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, 8: 1212-1242. PMID: 39735500, PMCID: PMC11674407, 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 MRIMultimodal predictive modeling: Scalable imaging informed approaches to predict future brain health
Ajith M, Spence J, Chapman S, Calhoun V. Multimodal predictive modeling: Scalable imaging informed approaches to predict future brain health. Journal Of Neuroscience Methods 2024, 414: 110322. PMID: 39608579, PMCID: PMC11687617, DOI: 10.1016/j.jneumeth.2024.110322.Peer-Reviewed Original ResearchStatic functional network connectivityHealth constructsNeuroimaging dataBrain healthResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingSupport vector regressionFunctional network connectivityRandom forestCognitive performanceAssessment-onlyRs-fMRINeural patternsBehavioral outcomesBehavioral dataDiverse data sourcesNeural connectionsPsychological stateTraining stageMagnetic resonance imagingLongitudinal changesNetwork connectivityBrainPerformance evaluationVector regressionInter-Modality Source Coupling: A Fully-Automated Whole-Brain Data-Driven Structure-Function Fingerprint Shows Replicable Links to Reading in a Large-Scale (N8K) Analysis
Kotoski A, Liu J, Morris R, Calhoun V. Inter-Modality Source Coupling: A Fully-Automated Whole-Brain Data-Driven Structure-Function Fingerprint Shows Replicable Links to Reading in a Large-Scale (N8K) Analysis. IEEE Transactions On Biomedical Engineering 2024, 71: 3383-3389. PMID: 38968021, PMCID: PMC11700228, DOI: 10.1109/tbme.2024.3423703.Peer-Reviewed Original ResearchReading abilityBrain structuresSchool-aged childrenResting-stateStructural magnetic resonance imagingInferior frontal areasFunctional brain changesInferior parietal lobuleHigher reading abilityFunctional connectivity patternsLow reading abilityLingual gyrusNeural basisParietal lobuleReading developmentBrain changesCognitive processesReplication linksRs-fMRICortical regionsReading scoresBrain functionCognitive growthFrontal areasConnectivity patternsA 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
Resting‐state dynamic functional network connectivity predicts cognition in 37,784 participants of UK Biobank
Sendi M, Zendehrouh E, Miller R, Salat D, Calhoun V. Resting‐state dynamic functional network connectivity predicts cognition in 37,784 participants of UK Biobank. Alzheimer's & Dementia 2023, 19 DOI: 10.1002/alz.065832.Peer-Reviewed Original ResearchFunctional network connectivityDynamic FNCDynamic functional network connectivityCognitive scoresFluid intelligenceCognitive declineAge-related cognitive declineGroup independent component analysisResting-state functional MRIBrain functional changesResting-state fMRIBrain functional network connectivityReaction timeParticipants of UK BiobankRT taskFunctional MRIRs-fMRIPairing taskCognitionIndependent component analysisUK BiobankBrainNetwork connectivityHealthy adultsData-driven componentsPrediction of sleep quality scores using dynamic functional network connectivity of young adults: A reproducibility analysis
Sendi M, Dini H, Zendehrouh E, Salat D, Calhoun V. Prediction of sleep quality scores using dynamic functional network connectivity of young adults: A reproducibility analysis. Alzheimer's & Dementia 2023, 19 DOI: 10.1002/alz.065778.Peer-Reviewed Original ResearchDynamic functional network connectivityHuman Connectome ProjectFunctional network connectivityHuman Connectome Project datasetDistinct statesResting-state fMRIRs-fMRIConnectome ProjectResting-state functional magnetic resonance imagingState vectorPittsburgh Sleep Quality IndexYoung adultsFunctional magnetic resonance imagingNight sleep timeRs-fMRI sessionsInterpretable 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
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