Bayesian model comparison in nonlinear BOLD fMRI hemodynamics  Daniel J. Jacobsen, Kristoffer H. Madsen, Lars Kai Hansen
 Abstract  Nonlinear hemodynamic models express the BOLD signal as a non
linear, parametric functional of the temporal sequence of local neural ac
tivity. Several models have been proposed both for the neural activity
and the hemodynamics. We compare two such combined models: the
'original' balloon model with a squarepulse neural model [5], and an ex
tended balloon model with a more sophisticated neural model [3]. We
learn the parameters of both models using a Bayesian approach, where
the distribution of the parameters conditioned on the data is estimated
using Markov chain Monte Carlo techniques. Using a splithalf resampling
procedure [14], we compare the generalization abilities of the models as
well as their reproducibility, both for synthetic and real data, recorded
from two diŽerent visual stimulation paradigms. The results show that
the simple model is the best one for these data.  Keywords  Bayes, model comparison, nonlinear, BOLD fMRI, MCMC  Type  Journal paper [Submitted]  Journal  Neural Computation  Year  2006  BibTeX data  [bibtex]  IMM Group(s)  Intelligent Signal Processing 
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