Combining Multiple Connectomes via Canonical Correlation Analysis Improves Predictive Models
Gao S, Greene A, Todd Constable R, Scheinost D. Combining Multiple Connectomes via Canonical Correlation Analysis Improves Predictive Models. Lecture Notes In Computer Science 2018, 11072: 349-356. DOI: 10.1007/978-3-030-00931-1_40.Peer-Reviewed Original ResearchTask conditionsDifferent cognitive tasksMultiple task conditionsDifferent task conditionsConnectivity dataDifferent cognitive conditionsFunctional connectivity dataComputational modelHuman Connectome ProjectPrediction of behaviorCognitive tasksIndividual differencesBehavioral measuresBehavioral predictionsCognitive conditionsMultiple connectomesSingle taskFunctional connectivityConnectome ProjectDifferent tasksComplementary informationMultiple tasksTaskPrincipled methodCanonical correlation analysisTask Integration for Connectome-Based Prediction Via Canonical Correlation Analysis
Gao S, Greene A, Constable R, Scheinost D. Task Integration for Connectome-Based Prediction Via Canonical Correlation Analysis. 2018, 87-91. DOI: 10.1109/isbi.2018.8363529.Peer-Reviewed Original ResearchTask conditionsDifferent tasksDifferent cognitive tasksMultiple task conditionsDifferent task conditionsConnectivity dataDifferent cognitive conditionsFunctional connectivity dataHuman Connectome ProjectComputational modelPrediction of behaviorCognitive tasksFluid intelligenceIndividual differencesBehavioral measuresBehavioral predictionsCognitive conditionsSingle taskFunctional connectivityConnectome ProjectComplementary informationTask integrationTaskProof of conceptCanonical correlation analysis