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 Experiment Video

Updated: Jan 29, 2026

Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder
09:13

Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder

Published on: April 22, 2015

17.0K

Lean-NET-Based Local Brain Connectome Analysis for Autism Spectrum Disorder Classification.

Aoumria Chelef1, Demet Yuksel Dal2, Mahmut Ozturk3

  • 1Department of Biomedical Engineering, Institute of Graduate Studies, Istanbul University-Cerrahpasa, Istanbul 34320, Türkiye.

Bioengineering (Basel, Switzerland)
|January 28, 2026
PubMed
Summary

Related Concept Videos

Autism Spectrum Disorder01:19

Autism Spectrum Disorder

1.1K
Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
1.1K
Classification of Titrimetric Analysis Based on Reaction Types01:01

Classification of Titrimetric Analysis Based on Reaction Types

1.7K
Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
Titrations between an acid and a base lead to neutralization reactions that form...
1.7K
The Electromagnetic Spectrum02:37

The Electromagnetic Spectrum

65.1K
The electromagnetic spectrum consists of all the types of electromagnetic radiation arranged according to their frequency and wavelength. Each of the various colors of visible light has specific frequencies and wavelengths associated with them, and you can see that visible light makes up only a small portion of the electromagnetic spectrum. Because the technologies developed to work in various parts of the electromagnetic spectrum are different, for reasons of convenience and historical...
65.1K
The Electromagnetic Spectrum01:24

The Electromagnetic Spectrum

33.6K
Electromagnetic waves are categorized according to their wavelengths and frequencies, giving the electromagnetic spectrum. These waves are classified as radio, infrared, ultraviolet, etc. Radio waves refer to electromagnetic radiation with wavelengths ranging from millimeters to kilometers. Radio waves are commonly used for audio communications (i.e., radios) and typically result from an alternating current in the wires of a broadcast antenna. They cover a broad wavelength range and are used...
33.6K
Disorders of Acid-Base Balance01:29

Disorders of Acid-Base Balance

2.0K
The human body maintains a precise pH range of arterial blood between 7.35 and 7.45. Deviations result in either acidosis (pH < 7.35) or alkalosis (pH > 7.45). These conditions are further classified as respiratory or metabolic disorders based on their underlying cause.
Respiratory Acidosis and Alkalosis
Respiratory acidosis occurs due to an increase in the partial pressure of carbon dioxide PCO2 in the blood. It often arises from shallow breathing or impaired gas exchange caused by...
2.0K
IR Spectrum01:19

IR Spectrum

2.1K
When infrared (IR) radiation passes through a molecule, the bonds stretch or bend by absorbing the radiation. This absorption creates the molecule's absorption spectrum, which is the plot of its percentage transmittance versus wavenumber.
Transmittance is defined as the ratio of the radiant power passing through a sample to that from the radiation's source. Multiplying the transmittance by 100 gives the percent transmittance (%T), which varies between 100% (no absorption) and 0%...
2.1K

You might also read

Related Articles

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

Sort by
Same author

Motor and Cognitive Imagery Detection from MEG Signals Using Wavelet-Based Common Spatial Pattern Analysis.

International journal of neural systems·2026
Same author

Effects of Steam Sterilization and Recycling on the Mechanical and Surface Properties of 3D-Printed Biodegradable PLA and Re-PLA Materials.

Polymers·2025
Same author

Two-Dimensional Latent Space Manifold of Brain Connectomes Across the Spectrum of Clinical Cognitive Decline.

Bioengineering (Basel, Switzerland)·2025
Same author

Reorganization of brain connectivity across the spectrum of clinical cognitive decline.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology·2024
Same author

Deep Learning-Based Classification of Epileptic Electroencephalography Signals Using a Concentrated Time-Frequency Approach.

International journal of neural systems·2023
Same journal

Correction: Komatsu et al. Three-Dimensional Visualization and Detection of the Pulmonary Venous-Left Atrium Connection Using Artificial Intelligence in Fetal Cardiac Ultrasound Screening. <i>Bioengineering</i> 2026, <i>13</i>, 100.

Bioengineering (Basel, Switzerland)·2026
Same journal

Comparison of CO<sub>2</sub> Laser and Microdebrider in the Surgical Treatment of Pediatric Recurrent Respiratory Papillomatosis: A Retrospective Analysis.

Bioengineering (Basel, Switzerland)·2026
Same journal

Toward More Translational Tumor Models: Breast dECM-Based 3D Systems Capture Native Microenvironmental Cues.

Bioengineering (Basel, Switzerland)·2026
Same journal

Postural Stability Changes During the 4 Phases of the Half Squat: Kinematics Profile of the Center of Pressure and Center of Mass in High-Performance Weightlifters-A Pilot Study.

Bioengineering (Basel, Switzerland)·2026
Same journal

Definite Implant Position as Novel Readout for Effectiveness of Ridge Preservation Indicates to Beneficial Effect of Combined Treatment with Platelet-Rich Fibrin (PRF) and Xenogenic Biomaterial in Bone Regeneration.

Bioengineering (Basel, Switzerland)·2026
Same journal

Trueness and Precision of Intraoral Scanners for 3D-Printed Orthodontic Models with Attachments: An In Vitro Comparative Study.

Bioengineering (Basel, Switzerland)·2026
See all related articles
This summary is machine-generated.

This study introduces a novel method for autism spectrum disorder (ASD) prediction using functional brain connectivity. Graph learning on resting-state fMRI data achieved high accuracy in distinguishing ASD from typically developing individuals.

Area of Science:

  • Neuroscience
  • Medical Imaging
  • Machine Learning

Background:

  • Autism spectrum disorder (ASD) is a neurodevelopmental condition impacting social interaction and communication.
  • Global prevalence of ASD is increasing, necessitating advanced diagnostic tools.
  • Resting-state functional magnetic resonance imaging (rs-fMRI) offers insights into brain functional connectivity.

Purpose of the Study:

  • To develop and validate a novel approach for ASD prediction using functional connectivity patterns.
  • To analyze subject-specific brain connectomes derived from rs-fMRI data.
  • To evaluate the efficacy of graph learning metrics for ASD characterization.

Main Methods:

  • Utilized the ABIDE II dataset comprising rs-fMRI data.
  • Employed the sparse functional brain connectome (Lean-NET) model to construct subject-specific connectomes.
Keywords:
autism spectrum disorder ASDfeature selectiongraph learninglocal graph metricsrs-FMRI BOLD signalsparse functional brain connectome (Lean-NET)support vector machine (SVM)

More Related Videos

Author Spotlight: Exploring Autism Spectrum Disorder Symptoms in Fruit Flies &#8212; Genetic Models and Behavioral Tests
08:30

Author Spotlight: Exploring Autism Spectrum Disorder Symptoms in Fruit Flies — Genetic Models and Behavioral Tests

Published on: September 6, 2024

2.6K
A Semantic Priming Event-related Potential ERP Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder
08:17

A Semantic Priming Event-related Potential ERP Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder

Published on: April 12, 2018

11.1K

Related Experiment Videos

Last Updated: Jan 29, 2026

Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder
09:13

Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder

Published on: April 22, 2015

17.0K
Author Spotlight: Exploring Autism Spectrum Disorder Symptoms in Fruit Flies &#8212; Genetic Models and Behavioral Tests
08:30

Author Spotlight: Exploring Autism Spectrum Disorder Symptoms in Fruit Flies — Genetic Models and Behavioral Tests

Published on: September 6, 2024

2.6K
A Semantic Priming Event-related Potential ERP Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder
08:17

A Semantic Priming Event-related Potential ERP Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder

Published on: April 12, 2018

11.1K
  • Extracted local graph metrics to quantify regional network properties, followed by statistical feature selection (Welch's t-test, FDR correction) and Support Vector Machine (SVM) classification.

    • Main Results:

    • Locally derived graph metrics demonstrated significant ability to discriminate between individuals with ASD and typically developing (TD) subjects.
    • Achieved classification accuracy ranging from 70% to 91% in ASD prediction.
    • Identified specific functional connectivity patterns associated with ASD.

    Conclusions:

    • Graph learning approaches show significant potential for accurate ASD characterization.
    • Functional connectivity analysis using graph metrics is a promising avenue for improving ASD diagnosis.
    • The Lean-NET model provides a robust framework for analyzing brain networks in ASD.