Jove
Visualize
Contact Us

Related Concept Videos

The Neuromuscular Junction01:19

The Neuromuscular Junction

The nervous system consists of complex motor neuron circuits, including upper motor neurons originating from the cerebral cortex and lower motor neurons starting in the spinal cord, coordinating both voluntary and involuntary movements. Among these, somatic motor neurons activate skeletal muscles and are classified into alpha, beta, and gamma types. Alpha neurons are vital for voluntary movement coordination, while gamma neurons adjust muscle spindle sensitivity, and the function of beta...
Motor Units01:13

Motor Units

The motor unit is a fundamental component of the neuromuscular system and plays a crucial role in coordinating muscle contractions. It consists of a somatic motor neuron, which connects and controls multiple skeletal muscle fibers, forming a single functional segment. The axon of the motor neuron branches out and establishes synaptic connections known as neuromuscular junctions with individual muscle fibers within the motor unit.
Motor units come in different sizes, with smaller units...
Introduction to Learning01:18

Introduction to Learning

Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...

You might also read

Related Articles

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

Sort by
Same author

At-Home Versus in-Clinic Vital Capacity Measurement: Insights From the HEALEY ALS Platform Trial.

Muscle & nerve·2026
Same author

Prospective Validation of the New PLS Diagnostic Criteria From PLS Natural History Study: EMG and Neurofilament Analyses.

Muscle & nerve·2026
Same author

The Tuesday lessons of ALS.

The Lancet. Neurology·2026
Same author

How Patients With Amyotrophic Lateral Sclerosis Perceive Respiratory Interventions: A Mixed-Methods Study to Inform Implementation Efforts.

Neurology. Clinical practice·2025
Same author

Predicting Amyotrophic Lateral Sclerosis Mortality With Machine Learning in Diverse Patient Databases.

Muscle & nerve·2025
Same author

Towards Reliable Prediction: A Bayesian Continual Learning Approach for Clinical Time-series Data.

IEEE journal of biomedical and health informatics·2025
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 Experiment Video

Updated: Jun 14, 2026

A Murine Model of Muscle Training by Neuromuscular Electrical Stimulation
08:24

A Murine Model of Muscle Training by Neuromuscular Electrical Stimulation

Published on: May 9, 2012

20.9K

A neuromuscular clinician's primer on machine learning.

Crystal Jing Jing Yeo1,2,3, Savitha Ramasamy4, F Joel Leong5

  • 1National Neuroscience Institute, Singapore.

Journal of Neuromuscular Diseases
|April 1, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) are transforming medicine, particularly in neuromuscular care. This primer helps neurologists understand AI/ML applications to improve patient outcomes.

Keywords:
Artificial IntelligenceElectrodiagnostic MedicineMachine LearningNeuromuscular ImagingNeuromuscular Medicine

More Related Videos

The Muscle Cuff Regenerative Peripheral Nerve Interface for the Amplification of Intact Peripheral Nerve Signals
07:30

The Muscle Cuff Regenerative Peripheral Nerve Interface for the Amplification of Intact Peripheral Nerve Signals

Published on: January 13, 2022

2.0K
Author Spotlight: Unveiling Neural Mechanisms Through Automated Evaluation of Motor Learning and Myelin Plasticity Studies Using the Erasmus Ladder
08:51

Author Spotlight: Unveiling Neural Mechanisms Through Automated Evaluation of Motor Learning and Myelin Plasticity Studies Using the Erasmus Ladder

Published on: December 15, 2023

1.2K

Related Experiment Videos

Last Updated: Jun 14, 2026

A Murine Model of Muscle Training by Neuromuscular Electrical Stimulation
08:24

A Murine Model of Muscle Training by Neuromuscular Electrical Stimulation

Published on: May 9, 2012

20.9K
The Muscle Cuff Regenerative Peripheral Nerve Interface for the Amplification of Intact Peripheral Nerve Signals
07:30

The Muscle Cuff Regenerative Peripheral Nerve Interface for the Amplification of Intact Peripheral Nerve Signals

Published on: January 13, 2022

2.0K
Author Spotlight: Unveiling Neural Mechanisms Through Automated Evaluation of Motor Learning and Myelin Plasticity Studies Using the Erasmus Ladder
08:51

Author Spotlight: Unveiling Neural Mechanisms Through Automated Evaluation of Motor Learning and Myelin Plasticity Studies Using the Erasmus Ladder

Published on: December 15, 2023

1.2K

Area of Science:

  • Clinical Neurology
  • Medical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Artificial intelligence (AI) and machine learning (ML) are increasingly integrated into medical management and research.
  • Generative AI, like ChatGPT3, has heightened public awareness of AI's potential for automating tasks.
  • Neurology risks lagging behind other specialties in adopting AI/ML technologies for clinical practice.

Purpose of the Study:

  • To provide a practical primer on machine learning (ML) fundamentals for clinicians.
  • To educate neurologists on ML applications in neuromuscular and electrodiagnostic medicine.
  • To address limitations, ethical concerns, and future directions of AI/ML in neurology.

Main Methods:

  • Review of current AI/ML applications in neuromuscular disease diagnosis, monitoring, prognosis, and treatment.
  • Discussion of ML in specific diagnostic modalities: nerve and muscle ultrasound, MRI, electrical impedance myography, nerve conduction studies, and electromyography.
  • Exploration of AI/ML in clinical cohort studies.

Main Results:

  • AI/ML applications are assisting in various aspects of patient care for neuromuscular diseases.
  • These applications are currently largely confined to the research domain.
  • Neurologists need to understand these technologies to avoid falling behind other medical specialties.

Conclusions:

  • AI/ML will inevitably change clinical practice in neurology; the focus should be on *when* and *how*.
  • The successful integration of AI/ML will be measured by improvements in patient outcomes.
  • Understanding the basics, applications, and challenges of AI/ML is crucial for future neurologists.