2024
Brain networks and intelligence: A graph neural network based approach to resting state fMRI data
Thapaliya B, Akbas E, Chen J, Sapkota R, Ray B, Suresh P, Calhoun V, Liu J. Brain networks and intelligence: A graph neural network based approach to resting state fMRI data. Medical Image Analysis 2024, 101: 103433. PMID: 39708510, PMCID: PMC11877132, DOI: 10.1016/j.media.2024.103433.Peer-Reviewed Original ResearchConceptsGraph neural networksNeural networkGraph isomorphism networkGraph convolutional layersGraph convolutional networkMachine learning modelsNetwork connectivity matrixCognitive processesConvolutional layersConvolutional networkPrediction taskModel architectureGraph architectureAdolescent Brain Cognitive Development datasetResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingLearning modelsMiddle frontal gyrusPredicting individual differencesResting state fMRI dataPredictive intelligenceIntelligenceNetworkFunctional network connectivity matricesArchitectureBrain networks and intelligence: A graph neural network based approach to resting state fMRI data
Thapaliya B, Akbas E, Chen J, Sapkota R, Ray B, Suresh P, Calhoun V, Liu J. Brain networks and intelligence: A graph neural network based approach to resting state fMRI data. Medical Image Analysis 2024, 101: 103433. PMID: 37986729, PMCID: PMC10659448, DOI: 10.1016/j.media.2024.103433.Peer-Reviewed Original ResearchGraph neural networksNeural networkGraph isomorphism networkGraph convolutional layersGraph convolutional networkMachine learning modelsMean square errorNetwork connectivity matrixCognitive processesConvolutional layersConvolutional networkPrediction taskModel architectureGraph architectureAdolescent Brain Cognitive Development datasetResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingLearning modelsMiddle frontal gyrusPredicting individual differencesResting state fMRI dataPredictive intelligenceIntelligenceNetworkFunctional network connectivity matricesIntra-Atlas Node Size Effects on Graph Metrics in fMRI Data: Implications for Alzheimer’s Disease and Cognitive Impairment
Kolla S, Falakshahi H, Abrol A, Fu Z, Calhoun V. Intra-Atlas Node Size Effects on Graph Metrics in fMRI Data: Implications for Alzheimer’s Disease and Cognitive Impairment. Sensors 2024, 24: 814. PMID: 38339531, PMCID: PMC10857295, DOI: 10.3390/s24030814.Peer-Reviewed Original ResearchConceptsGraph metricsFunctional network connectivityIndependent component analysisResting state fMRI dataData-driven methodologyNetwork connectivityNovel metricFunctional nodesNode sizeNodesLocal graph metricsMetricsNode dimensionsGraphAlzheimer's diseaseMild cognitive impairmentNetwork neuroscienceNeuroimaging researchNeuroimaging investigations
2023
REGRESSION-ASSISTED INDEPENDENT VECTOR ANALYSIS: A SOLUTION TO LARGE-SCALE FMRI DATA ANALYSIS
Yang H, Gabrielson B, Calhoun V, Adali T. REGRESSION-ASSISTED INDEPENDENT VECTOR ANALYSIS: A SOLUTION TO LARGE-SCALE FMRI DATA ANALYSIS. 2023, 00: 1443-1447. DOI: 10.1109/ieeeconf59524.2023.10476796.Peer-Reviewed Original ResearchThe Nonlinear Brain: Towards Uncovering Hidden Brain Networks Using Explicitly Nonlinear Functional Interaction
Iraji A, Kazimierczak K, Chen J, Motlaghian S, Specht K, Adali T, Calhoun V. The Nonlinear Brain: Towards Uncovering Hidden Brain Networks Using Explicitly Nonlinear Functional Interaction. 2023, 00: 1-4. DOI: 10.1109/isbi53787.2023.10230347.Peer-Reviewed Original Research
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply