global efficiency of the motor network is decreased in Parkinson's disease in comparison with essential tremor and healthy controls

2021 ◽  
Author(s):  
Natalia Pelizari Novaes ◽  
Joana Bisol Balardin ◽  
Fabiana Campos Hirata ◽  
Luciano Melo ◽  
Edson Amaro ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Essential Tremor ◽  
Healthy Controls ◽  
Global Efficiency ◽  
Motor Network

scholarly journals Limb and trunk accelerometer data collected with wearable sensors from subjects with Parkinson’s disease

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Gloria Vergara-Diaz ◽  
Jean-Francois Daneault ◽  
Federico Parisi ◽  
Chen Admati ◽  
Christina Alfonso ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Symptom Severity ◽  
Wearable Sensors ◽  
Motor Fluctuations ◽  
Sensor Data ◽  
Accelerometer Data ◽  
Motor Tasks ◽  
Specific Symptom ◽  
At Home

AbstractParkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. Dyskinesia and motor fluctuations are complications of PD medications. An objective measure of on/off time with/without dyskinesia has been sought for some time because it would facilitate the titration of medications. The objective of the dataset herein presented is to assess if wearable sensor data can be used to generate accurate estimates of limb-specific symptom severity. Nineteen subjects with PD experiencing motor fluctuations were asked to wear a total of five wearable sensors on both forearms and shanks, as well as on the lower back. Accelerometer data was collected for four days, including two laboratory visits lasting 3 to 4 hours each while the remainder of the time was spent at home and in the community. During the laboratory visits, subjects performed a battery of motor tasks while clinicians rated limb-specific symptom severity. At home, subjects were instructed to use a smartphone app that guided the periodic performance of a set of motor tasks.

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