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

Classification of Neurotransmitters

Classification of Neurotransmitters

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...

Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

You might also read

Related Articles

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

Sort by

Same author

Effects of cardiac rehabilitation on atrial fibrillation recurrence, mortality, hospitalization, and exercise capacity: a systematic review and meta-analysis.

PeerJ·2026

Same author

Activation of cryptic donor splice site due to an exonic MYPN variant in congenital myopathy.

Journal of human genetics·2026

Same author

Interpreting Pulmonary Hypertension Beyond Single Cells.

Arteriosclerosis, thrombosis, and vascular biology·2026

Same author

Functional Characterization of <i>Helicoverpa armigera</i> Nicotinic Acetylcholine Receptor Subunits Targeted by <i>cis</i>- and <i>trans</i>-Configuration Piperidine Alkaloids in <i>Solenopsis</i> Fire Ant Venom.

Journal of agricultural and food chemistry·2026

Same author

Ganglioneuroblastoma associated with neurofibromatosis type 1: a case report with a systematic review.

Frontiers in oncology·2026

Same author

Distance-adaptive geometric margins for residual rotational uncertainty in single-isocenter multitarget stereotactic radiosurgery.

Physics and imaging in radiation oncology·2026

Same journal

Assessing the sustainability of reef and demersal fish stocks in Northwest México under a data-limited approach.

PeerJ·2026

Same journal

The impact of virtual reality exercise programs on postpartum pelvic pain and disability among women with lumbopelvic pain.

PeerJ·2026

Same journal

Soil salinity modulates fatty acid composition and antioxidant capacity of rice bran oil.

PeerJ·2026

Same journal

The optimal dose of brisk walking for improving blood pressure in hypertensive patients: a systematic review and bayesian meta-analysis of randomized controlled trials.

PeerJ·2026

Same journal

Enhanced sweet pepper yield through high-intensity artificial lighting and optimized plant density in high-latitude winter production.

PeerJ·2026

Same journal

Prenatal corticosteroid use improves the severity and complications of necrotizing enterocolitis in preterm infants: a retrospective multicenter clinical study in China.

PeerJ·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 5, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

The effect of node features on GCN-based brain network classification: an empirical study.

Guangyu Wang¹, Limei Zhang^1,2, Lishan Qiao^1,2

¹Liaocheng University, Liaocheng, China.

|March 27, 2023

Summary

This summary is machine-generated.

This study explores how different node features impact graph convolutional network (GCN) performance for classifying brain functional networks (BFNs). Node correlation features generally yield higher accuracy in brain disorder identification.

Keywords:

Autism spectrum disorder Empirical study Graph convolutional network Mild cognitive impairment Node features

More Related Videos

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

Related Experiment Videos

Last Updated: Aug 5, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

Area of Science:

Neuroscience
Machine Learning
Medical Informatics

Background:

Brain functional network (BFN) analysis is crucial for identifying neurological and mental diseases.
Graph convolutional networks (GCNs) are suitable for BFN classification due to their graph structure.
GCNs require node features, but their impact on BFN classification is understudied.

Purpose of the Study:

To systematically investigate the influence of various node feature types on GCN-based brain disorder classification.
To compare the performance of different node feature strategies for GCNs applied to BFNs.

Main Methods:

Empirical study of five node feature types: original fMRI signals, one-hot encoding, node statistics, node correlation, and combinations.
Utilized GCNs for classification on two benchmark brain disorder databases.
Evaluated classification performance based on different node feature inputs.

Main Results:

Node feature selection significantly impacts GCN performance in brain disorder classification.
Node correlation features generally outperform original fMRI signals and manually extracted statistical features.
Combined node features may offer further improvements, warranting further investigation.

Conclusions:

The choice of node features is critical for optimizing GCNs in BFN analysis for disease identification.
Node correlation emerges as a highly effective feature for GCN-based brain disorder classification.
Future research should explore advanced feature engineering and combinations for enhanced diagnostic accuracy.