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: Jun 27, 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

Modeling the hemodynamic response function in fMRI: efficiency, bias and mis-modeling.

Martin A Lindquist1, Ji Meng Loh, Lauren Y Atlas

  • 1Department of Statistics, Columbia University, New York, NY 10027, USA. martin@stat.columbia.edu

Neuroimage
|December 17, 2008
PubMed
Summary
This summary is machine-generated.

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

Intensity-dependent topographical expansion of sensory representations.

bioRxiv : the preprint server for biology·2026
Same author

A Beta-Binomial Model for Estimating Zero- or One-inflated Pain Trajectories.

bioRxiv : the preprint server for biology·2026
Same author

Detection of multiple influential observations on model selection.

Biometrics·2026
Same author

A multiverse approach to heat-evoked skin conductance analysis: evaluating the influence of analytic pipeline on associations between skin conductance and pain.

Pain·2026
Same author

The Endocannabinoid System's Contribution to Placebo Analgesia.

bioRxiv : the preprint server for biology·2026
Same author

The Common Fund Data Ecosystem (CFDE).

bioRxiv : the preprint server for biology·2026
Same journal

Segmentation of the parasagittal dura mater on multi-center 3D-FLAIR MRI.

NeuroImage·2026
Same journal

Spatial frequency channels implement a mental ruler in spatial vision.

NeuroImage·2026
Same journal

Exploring the Link Between Intravoxel Incoherent Motion Measured Brain Diffusivity During Wakefulness and Sleep Macrostructure in the Elderly.

NeuroImage·2026
Same journal

Closed-loop adaptation of transcranial magnetic stimulation intensity with electroencephalography feedback.

NeuroImage·2026
Same journal

Volumetric postmortem MRI of the medial temporal lobe in Alzheimer's disease and related disorders: methodological advances and implications for in vivo biomarker development.

NeuroImage·2026
Same journal

Neural responses to equity and inequity when receiving vicarious rewards for self and charity during adolescence.

NeuroImage·2026
See all related articles

Accurately measuring brain activity using functional Magnetic Resonance Imaging (fMRI) is challenging. Different models show significant variations in detecting true brain signal changes, impacting cognitive and affective neuroscience research.

Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Affective Neuroscience

Background:

  • Traditional fMRI research primarily analyzes response amplitude.
  • Growing interest exists in quantifying response onset, peak latency, and duration.
  • Hemodynamic response function (HRF) modeling is crucial for interpreting fMRI data.

Purpose of the Study:

  • To compare various fMRI response modeling techniques.
  • To assess methods for estimating amplitude, peak latency, and duration.
  • To evaluate model sensitivity, bias, and parameter estimation accuracy.

Main Methods:

  • Comparison of multiple fMRI modeling techniques with differing assumptions and complexity.
  • Development of inference methods for multi-subject fMRI data.

More Related Videos

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

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

Related Experiment Videos

Last Updated: Jun 27, 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

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

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

  • Assessment of parameter sensitivity and cross-parameter attribution (e.g., duration influencing amplitude).

    • Main Results:

    • Accurate recovery of true task-evoked BOLD signal changes is difficult.
    • Substantial differences observed among models regarding statistical power, bias, and parameter confusability.
    • Models vary significantly in their ability to accurately estimate response characteristics.

    Conclusions:

    • The choice of fMRI model significantly impacts the interpretation of hemodynamic response estimates.
    • Researchers must carefully consider model assumptions and limitations in cognitive and affective neuroscience.
    • Findings highlight the challenges in precisely quantifying brain activity dynamics using current fMRI modeling approaches.