2025
An interpretable deep-learning approach to detect biomarkers in anxious-depressed symptoms from prefrontal fNIRS signals during an autobiographical memory test
Zhang Y, Xu Y, Cheng Y, Zhao Y, Potenza M, Shi H. An interpretable deep-learning approach to detect biomarkers in anxious-depressed symptoms from prefrontal fNIRS signals during an autobiographical memory test. Asian Journal Of Psychiatry 2025, 107: 104451. DOI: 10.1016/j.ajp.2025.104451.Peer-Reviewed Original ResearchAutobiographical Memory TestNon-anxious depressionFunctional near-infrared spectroscopyAnxious depressionMemory testDepressive symptomsAnxious-depressive symptomsNegative emotional valenceSevere mood disordersFrontal pole areasAD symptomsMood disordersEmotional valenceRight hemisphereNeuroimaging dataDiagnosed depressionSymptom groupsCognitive impairmentSymptom predictionHealthy controlsNear-infrared spectroscopyDepressionSymptomsRecall featuresArea under the receiver operating characteristic curve
2024
Neuroimaging Correlates of the NIH-Toolbox-Driven Cognitive Metrics in Children
Acosta-Rodriguez H, Yuan C, Bobba P, Stephan A, Zeevi T, Malhotra A, Tran A, Kaltenhauser S, Payabvash S. Neuroimaging Correlates of the NIH-Toolbox-Driven Cognitive Metrics in Children. Journal Of Integrative Neuroscience 2024, 23: 217. PMID: 39735971, PMCID: PMC11851640, DOI: 10.31083/j.jin2312217.Peer-Reviewed Original ResearchConceptsCognitive composite scoreAdolescent Brain Cognitive DevelopmentFluid cognition composite scoresStructural magnetic resonance imagingComposite scoreDiffusion tensor imagingNeuroimaging correlatesCognitive functionRs-fMRINational Institutes of Health (NIH) Toolbox Cognition BatteryCognitive scoresMicrostructural integrityResting-state functional connectivityCrystallized cognition composite scoreCortical surface areaTotal cognitive scoreWM microstructural integrityCognitive batteryCrystallized cognitionNeuroanatomical correlatesWhite matterCognitive performanceNeuroimaging metricsFunctional connectivityNeuroimaging dataMultimodal 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 regressionGeneralizable and replicable brain-based predictions of cognitive functioning across common psychiatric illness
Chopra S, Dhamala E, Lawhead C, Ricard J, Orchard E, An L, Chen P, Wulan N, Kumar P, Rubenstein A, Moses J, Chen L, Levi P, Holmes A, Aquino K, Fornito A, Harpaz-Rotem I, Germine L, Baker J, Yeo B, Holmes A. Generalizable and replicable brain-based predictions of cognitive functioning across common psychiatric illness. Science Advances 2024, 10: eadn1862. PMID: 39504381, PMCID: PMC11540040, DOI: 10.1126/sciadv.adn1862.Peer-Reviewed Original ResearchConceptsPrediction of cognitionCognitive functionPrediction of cognitive functionFunctional neuroimaging dataTransdiagnostic sampleComputational psychiatryPsychiatric illnessNeuroimaging dataCognitive impairmentCognitionPopulation-level datasetsPsychiatryAssociated with poor outcomesUK BiobankImpairmentBrainIllnessSymptomsPrediction studiesParticipantsPoor outcomeClinical studiesSamplesEfficient federated learning for distributed neuroimaging data
Thapaliya B, Ohib R, Geenjaar E, Liu J, Calhoun V, Plis S. Efficient federated learning for distributed neuroimaging data. Frontiers In Neuroinformatics 2024, 18: 1430987. PMID: 39315000, PMCID: PMC11416982, DOI: 10.3389/fninf.2024.1430987.Peer-Reviewed Original ResearchFederated learningCommunication overheadsSparse modelModel sparsityClient siteTraining phaseAdolescent Brain Cognitive DevelopmentData sharingEfficient communicationLarge modelsLocal trainingResource capabilitiesDatasetCommunicationLearningSparsityActual dataOverheadsPrivacyNeuroimaging dataCognitive developmentDataScientific communitySharingData 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 informationMedical comorbidities and lower myelin content are associated with poor cognition in young adults with perinatally acquired HIV
Patel P, Prince D, Bolenzius J, Ch’en P, Chiarella J, Kolind S, Vavasour I, Pedersen T, Levendovszky S, Spudich S, Marra C, Paul R. Medical comorbidities and lower myelin content are associated with poor cognition in young adults with perinatally acquired HIV. AIDS 2024, 38: 1932-1939. PMID: 39110577, PMCID: PMC11524773, DOI: 10.1097/qad.0000000000003989.Peer-Reviewed Original ResearchPoor cognitionCognitive impairmentHorizontally acquired HIVMarkers of cognitive reserveAssociated with poorer cognitionRisk factorsCross-sectional studyYoung adultsLower cognitive scoresYears of educationMultiple cognitive domainsExperience cognitive deficitsCognitive domain scoresHIV-uninfected controlsOlder adultsCorticospinal tractCognitive batteryDomain scoresMedical comorbidityCognitive deficitsCognitive domainsMyelin contentCognitive reserveNeuroimaging dataCognitive scoresEpigenetic molecular underpinnings of brain structural-functional connectivity decoupling in patients with major depressive disorder
Tang L, Zhao P, Pan C, Song Y, Zheng J, Zhu R, Wang F, Tang Y. Epigenetic molecular underpinnings of brain structural-functional connectivity decoupling in patients with major depressive disorder. Journal Of Affective Disorders 2024, 363: 249-257. PMID: 39029702, DOI: 10.1016/j.jad.2024.07.110.Peer-Reviewed Original ResearchMajor depressive disorderMajor depressive disorder patientsStructural-functional connectivityHPA axisDepressive disorderIncreased susceptibility to MDDSusceptibility to major depressive disorderMajor depressive disorder treatmentHealthy controlsStress-related disordersBrain network dynamicsMultimodal neuroimaging dataGender-matched healthy controlsSubcortical regionsNeuroimaging dataChronic stressCortical regionsNodal levelMedical statusFunctional networksCRHR1FKBP5CpG sitesDisordersMolecular underpinningsBrain‐age prediction: Systematic evaluation of site effects, and sample age range and size
Yu Y, Cui H, Haas S, New F, Sanford N, Yu K, Zhan D, Yang G, Gao J, Wei D, Qiu J, Banaj N, Boomsma D, Breier A, Brodaty H, Buckner R, Buitelaar J, Cannon D, Caseras X, Clark V, Conrod P, Crivello F, Crone E, Dannlowski U, Davey C, de Haan L, de Zubicaray G, Di Giorgio A, Fisch L, Fisher S, Franke B, Glahn D, Grotegerd D, Gruber O, Gur R, Gur R, Hahn T, Harrison B, Hatton S, Hickie I, Pol H, Jamieson A, Jernigan T, Jiang J, Kalnin A, Kang S, Kochan N, Kraus A, Lagopoulos J, Lazaro L, McDonald B, McDonald C, McMahon K, Mwangi B, Piras F, Rodriguez‐Cruces R, Royer J, Sachdev P, Satterthwaite T, Saykin A, Schumann G, Sevaggi P, Smoller J, Soares J, Spalletta G, Tamnes C, Trollor J, Ent D, Vecchio D, Walter H, Wang Y, Weber B, Wen W, Wierenga L, Williams S, Wu M, Zunta‐Soares G, Bernhardt B, Thompson P, Frangou S, Ge R, Group E. Brain‐age prediction: Systematic evaluation of site effects, and sample age range and size. Human Brain Mapping 2024, 45: e26768. PMID: 38949537, PMCID: PMC11215839, DOI: 10.1002/hbm.26768.Peer-Reviewed Original ResearchConceptsBrain-aging modelBrain-age predictionBrain-ageDiscovery sampleBrain morphometric measuresStructural neuroimaging dataSamples of healthy individualsSample age rangeNeuroimaging metricsNeuroimaging dataHealthy individualsLongitudinal consistencyBrain developmentIndependent samplesAge varianceAge rangeBrainSample sizeAge binsMorphometry dataIndividualsHuman lifespanEmpirical examinationMeaningful measuresFindingsLabel Noise-Robust Ensemble Deep Multimodal Framework For Neuroimaging Data
Rokham H, Falakshahi H, Calhoun V. Label Noise-Robust Ensemble Deep Multimodal Framework For Neuroimaging Data. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039505, DOI: 10.1109/embc53108.2024.10782672.Peer-Reviewed Original ResearchConceptsLabel noiseEffects of label noiseBrain-based markersSelf-report assessmentsLabel noise problemFunctional MRI dataDeep convolutional frameworkDeep learning modelsK-fold cross-validation techniqueAssessment of diagnosisNosological categoriesCross-validation techniqueNeuroimaging dataMental illnessClassification performanceConvolutional frameworkDiagnostic categoriesDiagnostic classificationEnsemble methodsMultimodal frameworkLearning modelsSubsets of dataBagging approachK-foldNeuroimagingA 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-beingBrain maps of pCO2, pO2 and pH in aging via homeostatic modeling of neuroimaging data across the lifespan
Mangia S, DiNuzzo M, Dienel G, Behar K, Benveniste H, Giove F, Herculano S, Wolf M, Li X, Filip P, Michaeli S, Rothman D. Brain maps of pCO2, pO2 and pH in aging via homeostatic modeling of neuroimaging data across the lifespan. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2024 DOI: 10.58530/2024/3889.Peer-Reviewed Original ResearchNeural Correlates of Novelty-Evoked Distress in 4-Month-Old Infants: A Synthetic Cohort Study
Filippi C, Winkler A, Kanel D, Elison J, Hardiman H, Sylvester C, Pine D, Fox N. Neural Correlates of Novelty-Evoked Distress in 4-Month-Old Infants: A Synthetic Cohort Study. Biological Psychiatry Cognitive Neuroscience And Neuroimaging 2024, 9: 905-914. PMID: 38641209, PMCID: PMC11381178, DOI: 10.1016/j.bpsc.2024.03.008.Peer-Reviewed Original ResearchBrain-behavior associationsParent-report measuresInfant temperamentNetwork pairsDorsal attention network connectivityAssessment of infant temperamentParent-reported temperamentAttention network connectivityControl network connectivitySocial anxietyFMRI studyNeural correlatesNeural basisFunctional connectivityParent reportNeuroimaging dataTemperamentAttention-controlComposite scoreDistressNetwork connectivityObservational assessmentNegative associationBehavior estimationDorsalFunctional and structural effects of repetitive transcranial magnetic stimulation (rTMS) for the treatment of auditory verbal hallucinations in schizophrenia: A systematic review
Mehta D, Siddiqui S, Ward H, Steele V, Pearlson G, George T. Functional and structural effects of repetitive transcranial magnetic stimulation (rTMS) for the treatment of auditory verbal hallucinations in schizophrenia: A systematic review. Schizophrenia Research 2024, 267: 86-98. PMID: 38531161, PMCID: PMC11531343, DOI: 10.1016/j.schres.2024.03.016.Peer-Reviewed Original ResearchAuditory verbal hallucinationsRepetitive transcranial magnetic stimulationVerbal hallucinationsTranscranial magnetic stimulationTreatment-resistant auditory verbal hallucinationsAVH patientsTreatment of auditory verbal hallucinationsImpact of repetitive transcranial magnetic stimulationEmotion regulation regionsLanguage processing regionsAberrant neural activityHigh-frequency repetitive transcranial magnetic stimulationMagnetic stimulationRTMS interventionNeural substratesNeural effectsNeural mechanismsSham-controlled studySchizophreniaBrain activityNeuroimaging dataProcessing regionsNeuroimaging analysisNeuroimaging outcomesBrain abnormalitiesMultimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data
Batta I, Abrol A, Calhoun V, Initiative A. Multimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data. Journal Of Neuroscience Methods 2024, 406: 110109. PMID: 38494061, PMCID: PMC11100582, DOI: 10.1016/j.jneumeth.2024.110109.Peer-Reviewed Original ResearchConceptsBrain subspacesStandard machine learning algorithmsHigh-dimensional neuroimaging dataTrain machine learning modelsUnsupervised decompositionMachine learning algorithmsMachine learning modelsFunctional sub-systemsSubspace analysisSubspace componentsLearning algorithmsSupervised approachBiological traitsLearning modelsSub-systemsAlzheimer's diseaseSubspaceComputational frameworkActive subspaceCross-validation procedureNeuroimaging dataAD-related brain regionsAutomated identificationPredictive performanceOrientation subspacesA Novel Deep Subspace Learning Framework to Automatically Uncover Assessment-Specific Independent Brain Networks
Batta I, Abrol A, Calhoun V. A Novel Deep Subspace Learning Framework to Automatically Uncover Assessment-Specific Independent Brain Networks. 2024, 00: 1-6. DOI: 10.1109/ciss59072.2024.10480204.Peer-Reviewed Original ResearchLearning frameworkBrain subsystemsSubspace learning frameworkBrain networksHigh-dimensional neuroimaging dataConvolutional neural networkLow-dimensional subspaceSupervised learning approachDeep learning frameworkStructural brain featuresPredictive performanceUnsupervised approachNeural networkAutomated frameworkDimensional subspaceAlzheimer's diseaseLearning approachBrain changesFeature importanceTraining procedureNeuroimaging dataBrain featuresSalient networkNetworkBrain disordersA Multi-dimensional Joint ICA Model with Gaussian Copula
Agcaoglu O, Silva R, Alacam D, Calhoun V. A Multi-dimensional Joint ICA Model with Gaussian Copula. Lecture Notes In Computer Science 2024, 14366: 152-163. DOI: 10.1007/978-3-031-51026-7_14.Peer-Reviewed Original ResearchIndependent component analysisBivariate distributionMarginal distributionsGaussian copulaLogistic distributionJoint ICAImage data miningSuper-Gaussian distributionImage datasetsFunctional magnetic resonance imaging datasetsInfomax principleAlzheimer's Disease Neuroimaging InitiativeProposed algorithmData miningIdentical marginalsMagnetic resonance imaging datasetICA modelMultimodal versionICA methodJoint independent component analysisCopulasDatasetMaximum likelihoodMixing matrixNeuroimaging data
2023
Spatiotemporal Assessment of Locus Coeruleus Integrity Predicting Cortical Tau and Cognition
Bueichekú E, Diez I, Kim C, Becker A, Koops E, Kwong K, Papp K, Salat D, Bennett D, Rentz D, Sperling R, Johnson K, Sepulcre J, Jacobs H. Spatiotemporal Assessment of Locus Coeruleus Integrity Predicting Cortical Tau and Cognition. Alzheimer's & Dementia 2023, 19 DOI: 10.1002/alz.079747.Peer-Reviewed Original ResearchLC integrityLocus coeruleusLower cognitive performanceLongitudinal tau accumulationCognitive performanceEmergence of cognitive declineAssociated with lower cognitive performanceTemporal cortex areasIn vivo neuroimaging dataTau accumulationTau progressionCortical tau depositionAlzheimer's diseaseNeuroimaging studiesMTL structuresNeuroimaging dataAD-related pathologyCognitive declineCortex areaPartial correlationMisfolded tau proteinContext of ADGLM analysisSites of tauLipid biosynthetic processSelf-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links
Fedorov A, Geenjaar E, Wu L, Sylvain T, DeRamus T, Luck M, Misiura M, Mittapalle G, Hjelm R, Plis S, Calhoun V. Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links. NeuroImage 2023, 285: 120485. PMID: 38110045, PMCID: PMC10872501, DOI: 10.1016/j.neuroimage.2023.120485.Peer-Reviewed Original ResearchConceptsBrain regionsMultimodal neuroimaging dataNeuroimaging dataBrain disordersComplex brain disordersMRI dataNeuroimaging researchGroup inferencesDeep InfoMaxSupervised modelsDiagnostic labelsDisordersBrainState-of-the-art unsupervised methodsAlzheimer's phenotypeNovel self-supervised frameworkSelf-supervised frameworkSelf-supervised methodologyCanonical correlation analysisSelf-supervised representationsState-of-the-artDeep learning approachSingle-modal dataMultimode linksComplex brainsA Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data
Rokham H, Falakshahi H, Calhoun V. A Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38082903, DOI: 10.1109/embc40787.2023.10339949.Peer-Reviewed Original ResearchConceptsStructural MRI dataResting-state functional MRI dataFunctional MRI dataFunctional magnetic resonance imaging dataMRI dataMagnetic resonance imaging dataSchizophrenia patientsFunctional connectivity featuresBrain imaging modalitiesMental disordersNeuroimaging dataNeuroimaging techniquesBorderline subjectsHealthy control groupSchizophrenia datasetSchizophreniaConnectivity featuresBrainPsychosisMoodNosologyControl groupDisordersLabel noiseSubjects
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