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

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

Interpersonal Physiological Linkage Between People With Behavioral Variant Frontotemporal Dementia and Alzheimer's Disease and Their Informal Caregivers.

Psychophysiology·2025
Same author

Corrigendum: Reduced insulin signaling targeted to serotonergic neurons but not other neuronal subtypes extends lifespan in <i>Drosophila melanogaster</i>.

Frontiers in aging neuroscience·2023
Same author

Prediction of water quality extremes with composite quantile regression neural network.

Environmental monitoring and assessment·2023
Same author

Reduced Insulin Signaling Targeted to Serotonergic Neurons but Not Other Neuronal Subtypes Extends Lifespan in <i>Drosophila melanogaster</i>.

Frontiers in aging neuroscience·2022
Same author

An Online Survey on the Perception of Mediated Social Touch Interaction and Device Design.

IEEE transactions on haptics·2022
Same author

Optimizing Household Water Decisions for Managing Intermittent Water Supply in Mexico City.

Environmental science & technology·2021

Related Experiment Video

Updated: May 18, 2026

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

Nonparametric inference of the hemodynamic response using multi-subject fMRI data.

Tingting Zhang1, Fan Li, Lane Beckes

  • 1Department of Statistics, University of Virginia, Charlottesville, VA 22904, USA. tz3b@virginia.edu

Neuroimage
|September 18, 2012
PubMed
Summary

This study introduces novel nonparametric methods for estimating hemodynamic response functions (HRF) from multi-subject fMRI data. These techniques improve accuracy by leveraging shared brain activity patterns across individuals, outperforming standard independent analysis.

More Related Videos

Brain Imaging Investigation of the Neural Correlates of Observing Virtual Social Interactions
10:45

Brain Imaging Investigation of the Neural Correlates of Observing Virtual Social Interactions

Published on: July 6, 2011

Deep Brain Stimulation with Simultaneous fMRI in Rodents
11:09

Deep Brain Stimulation with Simultaneous fMRI in Rodents

Published on: February 15, 2014

Related Experiment Videos

Last Updated: May 18, 2026

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

Brain Imaging Investigation of the Neural Correlates of Observing Virtual Social Interactions
10:45

Brain Imaging Investigation of the Neural Correlates of Observing Virtual Social Interactions

Published on: July 6, 2011

Deep Brain Stimulation with Simultaneous fMRI in Rodents
11:09

Deep Brain Stimulation with Simultaneous fMRI in Rodents

Published on: February 15, 2014

Area of Science:

  • Neuroimaging
  • Biostatistics
  • Cognitive Neuroscience

Background:

  • Accurate estimation of hemodynamic response functions (HRF) is crucial for analyzing functional magnetic resonance imaging (fMRI) data.
  • Traditional methods often analyze fMRI data from individual subjects independently, potentially missing shared neural activity patterns.
  • Existing hypothesis tests typically focus on summary statistics of the HRF, rather than the entire curve.

Purpose of the Study:

  • To propose and evaluate new nonparametric estimators for HRF within the General Linear Model framework.
  • To develop methods that utilize multi-subject fMRI data to improve the estimation of individual HRFs.
  • To enable hypothesis testing on the entire HRF curve, offering a more comprehensive analysis.

Main Methods:

  • Introduced a kernel-smoothed nonparametric estimator for HRF.
  • Developed a Tikhonov-regularized kernel-smoothed estimator to manage high data variance.
  • Implemented a bias-correction step using multi-subject averaged information to enhance individual HRF estimation efficiency and reduce bias.
  • Created a fast algorithm for optimal selection of regularization and smoothing parameters.

Main Results:

  • The proposed methods demonstrated improved estimation of HRFs compared to existing regularization techniques in simulations.
  • The bias-correction step effectively utilized cross-subject information, a key advantage over independent analysis.
  • The methods were successfully applied to fMRI data from the Monetary Incentive Delay (MID) task.

Conclusions:

  • The novel nonparametric estimators, particularly the regularized and bias-corrected approach, offer significant improvements for HRF estimation in multi-subject fMRI studies.
  • Leveraging shared brain activity across subjects enhances the accuracy and efficiency of individual HRF estimation.
  • These methods provide a robust framework for hypothesis testing on the complete HRF curve and are applicable to real-world fMRI data.