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 Videos

Unsupervised robust nonparametric estimation of the hemodynamic response function for any fMRI experiment.

Philippe Ciuciu1, Jean-Baptiste Poline, Guillaume Marrelec

  • 1SHFJ/CEA/INSERM U562, 91401 Orsay, France. ciuciu@shfj.cea.fr

IEEE Transactions on Medical Imaging
|October 14, 2003
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

Replicability of multivariate brain-behaviour associations depends on clinical profile.

Communications biology·2026
Same author

Multifractality in critical neural field dynamics.

Physical review. E·2026
Same author

Effects of pharmacological modulation of cortical excitability on resting-state EEG PAC in humans.

Scientific reports·2026
Same author

ABCD-ReproNim: An educational program for responsible and reproducible analyses of ABCD data.

Developmental cognitive neuroscience·2026
Same author

Open neuroinformatics infrastructure ecosystem for federated multisite studies.

bioRxiv : the preprint server for biology·2026
Same author

Lexique 4: A major upgrade of the Lexique French lexical database.

Behavior research methods·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

Generalised Medical Phrase Grounding.

IEEE transactions on medical imaging·2026
Same journal

EndoLRMGS: Combining Large Reconstruction Modelling and Gaussian Splatting for Complete Endoscopic Scene Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

A Neural-Analytical Fusion Scatter Correction Method for Multi-Source CT Using Equivalent High-Order Scatter.

IEEE transactions on medical imaging·2026
Same journal

SynReEM: Synapse Reconstruction via Instance Structure Encoding in Anisotropic Electron Microscopic Volumes.

IEEE transactions on medical imaging·2026
See all related articles

This study enhances hemodynamic response function (HRF) estimation in functional MRI (fMRI) by integrating asynchronous designs, multiple trial types, and sessions. The Bayesian approach balances data and prior knowledge for more accurate brain activation analysis.

Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Statistical Modeling

Background:

  • Accurate estimation of the blood oxygen level-dependent (BOLD) response is crucial for understanding brain activity in functional magnetic resonance imaging (fMRI).
  • Nonparametric methods using the hemodynamic response function (HRF) are common but have limitations with real fMRI data.
  • Existing techniques are not well-suited for complex experimental designs like asynchronous paradigms or multi-session analyses.

Purpose of the Study:

  • To develop an advanced method for estimating the hemodynamic response function (HRF) in fMRI data.
  • To extend previous nonparametric approaches by incorporating asynchronous event-related designs, multiple trial types, and multi-session data.
  • To improve the precision of BOLD response estimation for a better understanding of cerebral activations.

Related Experiment Videos

Main Methods:

  • A threefold extension to existing HRF estimation techniques was developed.
  • The approach accounts for asynchronous event-related paradigms, different trial types, and integrates multiple fMRI sessions.
  • Bayesian inference models temporal prior information, balancing data-driven insights with prior physiological knowledge using hyperparameters optimized via expectation-conditional maximization (ECM).

Main Results:

  • The proposed method successfully handles complex fMRI data, including asynchronous designs and multi-session integration.
  • The Bayesian framework effectively balances empirical data with prior physiological information.
  • The unsupervised approach was validated on both synthetic and real fMRI datasets, demonstrating its robustness.

Conclusions:

  • The developed Bayesian method offers a more robust and flexible approach to HRF estimation in fMRI.
  • This technique improves the analysis of brain activity, particularly for complex experimental designs.
  • The findings contribute to more accurate cerebral activation mapping in neuroscience research.