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

Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

You might also read

Related Articles

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

Sort by

Same author

Multiscale characterization of the human claustrum from histology to MRI.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

A systematic review exploring the safety of deep brain stimulation at MRI field strengths beyond conventional field strengths: Opening the door to new imaging tools for neuromodulation.

AJNR. American journal of neuroradiology·2026

Same author

Predictive acoustical processing in human cortical layers.

Nature communications·2026

Same author

Editorial: Advancing high-resolution 3T MRI for cognitive and clinical neuroscience.

Frontiers in neuroscience·2026

Same author

Safety of 3 Tesla Magnetic Resonance Imaging in Active Peripheral Nerve Field Stimulation Device for Facial Pain.

Stereotactic and functional neurosurgery·2026

Same author

Evaluating the quality of brainstem ROI registration using structural and diffusion MRI.

Frontiers in neuroscience·2026

Same journal

Vowel acoustic parameters in speech assessment and rehabilitation of minimally verbal and speech-motor-impaired autistic children: a narrative review.

Frontiers in human neuroscience·2026

Same journal

Toward clinical translation of TMS-EEG: an integrative review of multidimensional neurophysiological measures.

Frontiers in human neuroscience·2026

Same journal

The causal efficacy of consciousness: a neuroscientific analysis and explanation.

Frontiers in human neuroscience·2026

Same journal

Temporal-oscillatory entrainment: a multi-timescale framework for rhythmic coordination from neural to social frequencies.

Frontiers in human neuroscience·2026

Same journal

Role of AQP4 in ameliorating heat stress-induced cellular injury in a cell line model through active heat acclimation.

Frontiers in human neuroscience·2026

Same journal

Correction: Cognitive state monitoring for neuroadaptive information visualization.

Frontiers in human neuroscience·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 6, 2025

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

Dynamic Effective Connectivity using Physiologically informed Dynamic Causal Model with Recurrent Units: A functional

Sayan Nag^1,2, Kamil Uludag^1,2,3,4

¹Techna Institute & Koerner Scientist in MR Imaging, University Health Network, Toronto, ON, Canada.

Frontiers in Human Neuroscience

|March 20, 2023

Summary

This summary is machine-generated.

This study introduces a new method to analyze brain connectivity during complex tasks using physiologically informed Dynamic Causal Modeling (P-DCM). The approach accurately captures dynamic effective connectivity, offering deeper insights into brain function.

Keywords:

BOLD fMRI causality dynamic effective connectivity graphical models neuroscience

More Related Videos

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

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

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

Published on: July 1, 2014

Related Experiment Videos

Last Updated: Aug 6, 2025

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

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

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

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

Published on: July 1, 2014

Area of Science:

Neuroimaging
Computational Neuroscience
Systems Neuroscience

Background:

Functional MRI (fMRI) indirectly measures neuronal activity.
Dynamic Causal Model (DCM) infers effective connectivity but often assumes static connections.
Complex cognitive tasks may involve time-varying brain connectivity.

Purpose of the Study:

To develop and validate a novel approach for inferring dynamic effective connectivity in task-based fMRI.
To investigate temporal variations in brain region interactions during complex cognitive tasks.
To combine physiologically informed DCM (P-DCM) with a recurrent window approach.

Main Methods:

Utilized a physiologically informed Dynamic Causal Model (P-DCM).
Implemented a recurrent window approach with discretized equations for fMRI data.
Validated the method using simulation studies on 3- and 10-region brain models.

Main Results:

Accurately predicted blood oxygenation level-dependent (BOLD) responses and effective connectivity time-courses.
Successfully distinguished dynamic connectivity from static or faulty models.
Demonstrated the capability to infer underlying neuronal dynamics and time-varying connectivities.

Conclusions:

The proposed method effectively determines dynamic effective connectivity during complex cognitive tasks.
Combining P-DCM with recurrent units provides a robust framework for analyzing time-varying brain interactions.
This approach enhances the analysis of brain function in task-based fMRI studies.