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 hemisphereExplainabilityCortical similarities in psychiatric and mood disorders identified in federated VBM analysis via COINSTAC
Rootes-Murdy K, Panta S, Kelly R, Romero J, Quidé Y, Cairns M, Loughland C, Carr V, Catts S, Jablensky A, Green M, Henskens F, Kiltschewskij D, Michie P, Mowry B, Pantelis C, Rasser P, Reay W, Schall U, Scott R, Watkeys O, Roberts G, Mitchell P, Fullerton J, Overs B, Kikuchi M, Hashimoto R, Matsumoto J, Fukunaga M, Sachdev P, Brodaty H, Wen W, Jiang J, Fani N, Ely T, Lorio A, Stevens J, Ressler K, Jovanovic T, van Rooij S, Federmann L, Jockwitz C, Teumer A, Forstner A, Caspers S, Cichon S, Plis S, Sarwate A, Calhoun V. Cortical similarities in psychiatric and mood disorders identified in federated VBM analysis via COINSTAC. Patterns 2024, 5: 100987. PMID: 39081570, PMCID: PMC11284501, DOI: 10.1016/j.patter.2024.100987.Peer-Reviewed Original ResearchPsychiatric disordersStructural neuroimaging studiesPattern of gray matterAutism spectrum disorderGray matterDepressive disorderMood disordersNeuroimaging studiesNeuroanatomical basisSubcortical regionsGM alterationsSpectrum disorderVBM analysisMental illnessGM patternsDisordersCollaborative InformaticsSchizophreniaMoodNeuroimaging Suite ToolkitAutismNeuroimagingVulnerabilityLarge-scale dataDeficitsReconfiguration 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-injuryConnectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study
Georgiadis F, Larivière S, Glahn D, Hong L, Kochunov P, Mowry B, Loughland C, Pantelis C, Henskens F, Green M, Cairns M, Michie P, Rasser P, Catts S, Tooney P, Scott R, Schall U, Carr V, Quidé Y, Krug A, Stein F, Nenadić I, Brosch K, Kircher T, Gur R, Gur R, Satterthwaite T, Karuk A, Pomarol- Clotet E, Radua J, Fuentes-Claramonte P, Salvador R, Spalletta G, Voineskos A, Sim K, Crespo-Facorro B, Tordesillas Gutiérrez D, Ehrlich S, Crossley N, Grotegerd D, Repple J, Lencer R, Dannlowski U, Calhoun V, Rootes-Murdy K, Demro C, Ramsay I, Sponheim S, Schmidt A, Borgwardt S, Tomyshev A, Lebedeva I, Höschl C, Spaniel F, Preda A, Nguyen D, Uhlmann A, Stein D, Howells F, Temmingh H, Diaz Zuluaga A, López Jaramillo C, Iasevoli F, Ji E, Homan S, Omlor W, Homan P, Kaiser S, Seifritz E, Misic B, Valk S, Thompson P, van Erp T, Turner J, Bernhardt B, Kirschner M. Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study. Molecular Psychiatry 2024, 29: 1869-1881. PMID: 38336840, PMCID: PMC11371638, DOI: 10.1038/s41380-024-02442-7.Peer-Reviewed Original ResearchConnectivity profilesCortical alterationsCourse of schizophreniaBrain morphological alterationsAssociation of schizophreniaBrain network architectureAnatomical MRI scansTransdiagnostic comparisonsHuman Connectome ProjectDepressive disorderAffective disordersPathophysiological continuityPatient-specific symptomsSchizophreniaFrontal regionsDisease-related alterationsENIGMA studyCortical thinningNormative dataConnectome architectureIndividual symptomsConnectome ProjectDisease stageAlteration patternsHealthy controls
2023
A 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 networkMulti-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 biomarkersMulti-study evaluation of neuroimaging-based prediction of medication class in mood disorders
Salman M, Verner E, Bockholt H, Fu Z, Misiura M, Baker B, Osuch E, Sui J, Calhoun V. Multi-study evaluation of neuroimaging-based prediction of medication class in mood disorders. Psychiatry Research Neuroimaging 2023, 333: 111655. PMID: 37201216, PMCID: PMC10330565, DOI: 10.1016/j.pscychresns.2023.111655.Peer-Reviewed Original ResearchConceptsMood stabilizersMood disordersDSM-based diagnosesBipolar disorder patientsDepressive disorderDisorder patientsManic stateResponders to treatmentDepressive stateNeuroimaging dataMoodTreatment responseAntidepressantsDisordersDSMMedication classesComplex symptomsGold standardPatientsMDDSupport vector machineDiagnosisTreatmentComplex casesGeneralizabilityGroup independent components underpin responses to items from a depression scale.
Stoyanov D, Khorev V, Paunova R, Kandilarova S, Kurkin S, Calhoun V. Group independent components underpin responses to items from a depression scale. Acta Neuropsychiatrica 2023, 36: 9-16. PMID: 37088536, DOI: 10.1017/neu.2023.22.Peer-Reviewed Original ResearchConceptsFunctional magnetic resonance imagingGroup independent component analysisSelf-evaluation scaleFrontal gyrusCingulate cortexDepression ScaleRight superior frontal gyrusFunctional magnetic resonance imaging acquisitionHealthy controlsRight middle cingulate cortexAnterior cingulate cortexMiddle frontal gyrusSuperior frontal gyrusMiddle cingulate cortexIndependent component analysisGroup independent componentStatistical parametric mappingDepressive disorderDepressive episodeBrain circuitsBrain networksNeural network patternsDiagnostic conditionsParametric mappingGyrusCorrection: 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