scholarly journals Assessment of the Status of Patients with Parkinson’s Disease Using Neural Networks and Mobile Phone Sensors

Diagnostics ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 214 ◽  
Author(s):  
Yulia Shichkina ◽  
Elizaveta Stanevich ◽  
Yulia Irishina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Mobile Phone ◽  
Neurological Diseases ◽  
Clinical Manifestations ◽  
Training Sample ◽  
Recurrent Network ◽  
The Status

Parkinson’s disease (PD) is one of the most common chronic neurological diseases and one of the significant causes of disability for middle-aged and elderly people. Monitoring the patient’s condition and its compliance is the key to the success of the correction of the main clinical manifestations of PD, including the almost inevitable modification of the clinical picture of the disease against the background of prolonged dopaminergic therapy. In this article, we proposed an approach to assessing the condition of patients with PD using deep recurrent neural networks, trained on data measured using mobile phones. The data was received in two modes: background (data from the phone’s sensors) and interactive (data directly entered by the user). For the classification of the patient’s condition, we built various models of the neural network. Testing of these models showed that the most efficient was a recurrent network with two layers. The results of the experiment show that with a sufficient amount of the training sample, it is possible to build a neural network that determines the condition of the patient according to the data from the mobile phone sensors with a high probability.

Current Therapeutic Strategies and Perspectives for Neuroprotection in Parkinson’s Disease

2020 ◽  
Vol 26 (37) ◽  
pp. 4738-4746
Author(s):  
Mohan K. Ghanta ◽  
P. Elango ◽  
Bhaskar L. V. K. S.
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Diagnosis ◽  
Neurodegenerative Disorder ◽  
Therapeutic Strategies ◽  
Neuroprotective Agents ◽  
Striatal Neurons ◽  
The Status ◽  
Advanced Stages ◽  
Dopaminergic Pathways

Parkinson’s disease is a progressive neurodegenerative disorder of dopaminergic striatal neurons in basal ganglia. Treatment of Parkinson’s disease (PD) through dopamine replacement strategies may provide improvement in early stages and this treatment response is related to dopaminergic neuronal mass which decreases in advanced stages. This treatment failure was revealed by many studies and levodopa treatment became ineffective or toxic in chronic stages of PD. Early diagnosis and neuroprotective agents may be a suitable approach for the treatment of PD. The essentials required for early diagnosis are biomarkers. Characterising the striatal neurons, understanding the status of dopaminergic pathways in different PD stages may reveal the effects of the drugs used in the treatment. This review updates on characterisation of striatal neurons, electrophysiology of dopaminergic pathways in PD, biomarkers of PD, approaches for success of neuroprotective agents in clinical trials. The literature was collected from the articles in database of PubMed, MedLine and other available literature resources.

scholarly journals Validation of IMU-based gait event detection during curved walking and turning in older adults and Parkinson’s Disease patients

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Robbin Romijnders ◽  
Elke Warmerdam ◽  
Clint Hansen ◽  
Julius Welzel ◽  
Gerhard Schmidt ◽  
...  
Keyword(s):  
Older Adults ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Daily Life ◽  
Neurological Diseases ◽  
Detection Performance ◽  
Measurement Unit ◽  
Straight Line ◽  
Optical Motion ◽  
Task Conditions

Abstract Background Identification of individual gait events is essential for clinical gait analysis, because it can be used for diagnostic purposes or tracking disease progression in neurological diseases such as Parkinson’s disease. Previous research has shown that gait events can be detected from a shank-mounted inertial measurement unit (IMU), however detection performance was often evaluated only from straight-line walking. For use in daily life, the detection performance needs to be evaluated in curved walking and turning as well as in single-task and dual-task conditions. Methods Participants (older adults, people with Parkinson’s disease, or people who had suffered from a stroke) performed three different walking trials: (1) straight-line walking, (2) slalom walking, (3) Stroop-and-walk trial. An optical motion capture system was used a reference system. Markers were attached to the heel and toe regions of the shoe, and participants wore IMUs on the lateral sides of both shanks. The angular velocity of the shank IMUs was used to detect instances of initial foot contact (IC) and final foot contact (FC), which were compared to reference values obtained from the marker trajectories. Results The detection method showed high recall, precision and F1 scores in different populations for both initial contacts and final contacts during straight-line walking (IC: recall $$=$$ = 100%, precision $$=$$ = 100%, F1 score $$=$$ = 100%; FC: recall $$=$$ = 100%, precision $$=$$ = 100%, F1 score $$=$$ = 100%), slalom walking (IC: recall $$=$$ = 100%, precision $$\ge$$ ≥ 99%, F1 score $$=$$ = 100%; FC: recall $$=$$ = 100%, precision $$\ge$$ ≥ 99%, F1 score $$=$$ = 100%), and turning (IC: recall $$\ge$$ ≥ 85%, precision $$\ge$$ ≥ 95%, F1 score $$\ge$$ ≥ 91%; FC: recall $$\ge$$ ≥ 84%, precision $$\ge$$ ≥ 95%, F1 score $$\ge$$ ≥ 89%). Conclusions Shank-mounted IMUs can be used to detect gait events during straight-line walking, slalom walking and turning. However, more false events were observed during turning and more events were missed during turning. For use in daily life we recommend identifying turning before extracting temporal gait parameters from identified gait events.

scholarly journals Combine and conquer: event reconstruction with Bayesian Ensemble Neural Networks

2021 ◽  
Vol 2021 (4) ◽  
Author(s):  
Jack Y. Araz ◽  
Michael Spannowsky
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Top Quark ◽  
Feature Space ◽  
Training Sample ◽  
Bayesian Techniques ◽  
Training Sample Size ◽  
Event Reconstruction ◽  
Neural Network Classifiers ◽  
Quark Jets

Abstract Ensemble learning is a technique where multiple component learners are combined through a protocol. We propose an Ensemble Neural Network (ENN) that uses the combined latent-feature space of multiple neural network classifiers to improve the representation of the network hypothesis. We apply this approach to construct an ENN from Convolutional and Recurrent Neural Networks to discriminate top-quark jets from QCD jets. Such ENN provides the flexibility to improve the classification beyond simple prediction combining methods by linking different sources of error correlations, hence improving the representation between data and hypothesis. In combination with Bayesian techniques, we show that it can reduce epistemic uncertainties and the entropy of the hypothesis by simultaneously exploiting various kinematic correlations of the system, which also makes the network less susceptible to a limitation in training sample size.

scholarly journals An Innovative Personalised Management Program for Older Adults with Parkinson’s Disease: New Concepts and Future Directions

2021 ◽  
Vol 11 (1) ◽  
pp. 43
Author(s):  
Piyush Varma ◽  
Lakshanaa Narayan ◽  
Jane Alty ◽  
Virginia Painter ◽  
Chandrasekhara Padmakumar
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Older Persons ◽  
Clinical Manifestations ◽  
Clinical Syndrome ◽  
Management Program ◽  
Older Person ◽  
Care Needs ◽  
Future Directions ◽  
New Concepts

Introduction: Parkinson’s disease is a heterogeneous clinical syndrome. Parkinson’s disease in older persons presents with a diverse array of clinical manifestations leading to unique care needs. This raises the need for the healthcare community to proactively address the care needs of older persons with Parkinson’s disease. Though it is tempting to categorise different phenotypes of Parkinson’s disease, a strong evidence based for the same is lacking. There is considerable literature describing the varying clinical manifestations in old age. This article aims to review the literature looking for strategies in personalising the management of an older person with Parkinson’s disease.

scholarly journals High-throughput behavioral screen in C. elegans reveals Parkinson’s disease drug candidates

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Salman Sohrabi ◽  
Danielle E. Mor ◽  
Rachel Kaletsky ◽  
William Keyes ◽  
Coleen T. Murphy
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
High Throughput ◽  
Potential Candidate ◽  
Automated Scoring ◽  
C Elegans ◽  
Drug Candidates ◽  
Branched Chain ◽  
Approved Drugs

AbstractWe recently linked branched-chain amino acid transferase 1 (BCAT1) dysfunction with the movement disorder Parkinson’s disease (PD), and found that RNAi-mediated knockdown of neuronal bcat-1 in C. elegans causes abnormal spasm-like ‘curling’ behavior with age. Here we report the development of a machine learning-based workflow and its application to the discovery of potentially new therapeutics for PD. In addition to simplifying quantification and maintaining a low data overhead, our simple segment-train-quantify platform enables fully automated scoring of image stills upon training of a convolutional neural network. We have trained a highly reliable neural network for the detection and classification of worm postures in order to carry out high-throughput curling analysis without the need for user intervention or post-inspection. In a proof-of-concept screen of 50 FDA-approved drugs, enasidenib, ethosuximide, metformin, and nitisinone were identified as candidates for potential late-in-life intervention in PD. These findings point to the utility of our high-throughput platform for automated scoring of worm postures and in particular, the discovery of potential candidate treatments for PD.

scholarly journals Using Artificial Neural Network to Discriminate Parkinson’s Disease from Other Parkinsonisms by Focusing on Putamen of Dopamine Transporter SPECT Images

Biomedicines ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 12
Author(s):  
Chung-Yao Chien ◽  
Szu-Wei Hsu ◽  
Tsung-Lin Lee ◽  
Pi-Shan Sung ◽  
Chou-Ching Lin
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Dopamine Transporter ◽  
Support Vector ◽  
Dat Spect ◽  
Ann Classifier ◽  
Spect Images

Background: The challenge of differentiating, at an early stage, Parkinson’s disease from parkinsonism caused by other disorders remains unsolved. We proposed using an artificial neural network (ANN) to process images of dopamine transporter single-photon emission computed tomography (DAT-SPECT). Methods: Abnormal DAT-SPECT images of subjects with Parkinson’s disease and parkinsonism caused by other disorders were divided into training and test sets. Striatal regions of the images were segmented by using an active contour model and were used as the data to perform transfer learning on a pre-trained ANN to discriminate Parkinson’s disease from parkinsonism caused by other disorders. A support vector machine trained using parameters of semi-quantitative measurements including specific binding ratio and asymmetry index was used for comparison. Results: The predictive accuracy of the ANN classifier (86%) was higher than that of the support vector machine classifier (68%). The sensitivity and specificity of the ANN classifier in predicting Parkinson’s disease were 81.8% and 88.6%, respectively. Conclusions: The ANN classifier outperformed classical biomarkers in differentiating Parkinson’s disease from parkinsonism caused by other disorders. This classifier can be readily included into standalone computer software for clinical application.

scholarly journals Recent Insights into the Interplay of Alpha-Synuclein and Sphingolipid Signaling in Parkinson’s Disease

2021 ◽  
Vol 22 (12) ◽  
pp. 6277
Author(s):  
Joanna A. Motyl ◽  
Joanna B. Strosznajder ◽  
Agnieszka Wencel ◽  
Robert P. Strosznajder
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cell Fate ◽  
Neuronal Death ◽  
Neurological Diseases ◽  
Alpha Synuclein ◽  
Lipid Mediator ◽  
Cell Defense ◽  
Promising Therapeutic Approach ◽  
S1p Receptor

Molecular studies have provided increasing evidence that Parkinson’s disease (PD) is a protein conformational disease, where the spread of alpha-synuclein (ASN) pathology along the neuraxis correlates with clinical disease outcome. Pathogenic forms of ASN evoke oxidative stress (OS), neuroinflammation, and protein alterations in neighboring cells, thereby intensifying ASN toxicity, neurodegeneration, and neuronal death. A number of evidence suggest that homeostasis between bioactive sphingolipids with opposing function—e.g., sphingosine-1-phosphate (S1P) and ceramide—is essential in pro-survival signaling and cell defense against OS. In contrast, imbalance of the “sphingolipid biostat” favoring pro-oxidative/pro-apoptotic ceramide-mediated changes have been indicated in PD and other neurodegenerative disorders. Therefore, we focused on the role of sphingolipid alterations in ASN burden, as well as in a vast range of its neurotoxic effects. Sphingolipid homeostasis is principally directed by sphingosine kinases (SphKs), which synthesize S1P—a potent lipid mediator regulating cell fate and inflammatory response—making SphK/S1P signaling an essential pharmacological target. A growing number of studies have shown that S1P receptor modulators, and agonists are promising protectants in several neurological diseases. This review demonstrates the relationship between ASN toxicity and alteration of SphK-dependent S1P signaling in OS, neuroinflammation, and neuronal death. Moreover, we discuss the S1P receptor-mediated pathways as a novel promising therapeutic approach in PD.

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