3D Textural, Morphological and Statistical Analysis of Voxel of Interests in 3T MRI Scans for the Detection of Parkinson’s Disease Using Artificial Neural Networks

Parkinson’s disease is caused due to the progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc). Presently, with the exponential growth of the aging population across the world the number of people being affected by the disease is also increasing and it imposes a huge economic burden on the governments. However, to date, no therapy or treatment has been found that can completely eradicate the disease. Therefore, early detection of Parkinson’s disease is very important so that the progressive loss of dopaminergic neurons can be controlled to provide the patients with a better life. In this study, 3T T1-MRI scans were collected from 906 subjects, out of which, 203 are control subjects, 66 are prodromal subjects and 637 are Parkinson’s disease patients. To analyze the MRI scans for the detection of neurodegeneration and Parkinson’s disease, eight subcortical structures were segmented from the acquired MRI scans using atlas based segmentation. Further, on the extracted eight subcortical structures, feature extraction was performed to extract textural, morphological and statistical features, respectively. After the feature extraction process, an exhaustive set of 107 features were generated for each MRI scan. Therefore, a two-level feature extraction process was implemented for finding the best possible feature set for the detection of Parkinson’s disease. The two-level feature extraction procedure leveraged correlation analysis and recursive feature elimination, which at the end provided us with 20 best performing features out of the extracted 107 features. Further, all the features were trained using machine learning algorithms and a comparative analysis was performed between four different machine learning algorithms based on the selected performance metrics. And at the end, it was observed that artificial neural network (multi-layer perceptron) performed the best by providing an overall accuracy of 95.3%, overall recall of 95.41%, overall precision of 97.28% and f1-score of 94%, respectively.

Download Full-text

Detection of Parkinson’s Disease from 3T T1 Weighted MRI Scans Using 3D Convolutional Neural Network

Diagnostics ◽

10.3390/diagnostics10060402 ◽

2020 ◽

Vol 10 (6) ◽

pp. 402

Author(s):

Sabyasachi Chakraborty ◽

Satyabrata Aich ◽

Hee-Cheol Kim

Keyword(s):

Neural Network ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Convolutional Neural Network ◽

Dopaminergic Neurons ◽

Aging Population ◽

Substantia Nigra Pars Compacta ◽

Progressive Loss ◽

Mri Scans ◽

Magnetic Resonance Imaging Mri

Parkinson’s Disease is a neurodegenerative disease that affects the aging population and is caused by a progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc). With the onset of the disease, the patients suffer from mobility disorders such as tremors, bradykinesia, impairment of posture and balance, etc., and it progressively worsens in the due course of time. Additionally, as there is an exponential growth of the aging population in the world the number of people suffering from Parkinson’s Disease is increasing and it levies a huge economic burden on governments. However, until now no therapeutic method has been discovered for completely eradicating the disease from a person’s body after it’s onset. Therefore, the early detection of Parkinson’s Disease is of paramount importance to tackle the progressive loss of dopaminergic neurons in patients to serve them with a better life. In this study, 3T T1-weighted MRI scans were acquired from the Parkinson’s Progression Markers Initiative (PPMI) database of 406 subjects from baseline visit, where 203 were healthy and 203 were suffering from Parkinson’s Disease. Following data pre-processing, a 3D convolutional neural network (CNN) architecture was developed for learning the intricate patterns in the Magnetic Resonance Imaging (MRI) scans for the detection of Parkinson’s Disease. In the end, it was observed that the developed 3D CNN model performed superiorly by completely aligning with the hypothesis of the study and plotted an overall accuracy of 95.29%, average recall of 0.943, average precision of 0.927, average specificity of 0.9430, f1-score of 0.936, and Receiver Operating Characteristic—Area Under Curve (ROC-AUC) score of 0.98 for both the classes respectively.

Download Full-text

TFE3-Mediated Autophagy is Involved in Dopaminergic Neurodegeneration in Parkinson’s Disease

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.761773 ◽

2021 ◽

Vol 9 ◽

Author(s):

Xin He ◽

Yue Xie ◽

Qiongping Zheng ◽

Zeyu Zhang ◽

Shanshan Ma ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cell Death ◽

Transcription Factor ◽

Dopaminergic Neurons ◽

Essential Role ◽

Dopaminergic Neurodegeneration ◽

Promising Strategy ◽

Progressive Loss ◽

Mptp Model

Impairment of autophagy has been strongly implicated in the progressive loss of nigral dopaminergic neurons in Parkinson’s disease (PD). Transcription factor E3 (TFE3), an MiTF/TFE family transcription factor, has been identified as a master regulator of the genes that are associated with lysosomal biogenesis and autophagy. However, whether TFE3 is involved in parkinsonian neurodegeneration remains to be determined. In this study, we found decreased TFE3 expression in the nuclei of the dopaminergic neurons of postmortem human PD brains. Next, we demonstrated that TFE3 knockdown led to autophagy dysfunction and neurodegeneration of dopaminergic neurons in mice, implying that reduction of nuclear TFE3 may contribute to autophagy dysfunction-mediated cell death in PD. Further, we showed that enhancement of autophagy by TFE3 overexpression dramatically reversed autophagy downregulation and dopaminergic neurons loss in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) model of PD. Taken together, these findings demonstrate that TFE3 plays an essential role in maintaining autophagy and the survival of dopaminergic neurons, suggesting TFE3 activation may serve as a promising strategy for PD therapy.

Download Full-text

Tremor Identification Using Machine Learning in Parkinson's Disease

Early Detection of Neurological Disorders Using Machine Learning Systems - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-5225-8567-1.ch008 ◽

2019 ◽

pp. 128-151

Author(s):

Angana Saikia ◽

Vinayak Majhi ◽

Masaraf Hussain ◽

Sudip Paul ◽

Amitava Datta

Keyword(s):

Machine Learning ◽

Parkinson’S Disease ◽

Support Vector Machine ◽

Parkinson's Disease ◽

Discriminant Analysis ◽

Learning Algorithms ◽

The Body ◽

Machine Learning Algorithms ◽

Support Vector

Tremor is an involuntary quivering movement or shake. Characteristically occurring at rest, the classic slow, rhythmic tremor of Parkinson's disease (PD) typically starts in one hand, foot, or leg and can eventually affect both sides of the body. The resting tremor of PD can also occur in the jaw, chin, mouth, or tongue. Loss of dopamine leads to the symptoms of Parkinson's disease and may include a tremor. For some people, a tremor might be the first symptom of PD. Various studies have proposed measurable technologies and the analysis of the characteristics of Parkinsonian tremors using different techniques. Various machine-learning algorithms such as a support vector machine (SVM) with three kernels, a discriminant analysis, a random forest, and a kNN algorithm are also used to classify and identify various kinds of tremors. This chapter focuses on an in-depth review on identification and classification of various Parkinsonian tremors using machine learning algorithms.

Download Full-text

Parkinson's Disease

Advances in Medical Diagnosis, Treatment, and Care - Handbook of Research on Critical Examinations of Neurodegenerative Disorders ◽

10.4018/978-1-5225-5282-6.ch012 ◽

2019 ◽

pp. 252-273 ◽

Cited By ~ 1

Author(s):

Vaibhav Walia ◽

Ashish Gakkhar ◽

Munish Garg

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Substantia Nigra ◽

Dopaminergic Neurons ◽

Older Patients ◽

Neurodegenerative Disorder ◽

Progressive Loss ◽

The Brain

Parkinson's disease (PD) is a neurodegenerative disorder in which a progressive loss of the dopaminergic neurons occurs. The loss of the neurons is most prominent in the substantia nigra region of the brain. The prevalence of PD is much greater among the older patients suggesting the risk of PD increases with the increase of age. The exact cause of the neurodegeneration in PD is not known. In this chapter, the authors introduce PD, demonstrate its history, pathogenesis, neurobiology, sign and symptoms, diagnosis, and pharmacotherapy.

Download Full-text

Drosophila Models of Sporadic Parkinson’s Disease

International Journal of Molecular Sciences ◽

10.3390/ijms19113343 ◽

2018 ◽

Vol 19 (11) ◽

pp. 3343 ◽

Cited By ~ 16

Author(s):

Emi Nagoshi

Keyword(s):

Risk Factors ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Dopaminergic Neurons ◽

Future Perspectives ◽

Progressive Loss ◽

New Findings ◽

Sporadic Parkinson’S Disease ◽

Preclinical Animal Models ◽

Drosophila Models

Parkinson’s disease (PD) is the most common cause of movement disorders and is characterized by the progressive loss of dopaminergic neurons in the substantia nigra. It is increasingly recognized as a complex group of disorders presenting widely heterogeneous symptoms and pathology. With the exception of the rare monogenic forms, the majority of PD cases result from an interaction between multiple genetic and environmental risk factors. The search for these risk factors and the development of preclinical animal models are in progress, aiming to provide mechanistic insights into the pathogenesis of PD. This review summarizes the studies that capitalize on modeling sporadic (i.e., nonfamilial) PD using Drosophila melanogaster and discusses their methodologies, new findings, and future perspectives.

Download Full-text

Method of gait disorders in Parkinson's disease classification based on machine learning algorithms

2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) ◽

10.1109/itaic.2019.8785586 ◽

2019 ◽

Cited By ~ 1

Author(s):

Yajing Guo ◽

Xi Wu ◽

Linyong Shen ◽

Zhen Zhang ◽

Yanan Zhang

Keyword(s):

Machine Learning ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Learning Algorithms ◽

Gait Disorders ◽

Machine Learning Algorithms ◽

Disease Classification

Download Full-text

Can Gut Microbiota Be a Good Predictor for Parkinson’s Disease? A Machine Learning Approach

Brain Sciences ◽

10.3390/brainsci10040242 ◽

2020 ◽

Vol 10 (4) ◽

pp. 242 ◽

Cited By ~ 3

Author(s):

Daniele Pietrucci ◽

Adelaide Teofani ◽

Valeria Unida ◽

Rocco Cerroni ◽

Silvia Biocca ◽

...

Keyword(s):

Machine Learning ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Random Forest ◽

Gut Microbiota ◽

Biological Data ◽

Machine Learning Algorithms ◽

Support Vector ◽

Published Data ◽

Promising Tool

The involvement of the gut microbiota in Parkinson’s disease (PD), investigated in several studies, identified some common alterations of the microbial community, such as a decrease in Lachnospiraceae and an increase in Verrucomicrobiaceae families in PD patients. However, the results of other bacterial families are often contradictory. Machine learning is a promising tool for building predictive models for the classification of biological data, such as those produced in metagenomic studies. We tested three different machine learning algorithms (random forest, neural networks and support vector machines), analyzing 846 metagenomic samples (472 from PD patients and 374 from healthy controls), including our published data and those downloaded from public databases. Prediction performance was evaluated by the area under curve, accuracy, precision, recall and F-score metrics. The random forest algorithm provided the best results. Bacterial families were sorted according to their importance in the classification, and a subset of 22 families has been identified for the prediction of patient status. Although the results are promising, it is necessary to train the algorithm with a larger number of samples in order to increase the accuracy of the procedure.

Download Full-text

Lipid-Mediated Oxidative Stress and Inflammation in the Pathogenesis of Parkinson's Disease

Parkinson s Disease ◽

10.4061/2011/247467 ◽

2011 ◽

Vol 2011 ◽

pp. 1-9 ◽

Cited By ~ 28

Author(s):

Tahira Farooqui ◽

Akhlaq A. Farooqui

Keyword(s):

Oxidative Stress ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Dopaminergic Neurons ◽

Platelet Activating Factor ◽

Lipid Mediators ◽

Unknown Etiology ◽

Oxygen Species ◽

Progressive Loss ◽

Progressive Neurodegeneration

Parkinson's disease (PD) is a neurodegenerative movement disorder of unknown etiology. PD is characterized by the progressive loss of dopaminergic neurons in the substantia nigra, depletion of dopamine in the striatum, abnormal mitochondrial and proteasomal functions, and accumulation ofα-synuclein that may be closely associated with pathological and clinical abnormalities. Increasing evidence indicates that both oxidative stress and inflammation may play a fundamental role in the pathogenesis of PD. Oxidative stress is characterized by increase in reactive oxygen species (ROS) and depletion of glutathione. Lipid mediators for oxidative stress include 4-hydroxynonenal, isoprostanes, isofurans, isoketals, neuroprostanes, and neurofurans. Neuroinflammation is characterized by activated microglial cells that generate proinflammatory cytokines, such as TNF-αand IL-1β. Proinflammatory lipid mediators include prostaglandins and platelet activating factor, together with cytokines may play a prominent role in mediating the progressive neurodegeneration in PD.

Download Full-text

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

10.1101/2021.06.14.21258631 ◽

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.

Download Full-text