2024
Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures
Ellis C, Sancho M, Miller R, Calhoun V. Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures. Communications In Computer And Information Science 2024, 2156: 102-124. DOI: 10.1007/978-3-031-63803-9_6.Peer-Reviewed Original ResearchDeep learning modelsExplainability methodsExplainability analysisConvolutional neural network architectureLearning modelsRaw electroencephalogramNeural network architectureDeep learning architectureMajor depressive disorderLearning architectureNetwork architectureDeep learningModel architectureMultichannel electroencephalogramTraining approachArchitectureBiomarkers of depressionFrequency bandElectroencephalogramResearch contextDepressive disorderElectroencephalogram biomarkerAccuracyRight hemisphereExplainabilityReconfiguration of Structural and Functional Connectivity Coupling in Patient Subgroups With Adolescent Depression
Xu M, Li X, Teng T, Huang Y, Liu M, Long Y, Lv F, Zhi D, Li X, Feng A, Yu S, Calhoun V, Zhou X, Sui J. Reconfiguration of Structural and Functional Connectivity Coupling in Patient Subgroups With Adolescent Depression. JAMA Network Open 2024, 7: e241933. PMID: 38470418, PMCID: PMC10933730, DOI: 10.1001/jamanetworkopen.2024.1933.Peer-Reviewed Original ResearchConceptsAdolescent major depressive disorderMajor depressive disorderSC-FC couplingIncreased SC-FC couplingSC-FCMode networkSuicide attemptsFirst-episode major depressive disorderVisual networkMDD subgroupsNonsuicidal self-injurious behaviorRates of self-injuryHealthy controlsResting-state functional MRI dataMagnetic resonance imagingCross-sectional studySelf-injurious behaviorOutpatient psychiatry clinicFunctional MRI dataMajor life eventsFirst Affiliated Hospital of Chongqing Medical UniversityDepressive disorderNeurobiological mechanismsChildhood traumaSelf-injury
2023
Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data*
Ellis C, Sattiraju A, Miller R, Calhoun V. Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data*. 2023, 00: 2466-2473. DOI: 10.1109/bibm58861.2023.10385424.Peer-Reviewed Original ResearchDeep learning methodsLearning methodsTransfer learningEEG datasetManually engineered featuresTransfer learning approachDeep learning modelsDeep learning performanceMachine learning methodsClassification datasetsLearned representationsElectroencephalography classifierDeep learningEEG classificationResting-state electroencephalographyDiagnosis of major depressive disorderRaw electroencephalographyLearning approachLearning modelsMajor depressive disorder diagnosisMajor depressive disorderLearning performanceClassifierDatasetEngineering featuresA Brainwide Risk Score for Psychiatric Disorder Evaluated in a Large Adolescent Population Reveals Increased Divergence Among Higher-Risk Groups Relative to Control Participants
Yan W, Pearlson G, Fu Z, Li X, Iraji A, Chen J, Sui J, Volkow N, Calhoun V. A Brainwide Risk Score for Psychiatric Disorder Evaluated in a Large Adolescent Population Reveals Increased Divergence Among Higher-Risk Groups Relative to Control Participants. Biological Psychiatry 2023, 95: 699-708. PMID: 37769983, PMCID: PMC10942727, DOI: 10.1016/j.biopsych.2023.09.017.Peer-Reviewed Original ResearchFunctional network connectivityHealthy control individualsPsychiatric disordersRisk scoreEarly psychosisPsychiatric riskControl individualsStudy participantsHigh-risk groupMajor depressive disorderHigh-risk patternsPsychiatric risk assessmentCognitive Development StudyUnaffected adolescentsAdolescent Brain Cognitive Development (ABCD) studyLarge adolescent populationDepressive disorderHigh riskPsychosis scoresBipolar disorderPotential biomarkersEarly screeningPsychiatric vulnerabilityAdolescent populationDisordersICA-based Individualized Differential Structure Similarity Networks for Predicting Symptom Scores in Adolescents with Major Depressive Disorder
Li X, Xu M, Jiang R, Li X, Calhoun V, Zhou X, Sui J. ICA-based Individualized Differential Structure Similarity Networks for Predicting Symptom Scores in Adolescents with Major Depressive Disorder. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-5. PMID: 38082692, DOI: 10.1109/embc40787.2023.10340456.Peer-Reviewed Original ResearchConceptsMajor depressive disorderGray matter volumeDepressive disorderWhole-brain structural covariance networksConnectome-based predictive modelingAdolescent MDD patientsComplex mood disorderMeasure individual differencesDefault-mode networkStructural brain alterationsStructural covariance networksHamilton Depression ScaleHamilton Anxiety ScaleSpatially constrained ICAMDD patientsMood disordersBrain alterationsMatter volumeIndividual differencesBrain structuresCovariance networksAnxiety ScaleVisual networkDepression ScaleStructure similarity networkCorrection: Links between electroconvulsive therapy responsive and cognitive impairment multimodal brain networks in late-life major depressive disorder
Qi S, Calhoun V, Zhang D, Miller J, Deng Z, Narr K, Sheline Y, McClintock S, Jiang R, Yang X, Upston J, Jones T, Sui J, Abbott C. Correction: Links between electroconvulsive therapy responsive and cognitive impairment multimodal brain networks in late-life major depressive disorder. BMC Medicine 2023, 21: 113. PMID: 36978111, PMCID: PMC10052797, DOI: 10.1186/s12916-023-02800-2.Peer-Reviewed Original Research