State‐space models for optical imaging
Identifieur interne : 000101 ( Main/Exploration ); précédent : 000100; suivant : 000102State‐space models for optical imaging
Auteurs : Kary L. Myers [États-Unis] ; Anthony E. Brockwell [États-Unis] ; William F. Eddy [États-Unis]Source :
- Statistics in Medicine [ 0277-6715 ] ; 2007-09-20.
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Abstract
Measurement of stimulus‐induced changes in activity in the brain is critical to the advancement of neuroscience. Scientists use a range of methods, including electrode implantation, surface (scalp) electrode placement, and optical imaging of intrinsic signals, to gather data capturing underlying signals of interest in the brain. These data are usually corrupted by artifacts, complicating interpretation of the signal; in the context of optical imaging, two primary sources of corruption are the heartbeat and respiration cycles. We introduce a new linear state‐space framework that uses the Kalman filter to remove these artifacts from optical imaging data. The method relies on a likelihood‐based analysis under the specification of a formal statistical model, and allows for corrections to the signal based on auxiliary measurements of quantities closely related to the sources of contamination, such as physiological processes. Furthermore, the likelihood‐based modeling framework allows us to perform both goodness‐of‐fit testing and formal hypothesis testing on parameters of interest. Working with data collected by our collaborators, we demonstrate the method of data collection in an optical imaging study of a cat's brain. Copyright © 2007 John Wiley & Sons, Ltd.
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DOI: 10.1002/sim.2933
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Med.</title><idno type="ISSN">0277-6715</idno><idno type="eISSN">1097-0258</idno><imprint><publisher>John Wiley & Sons, Ltd.</publisher><pubPlace>Chichester, UK</pubPlace><date type="published" when="2007-09-20">2007-09-20</date><biblScope unit="volume">26</biblScope><biblScope unit="issue">21</biblScope><biblScope unit="page" from="3862">3862</biblScope><biblScope unit="page" to="3874">3874</biblScope></imprint><idno type="ISSN">0277-6715</idno></series><idno type="istex">32603085CBC21A0149FEF0BA08D6D645F251A6AE</idno><idno type="DOI">10.1002/sim.2933</idno><idno type="ArticleID">SIM2933</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0277-6715</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Kalman filtering</term><term>functional neuroimaging</term><term>linear state‐space models</term><term>optical imaging</term><term>orientation columns</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Measurement of stimulus‐induced changes in activity in the brain is critical to the advancement of neuroscience. Scientists use a range of methods, including electrode implantation, surface (scalp) electrode placement, and optical imaging of intrinsic signals, to gather data capturing underlying signals of interest in the brain. These data are usually corrupted by artifacts, complicating interpretation of the signal; in the context of optical imaging, two primary sources of corruption are the heartbeat and respiration cycles. We introduce a new linear state‐space framework that uses the Kalman filter to remove these artifacts from optical imaging data. The method relies on a likelihood‐based analysis under the specification of a formal statistical model, and allows for corrections to the signal based on auxiliary measurements of quantities closely related to the sources of contamination, such as physiological processes. Furthermore, the likelihood‐based modeling framework allows us to perform both goodness‐of‐fit testing and formal hypothesis testing on parameters of interest. Working with data collected by our collaborators, we demonstrate the method of data collection in an optical imaging study of a cat's brain. Copyright © 2007 John Wiley & Sons, Ltd.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Nouveau-Mexique</li><li>Pennsylvanie</li></region><settlement><li>Pittsburgh</li></settlement><orgName><li>Université Carnegie-Mellon</li></orgName></list><tree><country name="États-Unis"><region name="Nouveau-Mexique"><name sortKey="Myers, Kary L" sort="Myers, Kary L" uniqKey="Myers K" first="Kary L." last="Myers">Kary L. Myers</name></region><name sortKey="Brockwell, Anthony E" sort="Brockwell, Anthony E" uniqKey="Brockwell A" first="Anthony E." last="Brockwell">Anthony E. Brockwell</name><name sortKey="Eddy, William F" sort="Eddy, William F" uniqKey="Eddy W" first="William F." last="Eddy">William F. Eddy</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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