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

Constraining the general linear model for sensible hemodynamic response function waveforms.

Koray Ciftçi1, Bülent Sankur, Yasemin P Kahya

  • 1Institute of Biomedical Engineering, Boğaziçi University, Istanbul, Turkey. rciftci@boun.edu.tr

Medical & Biological Engineering & Computing
|April 23, 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

Multi-institutional deep learning for GTV segmentation and survival prediction in nasopharyngeal carcinoma.

BMC medical imaging·2026
Same author

Sex based differences in functional connectivity during a working memory task: an fNIRS study.

Frontiers in psychology·2024
Same author

Resonance based separation and energy based classification of lung sounds using tunable wavelet transform.

Computers in biology and medicine·2021
Same author

Differential Diagnosis of Asthma and COPD Based on Multivariate Pulmonary Sounds Analysis.

IEEE transactions on bio-medical engineering·2021
Same author

An open access database for the evaluation of respiratory sound classification algorithms.

Physiological measurement·2019
Same author

Wheeze type classification using non-dyadic wavelet transform based optimal energy ratio technique.

Computers in biology and medicine·2018
Same journal

Reduced mechanical strength correlates with decreased elastin content in aortic intima-media tissue: association with dissection in human ascending aortas.

Medical & biological engineering & computing·2026
Same journal

How plaque morphology and stenosis severity govern stent-artery interaction and deployment outcomes: a computational study.

Medical & biological engineering & computing·2026
Same journal

Investigating a relation between amyloid beta plaque burden and accumulated neurotoxicity caused by amyloid beta oligomers.

Medical & biological engineering & computing·2026
Same journal

A robot-assisted eye positioning method with high precision and repeatability for ocular particle therapy: mechanical and geometric assessment.

Medical & biological engineering & computing·2026
Same journal

Enhanced puncture event detection for teleoperated needle insertion robotic system.

Medical & biological engineering & computing·2026
Same journal

Energy-efficient real-time 4-stage sleep classification at 10-second resolution.

Medical & biological engineering & computing·2026
See all related articles

Constraining the general linear model (GLM) with realistic hemodynamic response function (HRF) parameters improves neuroimaging analysis. This method reduces false positives and unrealistic estimates in brain imaging data without impacting valid findings.

Area of Science:

  • Neuroimaging analysis
  • Statistical modeling in neuroscience
  • Biomedical signal processing

Background:

  • General Linear Model (GLM) is widely used for neuroimaging data analysis.
  • Hemodynamic Response Function (HRF) estimation is crucial but can yield unrealistic results.
  • Existing methods may not adequately constrain HRF parameters, leading to potential inaccuracies.

Purpose of the Study:

  • To introduce a constrained parameter estimation and inference method for neuroimaging data using GLM.
  • To prevent unrealistic hemodynamic response function (HRF) estimates in GLM analyses.
  • To improve the reliability and accuracy of neuroimaging statistical inference.

Main Methods:

  • Developed a constrained approach for GLM parameter estimation.

Related Experiment Videos

  • Defined permissible waveform parameter ranges based on plausible HRF shapes.
  • Integrated these parameter intervals as prior distributions in a Bayesian GLM analysis.
  • Employed Gibbs sampling to derive posterior distributions for model parameters.
  • Applied the method to simulated null data and near-infrared spectroscopy (NIRS) data.

    • Main Results:

    • The constrained GLM successfully eliminated unrealistic HRF waveforms.
    • A reduction in false positive activations was observed.
    • Inference for realistic activations, satisfying the constraints, remained unaffected.
    • The method demonstrated efficacy on both artificial and real NIRS data.

    Conclusions:

    • Constrained GLM parameter estimation offers a robust approach to neuroimaging analysis.
    • This method enhances the specificity of neuroimaging findings by removing spurious HRF estimates.
    • The approach improves the validity of statistical inference in neuroimaging studies, particularly with NIRS data.