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

Neural Regulation01:37

Neural Regulation

34.8K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
34.8K
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

5.3K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
5.3K
Parkinson's Disease: Treatment01:24

Parkinson's Disease: Treatment

1.4K
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...
1.4K
PD Controller: Design01:26

PD Controller: Design

761
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
761

You might also read

Related Articles

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

Sort by
Same author

Malicious user classification in cognitive 5G networks using novel improved bidirectional encoder representations from transformers model.

Scientific reports·2025
Same author

Exploring breast cancer knowledge, screening practices, needs among rural women, and challenges of community health workers in breast cancer screening in Tamil Nadu: A community-based mixed-method approach.

Journal of family medicine and primary care·2025
Same author

Prostate cancer prediction through a hybrid deep learning method applied to histopathological image.

Expert review of anticancer therapy·2025
Same author

Wastewater-Based Epidemiology for Analysis of Human Papillomavirus Infections in a Uruguayan Urban Area.

Food and environmental virology·2025
Same author

Attention dual transformer with adaptive temporal convolutional for diabetic retinopathy detection.

Scientific reports·2025
Same author

Glycemic variability after mechanical thrombectomy for anterior circulation acute ischemic stroke.

Revista de neurologia·2024

Related Experiment Video

Updated: Jun 8, 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

Modified graph neural network-oriented optimization model for the classification of PD.

S Castro1, P Poonkuzhali2, B Kaleeswari3

  • 1Department of Artificial Intelligence and Data Science, Karpagam College of Engineering, Coimbatore, 641032, India. suseelcastro@gmail.com.

Scientific Reports
|April 29, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning model for classifying Parkinson's Disease (PD) stages. The Classification of Parkinson's Disease (CPD) model accurately distinguishes between healthy individuals and different PD stages using advanced feature extraction and classification techniques.

Keywords:
Classification of PDFossa Optimization AlgorithmModified Graph Neural NetworkMultimodal data alignmentNormalizationShifted Window UNETRShort-Time Fourier Transform

Related Experiment Videos

Last Updated: Jun 8, 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

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Neurology

Background:

  • Accurate Parkinson's Disease (PD) classification is crucial for early detection and personalized treatment.
  • Current methods face challenges including symptom overlap, inconsistent progression, limited datasets, and diverse multi-modal data.
  • Early-stage PD detection remains difficult due to subtle symptom presentation.

Purpose of the Study:

  • To propose a novel deep learning model, the Classification of Parkinson's Disease (CPD) model, for accurate PD staging.
  • To enhance the classification accuracy and recall for distinguishing healthy individuals from early, mid, and late-stage PD patients.
  • To address the limitations of existing PD classification methods through advanced data processing and feature extraction.

Main Methods:

  • Utilized the Parkinson's Disease Classification Benchmark (PDCB) dataset for training and validation.
  • Applied multimodal data alignment and normalization for pre-processing.
  • Employed Shifted Window UNETR (Swin UNETR) for data segmentation and Short-Time Fourier Transform (STFT) for feature extraction.
  • Developed a Modified Graph Neural Network (MGNN) optimized by the Fossa Optimization Algorithm (FOA) for classification.

Main Results:

  • The proposed MGNN-FOA model achieved superior performance in classifying Parkinson's Disease stages.
  • Demonstrated a 12.52% increase in accuracy and a 12.68% increase in recall compared to conventional models.
  • Successfully classified individuals into four categories: Healthy Control, Early-Stage, Mid-Stage, and Late-Stage Parkinson's Disease.

Conclusions:

  • The novel MGNN-FOA approach offers a robust and accurate method for Parkinson's Disease classification.
  • This deep learning model shows significant potential for improving early detection and management of Parkinson's Disease.
  • The findings highlight the effectiveness of integrating advanced deep learning techniques for complex medical data analysis.