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