2019
Combining multiple connectomes improves predictive modeling of phenotypic measures
Gao S, Greene AS, Constable RT, Scheinost D. Combining multiple connectomes improves predictive modeling of phenotypic measures. NeuroImage 2019, 201: 116038. PMID: 31336188, PMCID: PMC6765422, DOI: 10.1016/j.neuroimage.2019.116038.Peer-Reviewed Original ResearchMeSH KeywordsAdultAlgorithmsConnectomeFemaleForecastingHumansMaleModels, NeurologicalPhenotypeYoung AdultConceptsMultiple connectomesLarge open-source datasetOpen-source datasetNovel prediction frameworkPredictive modelingSingle predictive modelPredictive modelArt algorithmsPrediction frameworkMultiple tasksPredictive model approachPrincipled waySpecific algorithmsFunctional connectivity matricesConnectivity matrixDifferent tasksPrediction performanceConnectome-based predictive modelingHuman Connectome ProjectTaskSuperior performanceAlgorithmComplementary informationNaïve extensionsConnectome ProjectTen simple rules for predictive modeling of individual differences in neuroimaging
Scheinost D, Noble S, Horien C, Greene AS, Lake EM, Salehi M, Gao S, Shen X, O’Connor D, Barron DS, Yip SW, Rosenberg MD, Constable RT. Ten simple rules for predictive modeling of individual differences in neuroimaging. NeuroImage 2019, 193: 35-45. PMID: 30831310, PMCID: PMC6521850, DOI: 10.1016/j.neuroimage.2019.02.057.Peer-Reviewed Original ResearchMeSH KeywordsBrainConnectomeHumansMachine LearningMagnetic Resonance ImagingModels, NeurologicalNeuroimagingConceptsBrain-behavior associations
2018
Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies
Fong AHC, Yoo K, Rosenberg MD, Zhang S, Li CR, Scheinost D, Constable RT, Chun MM. Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies. NeuroImage 2018, 188: 14-25. PMID: 30521950, PMCID: PMC6401236, DOI: 10.1016/j.neuroimage.2018.11.057.Peer-Reviewed Original ResearchMeSH KeywordsAttentionBrainHumansIndividualityMagnetic Resonance ImagingModels, NeurologicalNeural PathwaysRestTask Performance and AnalysisConceptsAttention task performanceDynamic functional connectivityTask performanceIndividual differencesExecutive control brain networksFunctional connectivityFunctional brain scansAttention performanceTask conditionsAttention scoresBrain networksFMRI dataBrain regionsBetter attentionFC featuresFC matricesDFC matrixPearson's rAttentionIndividualsOne-subjectBrain scansConnectivityConnectomeCross-validation approachTask-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 manipulationIntelligenceVariance
2017
Connectome-based Models Predict Separable Components of Attention in Novel Individuals
Rosenberg MD, Hsu WT, Scheinost D, Constable R, Chun MM. Connectome-based Models Predict Separable Components of Attention in Novel Individuals. Journal Of Cognitive Neuroscience 2017, 30: 160-173. PMID: 29040013, DOI: 10.1162/jocn_a_01197.Peer-Reviewed Original ResearchConceptsConnectome-based predictive modelingAttention Network TaskExecutive controlIntrinsic functional organizationRT variabilityANT performanceInfluential modelFunctional connectivityBrain's intrinsic functional organizationComponents of attentionExecutive control scoresResting-state functional connectivityResting-state dataFunctional brain networksFunctional organizationTask-based dataAttentional abilitiesUpcoming stimulusExplicit taskSustained attentionFMRI scanningAttention factorNovel individualsAdditional independent componentNetwork tasksCharacterizing Attention with Predictive Network Models
Rosenberg MD, Finn ES, Scheinost D, Constable RT, Chun MM. Characterizing Attention with Predictive Network Models. Trends In Cognitive Sciences 2017, 21: 290-302. PMID: 28238605, PMCID: PMC5366090, DOI: 10.1016/j.tics.2017.01.011.Peer-Reviewed Original ResearchConceptsAttention deficit hyperactivity disorderAttentional abilitiesLarge-scale brain networksLaboratory-based tasksDeficit hyperactivity disorderExplicit taskCognitive abilitiesHyperactivity disorderBrain networksBrain computationCognitive functionFunctional connectivityFunctional architectureTaskClinical dysfunctionEmpirical evidenceAttentionPredictive network modelsNeuromarkersNetwork modelAbilityRecent workNetwork propertiesDisordersPeopleUsing connectome-based predictive modeling to predict individual behavior from brain connectivity
Shen X, Finn ES, Scheinost D, Rosenberg MD, Chun MM, Papademetris X, Constable RT. Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nature Protocols 2017, 12: 506-518. PMID: 28182017, PMCID: PMC5526681, DOI: 10.1038/nprot.2016.178.Peer-Reviewed Original Research
2015
A neuromarker of sustained attention from whole-brain functional connectivity
Rosenberg MD, Finn ES, Scheinost D, Papademetris X, Shen X, Constable RT, Chun MM. A neuromarker of sustained attention from whole-brain functional connectivity. Nature Neuroscience 2015, 19: 165-171. PMID: 26595653, PMCID: PMC4696892, DOI: 10.1038/nn.4179.Peer-Reviewed Original ResearchThe (in)stability of functional brain network measures across thresholds
Garrison KA, Scheinost D, Finn ES, Shen X, Constable RT. The (in)stability of functional brain network measures across thresholds. NeuroImage 2015, 118: 651-661. PMID: 26021218, PMCID: PMC4554838, DOI: 10.1016/j.neuroimage.2015.05.046.Peer-Reviewed Original ResearchConceptsBinary graph
2008
Novel Interaction Techniques for Neurosurgical Planning and Stereotactic Navigation
Joshi A, Scheinost D, Vives KP, Spencer DD, Staib LH, Papademetris X. Novel Interaction Techniques for Neurosurgical Planning and Stereotactic Navigation. IEEE Transactions On Visualization And Computer Graphics 2008, 14: 1587-1594. PMID: 18989014, PMCID: PMC2633029, DOI: 10.1109/tvcg.2008.150.Peer-Reviewed Original Research