2024
Local-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophrenia
Xing Y, Pearlson G, Kochunov P, Calhoun V, Du Y. Local-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophrenia. NeuroImage 2024, 299: 120839. PMID: 39251116, PMCID: PMC11491165, DOI: 10.1016/j.neuroimage.2024.120839.Peer-Reviewed Original ResearchConceptsSelection methodClassification accuracy gainsGraph-based regularizationHigh-dimensional dataFeature selection methodLocal structural informationSparse regularizationAblation studiesFeature subsetPublic datasetsFeature selectionClassification accuracyExperimental evaluationAccuracy gainsSelection techniquesNetwork connectivityData transformationSuperior performanceDatasetConvergence analysisStructural informationClassificationRegularizationFeaturesDisorder prediction
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