2024
Neurodevelopmental subtypes of functional brain organization in the ABCD study using a rigorous analytic framework
DeRosa J, Friedman N, Calhoun V, Banich M. Neurodevelopmental subtypes of functional brain organization in the ABCD study using a rigorous analytic framework. NeuroImage 2024, 299: 120827. PMID: 39245397, DOI: 10.1016/j.neuroimage.2024.120827.Peer-Reviewed Original ResearchConceptsResting-state functional connectivityAdolescent Brain Cognitive DevelopmentIndividual’s resting-state functional connectivityAdolescent Brain Cognitive Development StudyFunctional brain organizationMental health profilesMental health characteristicsRsFC dataBrain organizationFunctional connectivityDevelopmental trajectoriesChildren aged 9Emotional functioningCognitive developmentLate childhoodAged 9SubtypesAdolescentsHealth characteristicsHealth profileChildhood4D 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 networksA 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 abnormalitiesCross‐cohort replicable resting‐state functional connectivity in predicting symptoms and cognition of schizophrenia
Zhao C, Jiang R, Bustillo J, Kochunov P, Turner J, Liang C, Fu Z, Zhang D, Qi S, Calhoun V. Cross‐cohort replicable resting‐state functional connectivity in predicting symptoms and cognition of schizophrenia. Human Brain Mapping 2024, 45: e26694. PMID: 38727014, PMCID: PMC11083889, DOI: 10.1002/hbm.26694.Peer-Reviewed Original ResearchConceptsFunctional magnetic resonance imagingNegative symptomsFunctional connectivityCognitive impairmentPrediction of negative symptomsResting-state functional connectivityAssociated with reduced cognitive functionDebilitating mental illnessHealthy controlsPredicting functional connectivityEarly adulthood onsetPositive symptomsNeural underpinningsSchizophreniaCognitive functionSensorimotor networkPredicting symptomsMental illnessConnectivity patternsClinical interventionsMagnetic resonance imagingAdulthood onsetSymptomsImpairmentResonance imagingThe overlap across psychotic disorders: A functional network connectivity analysis
Dini H, Bruni L, Ramsøy T, Calhoun V, Sendi M. The overlap across psychotic disorders: A functional network connectivity analysis. International Journal Of Psychophysiology 2024, 201: 112354. PMID: 38670348, PMCID: PMC11163820, DOI: 10.1016/j.ijpsycho.2024.112354.Peer-Reviewed Original ResearchConceptsFunctional network connectivitySchizoaffective disorderPsychotic disordersHealthy controlsBipolar-Schizophrenia NetworkFunctional network connectivity analysisStatic functional network connectivityResting-state fMRINetwork connectivity analysisPatterns of activityPsychiatric disordersDisorder groupSchizophreniaConnectivity analysisHC groupBipolarConnectivity patternsDisordersPatient groupSymptom scoresGroup of patientsPANSSSchizoaffectiveFMRINetwork connectivityRevealing complex functional topology brain network correspondences between humans and marmosets
Li Q, Calhoun V, Iraji A. Revealing complex functional topology brain network correspondences between humans and marmosets. Neuroscience Letters 2024, 822: 137624. PMID: 38218321, DOI: 10.1016/j.neulet.2024.137624.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsBrainBrain MappingCallithrixConnectomeHumansMagnetic Resonance ImagingNeural PathwaysConceptsWhole-brain functional connectivityFunctional brain connectivityDorsal attention networkFunctional connectivity patternsBrain connectivityMarmoset monkey brainBrain networksTopological characteristicsMode networkFunctional connectivityCognitive functionVisual networkNon-human primatesMonkey brainAttention networkConnectivity patternsNeural connectionsBrainFunctional correspondenceConnectome
2023
A Multivariate Method for Estimating and comparing whole brain functional connectomes from fMRI and PET data
Saha D, Bohsali A, Saha R, Hajjar I, Calhoun V. A Multivariate Method for Estimating and comparing whole brain functional connectomes from fMRI and PET data. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083351, DOI: 10.1109/embc40787.2023.10340631.Peer-Reviewed Original ResearchConceptsWhole-brain functional connectomePositron emission tomographyResting fMRIResting fMRI dataBrain positron emission tomographyBrain functional connectomePositron emission tomography dataResting networksMagnetic resonance imagingConnectomeFunctional connectomeBrain networksConnectome patternsFMRIFMRI dataBrain functionSubject expressionPiB-PET scansBrainEmission tomographySpatial mappingSpatial networksClinical Relevance-This studyPET scansResonance imagingICA-based Individualized Differential Structure Similarity Networks for Predicting Symptom Scores in Adolescents with Major Depressive Disorder
Li X, Xu M, Jiang R, Li X, Calhoun V, Zhou X, Sui J. ICA-based Individualized Differential Structure Similarity Networks for Predicting Symptom Scores in Adolescents with Major Depressive Disorder. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-5. PMID: 38082692, DOI: 10.1109/embc40787.2023.10340456.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentBrainConnectomeDepressive Disorder, MajorGray MatterHumansMagnetic Resonance ImagingConceptsMajor depressive disorderGray matter volumeDepressive disorderWhole-brain structural covariance networksConnectome-based predictive modelingAdolescent MDD patientsComplex mood disorderMeasure individual differencesDefault-mode networkStructural brain alterationsStructural covariance networksHamilton Depression ScaleHamilton Anxiety ScaleSpatially constrained ICAMDD patientsMood disordersBrain alterationsMatter volumeIndividual differencesBrain structuresCovariance networksAnxiety ScaleVisual networkDepression ScaleStructure similarity network