Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks
Qu G, Orlichenko A, Wang J, Zhang G, Xiao L, Zhang K, Wilson T, Stephen J, Calhoun V, Wang Y. Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks. IEEE Transactions On Medical Imaging 2024, 43: 1568-1578. PMID: 38109241, PMCID: PMC11090410, DOI: 10.1109/tmi.2023.3343365.Peer-Reviewed Original ResearchConceptsGraph transformation frameworkBrain imaging datasetsFunctional brain networksPhiladelphia Neurodevelopmental CohortConvolutional deep learningFeature embeddingPropagation weightsGraph embeddingHuman Connectome ProjectAttention mechanismImage datasetsDeep learningGraph transformationFunctional connectivityAnalyze functional brain networksTransformation frameworkDiffusion strategyBrain networksPositional encodingSpatial knowledgePrediction accuracyIndividual cognitive abilitiesEmbeddingNetworkGraphA Multi-dimensional Joint ICA Model with Gaussian Copula
Agcaoglu O, Silva R, Alacam D, Calhoun V. A Multi-dimensional Joint ICA Model with Gaussian Copula. Lecture Notes In Computer Science 2024, 14366: 152-163. DOI: 10.1007/978-3-031-51026-7_14.Peer-Reviewed Original ResearchIndependent component analysisBivariate distributionMarginal distributionsGaussian copulaLogistic distributionJoint ICAImage data miningSuper-Gaussian distributionImage datasetsFunctional magnetic resonance imaging datasetsInfomax principleAlzheimer's Disease Neuroimaging InitiativeProposed algorithmData miningIdentical marginalsMagnetic resonance imaging datasetICA modelMultimodal versionICA methodJoint independent component analysisCopulasDatasetMaximum likelihoodMixing matrixNeuroimaging data