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

Classification of Neurotransmitters01:30

Classification of Neurotransmitters

4.5K
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...
4.5K

You might also read

Related Articles

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

Sort by
Same author

EphB1-Mediated Transient Blood-Brain Barrier Opening Facilitates a Ferritin-Based Nanotherapeutic for Alzheimer's Disease.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Dietary Periplaneta americana chitin maintains inflammatory homeostasis and enhances innate immunity in juvenile common carp (Cyprinus carpio) via the Nrf2/NF-κB network.

Fish & shellfish immunology·2026
Same author

Atlantoaxial subluxation treated with exercise therapy combined with occipito-maxillary band traction: a case report.

Journal of medical case reports·2026
Same author

Different modulation patterns of theta and gamma dual-site HD-tACS on cognitive impairment.

iScience·2026
Same author

Self-adaptive hetero-phase superlattices in TaS<sub>2</sub> via layer-resolved 1T-to-1H transformations.

National science review·2026
Same author

Systematic Engineering of Intra-Articular Drug Release Profiles Reveals a Key Determinant of Disease-Modifying Efficacy in Post-Traumatic Osteoarthritis.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: Nov 17, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.2K

Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual

Jingjing Gao1, Mingren Chen2, Yuanyuan Li3

  • 1School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China.

Frontiers in Neuroscience
|February 15, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework using convolutional neural networks (CNNs) to classify autism spectrum disorder (ASD) from brain imaging. The new method achieves high accuracy, identifying key brain regions for potential early diagnosis of ASD.

Keywords:
autism spectrum disorderconvolutional neural networkgradient-weighted class activation mappingindividual morphological covariance brain networkstructural MRI

More Related Videos

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

16.0K
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.2K

Related Experiment Videos

Last Updated: Nov 17, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.2K
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

16.0K
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.2K

Area of Science:

  • Neuroscience
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Autism spectrum disorder (ASD) presents significant challenges due to behavioral and cognitive impairments.
  • Accurate ASD identification is crucial for early intervention and reducing societal burdens.
  • Existing structural MRI (sMRI) methods for ASD classification often overlook inter-regional covariance patterns.

Purpose of the Study:

  • To develop and validate a novel framework for classifying ASD patients using sMRI data.
  • To leverage convolutional neural networks (CNNs) and structural covariance networks for improved ASD detection.
  • To identify discriminative brain features indicative of ASD.

Main Methods:

  • Utilized a framework combining CNNs with individual structural covariance networks.
  • Applied gradient-weighted class activation mapping (Grad-CAM) to interpret feature importance.
  • Analyzed sMRI data from the Autism Brain Imaging Data Exchange (ABIDE) consortium.

Main Results:

  • The proposed framework achieved a classification accuracy of 71.8% for ASD detection across multiple sites.
  • This method demonstrated superior performance compared to existing classification techniques for ASD using ABIDE data.
  • Discriminative features were primarily identified in the prefrontal cortex and cerebellum.

Conclusions:

  • CNNs are effective tools for diagnosing ASD using structural covariance brain networks.
  • The prefrontal cortex and cerebellum show potential as early biomarkers for ASD diagnosis.
  • This research offers a promising advancement in the neuroimaging-based classification of ASD.