2025
Bridging animal models and humans: neuroimaging as intermediate phenotypes linking genetic or stress factors to anhedonia
Guo H, Xiao Y, Dong S, Yang J, Zhao P, Zhao T, Cai A, Tang L, Liu J, Wang H, Hua R, Liu R, Wei Y, Sun D, Liu Z, Xia M, He Y, Wu Y, Si T, Womer F, Xu F, Tang Y, Wang J, Zhang W, Zhang X, Wang F. Bridging animal models and humans: neuroimaging as intermediate phenotypes linking genetic or stress factors to anhedonia. BMC Medicine 2025, 23: 38. PMID: 39849528, PMCID: PMC11755933, DOI: 10.1186/s12916-025-03850-4.Peer-Reviewed Original ResearchConceptsIntermediate phenotypesCore symptoms of depressionAmplitude of low-frequency fluctuationNeuroimaging patternsSubtypes of depressionCore depressive symptomsExpression of risk genesDiverse clinical populationsSymptoms of depressionRodent modelsAssociated with depressionLow-frequency fluctuationsStress-related changesDepression subtypesCore symptomsCross-species validationPsychiatric disordersNeuropsychiatric disordersDepressive symptomsBehavioral manifestationsStress modelDepression cohortClinical populationsSensorimotor regionsAnhedoniaModality-level obstacles and initiatives to improve representation in fetal, infant, and toddler neuroimaging research samples
Margolis E, Nelson P, Fiske A, Champaud J, Olson H, Gomez M, Dineen Á, Bulgarelli C, Troller-Renfree S, Donald K, Spann M, Howell B, Scheinost D, Korom M. Modality-level obstacles and initiatives to improve representation in fetal, infant, and toddler neuroimaging research samples. Developmental Cognitive Neuroscience 2025, 72: 101505. PMID: 39954600, PMCID: PMC11875194, DOI: 10.1016/j.dcn.2024.101505.Peer-Reviewed Original ResearchConceptsFunctional near-infrared spectroscopyEarly brain developmentNeurobiological vulnerabilityNeuroimaging researchModality-specificNeuroimaging techniquesNeurodevelopmental processesNeuroimaging modalitiesBrain developmentResearch sampleMagnetic resonance imagingNear-infrared spectroscopyEquitable representationMarginalized communitiesExcluded peopleStudy early brain developmentToddlersCranial ultrasonographyResonance imagingInsufficient resourcesTangible solutionsEarly markersUnsupervised Dimensionality Reduction Techniques for the Assessment of ASD Biomarkers.
Jacokes Z, Adoremos I, Hussain A, Newman B, Pelphrey K, Van Horn J. Unsupervised Dimensionality Reduction Techniques for the Assessment of ASD Biomarkers. Biocomputing 2025, 30: 614-630. PMID: 39670400.Peer-Reviewed Original ResearchConceptsAutism spectrum disorderNeural basisDevelopmental disabilitiesCognitive traitsDiagnostic disparitiesSpectrum disorderNeuroimaging techniquesASD biomarkersSex differencesSocial functioningASD researchEffects of gene expressionNeuronal microstructureTailored interventionsBiological influencesGenetic interactionsGene expressionPhenotypic heterogeneityAutismCognitionPrincipal component analysis
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 dataSynergistic, multi-level understanding of psychedelics: three systematic reviews and meta-analyses of their pharmacology, neuroimaging and phenomenology
Shinozuka K, Jerotic K, Mediano P, Zhao A, Preller K, Carhart-Harris R, Kringelbach M. Synergistic, multi-level understanding of psychedelics: three systematic reviews and meta-analyses of their pharmacology, neuroimaging and phenomenology. Translational Psychiatry 2024, 14: 485. PMID: 39632810, PMCID: PMC11618481, DOI: 10.1038/s41398-024-03187-1.Peer-Reviewed Original ResearchMeSH KeywordsBrainDimethoxyphenylethylamineHallucinogensHumansLysergic Acid DiethylamideNeuroimagingPsilocybinConceptsFunctional connectivityBetween-network functional connectivityLevels of analysisWithin-network FCBetween-drug differencesMeta-analysesD2 receptorsPsychedelic effectsSerotonergic psychedelicsNeuroimaging resultsNeuropsychiatric disordersStates of consciousnessPsychedelicsNeural fingerprintsAltered statesNeuroimagingPsilocybinHigh dosesLSDSignificantly higher ratesMolecular pharmacologySystematic reviewAddictionDepressionPhenomenologyDeep learning analysis of fMRI data for predicting Alzheimer’s Disease: A focus on convolutional neural networks and model interpretability
Zhou X, Kedia S, Meng R, Gerstein M. Deep learning analysis of fMRI data for predicting Alzheimer’s Disease: A focus on convolutional neural networks and model interpretability. PLOS ONE 2024, 19: e0312848. PMID: 39630834, PMCID: PMC11616848, DOI: 10.1371/journal.pone.0312848.Peer-Reviewed Original ResearchConceptsConvolutional neural networkNeural networkAlzheimer's diseaseConvolutional neural network modelMultimodal medical datasetsDeep learning methodsPotential of deep learningGenetic risk factorsMedical datasetsAlzheimer's Disease Neuroimaging InitiativeAD predictionDeep learningDeep learning analysisLearning methodsMedical imagesPredicting Alzheimer's diseaseDetection of Alzheimer's diseaseModel interpretationEarly detection of Alzheimer's diseaseAccuracy levelGenetic factorsDatasetEarly detection of ADNetworkDetection of ADPortable, low-field magnetic resonance imaging for evaluation of Alzheimer’s disease
Sorby-Adams A, Guo J, Laso P, Kirsch J, Zabinska J, Garcia Guarniz A, Schaefer P, Payabvash S, de Havenon A, Rosen M, Sheth K, Gomez-Isla T, Iglesias J, Kimberly W. Portable, low-field magnetic resonance imaging for evaluation of Alzheimer’s disease. Nature Communications 2024, 15: 10488. PMID: 39622805, PMCID: PMC11612292, DOI: 10.1038/s41467-024-54972-x.Peer-Reviewed Original ResearchConceptsWhite matter hyperintensitiesMachine learning pipelineMild cognitive impairmentAlzheimer's diseaseWhite matter hyperintensities volumeLearning pipelineAssessment of patientsIncrease accessCognitive impairmentEvaluation of Alzheimer's diseaseDementiaLF-MRIPoint-of-care assessmentMagnetic resonance imagingHippocampal volumeResonance imagingImage qualityDiseaseReduce costsAnisotropic counterpartIncreasing availabilityManual segmentationRecent advances in portable, low-field magnetic resonance imaging in cerebrovascular disease
Zabinska J, de Havenon A, Sheth K. Recent advances in portable, low-field magnetic resonance imaging in cerebrovascular disease. Current Opinion In Neurology 2024, 38: 35-39. PMID: 39624032, DOI: 10.1097/wco.0000000000001338.Peer-Reviewed Original ResearchConceptsCerebrovascular diseaseWhite matter hyperintensityHealthcare settingsHemorrhage managementAlzheimer's dementiaClinical timepointsStages of cerebrovascular diseaseHemorrhagic strokeEmergency settingWhite matter hyperintensitiesIntracerebral hemorrhage managementCardiovascular intensive careThrombolytic administrationPatient populationCerebrovascular disease progressionIntensive careMagnetic resonance imagingStrokeEndovascular reperfusionCerebrovascularCost-effectiveDisease progressionResonance imagingChronic stageCareA multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data
Bi Y, Abrol A, Fu Z, Calhoun V. A multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data. Human Brain Mapping 2024, 45: e26783. PMID: 39600159, PMCID: PMC11599617, DOI: 10.1002/hbm.26783.Peer-Reviewed Original ResearchConceptsCross-attention mechanismVision transformerDeep learning modelsBrain disordersCharacteristics of schizophreniaDiagnosis of schizophreniaStructural neuroimaging dataNetwork connectivity matrixData fusion approachAttention mapsMultimodal baselinesFunctional network connectivityFuse informationDeep learningICA algorithmFusion approachGrey matter mapsAI algorithmsFunctional network connectivity matricesLeverage multiple sources of informationGray matter imagesLearning modelsMultiple sources of informationBrain imaging modalitiesNetwork connectivityNeuroimaging Biomarkers in Parkinson’s Disease
Holmes S, Tinaz S. Neuroimaging Biomarkers in Parkinson’s Disease. Advances In Neurobiology 2024, 40: 617-663. PMID: 39562459, DOI: 10.1007/978-3-031-69491-2_21.Peer-Reviewed Original ResearchMeSH KeywordsBiomarkersBrainHumansMagnetic Resonance ImagingNeuroimagingParkinson DiseasePositron-Emission TomographyConceptsPublic health burdenNeuroimaging biomarkersTreatment developmentRisk of developing PDParkinson's diseaseHealth burdenDiagnosis of PDMultimodal neuroimaging techniquesIdiopathic Parkinson's diseaseNon-motorDifferential diagnosis of PDNeuroimaging techniquesProdromal phaseDifferential diagnosisSymptomatic treatmentDisease progressionClinical applicationBiomarkersA 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 datasetImaging‐genomic spatial‐modality attentive fusion for studying neuropsychiatric disorders
Rahaman A, Garg Y, Iraji A, Fu Z, Kochunov P, Hong L, Van Erp T, Preda A, Chen J, Calhoun V. Imaging‐genomic spatial‐modality attentive fusion for studying neuropsychiatric disorders. Human Brain Mapping 2024, 45: e26799. PMID: 39562310, PMCID: PMC11576332, DOI: 10.1002/hbm.26799.Peer-Reviewed Original ResearchConceptsNeural networkDilated convolutional neural networkJoint learning frameworkAttention scoresState-of-the-artDeep neural networksNeural network decisionsConvolutional neural networkAttention fusionFusion moduleDiverse data sourcesArtificial intelligence modelsLearning frameworkAttention moduleJoint learningMultimodal clusteringNetwork decisionsInput streamMultimodal learningHigh-dimensionalIntermediate fusionFused dataSZ classificationIntelligence modelsContextual patternsThe Hierarchical Taxonomy of Psychopathology and the Search for Neurobiological Substrates of Mental Illness: A Systematic Review and Roadmap for Future Research
DeYoung C, Blain S, Latzman R, Grazioplene R, Haltigan J, Kotov R, Michelini G, Venables N, Docherty A, Goghari V, Kallen A, Martin E, Palumbo I, Patrick C, Perkins E, Shackman A, Snyder M, Tobin K. The Hierarchical Taxonomy of Psychopathology and the Search for Neurobiological Substrates of Mental Illness: A Systematic Review and Roadmap for Future Research. Journal Of Psychopathology And Clinical Science 2024, 133: 697-715. PMID: 39480338, PMCID: PMC11529694, DOI: 10.1037/abn0000903.Peer-Reviewed Original ResearchConceptsHierarchical Taxonomy of PsychopathologyTaxonomy of PsychopathologyClinical neuroscience researchHierarchical taxonomyNeuroscience researchAddictions Neuroclinical AssessmentDimensions of psychopathologyResearch Domain CriteriaDepressive symptom dimensionsHuman neuroimaging studiesTraditional psychiatric diagnosesDistress subfactorTransdiagnostic frameworkNeuroclinical AssessmentSymptom dimensionsCategorical nosologyNeurobiological substratesNeurobiological mechanismsNeuroimaging studiesPsychiatric diagnosisHiTOPInternalizing spectrumAssessment batteryPsychopathologyMental illnessBayesian pathway analysis over brain network mediators for survival data
Tian X, Li F, Shen L, Esserman D, Zhao Y. Bayesian pathway analysis over brain network mediators for survival data. Biometrics 2024, 80: ujae132. PMID: 39530270, PMCID: PMC11555425, DOI: 10.1093/biomtc/ujae132.Peer-Reviewed Original ResearchConceptsAccelerated failure time modelFailure time modelBrain connectivityAlzheimer's Disease Neuroimaging Initiative studyMaximum information extractionResponse regressionBayesian approachInformation extractionTime modelSurvival dataNoisy componentsUnique edgeWhite matter fiber tractsNetwork configurationBrain networksInterconnection networksNetworkNetwork mediatorsBrainLocal-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophrenia
Xing Y, Pearlson G, Kochunov P, Calhoun V, Du Y. Local-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophrenia. NeuroImage 2024, 299: 120839. PMID: 39251116, PMCID: PMC11491165, DOI: 10.1016/j.neuroimage.2024.120839.Peer-Reviewed Original ResearchMeSH KeywordsAdultBiomarkersBrainFemaleHumansMagnetic Resonance ImagingMaleNeuroimagingSchizophreniaConceptsSelection methodClassification accuracy gainsGraph-based regularizationHigh-dimensional dataFeature selection methodLocal structural informationSparse regularizationAblation studiesFeature subsetPublic datasetsFeature selectionClassification accuracyExperimental evaluationAccuracy gainsSelection techniquesNetwork connectivityData transformationSuperior performanceDatasetConvergence analysisStructural informationClassificationRegularizationFeaturesDisorder predictionAssociations of alcohol and tobacco use with psychotic, depressive and developmental disorders revealed via multimodal neuroimaging
Qiu L, Liang C, Kochunov P, Hutchison K, Sui J, Jiang R, Zhi D, Vergara V, Yang X, Zhang D, Fu Z, Bustillo J, Qi S, Calhoun V. Associations of alcohol and tobacco use with psychotic, depressive and developmental disorders revealed via multimodal neuroimaging. Translational Psychiatry 2024, 14: 326. PMID: 39112461, PMCID: PMC11306356, DOI: 10.1038/s41398-024-03035-2.Peer-Reviewed Original ResearchConceptsFronto-limbic networkSalience networkAssociated with cognitionFronto-basal gangliaDevelopmental disordersBrain networksLimbic systemAlcohol useAssociated with alcohol useMultimodal brain networksTobacco useAssociation of alcoholPsychiatric disordersMultimodal neuroimagingDMNBrain featuresCognitionAlcohol/tobacco useDisordersAssociated with tobacco useDepressionSymptomsFunctional abnormalitiesAlcoholBrainBrain Care Score and Neuroimaging Markers of Brain Health in Asymptomatic Middle-Age Persons
Rivier C, Singh S, Senff J, Tack R, Marini S, Clocchiatti-Tuozzo S, Huo S, Renedo D, Papier K, Conroy M, Littlejohns T, Chemali Z, Kourkoulis C, Payabvash S, Newhouse A, Westover M, Lazar R, Pikula A, Ibrahim S, Howard V, Howard G, Brouwers H, Van Duijn C, Fricchione G, Tanzi R, Yechoor N, Sheth K, Anderson C, Rosand J, Falcone G. Brain Care Score and Neuroimaging Markers of Brain Health in Asymptomatic Middle-Age Persons. Neurology 2024, 103: e209687. PMID: 39052961, PMCID: PMC11760050, DOI: 10.1212/wnl.0000000000209687.Peer-Reviewed Original ResearchConceptsCare scoresUK BiobankWhite matter hyperintensitiesHealth-related behaviorsMarkers of brain healthMiddle-aged personsStroke risk factorsProspective cohort studyMultivariate linear regressionEmpower patientsSilent cerebrovascular diseaseBrain careWhite matter hyperintensities progressionDementia historyHealth declineInvestigate associationsCohort studyBrain healthNeuroimaging markersHigher scoresRisk factorsMean diffusivityCerebrovascular diseaseFractional anisotropyImaging assessmentNeurostructural 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 1SubgroupsBrain‐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 measuresFindingsBrain community detection in the general children population
Farahdel B, Thapaliya B, Suresh P, Ray B, Calhoun V, Liu J. Brain community detection in the general children population. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-6. PMID: 40040186, DOI: 10.1109/embc53108.2024.10782157.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentBrainChildCluster AnalysisFemaleHumansMagnetic Resonance ImagingMaleNeuroimaging
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