2024
A confounder controlled machine learning approach: Group analysis and classification of schizophrenia and Alzheimer’s disease using resting-state functional network connectivity
Hassanzadeh R, Abrol A, Pearlson G, Turner J, Calhoun V. A confounder controlled machine learning approach: Group analysis and classification of schizophrenia and Alzheimer’s disease using resting-state functional network connectivity. PLOS ONE 2024, 19: e0293053. PMID: 38768123, PMCID: PMC11104643, DOI: 10.1371/journal.pone.0293053.Peer-Reviewed Original ResearchConceptsResting-state functional network connectivityFunctional network connectivityResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingAlzheimer's diseaseClassification of schizophreniaNetwork pairsPatients to healthy controlsSchizophrenia patientsNeurobiological mechanismsSZ patientsSubcortical networksCerebellum networkSchizophreniaRs-fMRIDisorder developmentMotor networkCompare patient groupsSubcortical domainSZ disorderHealthy controlsMagnetic resonance imagingDisordersNetwork connectivityFunctional abnormalities
2015
Identifying Brain Dynamic Network States VIA GIG-ICA: Application to Schizophrenia, Bipolar and Schizoaffective Disorders
Du Y, Pearlson G, He H, Wu L, Chen J, Calhoun V. Identifying Brain Dynamic Network States VIA GIG-ICA: Application to Schizophrenia, Bipolar and Schizoaffective Disorders. 2015, 478-481. DOI: 10.1109/isbi.2015.7163915.Peer-Reviewed Original ResearchFunctional connectivity statesSchizoaffective disorderBipolar disorderSAD patientsDynamic functional networksFunctional networksConnectivity statesResting-state fMRI dataBP patientsHealthy controlsPatientsSZ patientsFunctional connectivitySimilar symptomsFMRI dataDisordersSchizophreniaMental diseasesSignificant differences