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

Autism Spectrum Disorder01:19

Autism Spectrum Disorder

177
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.
177

You might also read

Related Articles

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

Sort by
Same author

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

iScience·2026
Same author

IQGAP3 bridges matrix stiffness with glioma stem cell maintenance and radioresistance by stabilizing SOX2.

Nature communications·2026
Same author

Synergistic-redundant dysfunction in autism spectrum disorder: Heterogeneity and molecular mechanisms.

Progress in neuro-psychopharmacology & biological psychiatry·2026
Same author

Molecular signatures of aberrant dynamic structure-function coupling in major depressive disorder.

Journal of affective disorders·2026
Same author

Relationship Between Dp140 Genotype and Cortical Similarity Network Phenotype in Duchenne Muscular Dystrophy: Preliminary T1 Weighted Study.

Journal of magnetic resonance imaging : JMRI·2026
Same author

Distinct and shared neurobiological patterns in PTSD and CPTSD: evidence from resting-state fMRI.

BMC psychiatry·2026
Same journal

Differentiation of cortical areas: effects of free energy minimization with broken symmetry.

Cerebral cortex (New York, N.Y. : 1991)·2026
Same journal

Prior exposure to speech rapidly modulates cortical processing of high-level linguistic structure.

Cerebral cortex (New York, N.Y. : 1991)·2026
Same journal

Beta bursts in SMA mediate anticipatory muscle inhibition.

Cerebral cortex (New York, N.Y. : 1991)·2026
Same journal

Cognitive load modulates the effects of social contexts on facial expression processing.

Cerebral cortex (New York, N.Y. : 1991)·2026
Same journal

The neural mechanisms of aligning spatial perspectives.

Cerebral cortex (New York, N.Y. : 1991)·2026
Same journal

Relationships between bilateral tapping skills and brain gray matter volumes: a voxel-based morphometry study.

Cerebral cortex (New York, N.Y. : 1991)·2026
See all related articles

Related Experiment Video

Updated: Aug 5, 2025

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
12:21

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

25.3K

Identifying autism spectrum disorder using edge-centric functional connectivity.

Ang Sun1, Jiaojian Wang2,3, Junran Zhang1

  • 1College of Electrical Engineering, Sichuan University, No. 24 South Section One of Yihuan Road, Wuhou district, Chengdu 610065, China.

Cerebral Cortex (New York, N.Y. : 1991)
|March 28, 2023
PubMed
Summary
This summary is machine-generated.

Edge-centric functional connectivity (eFC) analysis improves autism spectrum disorder (ASD) diagnosis. This novel approach enhances brain network analysis for identifying biomarkers and aiding early detection of neuropsychiatric disorders.

Keywords:
ABIDE Iautism spectrum disorderedge-centric functional connectivityfeature selectionfunctional MRI

More Related Videos

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

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

10.2K
Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

7.3K

Related Experiment Videos

Last Updated: Aug 5, 2025

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
12:21

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

25.3K
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

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

10.2K
Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

7.3K

Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Medical Imaging Analysis

Background:

  • Brain network analysis is crucial for understanding brain disorders like autism spectrum disorder (ASD).
  • Traditional node-centric functional connectivity (nFC) methods overlook edge interactions, potentially missing vital diagnostic information.
  • Existing diagnostic approaches for ASD may benefit from advanced network analysis techniques.

Purpose of the Study:

  • To introduce and validate an edge-centric functional connectivity (eFC) protocol for improved ASD classification.
  • To compare the diagnostic performance of eFC against traditional nFC using a multi-site dataset.
  • To explore the potential of eFC in identifying reliable biomarkers for ASD and other neuropsychiatric disorders.

Main Methods:

  • Utilized the Autism Brain Imaging Data Exchange I (ABIDE I) multi-site dataset.
  • Developed and applied an edge-centric functional connectivity (eFC) analysis protocol.
  • Employed a support vector machine (SVM) classifier for diagnostic modeling.

Main Results:

  • The eFC approach significantly enhanced classification performance for ASD.
  • Achieved high diagnostic accuracy (96.41%), sensitivity (98.30%), and specificity (94.25%) using SVM on the ABIDE I dataset.
  • Demonstrated the effectiveness of eFC in capturing co-fluctuations between brain region edges.

Conclusions:

  • Edge-centric functional connectivity (eFC) offers a powerful framework for diagnosing ASD and other mental disorders.
  • eFC facilitates the identification of stable and effective biomarkers for neuropsychiatric conditions.
  • This study provides a complementary perspective on ASD neural mechanisms, aiding future early diagnosis efforts.