A Computationally Efficient, Exploratory Approach to Brain Connectivity Incorporating False Discovery Rate Control, A Priori Knowledge, and Group Inference
Identifieur interne : 001297 ( Main/Exploration ); précédent : 001296; suivant : 001298A Computationally Efficient, Exploratory Approach to Brain Connectivity Incorporating False Discovery Rate Control, A Priori Knowledge, and Group Inference
Auteurs : Aiping Liu [Canada] ; Junning Li [États-Unis] ; Z. Jane Wang [Canada] ; Martin J. Mckeown [Canada]Source :
- Computational and Mathematical Methods in Medicine [ 1748-670X ] ; 2012.
English descriptors
- KwdEn :
- Algorithms, Bayes Theorem, Brain (metabolism), Brain (pathology), Brain (physiology), Brain Mapping (methods), Computer Graphics, Computer Simulation, False Positive Reactions, Humans, Levodopa (therapeutic use), Magnetic Resonance Imaging (methods), Models, Biological, Models, Statistical, Models, Theoretical, Multivariate Analysis, Parkinson Disease (metabolism), Probability.
- MESH :
- chemical , therapeutic use : Levodopa.
- metabolism : Brain, Parkinson Disease.
- methods : Brain Mapping, Magnetic Resonance Imaging.
- pathology : Brain.
- physiology : Brain.
- Algorithms, Bayes Theorem, Computer Graphics, Computer Simulation, False Positive Reactions, Humans, Models, Biological, Models, Statistical, Models, Theoretical, Multivariate Analysis, Probability.
Abstract
Graphical models appear well suited for inferring brain connectivity from fMRI data, as they can distinguish between direct and indirect brain connectivity. Nevertheless, biological interpretation requires not only that the multivariate time series are adequately modeled, but also that there is accurate error-control of the inferred edges. The PCfdr algorithm, which was developed by Li and Wang, was to provide a computationally efficient means to control the false discovery rate (FDR) of computed edges asymptotically. The original PCfdr algorithm was unable to accommodate
Url:
DOI: 10.1155/2012/967380
PubMed: 23251232
PubMed Central: 3509717
Affiliations:
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Jane" last="Wang">Z. Jane Wang</name><affiliation wicri:level="1"><nlm:aff id="I1">Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada V6T 1Z4</nlm:aff><country>Canada</country><wicri:regionArea>Department of Electrical and Computer Engineering, 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><affiliation wicri:level="1"><nlm:aff id="I1">Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada V6T 1Z4</nlm:aff><country>Canada</country><wicri:regionArea>Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC</wicri:regionArea><wicri:noRegion>BC</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:aff id="I3">Division of Neurology, Department of Medicine and Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, BC, Canada V5Z 1M9</nlm:aff><country>Canada</country><wicri:regionArea>Division of Neurology, Department of Medicine and Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, BC</wicri:regionArea><wicri:noRegion>BC</wicri:noRegion></affiliation></author></analytic><series><title level="j">Computational and Mathematical Methods in Medicine</title><idno type="ISSN">1748-670X</idno><idno type="eISSN">1748-6718</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Bayes Theorem</term><term>Brain (metabolism)</term><term>Brain (pathology)</term><term>Brain (physiology)</term><term>Brain Mapping (methods)</term><term>Computer Graphics</term><term>Computer Simulation</term><term>False Positive Reactions</term><term>Humans</term><term>Levodopa (therapeutic use)</term><term>Magnetic Resonance Imaging (methods)</term><term>Models, Biological</term><term>Models, Statistical</term><term>Models, Theoretical</term><term>Multivariate Analysis</term><term>Parkinson Disease (metabolism)</term><term>Probability</term></keywords><keywords scheme="MESH" type="chemical" qualifier="therapeutic use" xml:lang="en"><term>Levodopa</term></keywords><keywords scheme="MESH" qualifier="metabolism" xml:lang="en"><term>Brain</term><term>Parkinson Disease</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Brain Mapping</term><term>Magnetic Resonance Imaging</term></keywords><keywords scheme="MESH" qualifier="pathology" xml:lang="en"><term>Brain</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Brain</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Bayes Theorem</term><term>Computer Graphics</term><term>Computer Simulation</term><term>False Positive Reactions</term><term>Humans</term><term>Models, Biological</term><term>Models, Statistical</term><term>Models, Theoretical</term><term>Multivariate Analysis</term><term>Probability</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Graphical models appear well suited for inferring brain connectivity from fMRI data, as they can distinguish between direct and indirect brain connectivity. Nevertheless, biological interpretation requires not only that the multivariate time series are adequately modeled, but also that there is accurate error-control of the inferred edges. The PC<sub>fdr</sub> algorithm, which was developed by Li and Wang, was to provide a computationally efficient means to control the false discovery rate (FDR) of computed edges asymptotically. The original PC<sub>fdr</sub> algorithm was unable to accommodate <italic>a priori</italic> information about connectivity and was designed to infer connectivity from a single subject rather than a group of subjects. Here we extend the original PC<sub>fdr</sub> algorithm and propose a multisubject, error-rate-controlled brain connectivity modeling approach that allows incorporation of prior knowledge of connectivity. In simulations, we show that the two proposed extensions can still control the FDR around or below a specified threshold. When the proposed approach is applied to fMRI data in a Parkinson's disease study, we find robust group evidence of the disease-related changes, the compensatory changes, and the normalizing effect of L-dopa medication. The proposed method provides a robust, accurate, and practical method for the assessment of brain connectivity patterns from functional neuroimaging data.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Smith, Sm" uniqKey="Smith S">SM Smith</name></author><author><name sortKey="Miller, Kl" uniqKey="Miller K">KL Miller</name></author><author><name sortKey="Salimi Khorshidi, G" uniqKey="Salimi Khorshidi G">G Salimi-Khorshidi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Friston, Kj" uniqKey="Friston K">KJ Friston</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Mcintosh, Ar" uniqKey="Mcintosh A">AR McIntosh</name></author><author><name sortKey="Gonzalez Lima, F" uniqKey="Gonzalez Lima F">F Gonzalez-Lima</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Friston, Kj" uniqKey="Friston K">KJ Friston</name></author><author><name 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uniqKey="Fisher R">RA Fisher</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Palmer, Sj" uniqKey="Palmer S">SJ Palmer</name></author><author><name sortKey="Ng, B" uniqKey="Ng B">B Ng</name></author><author><name sortKey="Abugharbieh, R" uniqKey="Abugharbieh R">R Abugharbieh</name></author><author><name sortKey="Eigenraam, L" uniqKey="Eigenraam L">L Eigenraam</name></author><author><name sortKey="Mckeown, Mj" uniqKey="Mckeown M">MJ McKeown</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>Canada</li><li>États-Unis</li></country></list><tree><country name="Canada"><noRegion><name sortKey="Liu, Aiping" sort="Liu, Aiping" uniqKey="Liu A" first="Aiping" last="Liu">Aiping Liu</name></noRegion><name sortKey="Mckeown, Martin J" sort="Mckeown, Martin J" uniqKey="Mckeown M" first="Martin J." last="Mckeown">Martin J. Mckeown</name><name sortKey="Mckeown, Martin J" sort="Mckeown, Martin J" uniqKey="Mckeown M" first="Martin J." last="Mckeown">Martin J. Mckeown</name><name sortKey="Wang, Z Jane" sort="Wang, Z Jane" uniqKey="Wang Z" first="Z. Jane" last="Wang">Z. Jane Wang</name></country><country name="États-Unis"><noRegion><name sortKey="Li, Junning" sort="Li, Junning" uniqKey="Li J" first="Junning" last="Li">Junning Li</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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