A generative model for activations in functional MRI

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011. === Cataloged from PDF version of thesis. === Includes bibliographical references (p. 87-91). === Detection of brain activity and selectivity using functional magnetic resonance imaging...

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Main Author: Sridharan, Ramesh
Other Authors: Polina Golland.
Format: Others
Language:English
Published: Massachusetts Institute of Technology 2011
Subjects:
Online Access:http://hdl.handle.net/1721.1/64595
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spelling ndltd-MIT-oai-dspace.mit.edu-1721.1-645952019-05-02T16:07:20Z A generative model for activations in functional MRI Generative model for activations in functional magnetic resonance imaging Sridharan, Ramesh Polina Golland. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011. Cataloged from PDF version of thesis. Includes bibliographical references (p. 87-91). Detection of brain activity and selectivity using functional magnetic resonance imaging (fMRI) provides unique insight into the underlying functional properties of the brain. We propose a generative model that jointly explains neural activation and temporal activity in an fMRI experiment. We derive an algorithm for inferring activation patterns and estimating the temporal response from fMRI data, and present results on synthetic and actual fMRI data, showing that the model performs well in both settings, and provides insight into patterns of selectivity. by Ramesh Sridharan. S.M. 2011-06-20T15:57:27Z 2011-06-20T15:57:27Z 2011 2011 Thesis http://hdl.handle.net/1721.1/64595 727065384 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 91 p. application/pdf Massachusetts Institute of Technology
collection NDLTD
language English
format Others
sources NDLTD
topic Electrical Engineering and Computer Science.
spellingShingle Electrical Engineering and Computer Science.
Sridharan, Ramesh
A generative model for activations in functional MRI
description Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011. === Cataloged from PDF version of thesis. === Includes bibliographical references (p. 87-91). === Detection of brain activity and selectivity using functional magnetic resonance imaging (fMRI) provides unique insight into the underlying functional properties of the brain. We propose a generative model that jointly explains neural activation and temporal activity in an fMRI experiment. We derive an algorithm for inferring activation patterns and estimating the temporal response from fMRI data, and present results on synthetic and actual fMRI data, showing that the model performs well in both settings, and provides insight into patterns of selectivity. === by Ramesh Sridharan. === S.M.
author2 Polina Golland.
author_facet Polina Golland.
Sridharan, Ramesh
author Sridharan, Ramesh
author_sort Sridharan, Ramesh
title A generative model for activations in functional MRI
title_short A generative model for activations in functional MRI
title_full A generative model for activations in functional MRI
title_fullStr A generative model for activations in functional MRI
title_full_unstemmed A generative model for activations in functional MRI
title_sort generative model for activations in functional mri
publisher Massachusetts Institute of Technology
publishDate 2011
url http://hdl.handle.net/1721.1/64595
work_keys_str_mv AT sridharanramesh agenerativemodelforactivationsinfunctionalmri
AT sridharanramesh generativemodelforactivationsinfunctionalmagneticresonanceimaging
AT sridharanramesh generativemodelforactivationsinfunctionalmri
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