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 Experiment Video

Updated: Dec 9, 2025

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

1.4K

A temporal decomposition method for identifying venous effects in task-based fMRI.

Kendrick Kay1, Keith W Jamison2,3, Ru-Yuan Zhang2,4,5

  • 1Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA. kay@umn.edu.

Nature Methods
|September 8, 2020
PubMed
Summary

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

Molecular mechanisms and intervention strategies for age-related macular degeneration.

International journal of ophthalmology·2026
Same author

Integrating neuroscience across species and scales.

Nature neuroscience·2026
Same author

Herbicide-resistant weeds exploit kinship to reduce allelopathy from crops.

Journal of experimental botany·2026
Same author

Anxiety sensitivity and intolerance of uncertainty track distinct neurobehavioral dimensions of avoidance in anxiety-related disorders.

Molecular psychiatry·2026
Same author

An 80-channel receive array for 10.5T neuroimaging: Key considerations for SNR optimization.

bioRxiv : the preprint server for biology·2026
Same author

MMA++: Effective Multi-Modal Adaptation for Vision-Language Models.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RNAbpFlow: base pair-augmented SE(3) flow matching for conditional RNA 3D structure generation.

Nature methods·2026
Same journal

Spatio-DARLIN enables robust and efficient in situ lineage tracing in mice at single-cell resolution.

Nature methods·2026
Same journal

EasyGrid: a versatile platform for automated cryo-EM sample preparation and quality control.

Nature methods·2026
Same journal

Cloud-based microscope enables live neuroimaging for 24 h and beyond with worldwide access.

Nature methods·2026
Same journal

Deep molecular profiling in three dimensions.

Nature methods·2026
Same journal

3D pathology-guided microdissection.

Nature methods·2026
See all related articles
This summary is machine-generated.

This study introduces a new analysis method for functional magnetic resonance imaging (fMRI) to improve spatial accuracy by identifying and removing large draining vein signals. This technique enhances the precision of brain activity mapping in fMRI studies.

Area of Science:

  • Neuroimaging
  • Biophysics

Background:

  • Spatial resolution in functional magnetic resonance imaging (fMRI) is often compromised by signals originating from large draining veins.
  • These large venous vessels introduce biases, particularly affecting the superficial cortical depth of fMRI responses.

Purpose of the Study:

  • To develop and validate a data-driven analysis method for estimating and mitigating the impact of large draining vein effects in task-based fMRI.
  • To enhance the spatial accuracy and reliability of fMRI-based cortical activity mapping.

Main Methods:

  • A one-dimensional manifold fitting approach was employed to characterize response timecourse variations within fMRI datasets.
  • Identified early and late timecourses served as basis functions to decompose fMRI responses into microvasculature and macrovasculature components.

More Related Videos

fMRI Validation of fNIRS Measurements During a Naturalistic Task
10:36

fMRI Validation of fNIRS Measurements During a Naturalistic Task

Published on: June 15, 2015

21.3K
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.4K

Related Experiment Videos

Last Updated: Dec 9, 2025

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

1.4K
fMRI Validation of fNIRS Measurements During a Naturalistic Task
10:36

fMRI Validation of fNIRS Measurements During a Naturalistic Task

Published on: June 15, 2015

21.3K
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.4K
  • The method specifically isolates and removes late timecourse components attributed to large veins.

    • Main Results:

    • Removal of late timecourse components significantly reduced the superficial cortical depth bias observed in fMRI responses.
    • The analysis effectively minimized artifacts in cortical activity maps, leading to clearer representations of neural activity.
    • The method provides valuable insights into the physiological origins of the fMRI signal.

    Conclusions:

    • The developed analysis method offers a robust approach to address signal limitations imposed by large draining veins in fMRI.
    • This technique has the potential to substantially improve the spatial accuracy of fMRI, leading to more precise neuroimaging results.
    • This method aids in understanding fMRI signal sources and refining neuroimaging analysis pipelines.