2024
Improving 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
Interpretable LSTM model reveals transiently-realized patterns of dynamic brain connectivity that predict patient deterioration or recovery from very mild cognitive impairment
Gao Y, Lewis N, Calhoun V, Miller R. Interpretable LSTM model reveals transiently-realized patterns of dynamic brain connectivity that predict patient deterioration or recovery from very mild cognitive impairment. Computers In Biology And Medicine 2023, 161: 107005. PMID: 37211004, PMCID: PMC10365638, DOI: 10.1016/j.compbiomed.2023.107005.Peer-Reviewed Original ResearchConceptsMild cognitive impairmentDynamic functional network connectivityCognitive impairmentResting-state functional magnetic resonance imagingDementia interventionsFunctional magnetic resonance imagingAlzheimer's diseaseCognitive healthPatient deteriorationFunctional network connectivityCognitive abilitiesShort-term memoryRs-fMRIBrain connectivityResolved measurementsDynamic brain connectivityMagnetic resonance imaging