2024
A spatially constrained independent component analysis jointly informed by structural and functional network connectivity
Fouladivanda M, Iraji A, Wu L, van Erp T, Belger A, Hawamdeh F, Pearlson G, Calhoun V. A spatially constrained independent component analysis jointly informed by structural and functional network connectivity. Network Neuroscience 2024, 1-31. DOI: 10.1162/netn_a_00398.Peer-Reviewed Original ResearchIntrinsic connectivity networksFunctional brain connectivityBrain connectivityStructural connectivityFunctional connectivityIndependent component analysisResting-state functional MRIAnalysis of group differencesBrain functional organizationFunctional network connectivityStructural-functional connectivityNeuroimaging studiesFunctional MRIWhole-brain tractographyGroup differencesRs-fMRIBrain disordersFunctional couplingSchizophreniaStatistical analysis of group differencesSubject levelFunctional organizationConnectivity networksBrainDiffusion-weighted MRI
2023
Multi-model order spatially constrained ICA reveals highly replicable group differences and consistent predictive results from resting data: A large N fMRI schizophrenia study
Meng X, Iraji A, Fu Z, Kochunov P, Belger A, Ford J, McEwen S, Mathalon D, Mueller B, Pearlson G, Potkin S, Preda A, Turner J, van Erp T, Sui J, Calhoun V. Multi-model order spatially constrained ICA reveals highly replicable group differences and consistent predictive results from resting data: A large N fMRI schizophrenia study. NeuroImage Clinical 2023, 38: 103434. PMID: 37209635, PMCID: PMC10209454, DOI: 10.1016/j.nicl.2023.103434.Peer-Reviewed Original ResearchConceptsIndependent component analysisData-driven approachData miningF1 scoreClassification modelReference algorithmNetwork connectivityMagnetic resonance imaging dataNetworkImaging dataPredictive resultsPatient dataFunctional magnetic resonance imaging (fMRI) dataData acquisition timeConnectivity networksFrameworkConnectivityPromising approachNew subjectMiningAnalytic approachAlgorithmDatasetAcquisition timeComponent analysis
2022
Brain-behavior relationships of simulated naturalistic automobile driving under the influence of acute cannabis intoxication: A double-blind, placebo-controlled study
Meda S, Stevens M, Boer E, Boyle C, Book G, Ward N, Pearlson G. Brain-behavior relationships of simulated naturalistic automobile driving under the influence of acute cannabis intoxication: A double-blind, placebo-controlled study. 2022 DOI: 10.26828/cannabis.2022.02.000.32.Peer-Reviewed Original ResearchBrain-behavior relationshipsEffects of cannabisFunctional MRICar followingMotion-sensitive visual cortexSpecific driving skillsInferior frontal gyrusSuperior temporal gyrusFrequent cannabis usersComplex everyday activitiesGroup independent component analysisInfluence of cannabisAcute cannabis intoxicationBehavioral factorsKey brain areasIndependent component analysisFrontal gyrusDriving skillsBehavior factorsTemporal gyrusDifferent driving behaviorsSafe overtakingCannabis usersConnectivity differencesFMRI data
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