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

10.1K
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...
10.1K
Brain Imaging01:14

Brain Imaging

835
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...
835

You might also read

Related Articles

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

Sort by
Same author

Predicting Craving-Related Emotions among Opioid Use Disorder Patients: Preliminary Results.

... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks·2026
Same author

Brain age prediction in generalized anxiety disorder using a convolutional neural network.

Translational psychiatry·2026
Same author

Digitised histopathology slides now ready for artificial intelligence: predicting the molecular signatures of gliomas.

The Lancet. Digital health·2026
Same author

Spatial evolution in temporal dynamics of hemodynamic response function in human superior colliculi with ultra-high-resolution MRI at 9.4T.

Frontiers in neuroscience·2026
Same author

Stereotactic Radiosurgery for Brain Arteriovenous Malformations: A Radiosurgery Society Case-Based Guide.

Practical radiation oncology·2026
Same author

Network and receptor architectures shape brain morphometry in addiction.

medRxiv : the preprint server for health sciences·2026
Same journal

Identifying factors associated with sleep disturbance among adults seeking outpatient psychiatric services for anxiety and related disorders.

Bulletin of the Menninger Clinic·2026
Same journal

From unplanned attempts to planned deaths: A comparative analysis of suicidal behavior in rural Türkiye.

Bulletin of the Menninger Clinic·2026
Same journal

Toward a double bind theory of borderline personality disorder.

Bulletin of the Menninger Clinic·2026
Same journal

An evaluation of factor structure, measurement invariance, and psychometric properties of the Psychache Scale.

Bulletin of the Menninger Clinic·2026
Same journal

Psychiatric and behavioral symptoms after pediatric herpes simplex virus type 1 encephalitis: An exploratory case series.

Bulletin of the Menninger Clinic·2026
Same journal

Object relations moderate the relationship between emotion regulation and quality of life among psychiatric inpatients: A brief report.

Bulletin of the Menninger Clinic·2026
See all related articles

Related Experiment Video

Updated: Mar 10, 2026

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

11.5K

Real time functional MRI training to decrease motion in imaging studies: Lack of significant improvement.

Tessy M Lal1, Philip R Baldwin1,2, Ming-Bo Cai3

  • 1Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Texas.

Bulletin of the Menninger Clinic
|December 13, 2016
PubMed
Summary
This summary is machine-generated.

Providing real-time head movement feedback during functional magnetic resonance imaging (fMRI) scans yielded marginal improvements in subject stillness. This strategy, while potentially helpful for some, is not yet sufficiently successful for widespread implementation in brain imaging research.

More Related Videos

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
10:06

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

13.5K
Real-Time fMRI Brain Mapping in Animals
04:05

Real-Time fMRI Brain Mapping in Animals

Published on: September 24, 2020

4.1K

Related Experiment Videos

Last Updated: Mar 10, 2026

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

11.5K
High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
10:06

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

13.5K
Real-Time fMRI Brain Mapping in Animals
04:05

Real-Time fMRI Brain Mapping in Animals

Published on: September 24, 2020

4.1K

Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Psychiatric Research

Background:

  • Functional magnetic resonance imaging (fMRI) is crucial for studying brain function in health and disease.
  • Head motion during fMRI scans is a significant source of image artifacts and data confounding.
  • Reducing subject movement in the MRI scanner remains a persistent challenge in neuroimaging research.

Purpose of the Study:

  • To investigate the efficacy of real-time head motion feedback as a strategy to improve subject stillness during fMRI.
  • To determine if visual feedback of movement can train subjects to reduce motion during scanning.

Main Methods:

  • Participants underwent fMRI scanning while a real-time plot of their head movement was displayed.
  • The feedback plot featured three distinct regions to guide subjects on their movement levels.
  • Data analysis focused on the degree of motion reduction achieved with the feedback intervention.

Main Results:

  • The real-time feedback intervention resulted in marginal and inconsistent improvements in head stillness.
  • The observed benefits varied among participants, indicating limited generalizability.
  • The strategy did not prove sufficiently effective in the tested sample to warrant immediate adoption.

Conclusions:

  • Real-time visual feedback of head motion is not currently a sufficiently effective method to significantly reduce motion in fMRI studies.
  • Further research may be needed to refine feedback strategies or explore alternative motion reduction techniques.
  • Motion artifacts continue to pose a challenge for reliable fMRI data acquisition, particularly in clinical populations.