Parkinson's Disease

A deep learning-guided automated workflow in LipidOz for detailed characterization of fungal fatty acid unsaturation by ozonolysis.

Understanding fungal lipid biology and metabolism is critical for antifungal target discovery as lipids play central roles in cellular processes. Nuances in lipid structural differences can significantly impact their functions, making it necessary to characterize lipids in detail to understand their roles in these complex systems. In particular, lipid double bond (DB) locations are an important co...

Multimodal Retinal Imaging Classification for Parkinson's Disease Using a Convolutional Neural Network.

PURPOSE: Changes in retinal structure and microvasculature are connected to parallel changes in the brain. Two recent studies described machine learning algorithms trained on retinal images and quantitative data that identified Alzheimer's dementia and mild cognitive impairment with high accuracy. Prior studies also demonstrated retinal differences in individuals with PD. Herein, we developed a co...

Innovative Speech-Based Deep Learning Approaches for Parkinson's Disease Classification: A Systematic Review

Parkinson's disease (PD), the second most prevalent neurodegenerative disorder worldwide, frequently presents with early-stage speech impairments. R...

Preliminary Results of Neuromorphic Controller Design and a Parkinson's Disease Dataset Building for Closed-Loop Deep Brain Stimulation

Parkinson's Disease afflicts millions of individuals globally. Emerging as a promising brain rehabilitation therapy for Parkinson's Disease, Closed-...

2D and 3D Deep Learning Models for MRI-based Parkinson's Disease Classification: A Comparative Analysis of Convolutional Kolmogorov-Arnold Networks, Convolutional Neural Networks, and Graph Convolutional Networks

Parkinson's Disease (PD) diagnosis remains challenging. This study applies Convolutional Kolmogorov-Arnold Networks (ConvKANs), integrating learnabl...

Early Recognition of Parkinson's Disease Through Acoustic Analysis and Machine Learning

Parkinson's Disease (PD) is a progressive neurodegenerative disorder that significantly impacts both motor and non-motor functions, including speech...

A discontinuous Galerkin method for the three-dimensional heterodimer model with application to prion-like proteins' dynamics

Neurocognitive disorders, such as Alzheimer's and Parkinson's, have a wide social impact. These proteinopathies involve misfolded proteins accumulat...

CATD: Unified Representation Learning for EEG-to-fMRI Cross-Modal Generation

Multi-modal neuroimaging analysis is crucial for a comprehensive understanding of brain function and pathology, as it allows for the integration of ...

Enhancing Model Generalizability In Parkinson's Disease Automatic Assessment: A Semi-Supervised Approach Across Independent Experiments.

Machine learning in Parkinson's disease assessment uses data from clinically-coded movements, such as finger tapping, to objectively measure motor imp...

Shared-task Self-supervised Learning for Estimating Free Movement Unified Parkinson's Disease Rating Scale III.

The Unified Parkinson's Disease Rating Scale (UP-DRS) is used to recognize patients with Parkinson's disease (PD) and rate its severity in clinical se...

Video-based Clinical Gait Analysis in Parkinson's Disease: A Novel Approach Using Frontal Plane Videos and Machine Learning.

Gait can be significantly impaired by neurological conditions such as Parkinson's disease (PD). Gait impairments can be quantified by using instrument...

Dysarthria Detection with Deep Representation Learning for Patients with Parkinson's Disease.

Dysarthria is a very common motor speech symptom in Parkinson's disease impairing normal communications of patients. Detection of dysarthria could ass...

Simulating Accelerometer Signals of Parkinson's Gait Using Generative Adversarial Networks.

Wearable technologies have been demonstrated to have value in the objective assessment of Parkinson's disease. However, certain symptoms such as freez...

Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease

Omics data analysis is crucial for studying complex diseases, but its high dimensionality and heterogeneity challenge classical statistical and mach...

Rapid assessment of cosmic radiation exposure in aviation based on BP neural network method.

Cosmic radiation exposure is one of the important health concerns for aircrews. In this work, we constructed a back propagation neural network model f...

Generalizing Parkinson's disease detection using keystroke dynamics: a self-supervised approach.

OBJECTIVE: Passive monitoring of touchscreen interactions generates keystroke dynamic signals that can be used to detect and track neurological condit...

EEG-based machine learning models for the prediction of phenoconversion time and subtype in isolated rapid eye movement sleep behavior disorder.

STUDY OBJECTIVES: Isolated rapid eye movement sleep behavior disorder (iRBD) is a prodromal stage of α-synucleinopathies and eventually phenoconverts ...

Deep Learning-based Approach for Brainstem and Ventricular MR Planimetry: Application in Patients with Progressive Supranuclear Palsy.

Purpose To develop a fast and fully automated deep learning (DL)-based method for the MRI planimetric segmentation and measurement of the brainstem an...

Screening of key immunerelated gene in Parkinsons disease based on WGCNA and machine learning.

OBJECTIVES: Abnormal immune system activation and inflammation are crucial in causing Parkinson's disease. However, we still don't fully understand ho...

Parkinson's Disease Diagnosis Using miRNA Biomarkers and Deep Learning.

BACKGROUND: The current standard for Parkinson's disease (PD) diagnosis is often imprecise and expensive. However, the dysregulation patterns of micro...