2025
Prediction of alcohol intake patterns with olfactory and gustatory brain connectivity networks
Agarwal K, Chaudhary S, Tomasi D, Volkow N, Joseph P. Prediction of alcohol intake patterns with olfactory and gustatory brain connectivity networks. Neuropsychopharmacology 2025, 1-9. PMID: 39962224, DOI: 10.1038/s41386-025-02058-7.Peer-Reviewed Original ResearchVentral attention networkBrain connectivity patternsHuman Connectome ProjectResting-state fMRI dataConnectivity patternsRisk of AUDYoung adultsBrain network connectivityAlcohol intake patternsAlcohol intakeAlcohol consumption behaviorOlfactory perceptionFMRI dataFunctional connectomePast-weekLongitudinal researchAttention networkYoung adult cohortChemosensory cuesBrain connectivity networksConnectome ProjectPast-yearConnectivity networksAlcohol drinkersAlcoholImpaired spatial dynamic functional network connectivity and neurophysiological correlates in functional hemiparesis
Premi E, Cantoni V, Benussi A, Iraji A, Calhoun V, Corbo D, Gasparotti R, Tinazzi M, Borroni B, Magoni M. Impaired spatial dynamic functional network connectivity and neurophysiological correlates in functional hemiparesis. NeuroImage Clinical 2025, 45: 103731. PMID: 39764901, PMCID: PMC11762193, DOI: 10.1016/j.nicl.2025.103731.Peer-Reviewed Original ResearchDynamic functional network connectivitySomatomotor networkSalience networkFunctional network connectivityGABAergic neurotransmissionResting-state functional MRI scansResting-state fMRI dataFunctional MRI scansDynamic brain statesBrain network dynamicsStatic functional connectivityDynamic brain networksBrain networksGlutamatergic transmissionNeurophysiological correlatesFunctional connectivityTranscranial magnetic stimulation protocolFMRI dataGABAergic inhibitionMagnetic stimulation protocolBrain statesNeurotransmissionHealthy controlsDMNNetwork connectivity
2024
A core tensor sparsity enhancement method for solving Tucker-2 model of multi-subject fMRI data
Han Y, Lin Q, Kuang L, Zhao B, Gong X, Cong F, Wang Y, Calhoun V. A core tensor sparsity enhancement method for solving Tucker-2 model of multi-subject fMRI data. Biomedical Signal Processing And Control 2024, 95: 106471. DOI: 10.1016/j.bspc.2024.106471.Peer-Reviewed Original ResearchTucker-2 modelMulti-subject fMRI dataFactor matricesCore tensorHalf-quadratic splittingTensor structure informationLow-rank constraintTensor sparsitySparsity constraintQuadratic splittingTask-related fMRI dataImprovement of accuracyEnhancement methodOrthogonality constraintsFMRI dataProcrustes solutionSimulated fMRI dataTucker-3 modelSparsityTemporal evidenceResting-state fMRI dataIdentity matrixDecomposition modelIntrinsic relationshipStructural informationCortical hubs of highly superior autobiographical memory
Orwig W, Diez I, Bueichekú E, Pedale T, Parente F, Campolongo P, Schacter D, Sepulcre J, Santangelo V. Cortical hubs of highly superior autobiographical memory. Cortex 2024, 179: 14-24. PMID: 39094240, DOI: 10.1016/j.cortex.2024.06.018.Peer-Reviewed Original ResearchConceptsSuperior autobiographical memoryAutobiographical memoryCortical hubsWhole-brain connectivity analysisPattern of increased connectivityResting-state fMRI dataWhole-brain analysisAutobiographical memory networkPosterior cingulate cortexMidline areaSeed-based analysisFunctional brain connectivityGraph theory analysisCingulate cortexNeural underpinningsNeuroimaging studiesEnhance memoryRemembering eventsBrain regionsControl participantsConnectivity analysisFMRI dataBrain connectivityCortical regionsWhole-brainSubgroup Identification Through Multiplex Community Structure Within Functional Connectivity Networks
Yang H, Ortiz-Bouza M, Vu T, Laport F, Calhoun V, Aviyente S, Adali T. Subgroup Identification Through Multiplex Community Structure Within Functional Connectivity Networks. 2024, 00: 2141-2145. DOI: 10.1109/icassp48485.2024.10446076.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingFunctional networksResting-state fMRI dataMultiplex networksMulti-subject functional magnetic resonance imagingNature of psychiatric disordersFunctional connectivity networksDiagnostic heterogeneityPsychotic patientsIndividual functional networksPsychiatric disordersCommunity detectionGroup differencesFMRI dataData-driven methodMultiple networksConnectivity networksMagnetic resonance imagingIdentified subgroupsNetworkSubgroup identificationResonance imagingSubject correlationSubgroup structure
2023
Relationship between loss of awareness of cognitive decline and tau pathology in the Alzheimer’s disease spectrum: modulatory role of resting‐state functional connectivity
Cacciamani F, Gagliardi G, Mimmack K, Qiao K, Bueichekú E, Marshall G, Sepulcre J, Diez I, Vannini P. Relationship between loss of awareness of cognitive decline and tau pathology in the Alzheimer’s disease spectrum: modulatory role of resting‐state functional connectivity. Alzheimer's & Dementia 2023, 19 DOI: 10.1002/alz.079806.Peer-Reviewed Original ResearchAwareness of cognitive declineResting-state functional connectivityLinear regression modelsSelf-referential networkHigher tau burdenFunctional connectivityCognitive declineRegression modelsTau accumulationAlzheimer's diseaseImpaired resting-state functional connectivityAlzheimer's disease spectrumResting-state fMRI dataAssociated with tau accumulationDorsal attention networkReduced awarenessWithin-network connectivityModulatory roleLow awarenessTau burdenEffects of tauCognitive function indicesTau interactionsSalience networkTau pathologyAltered Resting-State Activity and Connectivity in Late-Life Depression with Suicidal Ideation
Wang L, Manning K, Pearlson G, Steffens D. Altered Resting-State Activity and Connectivity in Late-Life Depression with Suicidal Ideation. American Journal Of Geriatric Psychiatry 2023, 31: e22-e23. DOI: 10.1016/j.jagp.2023.02.031.Peer-Reviewed Original ResearchDefault mode networkVentrolateral prefrontal cortexLate-life depressionFunctional connectivityPrefrontal cortexOlder adultsOlder depressed adultsBrain activityDepressed adultsSuicidal ideationGreater brain activityVisual cortexWeaker functional connectivityStronger functional connectivityResting-state fMRI dataResting-state activitySuicidal behaviorWhole-brain voxelwiseNon-SI groupROI functional connectivityDepression severitySI groupCerebellar cortexCerebellar functional connectivityResting activityNew Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity using Dictionary Learning
Ghayem F, Yang H, Kantar F, Kim S, Calhoun V, Adali T. New Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity using Dictionary Learning. 2023, 00: 1-5. DOI: 10.1109/icassp49357.2023.10096473.Peer-Reviewed Original ResearchDictionary learningIndependent component analysisLearned atomsDiscovery of hidden informationNetwork connectivityMulti-subject functional magnetic resonance imagingFunctional magnetic resonance imagingFunctional network connectivityDiscriminative featuresFeature vectorHidden informationEffective classificationSZ groupHealthy controlsResting-state fMRI dataExperimental resultsICA resultsDictionaryBrain functional network connectivityBrain networksMental disordersFMRI dataLearningRepresentationMental diseasesHigher-Order Organization in the Human Brain From Matrix-Based Rényi’s Entropy
Li Q, Yu S, Madsen K, Calhoun V, Iraji A. Higher-Order Organization in the Human Brain From Matrix-Based Rényi’s Entropy. 2023, 00: 1-5. DOI: 10.1109/icasspw59220.2023.10193346.Peer-Reviewed Original ResearchS-entropyHigher-order interactionsResting-state fMRI dataEstimate statistical dependenciesProbability distribution functionFMRI dataInformation processingHuman brainHigher-orderInformation interactionMutual informationMultivariate mutual informationTotal correlationOrderMultivariate time seriesInteractionHigher-order informationDependenceEvaluation of boundaries between mood and psychosis disorder using dynamic functional network connectivity (dFNC) via deep learning classification
Rokham H, Falakshahi H, Fu Z, Pearlson G, Calhoun V. Evaluation of boundaries between mood and psychosis disorder using dynamic functional network connectivity (dFNC) via deep learning classification. Human Brain Mapping 2023, 44: 3180-3195. PMID: 36919656, PMCID: PMC10171526, DOI: 10.1002/hbm.26273.Peer-Reviewed Original ResearchConceptsDynamic functional network connectivityFunctional network connectivityDSM-IVFMRI-based measuresResting-state fMRI dataBiomarker-based approachPsychosis disordersClinical courseBipolar-Schizophrenia NetworkClinical evaluationSymptomatic measuresHealthy controlsPsychotic illnessHealthy individualsNeurological observationsMental disordersReliability of diagnosisStatistical group differencesMental healthNeuroimaging techniquesStatistical ManualDiagnostic problemsGroup differencesIntermediate phenotypesDisorders
2022
Characterization of Early Stage Parkinson's Disease From Resting-State fMRI Data Using a Long Short-Term Memory Network
Guo X, Tinaz S, Dvornek N. Characterization of Early Stage Parkinson's Disease From Resting-State fMRI Data Using a Long Short-Term Memory Network. Frontiers In Neuroimaging 2022, 1: 952084. PMID: 37555151, PMCID: PMC10406199, DOI: 10.3389/fnimg.2022.952084.Peer-Reviewed Original ResearchEarly-stage Parkinson's diseaseFunctional magnetic resonance imagingParkinson's Progression Markers InitiativeParkinson's diseaseProgression Markers InitiativeDiagnosis of PDEarly-stage diseaseFunctional brain changesBrain function alterationsStage Parkinson's diseaseFunctional connectivity differencesComplex neurodegenerative disorderMagnetic resonance imagingResting-state fMRI dataStage diseaseDisease stageDisease progressionBrain changesTreatment responseMotor impairmentFC changesNew therapiesFunction alterationsResonance imagingBrain regions
2021
Functional Connectivity for the Language Network in the Developing Brain: 30 Weeks of Gestation to 30 Months of Age
Scheinost D, Chang J, Lacadie C, Brennan-Wydra E, Constable RT, Chawarska K, Ment LR. Functional Connectivity for the Language Network in the Developing Brain: 30 Weeks of Gestation to 30 Months of Age. Cerebral Cortex 2021, 32: 3289-3301. PMID: 34875024, PMCID: PMC9340393, DOI: 10.1093/cercor/bhab415.Peer-Reviewed Original ResearchConceptsWeeks of gestationFirst postnatal monthMonths of agePostnatal monthFunctional connectivityWernicke's areaWeeks postmenstrual ageLanguage networkResting-state fMRI dataPostmenstrual ageFetal onsetInterhemispheric connectionsIntrahemispheric connectionsPrimary analysisOlder infantsNeurobehavioral disordersSecondary analysisGestationCross-sectional dataMonthsBrocaUnique participantsWeeksSignificant increaseFunctional connections
2020
Connectome-based models can predict early symptom improvement in major depressive disorder
Ju Y, Horien C, Chen W, Guo W, Lu X, Sun J, Dong Q, Liu B, Liu J, Yan D, Wang M, Zhang L, Guo H, Zhao F, Zhang Y, Shen X, Constable RT, Li L. Connectome-based models can predict early symptom improvement in major depressive disorder. Journal Of Affective Disorders 2020, 273: 442-452. PMID: 32560939, DOI: 10.1016/j.jad.2020.04.028.Peer-Reviewed Original ResearchConceptsMajor depressive disorderSymptom improvementAntidepressant treatmentMDD patientsDepressive disorderTreatment outcomesEarly symptom improvementIndividual therapeutic responseInitial MR scansUntreated MDD patientsResting-state functional connectivity patternsFirst-line treatmentThree-month time pointTime pointsAntidepressant treatment outcomeBaseline functional connectivityHamilton Rating ScaleSeverity of depressionResting-state connectivityFunctional connectivity patternsResting-state fMRI dataDifferent time pointsTherapeutic responseClinical practiceFunctional brain networks
2019
The dorsolateral prefrontal cortex is selectively involved in chemotherapy-related cognitive impairment in breast cancer patients with different hormone receptor expression.
Chen H, Ding K, Zhao J, Chao HH, Li CR, Cheng H. The dorsolateral prefrontal cortex is selectively involved in chemotherapy-related cognitive impairment in breast cancer patients with different hormone receptor expression. American Journal Of Cancer Research 2019, 9: 1776-1785. PMID: 31497358, PMCID: PMC6726991.Peer-Reviewed Original ResearchChemotherapy-related cognitive impairmentBreast cancer patientsHormone receptor expressionDorsolateral prefrontal cortexLeft dorsolateral prefrontal cortexCancer patientsSuperior frontal gyrusReceptor expressionFunctional connectivityPrefrontal cortexEstrogen receptorLeft precuneusCognitive impairmentFunctional magnetic resonance imaging (fMRI) examinationsMagnetic resonance imaging examinationsRight superior frontal gyrusNeuropsychological testsRight dorsolateral prefrontal cortexFunctional connectivity strengthResting-state fMRI dataER-/PRBC patientsDLPFC connectivityBreast cancerImaging examinationsAn information network flow approach for measuring functional connectivity and predicting behavior
Kumar S, Yoo K, Rosenberg MD, Scheinost D, Constable RT, Zhang S, Li C, Chun MM. An information network flow approach for measuring functional connectivity and predicting behavior. Brain And Behavior 2019, 9: e01346. PMID: 31286688, PMCID: PMC6710195, DOI: 10.1002/brb3.1346.Peer-Reviewed Original ResearchConceptsFunctional brain connectivityFunctional magnetic resonance imagingFMRI time coursesIndividual differencesTask performanceMeasures of attentionSustained attention taskAttention task performanceResting-state fMRI dataSample of individualsAttention taskFMRI dataFunctional connectivityFC patternsBrain connectivityPearson correlationInformation theory statisticsInformation flowMachine-learning modelsMeasuresMagnetic resonance imagingAttentionNetwork flow approachTime courseDifferent datasets
2018
Associations between children’s family environment, spontaneous brain oscillations, and emotional and behavioral problems
Sato J, Biazoli C, Salum G, Gadelha A, Crossley N, Vieira G, Zugman A, Picon F, Pan P, Hoexter M, Amaro E, Anés M, Moura L, Del’Aquilla M, Mcguire P, Rohde L, Miguel E, Bressan R, Jackowski A. Associations between children’s family environment, spontaneous brain oscillations, and emotional and behavioral problems. European Child & Adolescent Psychiatry 2018, 28: 835-845. PMID: 30392120, DOI: 10.1007/s00787-018-1240-y.Peer-Reviewed Original ResearchConceptsFamily environmentMental health outcomesFamily coherenceEmotional problemsOrbitofrontal cortexTemporal poleResting-state fMRI dataLeft temporal poleSpontaneous brain oscillationsChild's family environmentRight orbitofrontal cortexOscillatory neural activityHealth outcomesBrain oscillationsBehavioral problemsNeural activityFMRI dataFMRI metricsBrain areasLow-frequency fluctuationsFractional amplitudeLower incidenceSpontaneous activityHigh incidenceExploratory studyTask-induced brain state manipulation improves prediction of individual traits
Greene AS, Gao S, Scheinost D, Constable RT. Task-induced brain state manipulation improves prediction of individual traits. Nature Communications 2018, 9: 2807. PMID: 30022026, PMCID: PMC6052101, DOI: 10.1038/s41467-018-04920-3.Peer-Reviewed Original ResearchConceptsBrain statesIndividual differencesBrain-behavior relationshipsFluid intelligence scoresTask-based functional connectivity analysisResting-state fMRI dataBrain functional organizationFunctional connectivity analysisCognitive tasksFluid intelligenceIntelligence scoresFunctional connectivityFMRI dataConnectivity analysisHuman behaviorIndividual traitsTaskCertain tasksFunctional organizationOutperform modelsSuch relationshipsCognitionState manipulationIntelligenceVarianceCombining Phenotypic and Resting-State FMRI Data for Autism Classification with Recurrent Neural Networks
Dvornek NC, Ventola P, Duncan JS. Combining Phenotypic and Resting-State FMRI Data for Autism Classification with Recurrent Neural Networks. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2018, 2018: 725-728. PMID: 30288208, PMCID: PMC6166875, DOI: 10.1109/isbi.2018.8363676.Peer-Reviewed Original ResearchAutism spectrum disorderRecurrent neural networkNeural networkAutism Brain Imaging Data ExchangeSingle deep learning frameworkHeterogeneity of ASDFunctional magnetic resonance imagingDeep learning frameworkResting-state fMRI dataResting-state functional magnetic resonance imagingBetter classification accuracyAutism classificationSpectrum disorderData exchangeLearning frameworkFMRI dataClassification accuracyCross-validation frameworkChallenging taskStraightforward taskPrior workNetworkSuch dataRsfMRITask
2017
Association between abnormal brain functional connectivity in children and psychopathology: A study based on graph theory and machine learning
Sato J, Biazoli C, Salum G, Gadelha A, Crossley N, Vieira G, Zugman A, Picon F, Pan P, Hoexter M, Amaro E, Anés M, Moura L, Del’Aquilla M, Mcguire P, Rohde L, Miguel E, Jackowski A, Bressan R. Association between abnormal brain functional connectivity in children and psychopathology: A study based on graph theory and machine learning. The World Journal Of Biological Psychiatry 2017, 19: 119-129. PMID: 28635541, DOI: 10.1080/15622975.2016.1274050.Peer-Reviewed Original ResearchConceptsBilateral posterior temporal corticesAbnormal brain functional connectivityMental health disordersBilateral posterior cingulateBrain networksBrain functional connectivityResting-state fMRI dataPosterior temporal cortexBrain network organizationLevels of psychopathologyTemporal cortexHealth disordersTemporal polePosterior cingulateMental disordersBrain developmentFunctional connectivityBrain connectivitySignificant decreaseDisordersGraph theory measuresIndividual brain networksBiological measuresPsychopathologySubjects
2015
Resting-State Functional Connectivity of the Locus Coeruleus in Humans: In Comparison with the Ventral Tegmental Area/Substantia Nigra Pars Compacta and the Effects of Age
Zhang S, Hu S, Chao HH, Li CR. Resting-State Functional Connectivity of the Locus Coeruleus in Humans: In Comparison with the Ventral Tegmental Area/Substantia Nigra Pars Compacta and the Effects of Age. Cerebral Cortex 2015, 26: 3413-3427. PMID: 26223261, PMCID: PMC4961017, DOI: 10.1093/cercor/bhv172.Peer-Reviewed Original ResearchConceptsVentral tegmental area/substantia nigra pars compactaSubstantia nigra pars compactaLocus coeruleusPars compactaFunctional connectivityResting-state functional connectivityCerebral functional connectivityNumerous animal studiesFronto-parietal cortexRight anterior insulaResting-state fMRI dataEffect of ageCerebral cortexNoradrenergic inputCognitive motor controlAnimal studiesCortex decreasesHealthy adultsCerebellum increasesMidbrain nucleiBilateral amygdalaMidbrain structuresAnterior insulaCognitive manifestationsMotor control
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