Vince Calhoun, PhD
About
Research
Publications
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 hemisphereExplainabilityLearning Spatiotemporal Brain Dynamics in Adolescents via Multimodal MEG and fMRI Data Fusion Using Joint Tensor/Matrix Decomposition
Belyaeva I, Gabrielson B, Wang Y, Wilson T, Calhoun V, Stephen J, Adali T. Learning Spatiotemporal Brain Dynamics in Adolescents via Multimodal MEG and fMRI Data Fusion Using Joint Tensor/Matrix Decomposition. IEEE Transactions On Biomedical Engineering 2024, 71: 2189-2200. PMID: 38345949, PMCID: PMC11240882, DOI: 10.1109/tbme.2024.3364704.Peer-Reviewed Original ResearchSpatiotemporal brain dynamicsBrain dynamicsFunctional magnetic resonance imagingComplex spatiotemporal dynamicsStudy brain functionSpatial resolutionMillisecond scaleBrain functionTemporal resolutionBrain patternsHigh-level cognitive functionsBrain response patternsDynamicsSpatiotemporal dynamicsSensory processing pathwaysMagnetoencephalographyLow-performing subjectsResolutionFrequency modulation increases the specificity of time-resolved connectivity: A resting-state fMRI study
Faghiri A, Yang K, Faria A, Ishizuka K, Sawa A, Adali T, Calhoun V. Frequency modulation increases the specificity of time-resolved connectivity: A resting-state fMRI study. Network Neuroscience 2024, 1-28. DOI: 10.1162/netn_a_00372.Peer-Reviewed Original ResearchSliding window Pearson correlationTime-resolved networksSingle sideband modulationTime-resolved connectivityResting-state fMRI studiesSideband modulationFunctional magnetic resonance imagingFunctional network connectivityResting-state functional magnetic resonance imagingActivity time seriesTypical controlsFrequency modulationLow-frequency informationStateEpisode of psychosisNetwork connectivityHuman brainSub-corticalSuperior performanceFMRI studyCortical regionsA Trifecta of Deep Learning Models: Assessing Brain Health by Integrating Assessment and Neuroimaging Data
Ajith M, M. Aycock D, B. Tone E, Liu J, B. Misiura M, Ellis R, M. Plis S, Z. King T, M. Dotson V, Calhoun V. A Trifecta of Deep Learning Models: Assessing Brain Health by Integrating Assessment and Neuroimaging Data. Aperture Neuro 2024, 4 DOI: 10.52294/001c.118576.Peer-Reviewed Original ResearchStatic functional network connectivityBrain health indexBrain healthResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingPsychological assessment measuresAssessment dataFunctional network connectivityMental health disordersBrain systemsEvaluating brain healthNeuroimaging dataRs-fMRINeural patternsPhysical well-beingCognitive declineAssessment measuresHealth disordersVariational autoencoderNeuroimagingHealthy brainBrainMagnetic resonance imagingTesting phaseWell-beingGray matters: ViT-GAN framework for identifying schizophrenia biomarkers linking structural MRI and functional network connectivity
Bi Y, Abrol A, Jia S, Sui J, Calhoun V. Gray matters: ViT-GAN framework for identifying schizophrenia biomarkers linking structural MRI and functional network connectivity. NeuroImage 2024, 297: 120674. PMID: 38851549, DOI: 10.1016/j.neuroimage.2024.120674.Peer-Reviewed Original ResearchFunctional network connectivityMedial prefrontal cortexBrain structuresFunctional network connectivity matricesPrefrontal cortexStructural MRINetwork connectivityGray matterSelf-attention mechanismGenerative adversarial networkDeep learning architectureBrain disordersDorsolateral prefrontal cortexResearch of schizophreniaNeural signal processingIdentified functional connectivityCross-domain analysisAttention mapsStructural biomarkersAdversarial networkLearning architectureDL-PFCICA algorithmSchizophrenia patientsHigh-dimensional fMRI dataA Roundtable Discussion on Brain Connectivity
Laureys S, Raichle M, Friston K, Whitfield-Gabrieli S, Whitwell J, Calhoun V, Douw L, Boly M. A Roundtable Discussion on Brain Connectivity. Brain Connectivity 2024, 14: 263-273. PMID: 38814819, DOI: 10.1089/brain.2024.0037.Peer-Reviewed Original ResearchTesting the structural disconnection hypothesis: Myelin content correlates with memory in healthy aging
Mendez Colmenares A, Thomas M, Anderson C, Arciniegas D, Calhoun V, Choi I, Kramer A, Li K, Lee J, Lee P, Burzynska A. Testing the structural disconnection hypothesis: Myelin content correlates with memory in healthy aging. Neurobiology Of Aging 2024, 141: 21-33. PMID: 38810596, DOI: 10.1016/j.neurobiolaging.2024.05.013.Peer-Reviewed Original ResearchDisconnection hypothesisVirginia Cognitive Aging ProjectHypothesis of cognitive agingFractional anisotropyAssociated with reduced memory performanceDeterioration of white matterAge-related memory lossPrefrontal white matterGenu of the corpus callosumMyelin water fractionVoxel-wise analysisWhite matterCorpus callosumExecutive functionCognitive agingDiffusion tensor imagingMemory performancePrefrontal WMStructural equation modelingWM microstructureAge differencesProcessing speedStructural disconnectionCognitive declineHealthy participantsCross-continental environmental and genome-wide association study on children and adolescent anxiety and depression
Thapaliya B, Ray B, Farahdel B, Suresh P, Sapkota R, Holla B, Mahadevan J, Chen J, Vaidya N, Perrone-Bizzozero N, Benegal V, Schumann G, Calhoun V, Liu J. Cross-continental environmental and genome-wide association study on children and adolescent anxiety and depression. Frontiers In Psychiatry 2024, 15: 1384298. PMID: 38827440, PMCID: PMC11141390, DOI: 10.3389/fpsyt.2024.1384298.Peer-Reviewed Original ResearchEarly life stressAdolescent anxietyLife stressMega-analysisCognitive Development StudyHypothalamic-pituitary-adrenal axisSupport IndexAssociated with anxietyRisk of anxietyExternalizing disordersAdolescent brainGenome-wide association studiesGenetic vulnerabilityAnxietyGenome-wide association analysisPublic health concernDepressionMental healthLinear mixed-effects modelsEnvironmental factorsMixed-effects modelsAssociation studiesTissue enrichment analysisGenetic associationGenomic significanceTargeting the Amygdala in a TMS Clinical Trial for PTSD
van Rooij S, Hinojosa C, Teye-Botchway L, Minton S, Langhinrichsen-Rohling R, Job G, Ely T, Ressler K, Kaslow N, Rauch S, Jovanovic T, Holtzheimer P, Calhoun V, Riva-Posse P, Camprodon J, McDonald W. Targeting the Amygdala in a TMS Clinical Trial for PTSD. Biological Psychiatry 2024, 95: s31-s32. DOI: 10.1016/j.biopsych.2024.02.082.Peer-Reviewed Original Research156. Association Between Physical Frailty and Incident Depression
Jiang R, Noble S, Rosenblatt M, Ye J, Calhoun V, Sui J, Scheinost D. 156. Association Between Physical Frailty and Incident Depression. Biological Psychiatry 2024, 95: s163. DOI: 10.1016/j.biopsych.2024.02.391.Peer-Reviewed Original Research
Links & Media
News
- February 02, 2006
New MRI Technology at Yale: Brain Images that Show Structure and Function
- November 18, 2004
Brain Imaging Study of Drunk Drivers Pinpoints Neurological Changes
- July 20, 2004
NARSAD Funds 11 Yale University and Affiliated Researchers
- April 21, 2004
Imaging Test Could Be Used To Diagnose Schizophrenia