Identification of non-linear models of neural activity in bold fmri

Daniel Jakup Jacobsen; Kristoffer Hougaard Madsen; Lars Kai Hansen

doi:10.1109/ISBI.2006.1625077

Identification of non-linear models of neural activity in bold fmri

Daniel Jakup Jacobsen, Kristoffer Hougaard Madsen, Lars Kai Hansen

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Abstract

Non-linear hemodynamic models express the BOLD signal as a nonlinear, parametric functional of the temporal sequence of local neural activity. Several models have been proposed for this neural activity. We identify one such parametric model by estimating the distribution of its parameters. These distributions are themselves stochastic, therefore we estimate their variance by epoch based leave-one-out cross validation, using a Metropolis-Hastings algorithm for sampling of the posterior parameter distribution.

Original language	English
Title of host publication	3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano
Publisher	IEEE
Publication date	2006
Pages	952-955
ISBN (Print)	0-7803-9576-X
DOIs	https://doi.org/10.1109/ISBI.2006.1625077
Publication status	Published - 2006
Event	2006 IEEE International Symposium on Biomedical Imaging: Nano to Macro - Arlington, United States Duration: 6 Apr 2006 → 9 Apr 2006 Conference number: 3

Conference

Conference	2006 IEEE International Symposium on Biomedical Imaging
Number	3
Country/Territory	United States
City	Arlington
Period	06/04/2006 → 09/04/2006

Bibliographical note

Copyright: 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

Keywords

model comparison
BOLD fMRI
Bayes
learning
MCMC

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title = "Identification of non-linear models of neural activity in bold fmri",

abstract = "Non-linear hemodynamic models express the BOLD signal as a nonlinear, parametric functional of the temporal sequence of local neural activity. Several models have been proposed for this neural activity. We identify one such parametric model by estimating the distribution of its parameters. These distributions are themselves stochastic, therefore we estimate their variance by epoch based leave-one-out cross validation, using a Metropolis-Hastings algorithm for sampling of the posterior parameter distribution.",

keywords = "model comparison, BOLD fMRI, Bayes, learning, MCMC",

author = "Jacobsen, {Daniel Jakup} and Madsen, {Kristoffer Hougaard} and Hansen, {Lars Kai}",

note = "Copyright: 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE; 2006 IEEE International Symposium on Biomedical Imaging : Nano to Macro, ISBI 2006 ; Conference date: 06-04-2006 Through 09-04-2006",

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AB - Non-linear hemodynamic models express the BOLD signal as a nonlinear, parametric functional of the temporal sequence of local neural activity. Several models have been proposed for this neural activity. We identify one such parametric model by estimating the distribution of its parameters. These distributions are themselves stochastic, therefore we estimate their variance by epoch based leave-one-out cross validation, using a Metropolis-Hastings algorithm for sampling of the posterior parameter distribution.

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KW - Bayes

KW - learning

KW - MCMC

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BT - 3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano

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ER -

Identification of non-linear models of neural activity in bold fmri

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