2024
Frequency modulation increases the specificity of time-resolved connectivity: A resting-state fMRI study
Faghiri A, Yang K, Faria A, Ishizuka K, Sawa A, Adali T, Calhoun V. Frequency modulation increases the specificity of time-resolved connectivity: A resting-state fMRI study. Network Neuroscience 2024, 8: 734-761. PMID: 39355435, PMCID: PMC11349031, DOI: 10.1162/netn_a_00372.Peer-Reviewed Original ResearchSliding window Pearson correlationTime-resolved networksSingle sideband modulationTime-resolved connectivityResting-state fMRI studiesSideband modulationFunctional magnetic resonance imagingFunctional network connectivityResting-state functional magnetic resonance imagingActivity time seriesTypical controlsFrequency modulationLow-frequency informationStateEpisode of psychosisNetwork connectivityHuman brainSub-corticalSuperior performanceFMRI studyCortical regionsAssociation between the oral microbiome and brain resting state connectivity in schizophrenia
Lin D, Fu Z, Liu J, Perrone-Bizzozero N, Hutchison K, Bustillo J, Du Y, Pearlson G, Calhoun V. Association between the oral microbiome and brain resting state connectivity in schizophrenia. Schizophrenia Research 2024, 270: 392-402. PMID: 38986386, DOI: 10.1016/j.schres.2024.06.045.Peer-Reviewed Original ResearchOral microbiomeMicrobial speciesArea under curveResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingMicrobial 16S rRNA sequencingBrain circuit dysfunctionHealthy controlsBrain functional connectivity alterationsFunctional connectivity alterationsFunctional neuroimaging techniquesHypothalamic-pituitary-adrenal axisBrain functional connectivityFunctional network connectivityBrain functional activityBrain functional network connectivityHealthy control subjectsNeurotransmitter signaling pathwaysBeta diversityMicrobiome communitiesOral microbiome dysbiosisRRNA sequencingCircuit dysfunctionConnectivity alterationsSchizophreniaA 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 abnormalitiesAnalysis of High-Order Brain Networks Resolved in Time and Frequency Using CP Decomposition
Faghiri A, Iraji A, Adali T, Calhoun V. Analysis of High-Order Brain Networks Resolved in Time and Frequency Using CP Decomposition. 2024, 00: 13346-13350. DOI: 10.1109/icassp48485.2024.10446864.Peer-Reviewed Original ResearchHigh-order networksCP decompositionSubjective informationNetworkSpectral informationFunctional magnetic resonance imagingBrain networksComplex systemsModeling approachResting-state functional magnetic resonance imagingPipelineInformationDistribution of Connectivity Strengths Across Functional Regions has Higher Entropy in Schizophrenia Patients than in Controls
Maksymchuk N, Miller R, Calhoun V. Distribution of Connectivity Strengths Across Functional Regions has Higher Entropy in Schizophrenia Patients than in Controls. 2024, 00: 37-40. DOI: 10.1109/ssiai59505.2024.10508663.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingGroup independent component analysisSchizophrenia patientsCognitive controlResting-state functional magnetic resonance imagingIntrinsic connectivity networksHealthy controlsGender-matched healthy controlsSZ patientsNeuropsychiatric disordersBrain areasBrain networksSchizophreniaDisrupted integrityBrain domainsConnection strengthIndependent component analysisConnectivity networksMagnetic resonance imagingSomatomotorDistribution of connection strengthsResonance imagingCross-sectional dataPatientsDiagnostic testsA 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 pattern
2023
Multimodal deep learning for Alzheimer’s disease classification and clinical score prediction
Itkyal V, Batta I, Abrol A, Calhoun V. Multimodal deep learning for Alzheimer’s disease classification and clinical score prediction. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2023 DOI: 10.58530/2023/3053.Peer-Reviewed Original ResearchStructural magnetic resonance imagingResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingMagnetic resonance imagingInterpretable 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