2024
Multiscale Neuroimaging Features for the Identification of Medication Class and Non-Responders in Mood Disorder Treatment
Baker B, Salman M, Fu Z, Iraji A, Osuch E, Bockholt H, Calhoun V. Multiscale Neuroimaging Features for the Identification of Medication Class and Non-Responders in Mood Disorder Treatment. 2024, 00: 1-5. DOI: 10.1109/isbi56570.2024.10635741.Peer-Reviewed Original ResearchTreatment of mood disordersMood disordersCourse of antidepressantsFunctional network connectivity measuresMedication classesMood disorder treatmentNeuroimaging featuresComplex behavioral symptomsNetwork connectivity measuresMood stabilizersNon-respondersBehavioral symptomsDisorder treatmentNeuroimaging scansResting state functional networksNeural featuresConnectivity measuresFunctional networksMoodDisordersEffective courseAntidepressantsClinical decision supportClinical treatmentSymptomsCortical 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 dataDeficits
2023
A Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data
Rokham H, Falakshahi H, Calhoun V. A Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38082903, DOI: 10.1109/embc40787.2023.10339949.Peer-Reviewed Original ResearchConceptsStructural MRI dataResting-state functional MRI dataFunctional MRI dataFunctional magnetic resonance imaging dataMRI dataMagnetic resonance imaging dataSchizophrenia patientsFunctional connectivity featuresBrain imaging modalitiesMental disordersNeuroimaging dataNeuroimaging techniquesBorderline subjectsHealthy control groupSchizophrenia datasetSchizophreniaConnectivity featuresBrainPsychosisMoodNosologyControl groupDisordersLabel noiseSubjectsMulti-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 casesGeneralizability