2025
Yale Brain Atlas to interactively explore multimodal structural and functional neuroimaging data
Collins E, Chishti O, McGrath H, Obaid S, King A, Qiu E, Gabriel E, Shen X, Arora J, Papademetris X, Constable R, Spencer D, Zaveri H. Yale Brain Atlas to interactively explore multimodal structural and functional neuroimaging data. Frontiers In Network Physiology 2025, 5: 1585019. DOI: 10.3389/fnetp.2025.1585019.Peer-Reviewed Original ResearchMultimodal neuroimaging dataNeuroimaging dataAnalysis of multimodal neuroimaging dataBrain structure-function relationshipsFunctional neuroimaging dataFMRI activation dataWhite matter connectomeHuman Connectome ProjectBrain atlasesCortical thickness profilesConnectome ProjectHuman brainAnatomical parcellationBrainConnectivity matrixAtlas spaceYaleInteractive explorationHealthy subjectsWeb toolCognitionInteractive pagesEmotionsSubjectsConnectomeThe Transdiagnostic Connectome Project: an open dataset for studying brain-behavior relationships in psychiatry
Chopra S, Cocuzza C, Lawhead C, Ricard J, Labache L, Patrick L, Kumar P, Rubenstein A, Moses J, Chen L, Blankenbaker C, Gillis B, Germine L, Harpaz-Rotem I, Yeo B, Baker J, Holmes A. The Transdiagnostic Connectome Project: an open dataset for studying brain-behavior relationships in psychiatry. Scientific Data 2025, 12: 923. PMID: 40456751, PMCID: PMC12130183, DOI: 10.1038/s41597-025-04895-z.Peer-Reviewed Original ResearchConceptsTask-based functional MRIHigh-resolution anatomical scansBrain-behavior relationshipsHealthy comparison groupFeatures of brain functionFunctional network organizationClinically relevant symptomsPsychiatric illnessFunctional MRINeuroimaging dataResting-stateBrain functionClinical neuroscienceCognitive AssessmentBehavioral dataConnectome ProjectAnatomical scansComparison groupDiagnostic criteriaNetwork organizationRelevant symptomsPsychiatryActivating effectIndividualsTransdiagnosticNeurocognitive effects of psilocybin: A systematic and comprehensive review of neuroimaging studies in humans
Berkovitch L, Fauvel B, Preller K, Gaillard R. Neurocognitive effects of psilocybin: A systematic and comprehensive review of neuroimaging studies in humans. Neuroscience & Biobehavioral Reviews 2025, 175: 106239. PMID: 40456393, DOI: 10.1016/j.neubiorev.2025.106239.Peer-Reviewed Original ResearchConceptsEffects of psilocybinPsilocybin effectsNeuroimaging studiesBrain changesNeuroimaging techniquesTherapeutic effects of psilocybinTreat various psychiatric disordersFunctional brain changesSamples of healthy volunteersSerotonergic compoundsEmotional processingNeurobiological mechanismsMode networkPsychiatric disordersCognitive tasksPsychoactive effectsBrain activityNeuroimaging dataPsilocybinSelf-experienceAssociated with acute alterationsSocial functioningTherapeutic effectBrainNeuronal networksPenetrance of neurodevelopmental copy number variants is associated with variations in cortical morphology
Silva A, Sønderby I, Kirov G, Abdellaoui A, Agartz I, Ames D, Armstrong N, Artiges E, Banaschewski T, Bassett A, Bearden C, Blangero J, Boen R, Boomsma D, Bülow R, Butcher N, Calhoun V, Campbell L, Chow E, Ciufolini S, Craig M, Crespo-Farroco B, Cunningham A, Dalvie S, Daly E, Dazzan P, de Geus E, de Zubicaray G, Doherty J, Donohoe G, Drakesmith M, Espeseth T, Frouin V, Garavan H, Glahn D, Goodrich-Hunsaker N, Gowland P, Grabe H, Grigis A, Gudbrandsen M, Gutman B, Haavik J, Håberg A, Hall J, Heinz A, Hohmann S, Hottenga J, Jacquemont S, Jahanshad N, Jonas R, Jones D, Jönsson E, Koops S, Kumar K, Le Hellard S, Lemaitre H, Liu J, Lundervold A, Martinot J, Mather K, McDonald-McGinn D, McMahon K, McRae A, Medland S, Moreau C, Murphy K, Murphy D, Murray R, Nees F, Owen M, Martinot M, Orfanos D, Paus T, Poustka L, Marques T, Roalf D, Sachdev P, Scheffler F, Schmitt J, Schumann G, Steen V, Stein D, Strike L, Teumer A, Thalamuthu A, Thomopoulos S, Tordesillas-Gutiérrez D, Trollor J, Uhlmann A, Vajdi A, van ’t Ent D, van Amelsvoort T, van den Bree M, van der Meer D, Vázquez-Bourgon J, Villalón-Reina J, Völker U, Völzke H, Vorstman J, Westlye L, Williams N, Wittfeld K, Wright M, Thompson P, Andreassen O, Linden D, group E. Penetrance of neurodevelopmental copy number variants is associated with variations in cortical morphology. Biological Psychiatry Cognitive Neuroscience And Neuroimaging 2025 PMID: 40414598, DOI: 10.1016/j.bpsc.2025.05.010.Peer-Reviewed Original ResearchCopy number variantsDevelopmental disordersNeurobiological mechanismsPenetration scoresMechanisms of genetic riskAssociated with variationBrain magnetic resonance imagingCohort of patientsCortical surface areaT1-weighted brain magnetic resonance imagingMagnetic resonance imagingCortical morphometric featuresGenetic dataLingual gyrusClinical phenotypeSubcortical morphologyIncreased riskNeuroimaging dataSchizophreniaBrain abnormalitiesNeurodevelopmental conditionsIntracranial volumeCerebral cortexResonance imagingCortical morphologyNeurophysiological Progression in Alzheimer's Disease: Insights From Dynamic Causal Modelling of Longitudinal Magnetoencephalography
Jafarian A, Assem M, Kocagoncu E, Lanskey J, Fye H, Williams R, Quinn A, Pitt J, Raymont V, Lowe S, Singh K, Woolrich M, Nobre A, Henson R, Friston K, Rowe J. Neurophysiological Progression in Alzheimer's Disease: Insights From Dynamic Causal Modelling of Longitudinal Magnetoencephalography. Human Brain Mapping 2025, 46: e70234. PMID: 40396657, PMCID: PMC12093352, DOI: 10.1002/hbm.70234.Peer-Reviewed Original ResearchConceptsDynamic causal modelingAssociated with neurophysiological changesCognitive declineMedial prefrontal cortexAlzheimer's diseaseCausal modelEffects of Alzheimer's diseaseHuman neuroimaging dataExperimental medicine studiesRegion-specificLongitudinal changesApplication of dynamic causal modelingResting-state magnetoencephalographyMild cognitive impairmentPrefrontal cortexAmyloid-positive mild cognitive impairmentMode networkNMDA neurotransmissionTwenty-nine peopleNMDA type glutamate receptorsNeuroimaging dataCortical regionsEffective connectivityNeuropsychiatric diseasesNeuronal vulnerabilityThe MR neuroimaging protocol for the Accelerating Medicines Partnership® Schizophrenia Program
Harms M, Cho K, Anticevic A, Bolo N, Bouix S, Campbell D, Cannon T, Cecchi G, Goncalves M, Haidar A, Hughes D, Izyurov I, John O, Kapur T, Kim N, Kotler E, Kubicki M, Kuperman J, Laulette K, Lindberg U, Markiewicz C, Ning L, Poldrack R, Rathi Y, Romo P, Tamayo Z, Wannan C, Wickham A, Yassin W, Zhou J, Addington J, Alameda L, Arango C, Breitborde N, Broome M, Cadenhead K, Calkins M, Chen E, Choi J, Conus P, Corcoran C, Cornblatt B, Diaz-Caneja C, Ellman L, Fusar-Poli P, Gaspar P, Gerber C, Glenthøj L, Horton L, Hui C, Kambeitz J, Kambeitz-Ilankovic L, Keshavan M, Kim S, Koutsouleris N, Kwon J, Langbein K, Mamah D, Mathalon D, Mittal V, Nordentoft M, Pearlson G, Perez J, Perkins D, Powers A, Rogers J, Sabb F, Schiffman J, Shah J, Silverstein S, Smesny S, Stone W, Strauss G, Thompson J, Upthegrove R, Verma S, Wang J, Wolf D, Kahn R, Kane J, McGorry P, Nelson B, Woods S, Shenton M, Wood S, Bearden C, Pasternak O. The MR neuroimaging protocol for the Accelerating Medicines Partnership® Schizophrenia Program. Schizophrenia 2025, 11: 52. PMID: 40175382, PMCID: PMC11965426, DOI: 10.1038/s41537-025-00581-6.Peer-Reviewed Original ResearchClinical high riskSchizophrenia ProgramClinical high-risk cohortClinical high-risk stateResting-state fMRIFMRI scanningStudy of individualsNeuroimaging resultsBrain regionsNeuroimaging dataNeuroimaging protocolsStructural scansParticipation varianceDiffusion scansFMRIPercentage of participantsNeuroimagingVariance component analysisMulti-siteParticipantsPsychosisVarianceTime pointsDiffusion-weighted imagingHigh riskAn 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. PMID: 40158273, 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 abnormalities
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