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

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

  • Adaszewski S Laboratoire de Recherche en Neuroimagerie, DNC, CHUV, Lausanne, Switzerland; Instytut Radioelektroniki i Technik Multimedialnych, WEITI, PW, Warsaw, Poland. Electronic address: sadaszew@ire.pw.edu.pl.
  • Slater D Laboratoire de Recherche en Neuroimagerie, DNC, CHUV, Lausanne, Switzerland.
  • Melie-Garcia L Laboratoire de Recherche en Neuroimagerie, DNC, CHUV, Lausanne, Switzerland.
  • Draganski B Laboratoire de Recherche en Neuroimagerie, DNC, CHUV, Lausanne, Switzerland.
  • Bogorodzki P Instytut Radioelektroniki i Technik Multimedialnych, WEITI, PW, Warsaw, Poland.
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  • 2018-02-08
Published in:
  • NeuroImage. - 2018
Balloon-Windkessel hemodynamic model
Bayesian estimation
Markov Chain Monte Carlo
Metropolis-Hastings
Population receptive field
Posterior sampling
Algorithms
Brain Mapping
Computer Simulation
Humans
Magnetic Resonance Imaging
Markov Chains
Models, Neurological
Monte Carlo Method
Software
English 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.
Language
  • English
Open access status
green
Identifiers
Persistent URL
https://sonar.ch/global/documents/181762
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