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Bayesian model comparison in nonlinear BOLD fMRI hemodynamics.

Daniel J Jacobsen1, Lars Kai Hansen, Kristoffer Hougaard Madsen

  • 1Technical University of Denmark, Lyngby, N/A 2800, Denmark. dj@decision3.com

Neural Computation
|November 30, 2007
PubMed
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This study compared two nonlinear hemodynamic models for analyzing brain activity. The simpler balloon model with a square-pulse neural model demonstrated superior performance and reproducibility for both synthetic and real data.

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biophysics

Background:

  • Nonlinear hemodynamic models link neural activity to the blood oxygenation level dependent (BOLD) signal.
  • Various models exist for neural activity and hemodynamic responses.

Purpose of the Study:

  • To compare the performance of two combined nonlinear hemodynamic models.
  • To evaluate model generalization and reproducibility using Bayesian inference and resampling techniques.

Main Methods:

  • Bayesian parameter learning using Markov chain Monte Carlo (MCMC) methods.
  • Split-half resampling for assessing generalization and reproducibility.
  • Comparison of the original balloon model (square-pulse neural model) and an extended balloon model (sophisticated neural model).

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Main Results:

  • The simpler model (original balloon with square-pulse neural input) showed better generalization and reproducibility.
  • This finding held true for both synthetic and real fMRI data from visual stimulation paradigms.

Conclusions:

  • The original balloon model with a square-pulse neural input is more effective for the analyzed data.
  • Model selection in hemodynamic modeling is crucial for accurate interpretation of BOLD signals.