2015
Classification of Schizophrenia and Bipolar Patients Using Static and Time-Varying Resting-State FMRI Brain Connectivity
Rashid B, Arbabshirani M, Damaraju E, Millar R, Cetin M, Pearlson G, Calhoun V. Classification of Schizophrenia and Bipolar Patients Using Static and Time-Varying Resting-State FMRI Brain Connectivity. 2015, 251-254. DOI: 10.1109/isbi.2015.7163861.Peer-Reviewed Original ResearchClassification of schizophreniaHigh-dimensional dataAutomatic differential diagnosisAutomatic classificationAccurate classifierDimensional dataChallenging taskNetwork connectivityDiscriminative analysisHigh accuracyPowerful informationClassificationTraining subjectsLarge amountPrevious workDynamic functional network connectivityConnectivityClassifierFunctional network connectivityFNC analysisTaskBrain connectivityRobustnessFrameworkAccuracy
2012
Three-way FMRI-DTI-methylation data fusion based on mCCA+jICA and its application to schizophrenia
Sui J, He H, Liu J, Yu Q, Adali T, Pearlson G, Calhoun V. Three-way FMRI-DTI-methylation data fusion based on mCCA+jICA and its application to schizophrenia. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2012, 2012: 2692-2695. PMID: 23366480, DOI: 10.1109/embc.2012.6346519.Peer-Reviewed Original ResearchConceptsMulti-set canonical correlation analysisData fusionMulti-modal fusionDisparate data setsMultiple data typesJoint independent component analysisData typesFusion modelJoint informationData setsIndependent component analysisHigher decomposition accuracyEffective mannerCanonical correlation analysisDecomposition accuracyLimited viewEffective approachPromising approachBiomedical imagingFusionComponent analysisAccuracyIllness biomarkersInformationSet