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

Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

You might also read

Related Articles

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

Sort by

Same author

Polygenic risk scores for prediction of immune checkpoint inhibitor thyroid toxicity in diverse populations.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026

Same author

Learning brain dynamics across distinct scaling regimes reveals psychiatric signatures.

Communications biology·2026

Same author

Toward trustworthy clinical AI for obsessive-compulsive disorder: reliability, generalizability, and interpretability of a transformer model across the ENIGMA-OCD consortium.

medRxiv : the preprint server for health sciences·2026

Same author

Deciphering the Solvation Structure of Aqueous ZnCl<sub>2</sub> Solutions from X-ray Absorption Spectra Using the Interpretable Graph Neural Network.

The journal of physical chemistry. B·2026

Same author

Increasing value in the Veterans Affairs Healthcare System (VA) with precision health: a continuing landmark collaboration with the Department of Energy.

Journal of the American Medical Informatics Association : JAMIA·2026

Same author

Variable rate neural compression for sparse detector data.

Patterns (New York, N.Y.)·2026

Search research articles

Related Experiment Video

Updated: Sep 11, 2025

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Predicting task-related brain activity from resting-state brain dynamics with fMRI Transformer.

Junbeom Kwon¹, Jungwoo Seo¹, Heehwan Wang¹

¹Seoul National University, Seoul, South Korea.

Imaging Neuroscience (Cambridge, Mass.)

|August 13, 2025

Summary

This study introduces SwiFUN, a deep learning model that predicts brain task activity from resting-state fMRI scans. SwiFUN improves prediction accuracy, offering a potential alternative to task-based fMRI in clinical neuroscience.

Keywords:

deep learning individual differences resting-state fMRI task activation prediction

More Related Videos

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

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

Published on: March 21, 2019

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

Published on: May 12, 2014

Related Experiment Videos

Last Updated: Sep 11, 2025

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

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

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

Published on: March 21, 2019

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

Published on: May 12, 2014

Area of Science:

Neuroscience
Artificial Intelligence
Medical Imaging

Background:

Predicting brain task reactivity from resting-state fMRI is challenging.
Traditional methods struggle with complex spatiotemporal brain patterns.
Deep learning offers potential for improved prediction accuracy.

Purpose of the Study:

Introduce SwiFUN (Swin fMRI UNet Transformer), a novel deep learning framework.
Predict 3D task activation maps directly from resting-state fMRI.
Enhance understanding and prediction of individual brain function.

Main Methods:

Utilized a transformer-based deep learning architecture (SwiFUN).
Employed shifted window-based self-attention for pattern recognition.
Incorporated contrastive learning to capture individual subject differences.

Main Results:

SwiFUN achieved higher prediction accuracy than existing methods across all contrasts.
Demonstrated up to 27% improvement in prediction for the FACES-PLACES contrast (ABCD data).
Revealed individual differences in task activation maps related to sex, age, and depressive symptoms.

Conclusions:

SwiFUN provides a scalable, accurate method for predicting task reactivity from resting-state fMRI.
This approach may reduce reliance on task-based fMRI in clinical settings.
Offers a promising direction for future neuroscience and clinical research.