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

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

7.6K
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...
7.6K
Imaging Studies for Cardiovascular System IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

542
Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
542
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

432
Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
432

You might also read

Related Articles

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

Sort by
Same author

Evaluating Approaches for Inference Testing of Whole-Brain Densely Sampled Single-Subject Task fMRI Data.

bioRxiv : the preprint server for biology·2026
Same author

Objective quality assessment for precision functional MRI data.

Neuron·2026
Same author

Unraveling the Complexity of Multilingual Comprehension: Neuroimaging and Linguistic Profiling in 700+ Adults.

Scientific data·2026
Same author

Tactile force evokes a biphasic BOLD response in ipsilateral primary somatosensory cortex.

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

Iterative delay correction improves breath-hold cerebrovascular reactivity mapping in clinical populations.

bioRxiv : the preprint server for biology·2026
Same author

Stretch-evoked motor responses in the brainstem are modulated by task instructions.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: May 7, 2026

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis
10:33

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis

Published on: June 20, 2012

12.8K

Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA.

Neha A Reddy1,2, Kristina M Zvolanek1,2, Stefano Moia3,4,5

  • 1Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.

Imaging Neuroscience (Cambridge, Mass.)
|September 27, 2024
PubMed
Summary

Multi-echo independent component analysis (ME-ICA) effectively separates head motion from brain signals in motor fMRI tasks. This method improves data reliability for clinical populations, especially when group analysis is not feasible.

Keywords:
BOLD fMRIindependent component analysismotor taskmulti-echotask-correlated head motion

More Related Videos

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

1.0K
Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research
08:33

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research

Published on: January 5, 2024

1.1K

Related Experiment Videos

Last Updated: May 7, 2026

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis
10:33

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis

Published on: June 20, 2012

12.8K
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

1.0K
Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research
08:33

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research

Published on: January 5, 2024

1.1K

Area of Science:

  • Neuroimaging
  • Clinical Neuroscience

Background:

  • Motor-task functional magnetic resonance imaging (fMRI) is vital for studying neurological conditions like stroke and Parkinson's disease.
  • Task-correlated head motion is a significant confound in motor-task fMRI, particularly in clinical populations, affecting activation results.

Purpose of the Study:

  • To evaluate the effectiveness of multi-echo independent component analysis (ME-ICA) in mitigating head motion artifacts in motor-task fMRI data.
  • To compare ME-ICA with single-echo (SE) and multi-echo optimally combined (ME-OC) models in simulated high-motion scenarios.

Main Methods:

  • Collected fMRI data from healthy participants performing a hand grasp task, with and without amplified task-correlated head motion.
  • Analyzed data using single-echo (SE), multi-echo optimally combined (ME-OC), and multi-echo independent component analysis (ME-ICA) models.
  • Compared model performance at both subject and group levels for motion artifact mitigation.

Main Results:

  • ME-ICA demonstrated superior dissociation of head motion from the blood oxygenation level dependent (BOLD) signal and reduced noise at the subject level.
  • Both ME models increased t-statistics in motor regions; ME-ICA further mitigated artifacts and improved stability in high-motion scans.
  • Group-level analysis with all models revealed activation in motor areas, suggesting averaging can resolve subject-specific motion artifacts.

Conclusions:

  • ME-ICA is a valuable tool for subject-level analysis of motor-task fMRI data contaminated by significant head motion.
  • The improvements offered by ME-ICA enhance the reliability of subject-level activation maps, crucial for clinical populations where group analysis may be limited.