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

Modeling in Therapy01:26

Modeling in Therapy

583
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
583

You might also read

Related Articles

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

Sort by
Same author

Machine learning modeling to predict HCC locations in cirrhotic patients undergoing MRI - a proof-of-concept study.

Abdominal radiology (New York)·2026
Same author

STNAGNN: Data-driven Spatio-temporal Brain Connectivity beyond FC.

Proceedings of machine learning research·2026
Same author

<i>MFAP2</i> Promotes Glioblastoma Malignant Phenotypes via Autophagy-Dependent Activation of Wnt/β-Catenin Signaling.

Biomedicines·2026
Same author

HYPERBOLIC MODEL AGGREGATION FOR FEDERATED LEARNING IN FMRI.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same author

Centromere protein I promotes hepatocellular carcinoma progression by activating PI3K/AKT/mTOR-CDK2 cascade.

Cancer biology & therapy·2026
Same author

Chlorzoxazone-based One-Sample Method for Estimating In Vivo CYP2E1 Activity in Mice.

Current drug metabolism·2026

Related Experiment Video

Updated: Feb 24, 2026

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

25.9K

CAUSAL MODELING OF FMRI TIME-SERIES FOR INTERPRETABLE AUTISM SPECTRUM DISORDER CLASSIFICATION.

Peiyu Duan1, Nicha C Dvornek1,2, Jiyao Wang1

  • 1Department of Biomedical Engineering, Yale University, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|February 23, 2026
PubMed
Summary

A new deep learning model using functional magnetic resonance imaging (fMRI) data accurately identifies autism spectrum disorder (ASD). The model captures brain region causality, highlighting key areas like the precuneus and cerebellum in ASD.

Keywords:
Autism spectrum disorderCausal inferenceFunctional MRIInterpretability

More Related Videos

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.7K
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

7.7K

Related Experiment Videos

Last Updated: Feb 24, 2026

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

25.9K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.7K
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

7.7K

Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Autism spectrum disorder (ASD) is a developmental disorder impacting social and communication skills, necessitating early diagnosis for better outcomes.
  • Functional magnetic resonance imaging (fMRI) is crucial for understanding brain activity in ASD, but traditional models struggle with complex interactions.
  • Existing machine learning models often rely on correlations, failing to capture non-linear relationships crucial for ASD identification.

Purpose of the Study:

  • To develop a novel causality-inspired deep learning model for improved ASD classification using fMRI data.
  • To capture non-linear, causal interactions between brain regions for more accurate ASD detection.
  • To identify specific brain regions exhibiting altered causality in individuals with ASD.

Main Methods:

  • Implemented a deep learning model utilizing time-series fMRI data to infer causality among regions of interest (ROIs).
  • Validated the model on the ABIDE dataset, applying a data quality filter (mean FD < 15mm).
  • Compared performance against baseline and state-of-the-art models using 5-fold cross-validation.

Main Results:

  • The proposed causality-inspired model achieved the highest classification accuracy (71.9%) and AUC (75.8%).
  • Inter-ROI causality analysis identified the left precuneus, right precuneus, and cerebellum as significantly altered in ASD.
  • These identified ROIs were not top-ranked in the control group, suggesting ASD-specific causal network changes.

Conclusions:

  • The causality-inspired deep learning model offers a promising approach for ASD diagnosis using fMRI.
  • Altered inter-ROI causality in the precuneus and cerebellum may represent key neural markers for ASD.
  • This causal framework enhances understanding of brain network dynamics in autism spectrum disorder.