2020
Demographic-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 variablesInformationEstimating Reproducible Functional Networks Associated with Task Dynamics Using Unsupervised LSTMS
Dvornek NC, Ventola P, Duncan JS. Estimating Reproducible Functional Networks Associated with Task Dynamics Using Unsupervised LSTMS. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2020, 00: 1-4. PMID: 34422224, PMCID: PMC8375550, DOI: 10.1109/isbi45749.2020.9098377.Peer-Reviewed Original ResearchLong short-term memoryFunctional networksBiological motion perception taskTask activitiesMotion perception taskShort-term memoryLSTM modelPerception taskNeural correlatesTask paradigmFMRI activityTerm memoryRecurrent neural networkTask dynamicsTarget taskFunctional magnetic resonance imaging (fMRI) time series dataTaskUnsupervised manner
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 model
2018
Learning Generalizable Recurrent Neural Networks from Small Task-fMRI Datasets
Dvornek NC, Yang D, Ventola P, Duncan JS. Learning Generalizable Recurrent Neural Networks from Small Task-fMRI Datasets. Lecture Notes In Computer Science 2018, 11072: 329-337. PMID: 30873514, PMCID: PMC6411297, DOI: 10.1007/978-3-030-00931-1_38.Peer-Reviewed Original ResearchConceptsRecurrent neural networkNeural networkTask fMRI datasetsMedical image analysis problemsSuch deep networksImage analysis problemsTask fMRI scanTypical control subjectsDeep networkDeep learningTraining lossSmall datasetsLarge datasetsNumber of approachesAutism spectrum disorderAnalysis problemDatasetNetworkTraining runsImage analysisGeneralizable modelNon-imaging variablesSpectrum disorderFMRI analysisModel performanceCombining Phenotypic and Resting-State FMRI Data for Autism Classification with Recurrent Neural Networks
Dvornek NC, Ventola P, Duncan JS. Combining Phenotypic and Resting-State FMRI Data for Autism Classification with Recurrent Neural Networks. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2018, 2018: 725-728. PMID: 30288208, PMCID: PMC6166875, DOI: 10.1109/isbi.2018.8363676.Peer-Reviewed Original ResearchAutism spectrum disorderRecurrent neural networkNeural networkAutism Brain Imaging Data ExchangeSingle deep learning frameworkHeterogeneity of ASDFunctional magnetic resonance imagingDeep learning frameworkResting-state fMRI dataResting-state functional magnetic resonance imagingBetter classification accuracyAutism classificationSpectrum disorderData exchangeLearning frameworkFMRI dataClassification accuracyCross-validation frameworkChallenging taskStraightforward taskPrior workNetworkSuch dataRsfMRITask
2017
Identifying Autism from Resting-State fMRI Using Long Short-Term Memory Networks
Dvornek NC, Ventola P, Pelphrey KA, Duncan JS. Identifying Autism from Resting-State fMRI Using Long Short-Term Memory Networks. Lecture Notes In Computer Science 2017, 10541: 362-370. PMID: 29104967, PMCID: PMC5669262, DOI: 10.1007/978-3-319-67389-9_42.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingAutism spectrum disorderLong short-term memoryAutism Brain Imaging Data Exchange IResting-state functional connectivity measuresShort-term memoryLong short-term memory networkResting-state functional magnetic resonance imagingShort-term memory networkFunctional connectivity measuresPotential functional networksTypical controlsSpectrum disorderASD biomarkersMemory networkRecurrent neural networkExchange IMulti-site dataFMRI dataFunctional networksLSTM modelClassification of individualsCross-validation frameworkConnectivity measuresObjective biomarkers