2024
Explainable Multimodal Graph Isomorphism Network for Interpreting Sex Differences in Adolescent Neurodevelopment
Patel B, Orlichenko A, Patel A, Qu G, Wilson T, Stephen J, Calhoun V, Wang Y. Explainable Multimodal Graph Isomorphism Network for Interpreting Sex Differences in Adolescent Neurodevelopment. Applied Sciences 2024, 14: 4144. DOI: 10.3390/app14104144.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingBlood oxygen level-dependentGraph isomorphism networkGraph neural networksBrain networksFunctional magnetic resonance imaging paradigmFunctional magnetic resonance imaging blood oxygen level-dependentSex differencesClassification accuracyExploration of sex differencesInterpreting sex differencesOxygen level-dependentState-of-the-art algorithmsAdolescent neurodevelopmentState-of-the-artNeuropsychiatric conditionsFunctional connectivityTask-related dataDeep learning modelsLevel-dependentMouth movementsFMRI datasetsFunctional networksGraph structureAdolescentsMaximum Classifier Discrepancy Generative Adversarial Network for Jointly Harmonizing Scanner Effects and Improving Reproducibility of Downstream Tasks
Yan W, Fu Z, Jiang R, Sui J, Calhoun V. Maximum Classifier Discrepancy Generative Adversarial Network for Jointly Harmonizing Scanner Effects and Improving Reproducibility of Downstream Tasks. IEEE Transactions On Biomedical Engineering 2024, 71: 1170-1178. PMID: 38060365, PMCID: PMC11005005, DOI: 10.1109/tbme.2023.3330087.Peer-Reviewed Original ResearchDownstream tasksPerformance of downstream tasksOriginal feature spaceState-of-the-artAdversarial generative networkGAN generatorAdversarial networkFeature spaceOriginal imageGeneration networksClassification performanceSmall-sample problemTask objectivesGenerative modelImproved performanceTaskHarmony frameworkAnatomical layoutNetworkHarmonious methodsMulti-site collaborationSimulated dataLayoutScanner effectsDataset
2023
Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links
Fedorov A, Geenjaar E, Wu L, Sylvain T, DeRamus T, Luck M, Misiura M, Mittapalle G, Hjelm R, Plis S, Calhoun V. Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links. NeuroImage 2023, 285: 120485. PMID: 38110045, PMCID: PMC10872501, DOI: 10.1016/j.neuroimage.2023.120485.Peer-Reviewed Original ResearchConceptsBrain regionsMultimodal neuroimaging dataNeuroimaging dataBrain disordersComplex brain disordersMRI dataNeuroimaging researchGroup inferencesDeep InfoMaxSupervised modelsDiagnostic labelsDisordersBrainState-of-the-art unsupervised methodsAlzheimer's phenotypeNovel self-supervised frameworkSelf-supervised frameworkSelf-supervised methodologyCanonical correlation analysisSelf-supervised representationsState-of-the-artDeep learning approachSingle-modal dataMultimode linksComplex brainsDeep Generative Transfer Learning Predicts Conversion To Alzheimer’S Disease From Neuroimaging Genomics Data
Dolci G, Rahaman M, Galazzo I, Cruciani F, Abrol A, Chen J, Fu Z, Duan K, Menegaz G, Calhoun V. Deep Generative Transfer Learning Predicts Conversion To Alzheimer’S Disease From Neuroimaging Genomics Data. 2023, 00: 1-5. DOI: 10.1109/icasspw59220.2023.10193683.Peer-Reviewed Original ResearchTopological Correction of Subject-Level Intrinsic Connectivity Networks
Lewis N, Iraji A, Miller R, Calhoun V. Topological Correction of Subject-Level Intrinsic Connectivity Networks. 2023, 00: 1-4. DOI: 10.1109/isbi53787.2023.10230598.Peer-Reviewed Original ResearchContrast-to-noiseIntrinsic connectivity networksTopological propertiesState-of-the-art methodsFunctional magnetic resonance imagingState-of-the-artFMRI signalsFunctional networksSpatial mappingLow contrast-to-noiseConnectivity networksSimilarity constraintTopological correctnessSubject-specific informationSpatial informationNetworkVoxelHuman brain