2024
Chapter 13 Data-driven learning strategies for biomarker detection and outcome prediction in Autism from task-based fMRI
Duncan J, Staib L, Dvornek N, Li X, Zhuang J, Wang J, Ventola P. Chapter 13 Data-driven learning strategies for biomarker detection and outcome prediction in Autism from task-based fMRI. 2024, 357-393. DOI: 10.1016/b978-0-32-385124-4.00024-6.Peer-Reviewed Original ResearchLong short-term memoryGraph neural networksFunctional magnetic resonance imagingAutism spectrum disorderNeural ordinary differential equationsData-driven learning strategyDeep learning techniquesTask-based functional magnetic resonance imagingShort-term memoryNeural networkLearning techniquesImpaired social interactionTerm memoryBehavioral therapyRepetitive behaviorsSpectrum disorderDevelopmental disordersLearning strategiesSpatio-temporal characteristicsInherent dynamicsCharacterization of individualsModel of causalitySocial interactionNetworkPersonalized outcome predictions
2022
Characterization of Early Stage Parkinson's Disease From Resting-State fMRI Data Using a Long Short-Term Memory Network
Guo X, Tinaz S, Dvornek N. Characterization of Early Stage Parkinson's Disease From Resting-State fMRI Data Using a Long Short-Term Memory Network. Frontiers In Neuroimaging 2022, 1: 952084. PMID: 37555151, PMCID: PMC10406199, DOI: 10.3389/fnimg.2022.952084.Peer-Reviewed Original ResearchEarly-stage Parkinson's diseaseFunctional magnetic resonance imagingParkinson's Progression Markers InitiativeParkinson's diseaseProgression Markers InitiativeDiagnosis of PDEarly-stage diseaseFunctional brain changesBrain function alterationsStage Parkinson's diseaseFunctional connectivity differencesComplex neurodegenerative disorderMagnetic resonance imagingResting-state fMRI dataStage diseaseDisease stageDisease progressionBrain changesTreatment responseMotor impairmentFC changesNew therapiesFunction alterationsResonance imagingBrain regions
2018
Combining 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