Simultaneous estimation of population receptive field and hemodynamic parameters from single point BOLD responses using Metropolis-Hastings sampling.

Détails

ID Serval
serval:BIB_84BD1C937988
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Simultaneous estimation of population receptive field and hemodynamic parameters from single point BOLD responses using Metropolis-Hastings sampling.
Périodique
NeuroImage
Auteur(s)
Adaszewski S., Slater D., Melie-Garcia L., Draganski B., Bogorodzki P.
ISSN
1095-9572 (Electronic)
ISSN-L
1053-8119
Statut éditorial
Publié
Date de publication
15/05/2018
Peer-reviewed
Oui
Volume
172
Pages
175-193
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Résumé
We introduce a new approach to Bayesian pRF model estimation using Markov Chain Monte Carlo (MCMC) sampling for simultaneous estimation of pRF and hemodynamic parameters. To obtain high performance on commonly accessible hardware we present a novel heuristic consisting of interpolation between precomputed responses for predetermined stimuli and a large cross-section of receptive field parameters. We investigate the validity of the proposed approach with respect to MCMC convergence, tuning and biases. We compare different combinations of pRF - Compressive Spatial Summation (CSS), Dumoulin-Wandell (DW) and hemodynamic (5-parameter and 3-parameter Balloon-Windkessel) models within our framework with and without the usage of the new heuristic. We evaluate estimation consistency and log probability across models. We perform as well a comparison of one model with and without lookup table within the RStan framework using its No-U-Turn Sampler. We present accelerated computation of whole-ROI parameters for one subject. Finally, we discuss risks and limitations associated with the usage of the new heuristic as well as the means of resolving them. We found that the new algorithm is a valid sampling approach to joint pRF/hemodynamic parameter estimation and that it exhibits very high performance.
Mots-clé
Algorithms, Brain Mapping/methods, Computer Simulation, Humans, Magnetic Resonance Imaging/methods, Markov Chains, Models, Neurological, Monte Carlo Method, Software, Balloon-Windkessel hemodynamic model, Bayesian estimation, Markov Chain Monte Carlo, Metropolis-Hastings, Population receptive field, Posterior sampling
Pubmed
Web of science
Création de la notice
08/02/2018 18:22
Dernière modification de la notice
06/01/2019 7:26
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