2025
Balancing Data Quality and Bias: Investigating Functional Connectivity Exclusions in the Adolescent Brain Cognitive Development℠ (ABCD Study) Across Quality Control Pathways
Peverill M, Russell J, Keding T, Rich H, Halvorson M, King K, Birn R, Herringa R. Balancing Data Quality and Bias: Investigating Functional Connectivity Exclusions in the Adolescent Brain Cognitive Development℠ (ABCD Study) Across Quality Control Pathways. Human Brain Mapping 2025, 46: e70094. PMID: 39788921, PMCID: PMC11717557, DOI: 10.1002/hbm.70094.Peer-Reviewed Original Research
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 dataA 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 MRIMultimodal 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 regressionInter-Modality Source Coupling: A Fully-Automated Whole-Brain Data-Driven Structure-Function Fingerprint Shows Replicable Links to Reading in a Large-Scale (N8K) Analysis
Kotoski A, Liu J, Morris R, Calhoun V. Inter-Modality Source Coupling: A Fully-Automated Whole-Brain Data-Driven Structure-Function Fingerprint Shows Replicable Links to Reading in a Large-Scale (N8K) Analysis. IEEE Transactions On Biomedical Engineering 2024, 71: 3383-3389. PMID: 38968021, PMCID: PMC11700228, DOI: 10.1109/tbme.2024.3423703.Peer-Reviewed Original ResearchReading abilityBrain structuresSchool-aged childrenResting-stateStructural magnetic resonance imagingInferior frontal areasFunctional brain changesInferior parietal lobuleHigher reading abilityFunctional connectivity patternsLow reading abilityLingual gyrusNeural basisParietal lobuleReading developmentBrain changesCognitive processesReplication linksRs-fMRICortical regionsReading scoresBrain functionCognitive growthFrontal areasConnectivity patternsA 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 abnormalitiesBrain Connectomics Improve the Prediction of High‐Risk Depression Profiles in the First Year following Breast Cancer Diagnosis
Liang M, Chen P, Tang Y, Tang X, Molassiotis A, Knobf M, Liu M, Hu G, Sun Z, Yu Y, Ye Z. Brain Connectomics Improve the Prediction of High‐Risk Depression Profiles in the First Year following Breast Cancer Diagnosis. Depression And Anxiety 2024, 2024: 1-11. PMCID: PMC11919153, DOI: 10.1155/2024/3103115.Peer-Reviewed Original ResearchBreast cancer diagnosisLatent growth mixture modelingMultivoxel pattern analysisDepression profilesCancer diagnosisBrain connectomeBaseline resting-state functional magnetic resonance imagingResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingAssessment of depressionFrontal medial cortexBrain connectivity patternsGrowth mixture modelingDepression trajectoriesFrontal poleBrain areasRs-fMRIBreast cancerConnectivity patternsHigh riskLow riskBrainConnectomeMagnetic resonance imagingMedial cortexA 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 patternA deep learning approach for mental health quality prediction using functional network connectivity and assessment data
Ajith M, Aycock D, Tone E, Liu J, Misiura M, Ellis R, Plis S, King T, Dotson V, Calhoun V. A deep learning approach for mental health quality prediction using functional network connectivity and assessment data. Brain Imaging And Behavior 2024, 18: 630-645. PMID: 38340285, DOI: 10.1007/s11682-024-00857-y.Peer-Reviewed Original ResearchStatic functional network connectivityMental health qualityFunctional network connectivityMental health categoriesRs-fMRIMental healthPatterns of abnormal connectivityHealth categoriesHealth qualityDevelopment of personalized interventionsManagement of mental healthResting-state fMRIMeasure mental healthUK Biobank datasetNeural patternsBrain healthVisual domainAbnormal connectionPersonalized interventionsBiobank datasetTreatment responseHealthNetwork connectivityBehavioral aspectsAssessment dataA Longitudinal Correlational Study of Psychological Resilience, Depression Disorder, and Brain Functional–Structural Hybrid Connectome in Breast Cancer
Liang M, Zhou J, Chen P, Song Y, Li S, Liang Y, Hu G, Hu Q, Sun Z, Yu Y, Molassiotis A, Knobf M, Ye Z. A Longitudinal Correlational Study of Psychological Resilience, Depression Disorder, and Brain Functional–Structural Hybrid Connectome in Breast Cancer. Depression And Anxiety 2024, 2024 PMCID: PMC11918802, DOI: 10.1155/2024/9294268.Peer-Reviewed Original ResearchDepressive disorderMultivoxel pattern analysisDiffusion tensor imagingPsychological resilienceRs-fMRICorrelational tractographyResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingLongitudinal correlational studyDSM-5Frontal poleBrain areasRisk of DDHigher psychological resilienceBreast cancerMediator variablesTensor imagingConnectomeBrainMagnetic resonance imagingDepressionDisordersPsychiatry physiciansPattern analysisResonance imaging
2023
A systematic review and meta-analysis of resting-state fMRI in anxiety disorders: Need for data sharing to move the field forward
Zugman A, Jett L, Antonacci C, Winkler A, Pine D. A systematic review and meta-analysis of resting-state fMRI in anxiety disorders: Need for data sharing to move the field forward. Journal Of Anxiety Disorders 2023, 99: 102773. PMID: 37741177, PMCID: PMC10753861, DOI: 10.1016/j.janxdis.2023.102773.Peer-Reviewed Original ResearchConceptsAnxiety disordersSystematic reviewPrevalent psychiatric disordersStrict inclusion criteriaAnterior cingulate cortexMedial frontal gyrusActivation likelihood estimation (ALE) analysisResting-state fMRITime of scanningFunctional imaging modalitiesPatient groupHealthy groupInclusion criteriaHealthy volunteersPsychiatric disordersCingulate cortexStudy numbersRs-fMRIFrontal gyrusDisordersImaging modalitiesPatientsBrain activityFamily-wise errorGyrusResting‐state dynamic functional network connectivity predicts cognition in 37,784 participants of UK Biobank
Sendi M, Zendehrouh E, Miller R, Salat D, Calhoun V. Resting‐state dynamic functional network connectivity predicts cognition in 37,784 participants of UK Biobank. Alzheimer's & Dementia 2023, 19 DOI: 10.1002/alz.065832.Peer-Reviewed Original ResearchFunctional network connectivityDynamic FNCDynamic functional network connectivityCognitive scoresFluid intelligenceCognitive declineAge-related cognitive declineGroup independent component analysisResting-state functional MRIBrain functional changesResting-state fMRIBrain functional network connectivityReaction timeParticipants of UK BiobankRT taskFunctional MRIRs-fMRIPairing taskCognitionIndependent component analysisUK BiobankBrainNetwork connectivityHealthy adultsData-driven componentsPrediction of sleep quality scores using dynamic functional network connectivity of young adults: A reproducibility analysis
Sendi M, Dini H, Zendehrouh E, Salat D, Calhoun V. Prediction of sleep quality scores using dynamic functional network connectivity of young adults: A reproducibility analysis. Alzheimer's & Dementia 2023, 19 DOI: 10.1002/alz.065778.Peer-Reviewed Original ResearchDynamic functional network connectivityHuman Connectome ProjectFunctional network connectivityHuman Connectome Project datasetDistinct statesResting-state fMRIRs-fMRIConnectome ProjectResting-state functional magnetic resonance imagingState vectorPittsburgh Sleep Quality IndexYoung adultsFunctional magnetic resonance imagingNight sleep timeRs-fMRI sessionsInterpretable 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
2022
Graph theory demonstrates functional reorganization dynamics related to tumor grade and location in glioma
Pasquini L, Jenabi M, Peck K, Holodny A. Graph theory demonstrates functional reorganization dynamics related to tumor grade and location in glioma. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2022 DOI: 10.58530/2022/2564.Peer-Reviewed Original Research
2021
Identifying brain networks in synaptic density PET (11C-UCB-J) with independent component analysis
Fang XT, Toyonaga T, Hillmer AT, Matuskey D, Holmes SE, Radhakrishnan R, Mecca AP, van Dyck CH, D’Souza D, Esterlis I, Worhunsky PD, Carson RE. Identifying brain networks in synaptic density PET (11C-UCB-J) with independent component analysis. NeuroImage 2021, 237: 118167. PMID: 34000404, PMCID: PMC8452380, DOI: 10.1016/j.neuroimage.2021.118167.Peer-Reviewed Original ResearchConceptsSynaptic densityResting-state functional magnetic resonance imagingSynaptic vesicle protein 2ALevel-dependent signal fluctuationsBrain networksFunctional magnetic resonance imagingMagnetic resonance imagingAge-related changesHealthy controlsResonance imagingRs-fMRIEffects of sexProtein 2AMultiple comparisonsHuman brainAgePotential utilitySexFirst evidenceCovariance patterns
2020
Increased connectivity in several bilateral frontal and fronto‐parietal networks predicts depressive symptoms in mid‐ to late‐life diabetics
Salardini A, Shen X, Hashemi‐Aghdam A, Laltoo E, Savoia S, Tokoglu F, Constable T. Increased connectivity in several bilateral frontal and fronto‐parietal networks predicts depressive symptoms in mid‐ to late‐life diabetics. Alzheimer's & Dementia 2020, 16 DOI: 10.1002/alz.043619.Peer-Reviewed Original ResearchDepressive symptomsBeck Depression InventoryDiabetic individualsBDI scoresDepression InventoryPrefrontal cortexBackground Depressive symptomsLate-life diabetesVascular cognitive impairmentDiagnosis of diabetesHigher BDI scoresComprehensive neuropsychological testingDorsal prefrontal cortexBilateral ventromedial prefrontal cortexRs-fMRI dataDiabetic patientsFazekas scoreMoCA scoresNeuropsychological testingFunctional connectivity matricesCognitive impairmentDepression symptomsVentromedial prefrontal cortexSymptomsRs-fMRI
2019
Stepwise functional connectivity reveals altered sensory‐multimodal integration in medication‐naïve adults with attention deficit hyperactivity disorder
Pretus C, Marcos‐Vidal L, Martínez‐García M, Picado M, Ramos‐Quiroga J, Richarte V, Castellanos F, Sepulcre J, Desco M, Vilarroya Ó, Carmona S. Stepwise functional connectivity reveals altered sensory‐multimodal integration in medication‐naïve adults with attention deficit hyperactivity disorder. Human Brain Mapping 2019, 40: 4645-4656. PMID: 31322305, PMCID: PMC6865796, DOI: 10.1002/hbm.24727.Peer-Reviewed Original ResearchConceptsAttention-deficit/hyperactivity disorderFunctional connectivitySensory regionsNeural hubCognitive functionResting-state functional magnetic resonance imagingAttention deficit hyperactivity disorderFunctional magnetic resonance imagingSeed regionPatterns of functional connectivityDefault-mode networkDeficit hyperactivity disorderFunctional networksPrimary sensory regionsFunctional overconnectivityAttention-deficit/hyperactivityNeuroimaging studiesMode networkHyperactivity disorderLow connectivity degreeRs-fMRIPattern of alterationsSeverity ScaleSensory inputMagnetic resonance imaging
2017
The dynamic imprint of word learning on the dorsal language pathway
Palomar-García M, Sanjuán A, Bueichekú E, Ventura-Campos N, Ávila C. The dynamic imprint of word learning on the dorsal language pathway. NeuroImage 2017, 159: 261-269. PMID: 28774649, DOI: 10.1016/j.neuroimage.2017.07.064.Peer-Reviewed Original ResearchConceptsDorsal language pathwayRs-FCDorsal streamLanguage pathwaysMotor areaMeasured BOLD signal changesVerbal repetition taskHickok and PoeppelRs-fMRI analysisBOLD signal changesTraining groupWord learningFMRI sessionFMRI resultsNeural changesFunctional connectivityRs-fMRIVocabulary acquisitionLearning vocabularyNative wordsVocabulary learningHealthy participantsRepetitive tasksVocabularyLong-term imprint
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