2024
Spectral Brain Graph Neural Network for Prediction of Anxiety in Children with Autism Spectrum Disorder
Duan P, Dvornek N, Wang J, Eilbott J, Du Y, Sukhodolsky D, Duncan J. Spectral Brain Graph Neural Network for Prediction of Anxiety in Children with Autism Spectrum Disorder. 2024, 00: 1-5. DOI: 10.1109/isbi56570.2024.10635753.Peer-Reviewed Original ResearchGraph neural networksFunctional magnetic resonance imagingAutism spectrum disorderNeural networkCurrent graph neural networksSpectrum disorderMASC-2Spectral analysis algorithmAnalysis algorithmGraph-based networkMultidimensional Anxiety ScaleFast Fourier transformPredictive of anxietyDaily anxiety levelsExtract hidden informationBrain functional networksPower spectrum densityNode featuresNetwork performanceComorbid anxietyBrain mechanismsHidden informationCorrelated featuresAnxiety ScaleTotal score
2019
Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery
Li X, Dvornek NC, Zhou Y, Zhuang J, Ventola P, Duncan JS. Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery. Lecture Notes In Computer Science 2019, 11492: 718-730. PMID: 32982121, PMCID: PMC7519580, DOI: 10.1007/978-3-030-20351-1_56.Peer-Reviewed Original ResearchShapley value explanationAutism spectrum disorderFunctional magnetic resonance imagingDeep learning modelsDeep learning classifierCooperative game theoryLearning modelLearning classifiersGraph structureRandom forestGame theoryMachine learning methodsMNIST datasetTraditional learning strategiesSpectrum disorderFMRI biomarkersComputational complexityLearning methodsHuman perceptionHierarchical pipelineFeature importanceN featuresLearning strategiesInput dataEfficient interpretation
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
2016
Brain responses to biological motion predict treatment outcome in young children with autism
Yang D, Pelphrey KA, Sukhodolsky DG, Crowley MJ, Dayan E, Dvornek NC, Venkataraman A, Duncan J, Staib L, Ventola P. Brain responses to biological motion predict treatment outcome in young children with autism. Translational Psychiatry 2016, 6: e948-e948. PMID: 27845779, PMCID: PMC5314125, DOI: 10.1038/tp.2016.213.Peer-Reviewed Original ResearchConceptsAutism spectrum disorderYoung childrenSocial information processingMultivariate pattern analysisMotivation/rewardBiological motionCore deficitComplex neurodevelopmental disorderBrain responsesResponse treatmentSpectrum disorderNeurobiological markersNeural predictorsInformation processingBehavioral interventionsIndividual childrenNeurodevelopmental disordersCurrent findingsNeural circuitsBehavioral deficitsEarly childhoodChildrenUnsuccessful interventionsNeurobiomarkersPattern analysisPivotal response treatment prompts a functional rewiring of the brain among individuals with autism spectrum disorder
Venkataraman A, Yang D, Dvornek N, Staib LH, Duncan JS, Pelphrey KA, Ventola P. Pivotal response treatment prompts a functional rewiring of the brain among individuals with autism spectrum disorder. Neuroreport 2016, 27: 1081-1085. PMID: 27532879, PMCID: PMC5007196, DOI: 10.1097/wnr.0000000000000662.Peer-Reviewed Original ResearchConceptsPivotal Response TreatmentAutism spectrum disorderOccipital-temporal cortexAttentional systemResponse treatmentSpectrum disorderOrbitofrontal cortexPosterior cingulateHigh-level objectsBehavioral interventionsLearning mechanismPerception shiftProcessing areasNeural circuitsFunctional rewiringCortexTreatment regimenAutismInterventionNovel Bayesian frameworkCingulateFunctional changesIndividualsDisordersObjects
2015
An unbiased Bayesian approach to functional connectomics implicates social-communication networks in autism
Venkataraman A, Duncan JS, Yang D, Pelphrey KA. An unbiased Bayesian approach to functional connectomics implicates social-communication networks in autism. NeuroImage Clinical 2015, 8: 356-366. PMID: 26106561, PMCID: PMC4474177, DOI: 10.1016/j.nicl.2015.04.021.Peer-Reviewed Original ResearchConceptsAutism spectrum disorderAutism Brain Imaging Data ExchangeSuperior temporal sulcusMiddle temporal gyrusTemporal sulcusTemporal gyrusRight posterior superior temporal sulcusPosterior superior temporal sulcusFunctional magnetic resonance imaging studyFunctional connectomicsTemporo-parietal junctionResting-state functional magnetic resonance imaging studyRight temporal poleIntrinsic functional networksDefault mode networkPossible neural mechanismsPosterior cingulate cortexMeta-analytic databaseIntra-hemispheric connectivityInter-hemispheric connectivityMagnetic resonance imaging studyASD patientsResonance imaging studyNeural mechanismsSpectrum disorder