4D dynamic spatial brain networks at rest linked to cognition show atypical variability and coupling in schizophrenia
Pusuluri K, Fu Z, Miller R, Pearlson G, Kochunov P, Van Erp T, Iraji A, Calhoun V. 4D dynamic spatial brain networks at rest linked to cognition show atypical variability and coupling in schizophrenia. Human Brain Mapping 2024, 45: e26773. PMID: 39045900, PMCID: PMC11267451, DOI: 10.1002/hbm.26773.Peer-Reviewed Original ResearchConceptsBrain networksFunctional magnetic resonance imagingAssociated with cognitive performanceDynamics of functional brain networksAssociated with cognitionFunctional brain networksVoxel-wise changesVolumetric couplingDynamical variablesCognitive performanceTypical controlsSchizophreniaCognitive impairmentNetwork pairsMagnetic resonance imagingPair of networksCognitionAtypical variabilityResonance imagingCouplingNetwork connectivityNetwork growthImpairmentBrainStatic networksEdge-centric network control on the human brain structural network
Sun H, Rosenblatt M, Dadashkarimi J, Rodriguez R, Tejavibulya L, Scheinost D. Edge-centric network control on the human brain structural network. Imaging Neuroscience 2024, 2: 1-15. DOI: 10.1162/imag_a_00191.Peer-Reviewed Original ResearchHuman brain structural networksNetwork control theoryEdge controlWhole-brain networksHuman Connectome ProjectDiffusion MRI dataWhite matter connectivityConnectome ProjectBrain dynamicsExecutive functionBrain structural networksBrain network connectivityBrain connectivityFunctional connectomeState transitionsTransitionEnergy patternsTheory modelBrain energy consumptionDynamic processStructural networkStateNetwork control mechanismsCognitive statesNetwork pairsCapturing Stretching and Shrinking of Inter-Network Temporal Coupling in FMRI Via WARP Elasticity
Wiafe S, Faghiri A, Fu Z, Miller R, Calhoun V. Capturing Stretching and Shrinking of Inter-Network Temporal Coupling in FMRI Via WARP Elasticity. 2024, 00: 1-4. DOI: 10.1109/isbi56570.2024.10635377.Peer-Reviewed Original ResearchA 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 abnormalitiesNeural Correlates of Novelty-Evoked Distress in 4-Month-Old Infants: A Synthetic Cohort Study
Filippi C, Winkler A, Kanel D, Elison J, Hardiman H, Sylvester C, Pine D, Fox N. Neural Correlates of Novelty-Evoked Distress in 4-Month-Old Infants: A Synthetic Cohort Study. Biological Psychiatry Cognitive Neuroscience And Neuroimaging 2024, 9: 905-914. PMID: 38641209, PMCID: PMC11381178, DOI: 10.1016/j.bpsc.2024.03.008.Peer-Reviewed Original ResearchBrain-behavior associationsParent-report measuresInfant temperamentNetwork pairsDorsal attention network connectivityAssessment of infant temperamentParent-reported temperamentAttention network connectivityControl network connectivitySocial anxietyFMRI studyNeural correlatesNeural basisFunctional connectivityParent reportNeuroimaging dataTemperamentAttention-controlComposite scoreDistressNetwork connectivityObservational assessmentNegative associationBehavior estimationDorsal
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