TY - JOUR

T1 - Bayesian model comparison in nonlinear BOLD fMRI hemodynamics

A1 - Jacobsen,Danjal Jakup

A1 - Hansen,Lars Kai

A1 - Madsen,Kristoffer Hougaard

AU - Jacobsen,Danjal Jakup

AU - Hansen,Lars Kai

AU - Madsen,Kristoffer Hougaard

PB - M I T Press

PY - 2008

Y1 - 2008

N2 - Nonlinear hemodynamic models express the BOLD (blood oxygenation level dependent) signal as a nonlinear, parametric functional of the temporal sequence of local neural activity. Several models have been proposed for both the neural activity and the hemodynamics. We compare two such combined models: the original balloon model with a square-pulse neural model (Friston, Mechelli, Turner, & Price, 2000) and an extended balloon model with a more sophisticated neural model (Buxton, Uludag, Dubowitz, & Liu, 2004). 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 split-half resampling procedure (Strother, Anderson, & Hansen, 2002), we compare the generalization abilities of the models as well as their reproducibility for both synthetic and real data, recorded from two different visual stimulation paradigms. The results show that the simple model is the better one for these data.

AB - Nonlinear hemodynamic models express the BOLD (blood oxygenation level dependent) signal as a nonlinear, parametric functional of the temporal sequence of local neural activity. Several models have been proposed for both the neural activity and the hemodynamics. We compare two such combined models: the original balloon model with a square-pulse neural model (Friston, Mechelli, Turner, & Price, 2000) and an extended balloon model with a more sophisticated neural model (Buxton, Uludag, Dubowitz, & Liu, 2004). 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 split-half resampling procedure (Strother, Anderson, & Hansen, 2002), we compare the generalization abilities of the models as well as their reproducibility for both synthetic and real data, recorded from two different visual stimulation paradigms. The results show that the simple model is the better one for these data.

U2 - 10.1162/neco.2007.07-06-282

DO - 10.1162/neco.2007.07-06-282

JO - Neural Computation

JF - Neural Computation

SN - 0899-7667

IS - 3

VL - 20

SP - 738

EP - 755

ER -