Recursive Cluster Elimination Based Support Vector Machine for Disease State Prediction Using Resting State Functional and Effective Brain Connectivity
Identifieur interne : 003627 ( Main/Merge ); précédent : 003626; suivant : 003628Recursive Cluster Elimination Based Support Vector Machine for Disease State Prediction Using Resting State Functional and Effective Brain Connectivity
Auteurs : Gopikrishna Deshpande [États-Unis] ; Zhihao Li [États-Unis] ; Priya Santhanam [États-Unis] ; Claire D. Coles [États-Unis] ; Mary Ellen Lynch [États-Unis] ; Stephan Hamann [États-Unis] ; Xiaoping Hu [États-Unis]Source :
- PLoS ONE [ 1932-6203 ] ; 2010.
Abstract
Brain state classification has been accomplished using features such as voxel intensities, derived from functional magnetic resonance imaging (fMRI) data, as inputs to efficient classifiers such as support vector machines (SVM) and is based on the spatial localization model of brain function. With the advent of the connectionist model of brain function, features from brain networks may provide increased discriminatory power for brain state classification.
In this study, we introduce a novel framework where in both functional connectivity (FC) based on instantaneous temporal correlation and effective connectivity (EC) based on causal influence in brain networks are used as features in an SVM classifier. In order to derive those features, we adopt a novel approach recently introduced by us called correlation-purged Granger causality (CPGC) in order to obtain both FC and EC from fMRI data simultaneously without the instantaneous correlation contaminating Granger causality. In addition, statistical learning is accelerated and performance accuracy is enhanced by combining recursive cluster elimination (RCE) algorithm with the SVM classifier. We demonstrate the efficacy of the CPGC-based RCE-SVM approach using a specific instance of brain state classification exemplified by disease state prediction. Accordingly, we show that this approach is capable of predicting with 90.3% accuracy whether any given human subject was prenatally exposed to cocaine or not, even when no significant behavioral differences were found between exposed and healthy subjects.
The framework adopted in this work is quite general in nature with prenatal cocaine exposure being only an illustrative example of the power of this approach. In any brain state classification approach using neuroimaging data, including the directional connectivity information may prove to be a performance enhancer. When brain state classification is used for disease state prediction, our approach may aid the clinicians in performing more accurate diagnosis of diseases in situations where in non-neuroimaging biomarkers may be unable to perform differential diagnosis with certainty.
Url:
DOI: 10.1371/journal.pone.0014277
PubMed: 21151556
PubMed Central: 3000328
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type="wicri:Area/Main/Merge">003627</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Recursive Cluster Elimination Based Support Vector Machine for Disease State Prediction Using Resting State Functional and Effective Brain Connectivity</title><author><name sortKey="Deshpande, Gopikrishna" sort="Deshpande, Gopikrishna" uniqKey="Deshpande G" first="Gopikrishna" last="Deshpande">Gopikrishna Deshpande</name><affiliation wicri:level="2"><nlm:aff id="aff1"><addr-line>Department of Electrical and Computer Engineering, Auburn University MRI Research Center, Auburn University, Auburn, Alabama, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Electrical and Computer Engineering, Auburn University MRI Research Center, Auburn University, Auburn, Alabama</wicri:regionArea><placeName><region type="state">Alabama</region></placeName></affiliation></author><author><name sortKey="Li, Zhihao" sort="Li, Zhihao" uniqKey="Li Z" first="Zhihao" last="Li">Zhihao Li</name><affiliation wicri:level="2"><nlm:aff id="aff2"><addr-line>Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia</wicri:regionArea><placeName><region type="state">Géorgie (États-Unis)</region></placeName></affiliation></author><author><name sortKey="Santhanam, Priya" sort="Santhanam, Priya" uniqKey="Santhanam P" first="Priya" last="Santhanam">Priya Santhanam</name><affiliation wicri:level="2"><nlm:aff id="aff2"><addr-line>Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia</wicri:regionArea><placeName><region type="state">Géorgie (États-Unis)</region></placeName></affiliation></author><author><name sortKey="Coles, Claire D" sort="Coles, Claire D" uniqKey="Coles C" first="Claire D." last="Coles">Claire D. Coles</name><affiliation wicri:level="2"><nlm:aff id="aff3"><addr-line>Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia</wicri:regionArea><placeName><region type="state">Géorgie (États-Unis)</region></placeName></affiliation></author><author><name sortKey="Lynch, Mary Ellen" sort="Lynch, Mary Ellen" uniqKey="Lynch M" first="Mary Ellen" last="Lynch">Mary Ellen Lynch</name><affiliation wicri:level="2"><nlm:aff id="aff3"><addr-line>Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia</wicri:regionArea><placeName><region type="state">Géorgie (États-Unis)</region></placeName></affiliation></author><author><name sortKey="Hamann, Stephan" sort="Hamann, Stephan" uniqKey="Hamann S" first="Stephan" last="Hamann">Stephan Hamann</name><affiliation wicri:level="2"><nlm:aff id="aff4"><addr-line>Department of Psychology, Emory University, Atlanta, Georgia, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Psychology, Emory University, Atlanta, Georgia</wicri:regionArea><placeName><region type="state">Géorgie (États-Unis)</region></placeName></affiliation></author><author><name sortKey="Hu, Xiaoping" sort="Hu, Xiaoping" uniqKey="Hu X" first="Xiaoping" last="Hu">Xiaoping Hu</name><affiliation wicri:level="2"><nlm:aff id="aff2"><addr-line>Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia</wicri:regionArea><placeName><region type="state">Géorgie (États-Unis)</region></placeName></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2010">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>Brain state classification has been accomplished using features such as voxel intensities, derived from functional magnetic resonance imaging (fMRI) data, as inputs to efficient classifiers such as support vector machines (SVM) and is based on the spatial localization model of brain function. With the advent of the connectionist model of brain function, features from brain networks may provide increased discriminatory power for brain state classification.</p></sec><sec><title>Methodology/Principal Findings</title><p>In this study, we introduce a novel framework where in both functional connectivity (FC) based on instantaneous temporal correlation and effective connectivity (EC) based on causal influence in brain networks are used as features in an SVM classifier. In order to derive those features, we adopt a novel approach recently introduced by us called correlation-purged Granger causality (CPGC) in order to obtain both FC and EC from fMRI data simultaneously without the instantaneous correlation contaminating Granger causality. In addition, statistical learning is accelerated and performance accuracy is enhanced by combining recursive cluster elimination (RCE) algorithm with the SVM classifier. We demonstrate the efficacy of the CPGC-based RCE-SVM approach using a specific instance of brain state classification exemplified by disease state prediction. Accordingly, we show that this approach is capable of predicting with 90.3% accuracy whether any given human subject was prenatally exposed to cocaine or not, even when no significant behavioral differences were found between exposed and healthy subjects.</p></sec><sec><title>Conclusions/Significance</title><p>The framework adopted in this work is quite general in nature with prenatal cocaine exposure being only an illustrative example of the power of this approach. In any brain state classification approach using neuroimaging data, including the directional connectivity information may prove to be a performance enhancer. When brain state classification is used for disease state prediction, our approach may aid the clinicians in performing more accurate diagnosis of diseases in situations where in non-neuroimaging biomarkers may be unable to perform differential diagnosis with certainty.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Laconte, S" uniqKey="Laconte S">S LaConte</name></author><author><name sortKey="Peltier, S" uniqKey="Peltier S">S Peltier</name></author><author><name sortKey="Hu, X" uniqKey="Hu X">X Hu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="De Martino, F" uniqKey="De Martino F">F De Martino</name></author><author><name sortKey="Valente, G" uniqKey="Valente G">G Valente</name></author><author><name sortKey="Staeren, N" uniqKey="Staeren N">N Staeren</name></author><author><name sortKey="Ashburner, J" uniqKey="Ashburner J">J Ashburner</name></author><author><name sortKey="Goebel, R" 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James</name></author><author><name sortKey="Craddock, R" uniqKey="Craddock R">R Craddock</name></author><author><name sortKey="Mayberg, H" uniqKey="Mayberg H">H Mayberg</name></author><author><name sortKey="Hu, X" uniqKey="Hu X">X Hu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Weiskopf, N" uniqKey="Weiskopf N">N Weiskopf</name></author><author><name sortKey="Mathiak, K" uniqKey="Mathiak K">K Mathiak</name></author><author><name sortKey="Bock, S" uniqKey="Bock S">S Bock</name></author><author><name sortKey="Scharnowski, F" uniqKey="Scharnowski F">F Scharnowski</name></author><author><name sortKey="Veit, R" uniqKey="Veit R">R Veit</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Yoo, S" uniqKey="Yoo S">S Yoo</name></author><author><name sortKey="Fairneny, T" uniqKey="Fairneny T">T Fairneny</name></author><author><name sortKey="Chen, N" uniqKey="Chen N">N Chen</name></author><author><name sortKey="Choo, S" uniqKey="Choo S">S Choo</name></author><author><name sortKey="Panych, L" uniqKey="Panych L">L Panych</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Sitaram, R" uniqKey="Sitaram R">R Sitaram</name></author><author><name sortKey="Caria, A" uniqKey="Caria A">A Caria</name></author><author><name sortKey="Veit, R" uniqKey="Veit R">R Veit</name></author><author><name sortKey="Gaber, T" uniqKey="Gaber T">T Gaber</name></author><author><name sortKey="Rota, G" uniqKey="Rota G">G Rota</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Sitaram, R" uniqKey="Sitaram R">R Sitaram</name></author><author><name sortKey="Weiskopf, N" uniqKey="Weiskopf N">N Weiskopf</name></author><author><name sortKey="Caria, A" uniqKey="Caria A">A Caria</name></author><author><name sortKey="Veit, R" uniqKey="Veit R">R Veit</name></author><author><name sortKey="Erb, M" uniqKey="Erb M">M Erb</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Decharms, R" uniqKey="Decharms R">R deCharms</name></author><author><name sortKey="Maeda, F" uniqKey="Maeda F">F Maeda</name></author><author><name sortKey="Glover, G" uniqKey="Glover G">G Glover</name></author><author><name sortKey="Ludlow, D" uniqKey="Ludlow D">D Ludlow</name></author><author><name sortKey="Pauly, J" uniqKey="Pauly J">J Pauly</name></author></analytic></biblStruct></listBibl></div1></back></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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