Slater, D. | bioRxiv Science

bioRxiv2017

Simultaneous Estimation of Population Receptive Field and Hemodynamic Parameters from Single Point BOLD Responses using Metropolis-Hastings Sampling

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.

neuroscience↗

bioRxiv2017

Convex Optimized Population Receptive Field (CO-pRF) Mapping

Population receptive field (pRF) mapping represents an invaluable non-invasive tool for the study of sensory organization and plasticity within the human brain. Despite the very appealing result that fMRI derived pRF measures agree well with measurements made from other fields of neuroscience, current techniques often require very computationally expensive non-linear optimization procedures to fit the models to the data which are also vulnerable to bias due local minima issues. In this work we present a general framework for pRF model estimation termed Convex Optimized Population Receptive Field (CO-pRF) mapping and show how the pRF fitting problem can be linearized in order to be solved by extremely fast and efficient algorithms. The framework is general and can be readily applied to a variety of pRF models and measurement schemes. We provide an example of the CO-pRF methodology as applied to a computational neuroimaging approach used to map sensory processes in human visual cortex - the CSS-pRF model. Via simulation and in-vivo fMRI results we demonstrate that the CO-pRF approach achieves robust model fitting even in the presence of noise or reduced data, providing parameter estimates closer to the global optimum across 93% of in-vivo responses as compared to a typical nonlinear optimization procedure. Furthermore the example CO-pRF application substantially reduced model fitting times by a factor of 50. We hope that the availability of such highly accelerated and reliable pRF estimation algorithms will facilitate the spread of pRF techniques to larger imaging cohorts and the future study of neurological disorders and plasticity within the human brain.

neuroscience↗