2024
Consistent frontal-limbic-occipital connections in distinguishing treatment-resistant and non-treatment-resistant schizophrenia
Zhang Y, Gao S, Liang C, Bustillo J, Kochunov P, Turner J, Calhoun V, Wu L, Fu Z, Jiang R, Zhang D, Jiang J, Wu F, Peng T, Xu X, Qi S. Consistent frontal-limbic-occipital connections in distinguishing treatment-resistant and non-treatment-resistant schizophrenia. NeuroImage Clinical 2024, 45: 103726. PMID: 39700898, PMCID: PMC11721508, DOI: 10.1016/j.nicl.2024.103726.Peer-Reviewed Original ResearchNon-treatment-resistant schizophreniaTreatment-resistant schizophreniaFunctional connectivityDiagnosis of SZHealthy controlsFrontal-parietalResting-state functional connectivityAutomated anatomical labelingDysfunctional brain connectivityBrain functional connectivityAffiliated Brain Hospital of Nanjing Medical UniversityFrontal limbBrain connectivitySchizophreniaMedication dosageTreatment resistanceNeural pathwaysNanjing Medical UniversityDisease progressionMedical UniversityClinical practiceSpecific biomarkersDiagnosisAnatomical labelingA 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 MRINetworks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls
Kinsey S, Kazimierczak K, Camazón P, Chen J, Adali T, Kochunov P, Adhikari B, Ford J, van Erp T, Dhamala M, Calhoun V, Iraji A. Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls. Nature Mental Health 2024, 2: 1464-1475. PMID: 39650801, PMCID: PMC11621020, DOI: 10.1038/s44220-024-00341-y.Peer-Reviewed Original ResearchSelf-referential cognitionFunctional magnetic resonance imaging connectivityFunctional brain connectivityCingulo-opercularDefault-modeSchizophrenia diagnosisExecutive regionsFMRI connectivityFunctional connectivityConnectivity analysisSchizophreniaSensitive to differencesBrain connectivityFunctional connectivity structureWidespread alterationsImaging connectivityIndependent component analysisBrain phenomenaNetwork integrationHypoconnectivityPsychosisCognitionCore regionNonlinear networksCase-control datasetA Method for Multimodal IVA Fusion Within a MISA Unified Model Reveals Markers of Age, Sex, Cognition, and Schizophrenia in Large Neuroimaging Studies
Silva R, Damaraju E, Li X, Kochunov P, Ford J, Mathalon D, Turner J, van Erp T, Adali T, Calhoun V. A Method for Multimodal IVA Fusion Within a MISA Unified Model Reveals Markers of Age, Sex, Cognition, and Schizophrenia in Large Neuroimaging Studies. Human Brain Mapping 2024, 45: e70037. PMID: 39560198, PMCID: PMC11574741, DOI: 10.1002/hbm.70037.Peer-Reviewed Original ResearchConceptsMultimodal neuroimaging datasetSchizophrenia patientsNeuroimaging studiesCognitive performanceGroup differencesSchizophreniaSex effectsNeuroimaging datasetsMagnetic resonance imagingCognitionAge-associated declineControl subjectsMarkers of agingResonance imagingNon-imaging variablesSubject profilesSexNeuroimagingUK Biobank datasetFusion of Novel FMRI Features Using Independent Vector Analysis for a Multifaceted Characterization of Schizophrenia
Jia C, Abu Baker Siddique Akhonda M, Yang H, Calhoun V, Adali T. Fusion of Novel FMRI Features Using Independent Vector Analysis for a Multifaceted Characterization of Schizophrenia. 2015 23rd European Signal Processing Conference (EUSIPCO) 2024, 1112-1116. DOI: 10.23919/eusipco63174.2024.10715096.Peer-Reviewed Original ResearchFractional amplitude of low-frequency fluctuationAmplitude of low-frequency fluctuationResting-state functional magnetic resonanceCharacterization of schizophreniaFunctional magnetic resonanceBrain activity changesLow-frequency fluctuationsVisual cortexSchizophrenia patientsSchizophrenia NetworkBrain alterationsPsychiatric conditionsBrain regionsSchizophrenia biomarkersSchizophreniaFMRI featuresFractional amplitudeGroup differencesFMRI dataNeuroimaging analysisIndependent vector analysisActivity changesHealthy controlsBrainHigher-order statistical informationData augmentation for schizophrenia diagnosis via vision transformer-based latent diffusion model
Yang Y, Ma S, Cao S, Jia S, Bi Y, Calhoun V. Data augmentation for schizophrenia diagnosis via vision transformer-based latent diffusion model. Proceedings Of SPIE--the International Society For Optical Engineering 2024, 13252: 1325214-1325214-7. DOI: 10.1117/12.3044654.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingFunctional network connectivity matricesIndependent component analysisVision Transformer (ViTAdvanced artificial intelligence techniquesTraditional U-NetArtificial intelligence techniquesFunctional magnetic resonance imaging dataGroup independent component analysisNetwork connectivity matrixDenoising functionData augmentationImage generationIntelligence techniquesU-NetSmall datasetsDiagnosed schizophreniaSchizophrenia diagnosisGeneration taskNeuroimaging dataSchizophreniaComputational burdenConnectivity matrixMagnetic resonance imagingRelevant information4D 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 networksNeurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm
Jiang Y, Luo C, Wang J, Palaniyappan L, Chang X, Xiang S, Zhang J, Duan M, Huang H, Gaser C, Nemoto K, Miura K, Hashimoto R, Westlye L, Richard G, Fernandez-Cabello S, Parker N, Andreassen O, Kircher T, Nenadić I, Stein F, Thomas-Odenthal F, Teutenberg L, Usemann P, Dannlowski U, Hahn T, Grotegerd D, Meinert S, Lencer R, Tang Y, Zhang T, Li C, Yue W, Zhang Y, Yu X, Zhou E, Lin C, Tsai S, Rodrigue A, Glahn D, Pearlson G, Blangero J, Karuk A, Pomarol-Clotet E, Salvador R, Fuentes-Claramonte P, Garcia-León M, Spalletta G, Piras F, Vecchio D, Banaj N, Cheng J, Liu Z, Yang J, Gonul A, Uslu O, Burhanoglu B, Uyar Demir A, Rootes-Murdy K, Calhoun V, Sim K, Green M, Quidé Y, Chung Y, Kim W, Sponheim S, Demro C, Ramsay I, Iasevoli F, de Bartolomeis A, Barone A, Ciccarelli M, Brunetti A, Cocozza S, Pontillo G, Tranfa M, Park M, Kirschner M, Georgiadis F, Kaiser S, Van Rheenen T, Rossell S, Hughes M, Woods W, Carruthers S, Sumner P, Ringin E, Spaniel F, Skoch A, Tomecek D, Homan P, Homan S, Omlor W, Cecere G, Nguyen D, Preda A, Thomopoulos S, Jahanshad N, Cui L, Yao D, Thompson P, Turner J, van Erp T, Cheng W, Feng J. Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm. Nature Communications 2024, 15: 5996. PMID: 39013848, PMCID: PMC11252381, DOI: 10.1038/s41467-024-50267-3.Peer-Reviewed Original ResearchConceptsGray matter changesDisorder constructsEnlarged striatumPsychiatric conditionsMental disordersSubcortical regionsSchizophreniaBiological foundationsMatter changesBrain imagingStriatumDisordersBiological factorsIndividualsSubtypesHealthy subjectsCross-sectional brain imagingHippocampusTemporal trajectoriesInternational cohortSubgroup 2Subgroup 1SubgroupsA new transfer entropy method for measuring directed connectivity from complex-valued fMRI data
Li W, Lin Q, Zhang C, Han Y, Calhoun V. A new transfer entropy method for measuring directed connectivity from complex-valued fMRI data. Frontiers In Neuroscience 2024, 18: 1423014. PMID: 39050665, PMCID: PMC11266018, DOI: 10.3389/fnins.2024.1423014.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingFMRI dataBrain regionsAnatomical Automatic LabelingTransfer entropyFunctional magnetic resonance imaging dataConnectivity of brain regionsFrontal-parietal regionsConsistent with previous findingsSignificant group differencesRight frontal-parietal regionPartial transfer entropyPredicting mental disordersMental disordersParietal regionsGroup differencesMagnitude effectExperimental fMRI dataDirectional connectivityComplex-valued fMRI dataSchizophreniaMagnetic resonance imagingComplex-valued approachEntropyMagnitude dataAssociation 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 alterationsSchizophreniaNeural Complexity Unveiled: Doubly Functionally Independent Primitives (dFIPs) in Psychiatric Risk Score Assessment
Soleimani N, Calhoun V. Neural Complexity Unveiled: Doubly Functionally Independent Primitives (dFIPs) in Psychiatric Risk Score Assessment. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039582, DOI: 10.1109/embc53108.2024.10781623.Peer-Reviewed Original ResearchConceptsFunctional network connectivityAutism spectrum disorderBipolar disorderPsychiatric disordersDepressive disorderAdolescent brainNeural underpinningsPolygenic risk scoresPsychiatric riskSpectrum disorderDifferential contributionsDisordersMDDHigh-risk scoreSchizophreniaHealthy controlsRisk scoreScoresIndividualsPsychiatricAutismNetwork connectivityNeuroimagingRisk score assessmentElevated risk scoresUncovering Effects of Schizophrenia upon a Maximally Significant, Minimally Complex Subset of Default Mode Network Connectivity Features
Seraji M, Ellis C, Sendi M, Miller R, Calhoun V. Uncovering Effects of Schizophrenia upon a Maximally Significant, Minimally Complex Subset of Default Mode Network Connectivity Features. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039134, DOI: 10.1109/embc53108.2024.10782953.Peer-Reviewed Original ResearchConceptsDynamic functional network connectivityDFNC dataDynamic functional network connectivity stateResting state functional magnetic resonance imagingFunctional network connectivityFunctional magnetic resonance imagingHealthy controlsEffect of schizophreniaCingulate cortexNetwork connectivity featuresNeuropsychiatric disordersSchizophreniaAnticorrelationDynamicsVerbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis
Kennedy E, Liebel S, Lindsey H, Vadlamani S, Lei P, Adamson M, Alda M, Alonso-Lana S, Anderson T, Arango C, Asarnow R, Avram M, Ayesa-Arriola R, Babikian T, Banaj N, Bird L, Borgwardt S, Brodtmann A, Brosch K, Caeyenberghs K, Calhoun V, Chiaravalloti N, Cifu D, Crespo-Facorro B, Dalrymple-Alford J, Dams-O’Connor K, Dannlowski U, Darby D, Davenport N, DeLuca J, Diaz-Caneja C, Disner S, Dobryakova E, Ehrlich S, Esopenko C, Ferrarelli F, Frank L, Franz C, Fuentes-Claramonte P, Genova H, Giza C, Goltermann J, Grotegerd D, Gruber M, Gutierrez-Zotes A, Ha M, Haavik J, Hinkin C, Hoskinson K, Hubl D, Irimia A, Jansen A, Kaess M, Kang X, Kenney K, Keřková B, Khlif M, Kim M, Kindler J, Kircher T, Knížková K, Kolskår K, Krch D, Kremen W, Kuhn T, Kumari V, Kwon J, Langella R, Laskowitz S, Lee J, Lengenfelder J, Liou-Johnson V, Lippa S, Løvstad M, Lundervold A, Marotta C, Marquardt C, Mattos P, Mayeli A, McDonald C, Meinert S, Melzer T, Merchán-Naranjo J, Michel C, Morey R, Mwangi B, Myall D, Nenadić I, Newsome M, Nunes A, O’Brien T, Oertel V, Ollinger J, Olsen A, de la Foz V, Ozmen M, Pardoe H, Parent M, Piras F, Piras F, Pomarol-Clotet E, Repple J, Richard G, Rodriguez J, Rodriguez M, Rootes-Murdy K, Rowland J, Ryan N, Salvador R, Sanders A, Schmidt A, Soares J, Spalleta G, Španiel F, Sponheim S, Stasenko A, Stein F, Straube B, Thames A, Thomas-Odenthal F, Thomopoulos S, Tone E, Torres I, Troyanskaya M, Turner J, Ulrichsen K, Umpierrez G, Vecchio D, Vilella E, Vivash L, Walker W, Werden E, Westlye L, Wild K, Wroblewski A, Wu M, Wylie G, Yatham L, Zunta-Soares G, Thompson P, Pugh M, Tate D, Hillary F, Wilde E, Dennis E. Verbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis. Brain Sciences 2024, 14: 669. PMID: 39061410, PMCID: PMC11274572, DOI: 10.3390/brainsci14070669.Peer-Reviewed Original ResearchAttention-deficit/hyperactivity disorderVerbal learningNeuropsychiatric conditionsTraumatic brain injuryYears of educationMild cognitive impairmentComorbid disordersAttention-deficit/hyperactivityBipolar disorderMemory deficitsMemory performanceMemory recallClinical groupsMega-analysisCognitive impairmentEffects of dementiaAssociated with dementiaMemoryBrain injurySchizophreniaMemory impactDisordersDepressionParkinson's diseaseUnique participantsA Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia
Blair D, Miller R, Calhoun V. A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia. Entropy 2024, 26: 545. PMID: 39056908, PMCID: PMC11275472, DOI: 10.3390/e26070545.Peer-Reviewed Original ResearchSubjective trajectoriesBrain connectivity measuresPatient’s brain functionCognitive performancePsychiatric diseasesCourse of developmentBrain functionInformation theoryCortical hierarchyInformation processingConnectivity measuresSchizophreniaHealthy controlsDynamical systems theoryFunctional imagingTransit alterationsTransitionConnectivity statesPerspective of dynamical systemsStateTheoryEntropy generationDynamical systemsDynamicsNeuroimagingDouble Functionally Independent Primitives Provide Disorder Specific Fingerprints of Mental Illnesses
Soleimani N, Pearlson G, Iraji A, Calhoun V. Double Functionally Independent Primitives Provide Disorder Specific Fingerprints of Mental Illnesses. 2024, 00: 1-4. DOI: 10.1109/isbi56570.2024.10635116.Peer-Reviewed Original ResearchAutism spectrum disorderMental illnessBipolar disorderMental disordersManifestations of mental illnessAssociated with mental illnessFunctional network connectivityFunctional network connectivity patternsNetwork connectivity patternsDisorder-specificDepressive disorderNeural underpinningsSpectrum disorderPsychological disordersNeuroimaging techniquesConnectivity patternsDisordersSchizophreniaHealthy controlsIllnessBrainFunctional changesMDDAutismNetwork connectivitySpatial Sequence Attention Network for Schizophrenia Classification from Structural Brain MR Images
Shaik N, Cherukuri T, Calhoun V, Ye D. Spatial Sequence Attention Network for Schizophrenia Classification from Structural Brain MR Images. 2024, 00: 1-5. DOI: 10.1109/isbi56570.2024.10635528.Peer-Reviewed Original ResearchCognitive abilitiesStructural MRIAttention mechanismSchizophrenia classificationChronic mental disordersIndividual cognitive abilitiesTransfer learning paradigmDeep learning methodologyMental disordersSchizophreniaFeature mapsFeature representationConvolutional blocksAttention networkBrain MR imagesLearning paradigmSocial interactionClassification of individualsStructural brain MR imagesGray matterLearning methodologyExcitable networksClinical datasetsBrainManual observationA 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 imagingCortical similarities in psychiatric and mood disorders identified in federated VBM analysis via COINSTAC
Rootes-Murdy K, Panta S, Kelly R, Romero J, Quidé Y, Cairns M, Loughland C, Carr V, Catts S, Jablensky A, Green M, Henskens F, Kiltschewskij D, Michie P, Mowry B, Pantelis C, Rasser P, Reay W, Schall U, Scott R, Watkeys O, Roberts G, Mitchell P, Fullerton J, Overs B, Kikuchi M, Hashimoto R, Matsumoto J, Fukunaga M, Sachdev P, Brodaty H, Wen W, Jiang J, Fani N, Ely T, Lorio A, Stevens J, Ressler K, Jovanovic T, van Rooij S, Federmann L, Jockwitz C, Teumer A, Forstner A, Caspers S, Cichon S, Plis S, Sarwate A, Calhoun V. Cortical similarities in psychiatric and mood disorders identified in federated VBM analysis via COINSTAC. Patterns 2024, 5: 100987. PMID: 39081570, PMCID: PMC11284501, DOI: 10.1016/j.patter.2024.100987.Peer-Reviewed Original ResearchPsychiatric disordersStructural neuroimaging studiesPattern of gray matterAutism spectrum disorderGray matterDepressive disorderMood disordersNeuroimaging studiesNeuroanatomical basisSubcortical regionsGM alterationsSpectrum disorderVBM analysisMental illnessGM patternsDisordersCollaborative InformaticsSchizophreniaMoodNeuroimaging Suite ToolkitAutismNeuroimagingVulnerabilityLarge-scale dataDeficitsDifferences in the neural correlates of schizophrenia with positive and negative formal thought disorder in patients with schizophrenia in the ENIGMA dataset
Sharkey R, Bacon C, Peterson Z, Rootes-Murdy K, Salvador R, Pomarol-Clotet E, Karuk A, Homan P, Ji E, Omlor W, Homan S, Georgiadis F, Kaiser S, Kirschner M, Ehrlich S, Dannlowski U, Grotegerd D, Goltermann J, Meinert S, Kircher T, Stein F, Brosch K, Krug A, Nenadic I, Sim K, Spalletta G, Banaj N, Sponheim S, Demro C, Ramsay I, King M, Quidé Y, Green M, Nguyen D, Preda A, Calhoun V, Turner J, van Erp T, Nickl-Jockschat T. Differences in the neural correlates of schizophrenia with positive and negative formal thought disorder in patients with schizophrenia in the ENIGMA dataset. Molecular Psychiatry 2024, 29: 3086-3096. PMID: 38671214, PMCID: PMC11449795, DOI: 10.1038/s41380-024-02563-z.Peer-Reviewed Original ResearchNegative FTDBrain structural changesPositive FTDSymptom dimensionsBrain regionsENIGMA Schizophrenia Working GroupNegative formal thought disorderNeural correlates of schizophreniaRegional brain volume lossFormal thought disorderSchizophrenia Working GroupCorrelates of schizophreniaLateral temporal cortexBrain volume lossThought disorderNeurobiological underpinningsNeural correlatesNeuroimaging studiesTemporal cortexSchizophreniaCortical regionsCortical thicknessCellular underpinningsFTDMulti-site cohort
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