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, 1-76. 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 MRIA Trifecta of Deep Learning Models: Assessing Brain Health by Integrating Assessment and Neuroimaging Data
Ajith M, M. Aycock D, B. Tone E, Liu J, B. Misiura M, Ellis R, M. Plis S, Z. King T, M. Dotson V, Calhoun V. A Trifecta of Deep Learning Models: Assessing Brain Health by Integrating Assessment and Neuroimaging Data. Aperture Neuro 2024, 4 DOI: 10.52294/001c.118576.Peer-Reviewed Original ResearchStatic functional network connectivityBrain health indexBrain healthResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingPsychological assessment measuresAssessment dataFunctional network connectivityMental health disordersBrain systemsEvaluating brain healthNeuroimaging dataRs-fMRINeural patternsPhysical well-beingCognitive declineAssessment measuresHealth disordersVariational autoencoderNeuroimagingHealthy brainBrainMagnetic resonance imagingTesting phaseWell-beingA 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 abnormalitiesA whole-brain neuromark resting-state fMRI analysis of first-episode and early psychosis: Evidence of aberrant cortical-subcortical-cerebellar functional circuitry
Jensen K, Calhoun V, Fu Z, Yang K, Faria A, Ishizuka K, Sawa A, Andrés-Camazón P, Coffman B, Seebold D, Turner J, Salisbury D, Iraji A. A whole-brain neuromark resting-state fMRI analysis of first-episode and early psychosis: Evidence of aberrant cortical-subcortical-cerebellar functional circuitry. NeuroImage Clinical 2024, 41: 103584. PMID: 38422833, PMCID: PMC10944191, DOI: 10.1016/j.nicl.2024.103584.Peer-Reviewed Original ResearchConceptsFunctional network connectivityFirst-episodeEarly psychosisAberrant functional network connectivityResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingCorrelates of psychosisResting-state fMRI analysisWhole-brain approachPsychiatric disordersPsychiatric illnessSubcortical regionsCerebellar regionsFMRI analysisPsychosisControl participantsCognitive functionRs-fMRICerebellar connectivityMulti-site datasetFunctional circuitryMagnetic resonance imagingCircuitryResonance imagingProminent patternA deep learning approach for mental health quality prediction using functional network connectivity and assessment data
Ajith M, Aycock D, Tone E, Liu J, Misiura M, Ellis R, Plis S, King T, Dotson V, Calhoun V. A deep learning approach for mental health quality prediction using functional network connectivity and assessment data. Brain Imaging And Behavior 2024, 18: 630-645. PMID: 38340285, DOI: 10.1007/s11682-024-00857-y.Peer-Reviewed Original ResearchStatic functional network connectivityMental health qualityFunctional network connectivityMental health categoriesRs-fMRIMental healthPatterns of abnormal connectivityHealth categoriesHealth qualityDevelopment of personalized interventionsManagement of mental healthResting-state fMRIMeasure mental healthUK Biobank datasetNeural patternsBrain healthVisual domainAbnormal connectionPersonalized interventionsBiobank datasetTreatment responseHealthNetwork connectivityBehavioral aspectsAssessment data
2023
Interpretable LSTM model reveals transiently-realized patterns of dynamic brain connectivity that predict patient deterioration or recovery from very mild cognitive impairment
Gao Y, Lewis N, Calhoun V, Miller R. Interpretable LSTM model reveals transiently-realized patterns of dynamic brain connectivity that predict patient deterioration or recovery from very mild cognitive impairment. Computers In Biology And Medicine 2023, 161: 107005. PMID: 37211004, PMCID: PMC10365638, DOI: 10.1016/j.compbiomed.2023.107005.Peer-Reviewed Original ResearchConceptsMild cognitive impairmentDynamic functional network connectivityCognitive impairmentResting-state functional magnetic resonance imagingDementia interventionsFunctional magnetic resonance imagingAlzheimer's diseaseCognitive healthPatient deteriorationFunctional network connectivityCognitive abilitiesShort-term memoryRs-fMRIBrain connectivityResolved measurementsDynamic brain connectivityMagnetic resonance imaging