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
2023
Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia
Li W, Lin Q, Zhao B, Kuang L, Zhang C, Han Y, Calhoun V. Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia. Journal Of Neuroscience Methods 2023, 403: 110049. PMID: 38151187, DOI: 10.1016/j.jneumeth.2023.110049.Peer-Reviewed Original ResearchConceptsSchizophrenia patientsFMRI dataFunctional network connectivityHealthy controlsDynamic functional network connectivityPsychotic diagnosesMental disordersSchizophreniaComplex-valued fMRI dataPotential imaging biomarkersDetect functional alterationsFMRIState transitionsNetwork connectivityPhase informationFunctional alterationsComplex valuesBrain informationMutual informationDynamicsPhaseAutomated time-lapse data segmentation reveals in vivo cell state dynamics
Genuth M, Kojima Y, Jülich D, Kiryu H, Holley S. Automated time-lapse data segmentation reveals in vivo cell state dynamics. Science Advances 2023, 9: eadf1814. PMID: 37267354, PMCID: PMC10413672, DOI: 10.1126/sciadv.adf1814.Peer-Reviewed Original ResearchConceptsCell statesSingle-cell RNA sequencing dataCell state dynamicsCell behaviorEmbryonic development proceedsCell state transitionsRNA sequencing dataCollective cell behaviorIndividual cell behaviorsZebrafish tailbudLeft-right asymmetryCell tracking dataCollective cell motionGene expressionSequencing dataMolecular processesIndividual embryosDevelopment proceedsEmbryosCell motionParallel identificationBilateral symmetryReproducible patternTailbudState transitions
2022
Control of cell state transitions
Rukhlenko O, Halasz M, Rauch N, Zhernovkov V, Prince T, Wynne K, Maher S, Kashdan E, MacLeod K, Carragher N, Kolch W, Kholodenko B. Control of cell state transitions. Nature 2022, 609: 975-985. PMID: 36104561, PMCID: PMC9644236, DOI: 10.1038/s41586-022-05194-y.Peer-Reviewed Original ResearchConceptsCell state transitionsCell fateCell statesCell fate transitionsCell fate decisionsSingle-cell dataNew biological insightsFate transitionsMovement of cellsFate decisionsWaddington landscapePhenotypic dataBiological insightsOmics datasetsOmics dataCellular modelMechanistic modelLandscape1FateCellsDevelopment pathwaysLandscapeBiologyState transitionsTherapeutic interventions
2002
Applying Hidden Markov Models to the Analysis of Single Ion Channel Activity
Venkataramanan L, Sigworth F. Applying Hidden Markov Models to the Analysis of Single Ion Channel Activity. Biophysical Journal 2002, 82: 1930-1942. PMID: 11916851, PMCID: PMC1301989, DOI: 10.1016/s0006-3495(02)75542-2.Peer-Reviewed Original ResearchConceptsCorrelated background noiseHidden Markov ModelDigital inverse filterMarkov model parametersDiscrete timeBaum-Welch algorithmMarkov modelComputational intensityDeterministic interferenceModel parametersInverse filterMultiple data setsAlgorithmPrevious resultsPractical applicationsNoise ratioState transitionsSharp frequencyData setsChannel dataSingle ion channel currentsNoiseExtensionRandomnessBackground noise
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