Group replicator dynamics: a novel group-wise evolutionary approach for sparse brain network detection.
Identifieur interne : 001225 ( Main/Exploration ); précédent : 001224; suivant : 001226Group replicator dynamics: a novel group-wise evolutionary approach for sparse brain network detection.
Auteurs : Bernard Ng [Canada] ; Martin J. Mckeown ; Rafeef AbugharbiehSource :
- IEEE transactions on medical imaging [ 1558-254X ] ; 2012.
English descriptors
- KwdEn :
- MESH :
- methods : Magnetic Resonance Imaging.
- physiology : Brain.
- physiopathology : Brain, Parkinson Disease.
- Case-Control Studies, Databases, Factual, Humans, Image Processing, Computer-Assisted, Models, Neurological, Principal Component Analysis, Reproducibility of Results.
Abstract
Functional magnetic resonance imaging (fMRI) is increasingly used for studying functional integration of the brain. However, large inter-subject variability in functional connectivity, particularly in disease populations, renders detection of representative group networks challenging. In this paper, we propose a novel technique, "group replicator dynamics" (GRD), for detecting sparse functional brain networks that are common across a group of subjects. We extend the replicator dynamics (RD) approach, which we show to be a solution of the nonnegative sparse principal component analysis problem, by integrating group information into each subject's RD process. Our proposed strategy effectively coaxes all subjects' networks to evolve towards the common network of the group. This results in sparse networks comprising the same brain regions across subjects yet with subject-specific weightings of the identified brain regions. Thus, in contrast to traditional averaging approaches, GRD enables inter-subject variability to be modeled, which facilitates statistical group inference. Quantitative validation of GRD on synthetic data demonstrated superior network detection performance over standard methods. When applied to real fMRI data, GRD detected task-specific networks that conform well to prior neuroscience knowledge.
DOI: 10.1109/TMI.2011.2173699
PubMed: 22049362
Affiliations:
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Mckeown</name></author><author><name sortKey="Abugharbieh, Rafeef" sort="Abugharbieh, Rafeef" uniqKey="Abugharbieh R" first="Rafeef" last="Abugharbieh">Rafeef Abugharbieh</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2012">2012</date><idno type="RBID">pubmed:22049362</idno><idno type="pmid">22049362</idno><idno type="doi">10.1109/TMI.2011.2173699</idno><idno type="wicri:Area/PubMed/Corpus">000A94</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000A94</idno><idno type="wicri:Area/PubMed/Curation">000A94</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000A94</idno><idno type="wicri:Area/PubMed/Checkpoint">000A94</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">000A94</idno><idno type="wicri:Area/Ncbi/Merge">000F96</idno><idno type="wicri:Area/Ncbi/Curation">000F96</idno><idno type="wicri:Area/Ncbi/Checkpoint">000F96</idno><idno type="wicri:Area/Main/Merge">001250</idno><idno type="wicri:Area/Main/Curation">001225</idno><idno type="wicri:Area/Main/Exploration">001225</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Group replicator dynamics: a novel group-wise evolutionary approach for sparse brain network detection.</title><author><name sortKey="Ng, Bernard" sort="Ng, Bernard" uniqKey="Ng B" first="Bernard" last="Ng">Bernard Ng</name><affiliation wicri:level="1"><nlm:affiliation>Biomedical Signal and Image Computing Laboratory (BiSICL), The University of British Columbia, Vancouver, BC, Canada. bernardyng@gmail.com</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Biomedical Signal and Image Computing Laboratory (BiSICL), The University of British Columbia, Vancouver, BC</wicri:regionArea><wicri:noRegion>BC</wicri:noRegion></affiliation></author><author><name sortKey="Mckeown, Martin J" sort="Mckeown, Martin J" uniqKey="Mckeown M" first="Martin J" last="Mckeown">Martin J. Mckeown</name></author><author><name sortKey="Abugharbieh, Rafeef" sort="Abugharbieh, Rafeef" uniqKey="Abugharbieh R" first="Rafeef" last="Abugharbieh">Rafeef Abugharbieh</name></author></analytic><series><title level="j">IEEE transactions on medical imaging</title><idno type="eISSN">1558-254X</idno><imprint><date when="2012" type="published">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Brain (physiology)</term><term>Brain (physiopathology)</term><term>Case-Control Studies</term><term>Databases, Factual</term><term>Humans</term><term>Image Processing, Computer-Assisted</term><term>Magnetic Resonance Imaging (methods)</term><term>Models, Neurological</term><term>Parkinson Disease (physiopathology)</term><term>Principal Component Analysis</term><term>Reproducibility of Results</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Magnetic Resonance Imaging</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Brain</term></keywords><keywords scheme="MESH" qualifier="physiopathology" xml:lang="en"><term>Brain</term><term>Parkinson Disease</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Case-Control Studies</term><term>Databases, Factual</term><term>Humans</term><term>Image Processing, Computer-Assisted</term><term>Models, Neurological</term><term>Principal Component Analysis</term><term>Reproducibility of Results</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Functional magnetic resonance imaging (fMRI) is increasingly used for studying functional integration of the brain. However, large inter-subject variability in functional connectivity, particularly in disease populations, renders detection of representative group networks challenging. In this paper, we propose a novel technique, "group replicator dynamics" (GRD), for detecting sparse functional brain networks that are common across a group of subjects. We extend the replicator dynamics (RD) approach, which we show to be a solution of the nonnegative sparse principal component analysis problem, by integrating group information into each subject's RD process. Our proposed strategy effectively coaxes all subjects' networks to evolve towards the common network of the group. This results in sparse networks comprising the same brain regions across subjects yet with subject-specific weightings of the identified brain regions. Thus, in contrast to traditional averaging approaches, GRD enables inter-subject variability to be modeled, which facilitates statistical group inference. Quantitative validation of GRD on synthetic data demonstrated superior network detection performance over standard methods. When applied to real fMRI data, GRD detected task-specific networks that conform well to prior neuroscience knowledge.</div></front></TEI><affiliations><list><country><li>Canada</li></country></list><tree><noCountry><name sortKey="Abugharbieh, Rafeef" sort="Abugharbieh, Rafeef" uniqKey="Abugharbieh R" first="Rafeef" last="Abugharbieh">Rafeef Abugharbieh</name><name sortKey="Mckeown, Martin J" sort="Mckeown, Martin J" uniqKey="Mckeown M" first="Martin J" last="Mckeown">Martin J. Mckeown</name></noCountry><country name="Canada"><noRegion><name sortKey="Ng, Bernard" sort="Ng, Bernard" uniqKey="Ng B" first="Bernard" last="Ng">Bernard Ng</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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