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

You might also read

Related Articles

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

Sort by
Same author

Commonality and variability in functional networks in children under 5 years old.

Communications biology·2026
Same author

Patterns of brain-wide associations reflect socioeconomics.

Science (New York, N.Y.)·2026
Same author

Functional brain network correlates of pubertal timing and depressive symptoms in preadolescence.

bioRxiv : the preprint server for biology·2026
Same author

Frame-wise multi-echo distortion correction for superior functional MRI.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Cerebroretinal Microangiopathy with Calcifications and Cysts (CRMCC): A 5-Year Diagnostic Challenge.

Diagnostics (Basel, Switzerland)·2026
Same author

Task-induced topological and geometrical changes in whole-brain dynamics predict cognitive individual differences.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: Oct 9, 2025

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

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

Published on: June 30, 2018

11.9K

Enhancing task fMRI preprocessing via individualized model-based filtering of intrinsic activity dynamics.

Matthew F Singh1, Anxu Wang2, Michael Cole3

  • 1Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA; Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA.

Neuroimage
|December 23, 2021
PubMed
Summary

This study introduces a new filtering method using Mesoscale Individualized NeuroDynamic (MINDy) models to remove spontaneous brain activity from fMRI signals. This improves the accuracy and precision of detecting task-evoked brain responses.

Keywords:
Brain dynamicsCausal modelingCognitive controlIndividual differencesResting state fMRITask fMRI

More Related Videos

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training
07:05

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

Published on: August 24, 2017

11.2K
Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
08:19

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

1.2K

Related Experiment Videos

Last Updated: Oct 9, 2025

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

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

Published on: June 30, 2018

11.9K
A Protocol for the Administration of Real-Time fMRI Neurofeedback Training
07:05

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

Published on: August 24, 2017

11.2K
Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
08:19

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

1.2K

Area of Science:

  • Neuroscience
  • Cognitive Neuroscience
  • Neuroimaging

Background:

  • fMRI signals reflect both stimulus-evoked and spontaneous brain activity.
  • Distinguishing these components is crucial for accurate analysis of task-related neural effects.
  • Existing methods may not fully account for the propagation of intrinsic brain activity.

Purpose of the Study:

  • To develop and validate a novel method for enhancing the estimation of task-evoked brain activity in fMRI.
  • To improve the statistical power and temporal precision of detecting neural effects related to cognitive tasks.
  • To assess the impact of filtering spontaneous activity on behavioral prediction accuracy.

Main Methods:

  • Utilized Mesoscale Individualized NeuroDynamic (MINDy) models, built from resting-state fMRI data.
  • Developed a MINDy-based filtering technique to subtract the propagation of pre-event spontaneous activity from task-fMRI signals.
  • Applied conventional time-series analysis techniques to the filtered fMRI data.

Main Results:

  • MINDy-based filtering significantly enhanced statistical power and temporal precision of group-level fMRI effects.
  • The filtering method increased the similarity of neural activation profiles across related cognitive tasks.
  • Improved prediction accuracy for individual differences in cognitive control behavior was observed.

Conclusions:

  • Subtracting the propagation of intrinsic pre-event activity provides a more accurate estimate of task-related neural effects.
  • MINDy-based filtering offers a valuable approach to improve the analysis of fMRI data.
  • This method enhances the sensitivity and reliability of neuroimaging findings in cognitive neuroscience.