October 2020
Volume 20, Issue 11
Open Access
Vision Sciences Society Annual Meeting Abstract  |   October 2020
Bayesian adaptive stimulus selection with real-time fMRI
Author Affiliations
  • Steven M. Weisberg
    University of Florida
  • Geoffery K. Aguirre
    University of Pennsylvania
Journal of Vision October 2020, Vol.20, 394. doi:https://doi.org/10.1167/jov.20.11.394
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      Steven M. Weisberg, Geoffery K. Aguirre; Bayesian adaptive stimulus selection with real-time fMRI. Journal of Vision 2020;20(11):394. doi: https://doi.org/10.1167/jov.20.11.394.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

In contrast to the method of constant stimuli, adaptive procedures dynamically select the next stimulus based upon the prior responses of the subject, and an underlying stimulus-response model. For fMRI experiments that seek to measure the parameters of a neural response function, Bayesian adaptive stimulus selection (such as provided by QUEST+; Watson 2017) may provide better estimates of model parameters than traditional approaches. QUEST+ may be particularly useful in cases where the neural response is a function with multiple parameters, which must be fit simultaneously. Despite these advantages to adaptive stimulus selection, a complication is that QUEST+ considers responses as the proportion of outcomes within pre-defined, discrete bins, while BOLD fMRI data is a continuous signal with an uncertain amplitude and a varying baseline. To account for this, we have implemented a framework for QUEST+ fMRI that includes: 1) initial model fitting to the time-series to extract a gain parameter for each stimulus event, accounting for the hemodynamic response; 2) a procedure for dynamically updating the mapping between responses relative to a reference stimulus, and the fixed set of outcomes specified by QUEST+; 3) a Gaussian noise parameter that intercedes between the parameterized model of response and the proportions of observed outcomes. We examined the measurement of the V1 cortical response to a high-contrast stimulus flickering at different frequencies, as fit by a 4-parameter difference-of-exponential temporal sensitivity model. In simulations that model empirical data, we find that the QUEST+ approach recovers model parameters more accurately than does constant stimuli given a fixed acquisition length. This framework now allows us to optimize experimental design (e.g., the number and duration of stimulus types) given a neural response function to be measured.

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