Hypomimia Recognition in Parkinson’s Disease With Semantic Features

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-20
Author(s):  
Ge Su ◽  
Bo Lin ◽  
Wei Luo ◽  
Jianwei Yin ◽  
Shuiguang Deng ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Spatial Data ◽  
Semantic Analysis ◽  
Neurodegenerative Disorder ◽  
Machine Learning Algorithms ◽  
Semantic Feature ◽  
Semantic Features ◽  
Feature Maps ◽  
Real World Data

Parkinson’s disease is the second most common neurodegenerative disorder, commonly affecting elderly people over the age of 65. As the cardinal manifestation, hypomimia, referred to as impairments in normal facial expressions, stays covert. Even some experienced doctors may miss these subtle changes, especially in a mild stage of this disease. The existing methods for hypomimia recognition are mainly dominated by statistical variable-based methods with the help of traditional machine learning algorithms. Despite the success of recognizing hypomimia, they show a limited accuracy and lack the capability of performing semantic analysis. Therefore, developing a computer-aided diagnostic method for semantically recognizing hypomimia is appealing. In this article, we propose a Semantic Feature based Hypomimia Recognition network , named SFHR-NET , to recognize hypomimia based on facial videos. First, a Semantic Feature Classifier (SF-C) is proposed to adaptively adjust feature maps salient to hypomimia, which leads the encoder and classifier to focus more on areas of hypomimia-interest. In SF-C, the progressive confidence strategy (PCS) ensures more reliable semantic features. Then, a two-stream framework is introduced to fuse the spatial data stream and temporal optical stream, which allows the encoder to semantically and progressively characterize the rigid process of hypomimia. Finally, to improve the interpretability of the model, Gradient-weighted Class Activation Mapping (Grad-CAM) is integrated to generate attention maps that cast our engineered features into hypomimia-interest regions. These highlighted regions provide visual explanations for decisions of our network. Experimental results based on real-world data demonstrate the effectiveness of our method in detecting hypomimia.

scholarly journals Machine Learning Prediction of Parkinson's Disease Onset and Subtype Using Germline Variants

2021 ◽  
Author(s):  
Saya R Dennis ◽  
Tanya Simuni ◽  
Yuan Luo
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Neurodegenerative Disorder ◽  
Disease Onset ◽  
The United States ◽  
Machine Learning Algorithms ◽  
Progression Rate ◽  
High Importance ◽  
Germline Variants

Parkinson's Disease is the second most common neurodegenerative disorder in the United States, and is characterized by a largely irreversible worsening of motor and non-motor symptoms as the disease progresses. A prominent characteristic of the disease is its high heterogeneity in manifestation as well as the progression rate. For sporadic Parkinson's Disease, which comprises ~90% of all diagnoses, the relationship between the patient genome and disease onset or progression subtype remains largely elusive. Machine learning algorithms are increasingly adopted to study the genomics of diseases due to their ability to capture patterns within the vast feature space of the human genome that might be contributing to the phenotype of interest. In our study, we develop two machine learning models that predict the onset as well as the progression subtype of Parkinson's Disease based on subjects' germline mutations. Our best models achieved an ROC of 0.77 and 0.61 for disease onset and subtype prediction, respectively. To the best of our knowledge, our models present state-of-the-art prediction performances of PD onset and subtype solely based on the subjects' germline variants. The genes with high importance in our best-performing models were enriched for several canonical pathways related to signaling, immune system, and protein modifications, all of which have been previously associated with PD symptoms or pathogenesis. These high-importance gene sets provide us with promising candidate genes for future biomedical and clinical research.

Machine Learning Approaches in Parkinson’s Disease

2021 ◽  
Vol 28 ◽  
Author(s):  
Annamaria Landolfi ◽  
Carlo Ricciardi ◽  
Leandro Donisi ◽  
Giuseppe Cesarelli ◽  
Jacopo Troisi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Neurodegenerative Disorder ◽  
Learning Algorithms ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Clinical Aspects ◽  
Learning Approaches

Background:: Parkinson’s disease is the second most frequent neurodegenerative disorder. Its diagnosis is challenging and mainly relies on clinical aspects. At present, no biomarker is available to obtain a diagnosis of certainty in vivo. Objective:: The present review aims at describing machine learning algorithms as they have been variably applied to different aspects of Parkinson’s disease diagnosis and characterization. Methods:: A systematic search was conducted on PubMed in December 2019, resulting in 230 publications obtained with the following search query: “Machine Learning” “AND” “Parkinson Disease”. Results:: the obtained publications were divided into 6 categories, based on different application fields: “Gait Analysis - Motor Evaluation”, “Upper Limb Motor and Tremor Evaluation”, “Handwriting and typing evaluation”, “Speech and Phonation evaluation”, “Neuroimaging and Nuclear Medicine evaluation”, “Metabolomics application”, after excluding the papers of general topic. As a result, a total of 166 articles were analyzed, after elimination of papers written in languages other than English or not directly related to the selected topics. Conclusion:: Machine learning algorithms are computer-based statistical approaches which can be trained and are able to find common patterns from big amounts of data. The machine learning approaches can help clinicians in classifying patients according to several variables at the same time.

scholarly journals Diagnosis of Parkinson’s Disorder through Speech Data using Machine Learning Algorithms

2020 ◽  
Vol 9 (3) ◽  
pp. 69-72
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Health Care ◽  
Decision Making ◽  
Parkinson's Disease ◽  
Neurodegenerative Disorder ◽  
Machine Learning Algorithms ◽  
Speech Data ◽  
Computer Aided ◽  
Comprehensive Comparison

Parkinson's disease is a neurodegenerative disorder that affects millions of people around the globe. Detecting Parkinson's disease at an earlier stage could help to better diagnose the disease. Machine learning provides potentially large opportunities for computer-aided identification and diagnosis that could minimize unavoidable health care errors and inherent clinical uncertainty, provide guidance, and improve decision-making. In this paper, we explore the feature extraction and prediction algorithms used to predict Parkinson's disease and provide a comprehensive comparison of these algorithms

Rapid Diagnosis of Parkinson’s Disease from Sebum using Paper Spray Ionisation Ion Mobility Mass Spectrometry

2020 ◽  
Author(s):  
Depanjan Sarkar ◽  
Drupad Trivedi ◽  
Eleanor Sinclair ◽  
Sze Hway Lim ◽  
Caitlin Walton-Doyle ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Ion Mobility ◽  
Neurodegenerative Disorder ◽  
Treatment Options ◽  
Ion Mobility Mass Spectrometry ◽  
Paper Spray ◽  
Molecular Features ◽  
Validation Set

Parkinson’s disease (PD) is the second most common neurodegenerative disorder for which identification of robust biomarkers to complement clinical PD diagnosis would accelerate treatment options and help to stratify disease progression. Here we demonstrate the use of paper spray ionisation coupled with ion mobility mass spectrometry (PSI IM-MS) to determine diagnostic molecular features of PD in sebum. PSI IM-MS was performed directly from skin swabs, collected from 34 people with PD and 30 matched control subjects as a training set and a further 91 samples from 5 different collection sites as a validation set. PSI IM-MS elucidates ~ 4200 features from each individual and we report two classes of lipids (namely phosphatidylcholine and cardiolipin) that differ significantly in the sebum of people with PD. Putative metabolite annotations are obtained using tandem mass spectrometry experiments combined with accurate mass measurements. Sample preparation and PSI IM-MS analysis and diagnosis can be performed ~5 minutes per sample offering a new route to for rapid and inexpensive confirmatory diagnosis of this disease.

Visual Effects of Deep Brain Stimulation in a Patient with Parkinson’s Disease

2019 ◽  
pp. 158-173
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Visual System ◽  
Brain Stimulation ◽  
Neurodegenerative Disorder ◽  
Initial Examination ◽  
Before And After ◽  
Deep Brain ◽  
Cover Test

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder caused by a dopamine deficiency that presents with motor symptoms. Visual disorders can occur concomitantly but are frequently overlooked. Deep brain stimulation (DBS) has been an effective treatment to improve tremors, stiffness and overall mobility, but little is known about its effects on the visual system. Case Report: A 75-year-old Caucasian male with PD presented with longstanding binocular diplopia. On baseline examination, the best-corrected visual acuity was 20/25 in each eye. On observation, he had noticeable tremors with an unsteady gait. Distance alternating cover test showed exophoria with a right hyperphoria. Near alternating cover test revealed a significantly larger exophoria accompanied by a reduced near point of convergence. Additional testing with a 24-2 Humphrey visual field and optical coherence tomography (OCT) of the nerve and macula were unremarkable. The patient underwent DBS implantation five weeks after initial examination, and the device was activated four weeks thereafter. At follow up, the patient still complained of intermittent diplopia. There was no significant change in the manifest refraction or prism correction. On observation, the patient had remarkably improved tremors with a steady gait. All parameters measured were unchanged. The patient was evaluated again seven months after device activation. Although vergence ranges at all distances were improved, the patient was still symptomatic for intermittent diplopia. OCT scans of the optic nerve showed borderline but symmetric thinning in each eye. All other parameters measured were unchanged. Conclusion: The case found no significant changes on ophthalmic examination after DBS implantation and activation in a patient with PD. To the best of the authors’ knowledge, there are no other cases in the literature that investigated the effects of DBS on the visual system pathway in a patient with PD before and after DBS implantation and activation.

Peripheral Immunity, Immunoaging and Neuroinflammation in Parkinson’s Disease

2019 ◽  
Vol 26 (20) ◽  
pp. 3719-3753 ◽  
Author(s):  
Natasa Kustrimovic ◽  
Franca Marino ◽  
Marco Cosentino
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Immune System ◽  
Neurodegenerative Disorder ◽  
Neurodegenerative Process ◽  
Progressive Degeneration ◽  
Cytokine Levels ◽  
Inflammatory Phenotype ◽  
Immune Challenges

:Parkinson’s disease (PD) is the second most common neurodegenerative disorder among elderly population, characterized by the progressive degeneration of dopaminergic neurons in the midbrain. To date, exact cause remains unknown and the mechanism of neurons death uncertain. It is typically considered as a disease of central nervous system (CNS). Nevertheless, numerous evidence has been accumulated in several past years testifying undoubtedly about the principal role of neuroinflammation in progression of PD. Neuroinflammation is mainly associated with presence of activated microglia in brain and elevated levels of cytokine levels in CNS. Nevertheless, active participation of immune system as well has been noted, such as, elevated levels of cytokine levels in blood, the presence of auto antibodies, and the infiltration of T cell in CNS. Moreover, infiltration and reactivation of those T cells could exacerbate neuroinflammation to greater neurotoxic levels. Hence, peripheral inflammation is able to prime microglia into pro-inflammatory phenotype, which can trigger stronger response in CNS further perpetuating the on-going neurodegenerative process.:In the present review, the interplay between neuroinflammation and the peripheral immune response in the pathobiology of PD will be discussed. First of all, an overview of regulation of microglial activation and neuroinflammation is summarized and discussed. Afterwards, we try to collectively analyze changes that occurs in peripheral immune system of PD patients, suggesting that these peripheral immune challenges can exacerbate the process of neuroinflammation and hence the symptoms of the disease. In the end, we summarize some of proposed immunotherapies for treatment of PD.

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 Evaluation of fecal microbiota transplantation in Parkinson's disease patients with constipation

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Xiao-yi Kuai ◽  
Xiao-han Yao ◽  
Li-juan Xu ◽  
Yu-qing Zhou ◽  
Li-ping Zhang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scale ◽  
Neurodegenerative Disorder ◽  
Fecal Microbiota Transplantation ◽  
Fecal Microbiota ◽  
Motor Symptoms ◽  
Gastrointestinal Dysfunction ◽  
Before And After ◽  
Non Motor Symptoms

AbstractParkinson’s disease (PD) is a neurodegenerative disorder and 70–80% of PD patients suffer from gastrointestinal dysfunction such as constipation. We aimed to assess the efficacy and safety of fecal microbiota transplantation (FMT) for treating PD related to gastrointestinal dysfunction. We conducted a prospective, single- study. Eleven patients with PD received FMT. Fecal samples were collected before and after FMT and subjected to 16S ribosomal DNA (rDNA) gene sequencing. Hoehn-Yahr (H-Y) grade, Unified Parkinson's Disease Rating Scale (UPDRS) score, and the Non-Motion Symptom Questionnaire (NMSS) were used to assess improvements in motor and non-motor symptoms. PAC-QOL score and Wexner constipation score were used to assess the patient's constipation symptoms. All patients were tested by the small intestine breath hydrogen test, performed before and after FMT. Community richness (chao) and microbial structure in before-FMT PD patients were significantly different from the after-FMT. We observed an increased abundance of Blautia and Prevotella in PD patients after FMT, while the abundance of Bacteroidetes decreased dramatically. After FMT, the H-Y grade, UPDRS, and NMSS of PD patients decreased significantly. Through the lactulose H2 breath test, the intestinal bacterial overgrowth (SIBO) in PD patients returned to normal. The PAC-QOL score and Wexner constipation score in after-FMT patients decreased significantly. Our study profiles specific characteristics and microbial dysbiosis in the gut of PD patients. FMT might be a therapeutic potential for reconstructing the gut microbiota of PD patients and improving their motor and non-motor symptoms.

Vestibular semicircular canal function as detected by video Head Impulse Test (vHIT) is essentially unchanged in people with Parkinson’s disease compared to healthy controls

2021 ◽  
pp. 1-9
Author(s):  
Kim E. Hawkins ◽  
Elodie Chiarovano ◽  
Serene S. Paul ◽  
Ann M Burgess ◽  
Hamish G. MacDougall ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Semicircular Canal ◽  
Neurodegenerative Disorder ◽  
Vestibular Function ◽  
Head Impulse Test ◽  
Healthy Controls ◽  
Video Head Impulse Test ◽  
Head Impulse ◽  
Vestibulo Ocular Reflex

BACKGROUND: Parkinson’s disease (PD) is a common multi-system neurodegenerative disorder with possible vestibular system dysfunction, but prior vestibular function test findings are equivocal. OBJECTIVE: To report and compare vestibulo-ocular reflex (VOR) gain as measured by the video head impulse test (vHIT) in participants with PD, including tremor dominant and postural instability/gait dysfunction phenotypes, with healthy controls (HC). METHODS: Forty participants with PD and 40 age- and gender-matched HC had their vestibular function assessed. Lateral and vertical semicircular canal VOR gains were measured with vHIT. VOR canal gains between PD participants and HC were compared with independent samples t-tests. Two distinct PD phenotypes were compared to HC using Tukey’s ANOVA. The relationship of VOR gain with PD duration, phenotype, severity and age were investigated using logistic regression. RESULTS: There were no significant differences between groups in vHIT VOR gain for lateral or vertical canals. There was no evidence of an effect of PD severity, phenotype or age on VOR gains in the PD group. CONCLUSION: The impulsive angular VOR pathways are not significantly affected by the pathophysiological changes associated with mild to moderate PD.

Neuroprotective Effects of a GLP-2 Analogue in the MPTP Parkinson’s Disease Mouse Model

2021 ◽  
pp. 1-15
Author(s):  
Zijuan Zhang ◽  
Li Hao ◽  
Ming Shi ◽  
Ziyang Yu ◽  
Simai Shao ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Substantia Nigra ◽  
Mouse Model ◽  
Neurodegenerative Disorder ◽  
Enzyme Linked Immunosorbent Assay ◽  
Protective Effects ◽  
Neuroprotective Effects ◽  
Expression Levels ◽  
Dual Agonist

Background: Glucagon-like peptide 2 (GLP-2) is a peptide hormone derived from the proglucagon gene expressed in the intestines, pancreas and brain. Some previous studies showed that GLP-2 improved aging and Alzheimer’s disease related memory impairments. Parkinson’s disease (PD) is a progressive neurodegenerative disorder, and to date, there is no particular medicine reversed PD symptoms effectively. Objective: The aim of this study was to evaluate neuroprotective effects of a GLP-2 analogue in the 1-Methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine (MPTP) PD mouse model. Methods: In the present study, the protease resistant Gly(2)-GLP-2 (50 nmol/kg ip.) analogue has been tested for 14 days by behavioral assessment, transmission electron microscope, immunofluorescence histochemistry, enzyme-linked immunosorbent assay and western blot in an acute PD mouse model induced by MPTP. For comparison, the incretin receptor dual agonist DA5-CH was tested in a separate group. Results: The GLP-2 analogue treatment improved the locomotor and exploratory activity of mice, and improved bradykinesia and movement imbalance of mice. Gly(2)-GLP-2 treatment also protected dopaminergic neurons and restored tyrosine hydroxylase expression levels in the substantia nigra. Gly(2)-GLP-2 furthermore reduced the inflammation response as seen in lower microglia activation, and decreased NLRP3 and interleukin-1β pro-inflammatory cytokine expression levels. In addition, the GLP-2 analogue improved MPTP-induced mitochondrial dysfunction in the substantia nigra. The protective effects were comparable to those of the dual agonist DA5-CH. Conclusion: The present results demonstrate that Gly(2)-GLP-2 can attenuate NLRP3 inflammasome-mediated inflammation and mitochondrial damage in the substantia nigra induced by MPTP, and Gly(2)-GLP-2 shows neuroprotective effects in this PD animal model.

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