2024
Edge-centric network control on the human brain structural network
Sun H, Rosenblatt M, Dadashkarimi J, Rodriguez R, Tejavibulya L, Scheinost D. Edge-centric network control on the human brain structural network. Imaging Neuroscience 2024, 2: 1-15. DOI: 10.1162/imag_a_00191.Peer-Reviewed Original ResearchHuman brain structural networksNetwork control theoryEdge controlWhole-brain networksHuman Connectome ProjectDiffusion MRI dataWhite matter connectivityConnectome ProjectBrain dynamicsExecutive functionBrain structural networksBrain network connectivityBrain connectivityFunctional connectomeState transitionsTransitionEnergy patternsTheory modelBrain energy consumptionDynamic processStructural networkStateNetwork control mechanismsCognitive statesNetwork pairs
2020
Transdiagnostic, Connectome-Based Prediction of Memory Constructs Across Psychiatric Disorders
Barron DS, Gao S, Dadashkarimi J, Greene AS, Spann MN, Noble S, Lake EMR, Krystal JH, Constable RT, Scheinost D. Transdiagnostic, Connectome-Based Prediction of Memory Constructs Across Psychiatric Disorders. Cerebral Cortex 2020, 31: 2523-2533. PMID: 33345271, PMCID: PMC8023861, DOI: 10.1093/cercor/bhaa371.Peer-Reviewed Original ResearchConceptsMacroscale brain networksIndividual differencesBrain networksMemory deficitsFunctional connectivityAttention deficit hyper-activity disorderTask-based functional MRI dataLong-term memoryWhole-brain functional connectivityDiagnostic groupsWhole-brain patternsDefault mode networkFunctional MRI dataHuman Connectome ProjectPsychiatric disordersMemory constructsMemory performanceTransdiagnostic sampleBrain correlatesMode networkFunctional connectomeConnectome ProjectLimbic networkHealthy participantsMemoryThe Constrained Network-Based Statistic: A New Level of Inference for Neuroimaging
Noble S, Scheinost D. The Constrained Network-Based Statistic: A New Level of Inference for Neuroimaging. Lecture Notes In Computer Science 2020, 12267: 458-468. PMID: 33870336, PMCID: PMC8052680, DOI: 10.1007/978-3-030-59728-3_45.Peer-Reviewed Original ResearchNetwork-based statisticsLarge-scale networksConstrained networksLarge-scale brain networksHuman Connectome ProjectHigher effect sizesBrain networksGround truth mapConnectome ProjectTask dataTruth effectNew levelReproducible discoveryEffect sizeNBS methodTruth mapNetwork organizationNetworkLocal neighborhoodValid inferencesInferenceNeuroscienceImportant formCluster levelMajor initiativesDistributed Patterns of Functional Connectivity Predict Working Memory Performance in Novel Healthy and Memory-impaired Individuals
Avery EW, Yoo K, Rosenberg MD, Greene AS, Gao S, Na DL, Scheinost D, Constable TR, Chun MM. Distributed Patterns of Functional Connectivity Predict Working Memory Performance in Novel Healthy and Memory-impaired Individuals. Journal Of Cognitive Neuroscience 2020, 32: 241-255. PMID: 31659926, PMCID: PMC8004893, DOI: 10.1162/jocn_a_01487.Peer-Reviewed Original ResearchConceptsFunctional connectivity patternsFluid intelligenceMemory performanceIndividual differencesAttention modelConnectome-based predictive modelingConnectome-based predictive modelsWhole-brain functional connectivity patternsGeneral cognitive abilitySuch individual differencesConnectivity patternsAdult life spanHuman Connectome ProjectHuman Connectome Project dataMemory relateCognitive abilitiesNeural basisSustained attentionMemory scoresParietal regionsFunctional connectivityConnectome ProjectMemory modelOlder adultsMemory
2019
Cluster failure or power failure? Evaluating sensitivity in cluster-level inference
Noble S, Scheinost D, Constable RT. Cluster failure or power failure? Evaluating sensitivity in cluster-level inference. NeuroImage 2019, 209: 116468. PMID: 31852625, PMCID: PMC8061745, DOI: 10.1016/j.neuroimage.2019.116468.Peer-Reviewed Original ResearchCombining 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 ResearchConceptsMultiple 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 Project
2017
Can brain state be manipulated to emphasize individual differences in functional connectivity?
Finn ES, Scheinost D, Finn DM, Shen X, Papademetris X, Constable RT. Can brain state be manipulated to emphasize individual differences in functional connectivity? NeuroImage 2017, 160: 140-151. PMID: 28373122, PMCID: PMC8808247, DOI: 10.1016/j.neuroimage.2017.03.064.Peer-Reviewed Original ResearchConceptsIndividual differencesFunctional connectivityBrain statesIndividual differences researchBrain functional organizationHuman Connectome ProjectDifferences researchBrain activityConnectome ProjectSubject variabilityNetworks of interestBehavioral phenotypesCertain tasksFunctional organizationDefault stateNeutral backdropOutline questionsFuture studiesConnectivityTask