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Estimating neural response functions from fMRI.

Kumar, S; Penny, W; (2014) Estimating neural response functions from fMRI. Front Neuroinform , 8 , Article 48. 10.3389/fninf.2014.00048. Green open access


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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.

Type: Article
Title: Estimating neural response functions from fMRI.
Location: Switzerland
Open access status: An open access version is available from UCL Discovery
DOI: 10.3389/fninf.2014.00048
Publisher version: http://dx.doi.org/10.3389/fninf.2014.00048
Language: English
Additional information: © 2014 Kumar and Penny. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. PMCID: PMC4021120
Keywords: Balloon model, Bayesian inference, Tonotopic Mapping, auditory perception, neural response function, parametric modulation, population receptive field, repetition suppression
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
URI: https://discovery.ucl.ac.uk/id/eprint/1431205
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