2024
A telescopic independent component analysis on functional magnetic resonance imaging dataset
Mirzaeian S, Faghiri A, Calhoun V, Iraji A. A telescopic independent component analysis on functional magnetic resonance imaging dataset. Network Neuroscience 2024, 1-16. DOI: 10.1162/netn_a_00421.Peer-Reviewed Original ResearchRight frontoparietal networkVisual networkIndependent component analysisBrain functionExtraction of informationFunctional magnetic resonance imaging datasetsImage datasetsFrontoparietal networkMagnetic resonance imaging datasetFMRI dataGroup differencesLeverage informationSmall networksDMNNetworkComponent analysisIncomplete viewAbstract Brain functionFunctional sourceReconfiguration of Structural and Functional Connectivity Coupling in Patient Subgroups With Adolescent Depression
Xu M, Li X, Teng T, Huang Y, Liu M, Long Y, Lv F, Zhi D, Li X, Feng A, Yu S, Calhoun V, Zhou X, Sui J. Reconfiguration of Structural and Functional Connectivity Coupling in Patient Subgroups With Adolescent Depression. JAMA Network Open 2024, 7: e241933. PMID: 38470418, PMCID: PMC10933730, DOI: 10.1001/jamanetworkopen.2024.1933.Peer-Reviewed Original ResearchConceptsAdolescent major depressive disorderMajor depressive disorderSC-FC couplingIncreased SC-FC couplingSC-FCMode networkSuicide attemptsFirst-episode major depressive disorderVisual networkMDD subgroupsNonsuicidal self-injurious behaviorRates of self-injuryHealthy controlsResting-state functional MRI dataMagnetic resonance imagingCross-sectional studySelf-injurious behaviorOutpatient psychiatry clinicFunctional MRI dataMajor life eventsFirst Affiliated Hospital of Chongqing Medical UniversityDepressive disorderNeurobiological mechanismsChildhood traumaSelf-injuryRevealing 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 ResearchConceptsWhole-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
How Does Aging Affect Whole-brain Functional Network Connectivity? Evidence from An ICA Method
Du Y, Guo Y, Calhoun V. How Does Aging Affect Whole-brain Functional Network Connectivity? Evidence from An ICA Method. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083384, DOI: 10.1109/embc40787.2023.10340189.Peer-Reviewed Original ResearchICA-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 ResearchConceptsMajor 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 networkNeuropsychiatric Disorder Subtyping Via Clustered Deep Learning Classifier Explanations *
Ellis C, Miller R, Calhoun V. Neuropsychiatric Disorder Subtyping Via Clustered Deep Learning Classifier Explanations *. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083012, DOI: 10.1109/embc40787.2023.10340837.Peer-Reviewed Original ResearchConceptsDynamic functional network connectivityResting-state functional magnetic resonanceFunctional magnetic resonanceNeuropsychiatric disordersFunctional network connectivityCharacterization of schizophreniaCognitive controlDeep learning classifierContext of schizophreniaAuditory networkBrain activityBrain networksVisual networkSubcortical networksCerebellar networkAging brain shows joint declines in brain within-network connectivity and between-network connectivity: a large-sample study (N > 6,000)
Du Y, Guo Y, Calhoun V. Aging brain shows joint declines in brain within-network connectivity and between-network connectivity: a large-sample study (N > 6,000). Frontiers In Aging Neuroscience 2023, 15: 1159054. PMID: 37273655, PMCID: PMC10233064, DOI: 10.3389/fnagi.2023.1159054.Peer-Reviewed Original ResearchFunctional network connectivityWithin-network connectivityCognitive control networkFunctional networksIncreased within-network connectivityDecreased within-network connectivityWhole-brain functional networksControl networkReduced functional network connectivityBetween-network connectivityWhole-brain levelNon-pathological agingSub-cortical networksIndependent component analysisBrain functional networksMode networkAging-related changesSensorimotor networkNeuroimaging dataConnectivity declineVisual networkWhole-brainBrain agingAging brainEffects of age