2026-09-18T22:51:49Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/645952022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Polina Golland.
Sridharan, Ramesh
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2011-06-20T15:57:27Z
2011-06-20T15:57:27Z
2011
2011
http://hdl.handle.net/1721.1/64595
727065384
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.
91 p.
eng
Massachusetts Institute of Technology
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
Electrical Engineering and Computer Science.
A generative model for activations in functional MRI
Generative model for activations in functional magnetic resonance imaging
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="853658a1-8467-41c7-99d4-be09e39e6449">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>A generative model for activations in functional MRI</Title>
<Subtitle>Generative model for activations in functional magnetic resonance imaging</Subtitle>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2011</PublicationDate>
<Authors>
<Author>
<DisplayName>Sridharan, Ramesh</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>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.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>