2025
An Explainable Unified Framework of Spatio-Temporal Coupling Learning With Application to Dynamic Brain Functional Connectivity Analysis
Gao B, Yu A, Qiao C, Calhoun V, Stephen J, Wilson T, Wang Y. An Explainable Unified Framework of Spatio-Temporal Coupling Learning With Application to Dynamic Brain Functional Connectivity Analysis. IEEE Transactions On Medical Imaging 2025, 44: 941-951. PMID: 39320999, PMCID: PMC11977455, DOI: 10.1109/tmi.2024.3467384.Peer-Reviewed Original ResearchSpatio-temporal informationDeep learning networkInter-node connectivitySpatio-temporal correlationMachine learning modelsNode representationsPoor explainabilityCoupling learningLearning frameworkDeep learningLearning networkLearning modelsExplainabilityTime series dataExperimental resultsCoupling associationFramework constructionLearningDynamic functional connectivityFrameworkBrain functional connectivity analysisBrain dynamic functional connectivityInformationConnectionNetwork
2024
An Efficient Contrastive Unimodal Pretraining Method for EHR Time Series Data
King R, Kodali S, Krueger C, Yang T, Mortazavi B. An Efficient Contrastive Unimodal Pretraining Method for EHR Time Series Data. 2024, 00: 1-8. DOI: 10.1109/bhi62660.2024.10913624.Peer-Reviewed Original ResearchDeep neural networksElectronic health recordsMachine learningSelf-supervised taskSelf-supervised learningSemi-supervised learningEffective feature extractionMIMIC-III datasetExtract meaningful informationTimeseries dataExtract valuable insightsSOTA methodsContrastive pretrainingLabeled dataFeature extractionNeural networkData batchesEICU datasetTime series dataMeaningful informationMIMIC-IIILinear evaluationComplex mappingComputational demandsHealth recordsA simple but tough-to-beat baseline for fMRI time-series classification
Popov P, Mahmood U, Fu Z, Yang C, Calhoun V, Plis S. A simple but tough-to-beat baseline for fMRI time-series classification. NeuroImage 2024, 303: 120909. PMID: 39515403, PMCID: PMC11625415, DOI: 10.1016/j.neuroimage.2024.120909.Peer-Reviewed Original ResearchConceptsComplex machine learning modelsBlack-box natureMulti-layer perceptronMachine learning modelsPrediction accuracyBlack-box modelsFMRI classificationComplex classifiersClassification accuracySequential informationHuman fMRI dataLearning modelsBlack-boxRich modelsSuperior performanceComplex model developmentFMRI dataTime-series fMRI dataTime series dataClassifierStand-alone pieceClassificationAccuracyDesign modelSeries dataImproving Age Prediction: Utilizing LSTM-Based Dynamic Forecasting For Data Augmentation in Multivariate Time Series Analysis
Gao Y, Ellis C, Calhoun V, Miller R. Improving Age Prediction: Utilizing LSTM-Based Dynamic Forecasting For Data Augmentation in Multivariate Time Series Analysis. 2024, 00: 125-128. DOI: 10.1109/ssiai59505.2024.10508611.Peer-Reviewed Original ResearchLong short-term memoryDeep learning modelsData augmentationPerformance deep learning modelsLearning modelsMultivariate time series dataAge prediction taskShort-term memoryPrediction taskAugmented datasetDynamical forecastsComponent networksMultivariate time series analysisDatasetNeuroimaging datasetsRobust solutionTime series dataOriginal dataValidation frameworkTime series analysisSeries dataNetworkNeuroimaging fieldDataModel performance
2023
Pynapple, a toolbox for data analysis in neuroscience
Viejo G, Levenstein D, Skromne Carrasco S, Mehrotra D, Mahallati S, Vite G, Denny H, Sjulson L, Battaglia F, Peyrache A. Pynapple, a toolbox for data analysis in neuroscience. ELife 2023, 12 DOI: 10.7554/elife.85786.3.Peer-Reviewed Original ResearchData streamsHigh-dimensional time series dataLow-level data processingCollaborative repositoryProgramming environmentData acquisition modalitiesData formatError-prone stepsData processingTime series dataAcquisition modalitiesDataTask parametersDatasetSystems neuroscienceSeries dataCore packageAnalysis routinesAnalysis packageAnalysis pipelineUsersData analysisFrameworkPythonRepositoryPynapple, a toolbox for data analysis in neuroscience
Viejo G, Levenstein D, Carrasco S, Mehrotra D, Mahallati S, Vite G, Denny H, Sjulson L, Battaglia F, Peyrache A. Pynapple, a toolbox for data analysis in neuroscience. ELife 2023, 12: rp85786. PMID: 37843985, PMCID: PMC10578930, DOI: 10.7554/elife.85786.Peer-Reviewed Original ResearchConceptsData streamsHigh-dimensional time series dataLow-level data processingCollaborative repositoryProgramming environmentData acquisition modalitiesData formatError-prone stepsData processingTime series dataAcquisition modalitiesDataTask parametersDatasetSystems neuroscienceSeries dataCore packageAnalysis routinesAnalysis packageAnalysis pipelineUsersData analysisFrameworkPythonRepositoryNovel methods for elucidating modality importance in multimodal electrophysiology classifiers
Ellis C, Sendi M, Zhang R, Carbajal D, Wang M, Miller R, Calhoun V. Novel methods for elucidating modality importance in multimodal electrophysiology classifiers. Frontiers In Neuroinformatics 2023, 17: 1123376. PMID: 37006636, PMCID: PMC10050434, DOI: 10.3389/fninf.2023.1123376.Peer-Reviewed Original ResearchExplainability approachesExplainability methodsAutomated sleep stage classificationRaw time series dataConvolutional neural networkDeep learning classifierSleep stage classificationNovel methodMultimodal classificationLearning classifiersNeural networkClassifierLocal explanationsGlobal explanationsExplainabilitySubject-level differencesTime series dataAdvancement of personalized medicineGlobal methodClinical classifierClassificationClinical variablesElectrophysiological studiesStage classificationElectrophysiological classification
2021
Considerations to address missing data when deriving clinical trial endpoints from digital health technologies
Di J, Demanuele C, Kettermann A, Karahanoglu F, Cappelleri J, Potter A, Bury D, Cedarbaum J, Byrom B. Considerations to address missing data when deriving clinical trial endpoints from digital health technologies. Contemporary Clinical Trials 2021, 113: 106661. PMID: 34954098, DOI: 10.1016/j.cct.2021.106661.Peer-Reviewed Original ResearchConceptsStatistical approachFunctional data analysisStatistical methodsRobust modelingImputation approachComplex dataTime series dataSeries dataMissingnessHigh-frequency time series dataStarting pointLearning methodsData analysisApproachSummary measuresModelingImputationDeep learning methodsDigital health technologies
2020
DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-Rate Variability (HRV) Data
Oskooei A, Chau S, Weiss J, Sridhar A, Martínez M, Michel B. DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-Rate Variability (HRV) Data. Studies In Computational Intelligence 2020, 914: 93-105. DOI: 10.1007/978-3-030-53352-6_9.Peer-Reviewed Original ResearchConvolutional autoencoderK-Nearest Neighbor ClassificationTraditional K-means clusteringFrequency domain featuresK-Nearest NeighborK-means clusteringShort-term memoryLSTM autoencoderUnsupervised methodDeep learningDomain featuresAutoencoderDBSCAN clusteringK-meansInterval time series dataUnsupervised identificationStress detectionData pointsTime series dataEngineering featuresNormal clusterLSTMSize of clustersDemographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity
Dvornek NC, Li X, Zhuang J, Ventola P, Duncan JS. Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity. Lecture Notes In Computer Science 2020, 12436: 363-372. PMID: 34308438, PMCID: PMC8299434, DOI: 10.1007/978-3-030-59861-7_37.Peer-Reviewed Original ResearchRecurrent neural network modelRecurrent neural networkNeural network modelFunctional magnetic resonance imaging (fMRI) time series dataAttention mechanismArt resultsNeural networkCross-validation frameworkNetwork modelTime series dataIndividual demographic informationABIDE IImproved classificationNetwork differencesNetworkClassificationFunctional network differencesFrameworkIndividual demographic variablesInformation
2019
Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI
Dvornek NC, Li X, Zhuang J, Duncan JS. Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI. Lecture Notes In Computer Science 2019, 11861: 382-390. PMID: 32274470, PMCID: PMC7143657, DOI: 10.1007/978-3-030-32692-0_44.Peer-Reviewed Original ResearchRecurrent neural networkAutism Brain Imaging Data ExchangeFunctional magnetic resonance imaging (fMRI) dataLarge fMRI datasetShort-term memory structureGenerative modelLong short-term memory (LSTM) structureGenerative recurrent neural networkNeural networkLSTM nodesFMRI time series dataTime series dataMagnetic resonance imaging dataRNN-based modelsNetwork interpretabilityFMRI datasetsMemory structureClassification taskData exchangeDiscriminative modelUsing the Shapes of Clinical Data Trajectories to Predict Mortality in ICUs
Ma J, Lee DKK, Perkins ME, Pisani MA, Pinker E. Using the Shapes of Clinical Data Trajectories to Predict Mortality in ICUs. Critical Care Explorations 2019, 1: e0010. PMID: 32166256, PMCID: PMC7063876, DOI: 10.1097/cce.0000000000000010.Peer-Reviewed Original ResearchPrecision-recall curveTrajectory informationData trajectoriesStatistical learning techniquesRandom forest classifierRelevant shape featuresStatistical learning modelsLearning techniquesMachine learningElectronic health recordsTrajectory featuresLearning modelShape featuresForest classifierTime series dataManual extractionHealth recordsPredictive modelingSeries dataPredictive performanceInformationPatients' clinical dataDynamic predictionClassifierTime series
2018
iDREM: Interactive visualization of dynamic regulatory networks
Ding J, Hagood JS, Ambalavanan N, Kaminski N, Bar-Joseph Z. iDREM: Interactive visualization of dynamic regulatory networks. PLOS Computational Biology 2018, 14: e1006019. PMID: 29538379, PMCID: PMC5868853, DOI: 10.1371/journal.pcbi.1006019.Peer-Reviewed Original ResearchConceptsDynamic regulatory networksRegulatory networksHigh-throughput time series dataInteraction dataProtein-DNA interaction dataSingle-cell RNA-seqTime series gene expression dataStatic datasetsInteractive visualizationGene expression dataData typesRNA-seqTime series dataBiological processesExpression dataMiRNA expressionNetworkSeries dataImportant challengeNew versionDevelopmental dataNovel hypothesisUnified modelMultiple labsRecent years
2017
Robust distributed lag models using data adaptive shrinkage
Chen Y, Mukherjee B, Adar S, Berrocal V, Coull B. Robust distributed lag models using data adaptive shrinkage. Biostatistics 2017, 19: 461-478. PMID: 29040386, PMCID: PMC6454578, DOI: 10.1093/biostatistics/kxx041.Peer-Reviewed Original ResearchConceptsDistributed lag modelsDistributed lagLag modelTime series dataEffects of air pollutionBias-variance trade-offGeneralized ridge regressionShrinkage methodAir pollution studiesHierarchical Bayes approachShrinkage approachTime seriesDl functionAir pollutionPollution studiesEffect estimatesTrade-OffsExtensive simulation studyDependent variableShrinking coefficientsMean square errorLagSimulation studyBayes approachRidge regression
2013
Bayesian Analysis of Time-Series Data under Case-Crossover Designs: Posterior Equivalence and Inference
Li S, Mukherjee B, Batterman S, Ghosh M. Bayesian Analysis of Time-Series Data under Case-Crossover Designs: Posterior Equivalence and Inference. Biometrics 2013, 69: 925-936. PMID: 24289144, PMCID: PMC4108592, DOI: 10.1111/biom.12102.Peer-Reviewed Original ResearchConceptsSemi-parametric Bayesian approachLikelihood-based approachRandom nuisance parametersTime series analysisFrequentist literatureNuisance parametersDirichlet processInferential issuesConditional likelihoodPosterior distributionRisk functionTime seriesBayesian workFrequentist approachCase-crossover designSimulation studyRestrictive assumptionsBayesian approachTime series dataLikelihood formulationBayesian methodsEquivalent resultsBayesian analysisCase-crossoverBayesian framework
2007
International Graduate Education and Innovation: Evidence and Issues for East Asian Technology Policy
Maskus K, Mobarak A, Stuen E. International Graduate Education and Innovation: Evidence and Issues for East Asian Technology Policy. Asian Economic Papers 2007, 6: 78-94. DOI: 10.1162/asep.2007.6.3.78.Peer-Reviewed Original ResearchInternational graduate studentsGraduate studentsStudent-level panel dataOwn innovation capacityForeign doctoral studentsAmerican research universitiesEast Asian economiesU.S. immigration authoritiesTime series dataDoctoral studentsForeign studentsGraduate educationSuch enrollmentScientific journal publicationsResearch universitiesStudentsAsian economiesPanel dataUniversity officialsInnovation policyEngineering DepartmentEconomic innovationPolicy questionsInnovation capacityGrowth benefits
2004
Functional Brain Image Analysis Using Joint Function-Structure Priors
Yang J, Papademetris X, Staib LH, Schultz RT, Duncan JS. Functional Brain Image Analysis Using Joint Function-Structure Priors. Lecture Notes In Computer Science 2004, 3217: 736-744. PMID: 20543899, PMCID: PMC2883266, DOI: 10.1007/978-3-540-30136-3_90.Peer-Reviewed Original Research
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