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Lookup NU author(s): Dr Sukhbinder Kumar
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
This paper proposes a methodology for estimating Neural Response Functions (NRFs) from fMRI data. These NRFs describe non-linear relationships between experimental stimuli and neuronal population responses. The method is based on a two-stage model comprising an NRF and a Hemodynamic Response Function (HRF) that are simultaneously fitted to fMRI data using a Bayesian optimization algorithm. This algorithm also produces a model evidence score, providing a formal model comparison method for evaluating alternative NRFs. The HRF is characterized using previously established "Balloon" and BOLD signal models. We illustrate the method with two example applications based on fMRI studies of the auditory system. In the first, we estimate the time constants of repetition suppression and facilitation, and in the second we estimate the parameters of population receptive fields in a tonotopic mapping study.
Author(s): Kumar S, Penny W
Publication type: Article
Publication status: Published
Journal: Frontiers in Neuroinformatics
Online publication date: 08/05/2014
Acceptance date: 14/04/2014
Date deposited: 06/04/2016
ISSN (electronic): 1662-5196
Publisher: Frontiers Research Foundation
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