Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Parkinson's Disease: Treatment01:24

Parkinson's Disease: Treatment

Neurodegenerative disorders, such as Parkinson's Disease (PD), involve the gradual and irreversible destruction of neurons in particular brain areas. These disorders exhibit standard features like proteinopathies, selective vulnerability of some neurons, and an interaction of intrinsic properties, genetics, and environmental influences in neural injury.
Parkinson's Disease is primarily a result of the loss of dopaminergic neurons in the substantia nigra pars compacta. The cornerstone of its...
Parkinson Disease l: Introduction01:24

Parkinson Disease l: Introduction

Parkinson’s disease is a chronic, progressive neurodegenerative disorder that primarily affects movement. It is characterized by motor symptoms such as resting tremors, muscle rigidity, bradykinesia (slowness of movement), and postural instability. Patients may notice hand tremors at rest, stiffness during movement, or a shuffling gait. In addition to motor features, non-motor symptoms include sleep disturbances, mood and behavioral changes, constipation, and cognitive impairment, all of which...
Parkinson Disease ll: Pathophysiology01:24

Parkinson Disease ll: Pathophysiology

Parkinson disease (PD) is a progressive neurodegenerative disorder primarily affecting movement, with additional non-motor features. Its pathophysiology involves complex interactions among genetic susceptibility, environmental exposures, and cellular dysfunction, including dopaminergic neuron loss, protein aggregation, and mitochondrial impairment.Selective NeurodegenerationA key feature is the degeneration of dopaminergic neurons in the substantia nigra pars compacta, leading to reduced...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

MR Imaging-Based Biomarkers for Strength Prediction: A Statistical Shape and Architecture Modeling of Quadriceps Muscles.

Journal of magnetic resonance imaging : JMRI·2026
Same author

MR-Transformer: A Vision Transformer-based Deep Learning Model for Total Knee Replacement Prediction Using MRI.

Radiology. Artificial intelligence·2025
Same author

Preliminary results of a new endoscopic underlay cartilage tympanoplasty with lateral malleolar flap.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery·2025
Same author

Estimating time-to-total knee replacement on radiographs and MRI: a multimodal approach using self-supervised deep learning.

Radiology advances·2025
Same author

Artificial intelligence in knee osteoarthritis: A comprehensive review for 2022.

Osteoarthritis imaging·2024
Same author

Deep learning for diagnosis of COVID-19 using 3D CT scans.

Computers in biology and medicine·2021

Related Experiment Video

Updated: Jul 24, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Performance analysis of different classification algorithms using different feature selection methods on Parkinson's

Ozkan Cigdem1, Hasan Demirel1

  • 1Department of Electrical and Electronics Engineering, Eastern Mediterranean University, Gazimagusa, Mersin 10, Turkey.

Journal of Neuroscience Methods
|September 4, 2018
PubMed
Summary

This study enhances Parkinson

Keywords:
DARTELDecision fusionFeature selectionParkinson's diseaseSource fusionStructural MRI

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Related Experiment Videos

Last Updated: Jul 24, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Area of Science:

  • Neuroimaging
  • Medical Diagnostics
  • Machine Learning

Background:

  • Neurodegenerative diseases, including Parkinson's disease (PD), are a significant health concern.
  • Three-dimensional magnetic resonance imaging (3D-MRI) is a key technology for diagnosing neurodegenerative disorders.

Purpose of the Study:

  • To improve the accuracy of Parkinson's disease detection using 3D-MRI data.
  • To evaluate the effectiveness of various feature selection and classification methods for PD diagnosis.

Main Methods:

  • Utilized gray matter (GM) and white matter (WM) tissue maps from 3D-MRI.
  • Implemented source fusion of GM/WM data and decision fusion of multiple classifiers.
  • Employed correlation-based feature selection (CFS) and adaptive Fisher stopping criteria for feature selection.

Main Results:

  • Correlation-based feature selection (CFS) outperformed other methods across all classifiers.
  • Support Vector Machine (SVM) showed the best performance among classification algorithms.
  • Fusion of GM and WM datasets significantly improved classification accuracy to 95.00%.

Conclusions:

  • Combining 3D masked GM and WM tissue maps with a decision fusion technique using CFS achieves high accuracy in PD detection.
  • The proposed fusion methodology offers a promising approach for accurate Parkinson's disease diagnosis.