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 matricesArchitectureImaging‐genomic spatial‐modality attentive fusion for studying neuropsychiatric disorders
Rahaman A, Garg Y, Iraji A, Fu Z, Kochunov P, Hong L, Van Erp T, Preda A, Chen J, Calhoun V. Imaging‐genomic spatial‐modality attentive fusion for studying neuropsychiatric disorders. Human Brain Mapping 2024, 45: e26799. PMID: 39562310, PMCID: PMC11576332, DOI: 10.1002/hbm.26799.Peer-Reviewed Original ResearchConceptsNeural networkDilated convolutional neural networkJoint learning frameworkAttention scoresState-of-the-artDeep neural networksNeural network decisionsConvolutional neural networkAttention fusionFusion moduleDiverse data sourcesArtificial intelligence modelsLearning frameworkAttention moduleJoint learningMultimodal clusteringNetwork decisionsInput streamMultimodal learningHigh-dimensionalIntermediate fusionFused dataSZ classificationIntelligence modelsContextual patternsBrain 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 matricesAdding the third dimension: 3D convolutional neural network diagnosis of temporal lobe epilepsy
Kaestner E, Hassanzadeh R, Gleichgerrcht E, Hasenstab K, Roth R, Chang A, Rüber T, Davis K, Dugan P, Kuzniecky R, Fridriksson J, Parashos A, Bagić A, Drane D, Keller S, Calhoun V, Abrol A, Bonilha L, McDonald C. Adding the third dimension: 3D convolutional neural network diagnosis of temporal lobe epilepsy. Brain Communications 2024, 6: fcae346. PMID: 39474046, PMCID: PMC11520928, DOI: 10.1093/braincomms/fcae346.Peer-Reviewed Original ResearchConvolutional neural networkTwo-dimension convolutional neural networkThree-dimension convolutional neural networksNeural network diagnosisSaliency mapNetwork diagnosisImage harmonizationTraining 3DNeural networkModel trainingMedical imagesTemporal lobe epilepsyModel performanceSubcortical regionsMedian accuracySignificant outperformanceLobe epilepsyStructural abnormalitiesAccuracyClassificationDatapointsEpilepsy lesionsCNN diagnosisPerformanceA Deep Biclustering Framework for Brain Network Analysis
Rahaman A, Fu Z, Iraji A, Calhoun V. A Deep Biclustering Framework for Brain Network Analysis. 2024, 00: 5075-5085. DOI: 10.1109/cvprw63382.2024.00514.Peer-Reviewed Original ResearchDeep neural networksBrain networksState-of-the-artFunctional connectivityNeural networkFeature dimensionsBiclustering frameworkSuboptimal solutionBrain functional connectivityNeuroimaging datasetsBrain network analysisHuman brain dynamicsNetworkNeurobiological mechanismsBiclustering methodsNeural systemsAssigned probability distributionsProbability distributionBrain componentsBrain dynamicsCluster generalizationBiclusteringBrainFrameworkBN edgesCross-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 studiesElectroencephalographyVoxelwise Intensity Projection for the Spatial Representation of Resting State Functional MRI Networks and Multimodal Deep Learning
Itkyal V, Abrol A, LaGrow T, Calhoun V. Voxelwise Intensity Projection for the Spatial Representation of Resting State Functional MRI Networks and Multimodal Deep Learning. 2024, 00: 1-4. DOI: 10.1109/isbi56570.2024.10635831.Peer-Reviewed Original ResearchMulti-channel convolutional neural networkConvolutional neural networkMultimodal deep learningSpatial information extractionAmplitude of low-frequency fluctuationSaliency visualizationInformation extractionClassification performanceDeep learningAlzheimer's Disease Neuroimaging Initiative datasetFusion resultsNeural networkAUC scoreAD classificationTemporal dependenciesFMRI networksTraditional metricsTest accuracyInitiative datasetNetworkLow-frequency fluctuationsA Novel Deep Subspace Learning Framework to Automatically Uncover Assessment-Specific Independent Brain Networks
Batta I, Abrol A, Calhoun V. A Novel Deep Subspace Learning Framework to Automatically Uncover Assessment-Specific Independent Brain Networks. 2024, 00: 1-6. DOI: 10.1109/ciss59072.2024.10480204.Peer-Reviewed Original ResearchLearning frameworkBrain subsystemsSubspace learning frameworkBrain networksHigh-dimensional neuroimaging dataConvolutional neural networkLow-dimensional subspaceSupervised learning approachDeep learning frameworkStructural brain featuresPredictive performanceUnsupervised approachNeural networkAutomated frameworkDimensional subspaceAlzheimer's diseaseLearning approachBrain changesFeature importanceTraining procedureNeuroimaging dataBrain featuresSalient networkNetworkBrain disorders
2023
An Explainable and Robust Deep Learning Approach for Automated Electroencephalography-Based Schizophrenia Diagnosis
Sattiraju A, Ellis C, Miller R, Calhoun V. An Explainable and Robust Deep Learning Approach for Automated Electroencephalography-Based Schizophrenia Diagnosis. 2023, 00: 255-259. DOI: 10.1109/bibe60311.2023.00048.Peer-Reviewed Original ResearchConvolutional neural networkRobust deep learning approachBaseline convolutional neural networkChannel lossDeep learning methodsDeep learning modelsDeep learning approachDecision support roleExplainability approachesClassifier performanceRobust modelNeural networkExplainable modelsLearning methodsLearning approachLearning modelsAutomated diagnosisImplementation environmentEEG dataDiagnosis of SZExplainabilityRaw EEGTest dataRobustnessBiomarkers of SZMulti-scale convolutional recurrent neural network for psychiatric disorder identification in resting-state EEG
Yan W, Yu L, Liu D, Sui J, Calhoun V, Lin Z. Multi-scale convolutional recurrent neural network for psychiatric disorder identification in resting-state EEG. Frontiers In Psychiatry 2023, 14: 1202049. PMID: 37441141, PMCID: PMC10333510, DOI: 10.3389/fpsyt.2023.1202049.Peer-Reviewed Original ResearchConvolutional recurrent neural networkRecurrent neural networkResting-state EEGNeural networkPsychiatric disordersDeep learning classification modelLow-dimensional subspaceTwo-class classificationDesigning individualized treatmentLearning classification modelsEEG backgroundClassification modelHealthy controlsDepressive disorderSpatiotemporal informationClinical observationsDisease severityAccurate classificationIndividualized treatmentBiomarkersDisorder classificationDisorder identificationDisordersClassificationNeuroimaging biomarkersEffective Training Strategy for NN Models of Working Memory Classification with Limited Samples
Suresh P, Ray B, Thapaliya B, Farahdel B, Kazemivash B, Chen J, Duan K, Calhoun V, Liu J. Effective Training Strategy for NN Models of Working Memory Classification with Limited Samples. 2023, 00: 1-4. DOI: 10.1109/isbi53787.2023.10230722.Peer-Reviewed Original ResearchTraining strategyNeural networkData-hungry techniquesNN modelImage featuresSets of hyperparametersMachine learning methodsMachine learning modelsTrained NN modelModel performanceHigh memory capacityImbalanced samplesLearning methodsMemory capacityBrain imaging featuresSuboptimal solutionLearning modelsNetwork configurationEffective training strategyEfficient reuseWorking memory capacityTask-specificData conditionsBiomedical imagingNetworkNovel methods for elucidating modality importance in multimodal electrophysiology classifiers
Ellis C, Sendi M, Zhang R, Carbajal D, Wang M, Miller R, Calhoun V. Novel methods for elucidating modality importance in multimodal electrophysiology classifiers. Frontiers In Neuroinformatics 2023, 17: 1123376. PMID: 37006636, PMCID: PMC10050434, DOI: 10.3389/fninf.2023.1123376.Peer-Reviewed Original ResearchExplainability approachesExplainability methodsAutomated sleep stage classificationRaw time series dataConvolutional neural networkDeep learning classifierSleep stage classificationNovel methodMultimodal classificationLearning classifiersNeural networkClassifierLocal explanationsGlobal explanationsExplainabilitySubject-level differencesTime series dataAdvancement of personalized medicineGlobal methodClinical classifierClassificationClinical variablesElectrophysiological studiesStage classificationElectrophysiological classification
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