Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Motor Unit Stimulation

Motor Unit Stimulation

When the neuron of a motor unit fires an action potential, it triggers a series of events, leading to a twitch contraction in the muscle fibers. The process of excitation-contraction coupling is crucial in relaying the action potential to the muscle fibers.
The latent period of contraction marks the onset of excitation-contraction coupling, when the action potential propagates across the sarcolemma, preparing the muscle fibers for contraction. As the fibers enter the contraction phase, the...

You might also read

Related Articles

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

Sort by

Same author

Deep brain stimulation of the nucleus accumbens for severe self-injurious behaviour in children: long-term outcomes from a first-in-human pilot trial.

Molecular psychiatry·2026

Same author

Pediatric epilepsy surgery: Global survey of invasive explorations.

Epilepsia·2026

Same author

Diagnosis and management guidelines for infantile epileptic spasms syndrome around the world: A scoping review and comparative study of international approaches.

Epilepsia·2026

Same author

Use of artificial intelligence in magnetic resonance imaging across the epileptic patient's journey: A meta-analysis of four clinical applications.

Epilepsia·2026

Same author

Pediatric epilepsy surgery: Global survey of referral and presurgical evaluation practices.

Epilepsia·2026

Same author

From angiography-derived physiology guided PCI to INOCA treatment and to coronary sinus reducer: navigating multiple pathophysiological targets in a single patient: a case report.

European heart journal. Case reports·2026

Same journal

Interplay between oxygen redox and interfacial stability of Li-rich positive electrodes in sulfide-based all-solid-state batteries.

Nature communications·2026

Same journal

Breaking dependence on melanisation imparts diversity to a dogmatic invasion strategy of phytopathogenic fungi.

Nature communications·2026

Same journal

Hydroxyl-rich nanocavities on perovskite enable nearly barrierless intramolecular hydrogen transfer for nitrate electroreduction to ammonia.

Nature communications·2026

Same journal

Household mobility responses to weather extremes in Kyrgyzstan.

Nature communications·2026

Same journal

Autonomous Motion Vision with Tri-bulk-heterojunctioned Organic Adaptation Transistor.

Nature communications·2026

Same journal

Tissue-adhesive hydrogel optical fiber for peripheral optogenetic neuromodulation.

Nature communications·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 9, 2026

Author Spotlight: Exploring Abdominal VNS for Inflammatory Conditions

Author Spotlight: Exploring Abdominal VNS for Inflammatory Conditions

Published on: January 19, 2024

A deep representation learning model to predict response to vagus nerve stimulation.

Hrishikesh Suresh^1,2,3, Karim Mithani^1,2,3, Vicki Li^1,2

¹Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.

Nature Communications

|April 7, 2026

Summary

This summary is machine-generated.

A new deep learning model, VQ-VNS, predicts vagus nerve stimulation (VNS) success in pediatric epilepsy using MRI scans. This AI tool identifies patients likely to benefit, improving treatment decisions and reducing unnecessary surgeries.

More Related Videos

Preparation of Peripheral Nerve Stimulation Electrodes for Chronic Implantation in Rats

Preparation of Peripheral Nerve Stimulation Electrodes for Chronic Implantation in Rats

Published on: July 14, 2020

Vagus Nerve Stimulation as a Tool to Induce Plasticity in Pathways Relevant for Extinction Learning

Vagus Nerve Stimulation as a Tool to Induce Plasticity in Pathways Relevant for Extinction Learning

Published on: August 21, 2015

Related Experiment Videos

Last Updated: Apr 9, 2026

Author Spotlight: Exploring Abdominal VNS for Inflammatory Conditions

Author Spotlight: Exploring Abdominal VNS for Inflammatory Conditions

Published on: January 19, 2024

Preparation of Peripheral Nerve Stimulation Electrodes for Chronic Implantation in Rats

Preparation of Peripheral Nerve Stimulation Electrodes for Chronic Implantation in Rats

Published on: July 14, 2020

Vagus Nerve Stimulation as a Tool to Induce Plasticity in Pathways Relevant for Extinction Learning

Vagus Nerve Stimulation as a Tool to Induce Plasticity in Pathways Relevant for Extinction Learning

Published on: August 21, 2015

Area of Science:

Neuroscience
Artificial Intelligence
Medical Imaging

Background:

Pediatric epilepsy affects many children, with vagus nerve stimulation (VNS) being a common but inconsistently effective treatment.
Current methods cannot predict VNS outcomes, leading to surgeries without benefit for approximately half of pediatric patients.
Preoperative T1-weighted magnetic resonance imaging (T1w) shows potential for predicting VNS response but faces challenges due to high dimensionality.

Purpose of the Study:

To develop a predictive model for VNS treatment outcomes in pediatric epilepsy using preoperative T1w MRI data.
To overcome the limitations of traditional predictive modeling with high-dimensional imaging data.
To improve clinical decision-making for VNS implantation in children with epilepsy.

Main Methods:

A deep representation learning model, VQ-VNS, was developed to predict VNS response using preoperative T1w images (n=263).
The model was pre-trained on a large dataset of 7433 T1w images to learn compact anatomical representations.
Performance was evaluated on the largest pediatric VNS cohort (n=1046), comparing VQ-VNS predictions to clinical data-based predictions.

Main Results:

Presurgical clinical data showed poor predictive power for VNS response (AUC 0.54, p>0.99).
The VQ-VNS model accurately predicted VNS response with an AUC of 0.73 (p=0.007).
Model predictions highlighted disruptions in serotonin-rich brain regions and large-scale network connectivity in non-responders.

Conclusions:

VQ-VNS, a deep learning model, effectively predicts VNS outcomes in pediatric epilepsy using routine structural MRI.
The model offers biological interpretability, identifying network disruptions associated with non-response.
This approach enhances clinical decision-making, potentially improving treatment efficacy and reducing healthcare costs.