ArticleFrontiers in neuroinformatics2014
Estimating neural response functions from fMRI.
Article in Frontiers in neuroinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Does higher sampling rate (multiband + SENSE) improve group statistics - An example from social neuroscience block design at 3T.NeuroImage · 2020Article
- Network structure and input integration in competing firing rate models for decision-making.Journal of computational neuroscience · 2019Article
- Bayesian population receptive field modelling.NeuroImage · 2018Article
- Dynamic causal modelling for functional near-infrared spectroscopy.NeuroImage · 2015Article
- Computational parametric mapping of functional neuroimaging data.Imaging neuroscience (Cambridge, Mass.)Article
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2 authors.
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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.
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