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Estimating neural response functions from fMRI

Lookup NU author(s): Dr Sukhbinder Kumar

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Abstract

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.

Publication metadata

Author(s): Kumar S, Penny W

Publication type: Article

Publication status: Published

Journal: Frontiers in Neuroinformatics

Year: 2014

Volume: 8

Online publication date: 08/05/2014

Acceptance date: 14/04/2014

Date deposited: 06/04/2016

ISSN (electronic): 1662-5196

Publisher: Frontiers Research Foundation

URL: http://dx.doi.org/10.3389/fninf.2014.00048

DOI: 10.3389/fninf.2014.00048

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