A probabilistic model for discovering high level brain activities from fMRI

Li, J; Tao, D

A probabilistic model for discovering high level brain activities from fMRI

Li, J

Tao, D

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, 7062 LNCS (PART 1), pp. 329 - 336
Issue Date:: 2011-11-28

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Field	Value	Language
dc.contributor.author	Li, J https://orcid.org/0000-0002-1336-2241	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2011-11-28	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, 7062 LNCS (PART 1), pp. 329 - 336	en_US
dc.identifier.isbn	9783642249549	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31237
dc.description.abstract	Functional magnetic resonance imaging (fMRI) has provided an invaluable method of investing real time neuron activities. Statistical tools have been developed to recognise the mental state from a batch of fMRI observations over a period. However, an interesting question is whether it is possible to estimate the real time mental states at each moment during the fMRI observation. In this paper, we address this problem by building a probabilistic model of the brain activity. We model the tempo-spatial relations among the hidden high-level mental states and observable low-level neuron activities. We verify our model by experiments on practical fMRI data. The model also implies interesting clues on the task-responsible regions in the brain. © 2011 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-24955-6_40	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A probabilistic model for discovering high level brain activities from fMRI	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 1	en_US
utslib.citation.volume	7062 LNCS	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Shanghai, China
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	PART 1	en_US
pubs.publication-status	Published	en_US
pubs.volume	7062 LNCS	en_US

Abstract:

Functional magnetic resonance imaging (fMRI) has provided an invaluable method of investing real time neuron activities. Statistical tools have been developed to recognise the mental state from a batch of fMRI observations over a period. However, an interesting question is whether it is possible to estimate the real time mental states at each moment during the fMRI observation. In this paper, we address this problem by building a probabilistic model of the brain activity. We model the tempo-spatial relations among the hidden high-level mental states and observable low-level neuron activities. We verify our model by experiments on practical fMRI data. The model also implies interesting clues on the task-responsible regions in the brain. © 2011 Springer-Verlag.

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http://hdl.handle.net/10453/31237