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 matricesCross-Sampling Rate Transfer Learning for Enhanced Raw EEG Deep Learning Classifier Performance in Major Depressive Disorder Diagnosis
Ellis C, Miller R, Calhoun V. Cross-Sampling Rate Transfer Learning for Enhanced Raw EEG Deep Learning Classifier Performance in Major Depressive Disorder Diagnosis. 2024, 00: 1-5. DOI: 10.1109/isbi56570.2024.10635743.Peer-Reviewed Original ResearchTransfer learningDeep learning classifier’s performanceEarly convolutional layersConvolutional neural networkDeep learning modelsDeep learning studiesConvolutional layersClassifier performanceDiagnosis tasksExplainability analysisNeural networkSleep datasetsRaw electroencephalographyLearning modelsIncreased robustnessDatasetChannel lossSampling rateModel accuracyMDD modelLearningRepresentationTaskLearning studiesElectroencephalography
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