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, 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
A 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
Novel 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
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