scholarly journals Analysis and evaluation of handwriting in patients with Parkinson’s disease using kinematic, geometrical, and non-linear features

2019 ◽  
Vol 173 ◽  
pp. 43-52 ◽  
Author(s):  
C.D. Rios-Urrego ◽  
J.C. Vásquez-Correa ◽  
J.F. Vargas-Bonilla ◽  
E. Nöth ◽  
F. Lopera ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Linear Features ◽  
Analysis And Evaluation ◽  
Non Linear

scholarly journals Non-Linear Dynamical Analysis of EEG Time Series Distinguishes Patients with Parkinson’s Disease from Healthy Individuals

2013 ◽  
Vol 4 ◽  
Author(s):  
Claudia Lainscsek ◽  
Manuel E. Hernandez ◽  
Jonathan Weyhenmeyer ◽  
Terrence J. Sejnowski ◽  
Howard Poizner
Keyword(s):  
Parkinson’S Disease ◽  
Time Series ◽  
Parkinson's Disease ◽  
Dynamical Analysis ◽  
Healthy Individuals ◽  
Non Linear

Linear and non-linear acoustic voice analysis of Persian speaking Parkinson's disease patients

2014 ◽  
Vol 136 (4) ◽  
pp. 2312-2312
Author(s):  
Fatemeh Majdinasab ◽  
Maryam Mollashahi ◽  
Mansour Vali ◽  
Hedieh Hashemi
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Voice Analysis ◽  
Acoustic Voice Analysis ◽  
Non Linear

scholarly journals The organization of the basal ganglia functional connectivity network is non-linear in Parkinson's disease

2019 ◽  
Vol 22 ◽  
pp. 101708 ◽  
Author(s):  
Clara Rodriguez-Sabate ◽  
Ingrid Morales ◽  
Jesus N. Lorenzo ◽  
Manuel Rodriguez
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Basal Ganglia ◽  
Functional Connectivity ◽  
Non Linear

scholarly journals A Non-linear Dynamics Approach to Classify Gait Signals of Patients with Parkinson’s Disease

2018 ◽  
pp. 268-278 ◽  
Author(s):  
Paula Andrea Pérez-Toro ◽  
Juan Camilo Vásquez-Correa ◽  
Tomas Arias-Vergara ◽  
Nicanor Garcia-Ospina ◽  
Juan Rafael Orozco-Arroyave ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Linear Dynamics ◽  
Non Linear

scholarly journals Independently together: subthalamic theta and beta opposite roles in predicting Parkinson’s tremor

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Nir Asch ◽  
Yehuda Herschman ◽  
Rotem Maoz ◽  
Carmel R Auerbach-Asch ◽  
Dan Valsky ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Negative Correlation ◽  
Brain Stimulation ◽  
Significant Negative Correlation ◽  
Positive Correlation ◽  
Resting Tremor ◽  
Non Linear ◽  
Deep Brain

Abstract Tremor is a core feature of Parkinson’s disease and the most easily recognized Parkinsonian sign. Nonetheless, its pathophysiology remains poorly understood. Here, we show that multispectral spiking activity in the posterior-dorso-lateral oscillatory (motor) region of the subthalamic nucleus distinguishes resting tremor from the other Parkinsonian motor signs and strongly correlates with its severity. We evaluated microelectrode-spiking activity from the subthalamic dorsolateral oscillatory region of 70 Parkinson’s disease patients who underwent deep brain stimulation surgery (114 subthalamic nuclei, 166 electrode trajectories). We then investigated the relationship between patients’ clinical Unified Parkinson’s Disease Rating Scale score and their peak theta (4–7 Hz) and beta (13–30 Hz) powers. We found a positive correlation between resting tremor and theta activity (r = 0.41, P < 0.01) and a non-significant negative correlation with beta activity (r = −0.2, P = 0.5). Hypothesizing that the two neuronal frequencies mask each other’s relationship with resting tremor, we created a non-linear model of their proportional spectral powers and investigated its relationship with resting tremor. As hypothesized, patients’ proportional scores correlated better than either theta or beta alone (r = 0.54, P < 0.001). However, theta and beta oscillations were frequently temporally correlated (38/70 patients manifested significant positive temporal correlations and 1/70 exhibited significant negative correlation between the two frequency bands). When comparing theta and beta temporal relationship (r θ β) to patients’ resting tremor scores, we found a significant negative correlation between the two (r = −0.38, P < 0.01). Patients manifesting a positive correlation between the two bands (i.e. theta and beta were likely to appear simultaneously) were found to have lower resting tremor scores than those with near-zero correlation values (i.e. theta and beta were likely to appear separately). We therefore created a new model incorporating patients’ proportional theta–beta power and r θ βscores to obtain an improved neural correlate of resting tremor (r = 0.62, P < 0.001). We then used the Akaike and Bayesian information criteria for model selection and found the multispectral model, incorporating theta–beta proportional power and their correlation, to be the best fitting model, with 0.96 and 0.89 probabilities, respectively. Here we found that as theta increases, beta decreases and the two appear separately—resting tremor is worsened. Our results therefore show that theta and beta convey information about resting tremor in opposite ways. Furthermore, the finding that theta and beta coactivity is negatively correlated with resting tremor suggests that theta–beta non-linear scale may be a valuable biomarker for Parkinson’s resting tremor in future adaptive deep brain stimulation techniques.

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