Estimation of complete mutual information exploiting nonlinear magnitude-phase dependence: Application to spatial FNC for complex-valued fMRI data
Li W, Lin Q, Zhang C, Han Y, Li H, Calhoun V. Estimation of complete mutual information exploiting nonlinear magnitude-phase dependence: Application to spatial FNC for complex-valued fMRI data. Journal Of Neuroscience Methods 2024, 409: 110207. PMID: 38944128, DOI: 10.1016/j.jneumeth.2024.110207.Peer-Reviewed Original ResearchConceptsComplex-valued fMRI dataMutual informationJoint entropyNetwork connectivityComplex-valued signalsFunctional network connectivityMagnitude-phase dependenceDensity estimation methodMI estimationHistogram-basedKernel density estimation methodFMRI dataEstimation accuracyProbability density functionJoint probability density functionSimulated signalsChain rulePhase dependenceEstimation methodHigh-orderDensity functionControl networkInaccurate estimationNonlinear dependenceDependenceMultimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data
Batta I, Abrol A, Calhoun V, Initiative A. Multimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data. Journal Of Neuroscience Methods 2024, 406: 110109. PMID: 38494061, PMCID: PMC11100582, DOI: 10.1016/j.jneumeth.2024.110109.Peer-Reviewed Original ResearchConceptsBrain subspacesStandard machine learning algorithmsHigh-dimensional neuroimaging dataTrain machine learning modelsUnsupervised decompositionMachine learning algorithmsMachine learning modelsFunctional sub-systemsSubspace analysisSubspace componentsLearning algorithmsSupervised approachBiological traitsLearning modelsSub-systemsAlzheimer's diseaseSubspaceComputational frameworkActive subspaceCross-validation procedureNeuroimaging dataAD-related brain regionsAutomated identificationPredictive performanceOrientation subspaces