Vince Calhoun, PhD

Research & Publications
Biography
News

Coauthors

Selected Publications

Learning Spatiotemporal Brain Dynamics in Adolescents via Multimodal MEG and fMRI Data Fusion Using Joint Tensor/Matrix DecompositionBelyaeva 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, DOI: 10.1109/tbme.2024.3364704.
Frequency modulation increases the specificity of time-resolved connectivity: A resting-state fMRI studyFaghiri 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.
A Trifecta of Deep Learning Models: Assessing Brain Health by Integrating Assessment and Neuroimaging DataAjith 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.
Gray matters: ViT-GAN framework for identifying schizophrenia biomarkers linking structural MRI and functional network connectivityBi 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.
A Roundtable Discussion on Brain ConnectivityLaureys 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.
Testing the structural disconnection hypothesis: Myelin content correlates with memory in healthy agingMendez 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.
Cross-continental environmental and genome-wide association study on children and adolescent anxiety and depressionThapaliya 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.
Targeting the Amygdala in a TMS Clinical Trial for PTSDvan 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.
156. Association Between Physical Frailty and Incident DepressionJiang 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.
Multimodal Imaging Feature Extraction with Reference Canonical Correlation Analysis Underlying IntelligenceSapkota R, Thapaliya B, Suresh P, Ray B, Calhoun V, Liu J. Multimodal Imaging Feature Extraction with Reference Canonical Correlation Analysis Underlying Intelligence. 2024, 2071-2075. DOI: 10.1109/icassp48485.2024.10448219.
Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain NetworksQu G, Orlichenko A, Wang J, Zhang G, Xiao L, Zhang K, Wilson T, Stephen J, Calhoun V, Wang Y. Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks. IEEE Transactions On Medical Imaging 2024, 43: 1568-1578. PMID: 38109241, PMCID: PMC11090410, DOI: 10.1109/tmi.2023.3343365.
Maximum Classifier Discrepancy Generative Adversarial Network for Jointly Harmonizing Scanner Effects and Improving Reproducibility of Downstream TasksYan W, Fu Z, Jiang R, Sui J, Calhoun V. Maximum Classifier Discrepancy Generative Adversarial Network for Jointly Harmonizing Scanner Effects and Improving Reproducibility of Downstream Tasks. IEEE Transactions On Biomedical Engineering 2024, 71: 1170-1178. PMID: 38060365, PMCID: PMC11005005, DOI: 10.1109/tbme.2023.3330087.
Reconfiguration of Structural and Functional Connectivity Coupling in Patient Subgroups With Adolescent DepressionXu 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.
A Method to Estimate Longitudinal Change Patterns in Functional Network Connectivity of the Developing Brain Relevant to Psychiatric Problems, Cognition, and AgeSaha R, Saha D, Rahaman A, Fu Z, Liu J, Calhoun V. A Method to Estimate Longitudinal Change Patterns in Functional Network Connectivity of the Developing Brain Relevant to Psychiatric Problems, Cognition, and Age. Brain Connectivity 2024, 14: 130-140. PMID: 38308475, PMCID: PMC10954605, DOI: 10.1089/brain.2023.0040.
Decentralized Mixed Effects Modeling in COINSTACBasodi S, Raja R, Gazula H, Romero J, Panta S, Maullin-Sapey T, Nichols T, Calhoun V. Decentralized Mixed Effects Modeling in COINSTAC. Neuroinformatics 2024, 22: 163-175. PMID: 38424371, DOI: 10.1007/s12021-024-09657-7.
Explainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophreniaEllis C, Miller R, Calhoun V. Explainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophrenia. Frontiers In Psychiatry 2024, 15: 1165424. PMID: 38495909, PMCID: PMC10941842, DOI: 10.3389/fpsyt.2024.1165424.
A deep learning approach for mental health quality prediction using functional network connectivity and assessment dataAjith M, Aycock D, Tone E, Liu J, Misiura M, Ellis R, Plis S, King T, Dotson V, Calhoun V. A deep learning approach for mental health quality prediction using functional network connectivity and assessment data. Brain Imaging And Behavior 2024, 18: 630-645. PMID: 38340285, DOI: 10.1007/s11682-024-00857-y.
Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA studyGeorgiadis 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, 1-13. PMID: 38336840, DOI: 10.1038/s41380-024-02442-7.
Functional MRI of the Brainstem for Assessing Its Autonomic Functions: From Imaging Parameters and Analysis to Functional AtlasMohamed A, Kwiatek R, Del Fante P, Calhoun V, Lagopoulos J, Shan Z. Functional MRI of the Brainstem for Assessing Its Autonomic Functions: From Imaging Parameters and Analysis to Functional Atlas. Journal Of Magnetic Resonance Imaging 2024 PMID: 38339792, DOI: 10.1002/jmri.29286.
Better with age: Developmental changes in oscillatory activity during verbal working memory encoding and maintenanceKillanin A, Ward T, Embury C, Calhoun V, Wang Y, Stephen J, Picci G, Heinrichs-Graham E, Wilson T. Better with age: Developmental changes in oscillatory activity during verbal working memory encoding and maintenance. Developmental Cognitive Neuroscience 2024, 66: 101354. PMID: 38330526, PMCID: PMC10864839, DOI: 10.1016/j.dcn.2024.101354.
Intra-Atlas Node Size Effects on Graph Metrics in fMRI Data: Implications for Alzheimer’s Disease and Cognitive ImpairmentKolla S, Falakshahi H, Abrol A, Fu Z, Calhoun V. Intra-Atlas Node Size Effects on Graph Metrics in fMRI Data: Implications for Alzheimer’s Disease and Cognitive Impairment. Sensors 2024, 24: 814. PMID: 38339531, PMCID: PMC10857295, DOI: 10.3390/s24030814.
A Multi-dimensional Joint ICA Model with Gaussian CopulaAgcaoglu O, Silva R, Alacam D, Calhoun V. A Multi-dimensional Joint ICA Model with Gaussian Copula. 2024, 14366: 152-163. DOI: 10.1007/978-3-031-51026-7_14.
Correlates of axonal content in healthy adult span: Age, sex, myelin, and metabolic healthBurzynska A, Anderson C, Arciniegas D, Calhoun V, Choi I, Mendez Colmenares A, Kramer A, Li K, Lee J, Lee P, Thomas M. Correlates of axonal content in healthy adult span: Age, sex, myelin, and metabolic health. Cerebral Circulation - Cognition And Behavior 2024, 6: 100203. PMID: 38292016, PMCID: PMC10827486, DOI: 10.1016/j.cccb.2024.100203.
Sexual dimorphism in cortical theta rhythms relates to elevated internalizing symptoms during adolescencePetro N, Picci G, Ott L, Rempe M, Embury C, Penhale S, Wang Y, Stephen J, Calhoun V, Taylor B, Wilson T. Sexual dimorphism in cortical theta rhythms relates to elevated internalizing symptoms during adolescence. Imaging Neuroscience 2024, 2: 1-13. DOI: 10.1162/imag_a_00062.
Revealing complex functional topology brain network correspondences between humans and marmosetsLi Q, Calhoun V, Iraji A. Revealing complex functional topology brain network correspondences between humans and marmosets. Neuroscience Letters 2024, 822: 137624. PMID: 38218321, DOI: 10.1016/j.neulet.2024.137624.
Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophreniaLi W, Lin Q, Zhao B, Kuang L, Zhang C, Han Y, Calhoun V. Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia. Journal Of Neuroscience Methods 2023, 403: 110049. PMID: 38151187, DOI: 10.1016/j.jneumeth.2023.110049.
Triple Interactions Between the Environment, Brain, and Behavior in Children: An ABCD StudyZhi D, Jiang R, Pearlson G, Fu Z, Qi S, Yan W, Feng A, Xu M, Calhoun V, Sui J. Triple Interactions Between the Environment, Brain, and Behavior in Children: An ABCD Study. Biological Psychiatry 2023, 95: 828-838. PMID: 38151182, PMCID: PMC11006588, DOI: 10.1016/j.biopsych.2023.12.019.
Identifying psychosis subtypes use individualized covariance structural differential networks and multi-site clusteringJi Y, Pearlson G, Bustillo J, Kochunov P, Turner J, Jiang R, Shao W, Zhang X, Fu Z, Li K, Liu Z, Xu X, Zhang D, Qi S, Calhoun V. Identifying psychosis subtypes use individualized covariance structural differential networks and multi-site clustering. Schizophrenia Research 2023, 264: 130-139. PMID: 38128344, DOI: 10.1016/j.schres.2023.12.013.
Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal linksFedorov A, Geenjaar E, Wu L, Sylvain T, DeRamus T, Luck M, Misiura M, Mittapalle G, Hjelm R, Plis S, Calhoun V. Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links. NeuroImage 2023, 285: 120485. PMID: 38110045, PMCID: PMC10872501, DOI: 10.1016/j.neuroimage.2023.120485.
Theta oscillatory dynamics serving cognitive control index psychosocial distress in youthSchantell M, Taylor B, Mansouri A, Arif Y, Coutant A, Rice D, Wang Y, Calhoun V, Stephen J, Wilson T. Theta oscillatory dynamics serving cognitive control index psychosocial distress in youth. Neurobiology Of Stress 2023, 29: 100599. PMID: 38213830, PMCID: PMC10776433, DOI: 10.1016/j.ynstr.2023.100599.
Dynamic phase-locking states and personality in sub-acute mild traumatic brain injury: An exploratory studyvan der Horn H, de Koning M, Visser K, Kok M, Spikman J, Scheenen M, Renken R, Calhoun V, Vergara V, Cabral J, Mayer A, van der Naalt J. Dynamic phase-locking states and personality in sub-acute mild traumatic brain injury: An exploratory study. PLOS ONE 2023, 18: e0295984. PMID: 38100479, PMCID: PMC10723684, DOI: 10.1371/journal.pone.0295984.
CfMRIPrep: A MATLAB toolbox for integrated preprocessing of complex-valued fMRI dataLi S, Ma M, Lin Q, Calhoun V. CfMRIPrep: A MATLAB toolbox for integrated preprocessing of complex-valued fMRI data. 2023, 00: 167-171. DOI: 10.1109/icist59754.2023.10367157.
Longitudinal resting-state network connectivity changes in electroconvulsive therapy patients compared to healthy controlsVerdijk J, van de Mortel L, Doesschate F, Pottkämper J, Stuiver S, Bruin W, Abbott C, Argyelan M, Ousdal O, Bartsch H, Narr K, Tendolkar I, Calhoun V, Lukemire J, Guo Y, Oltedal L, van Wingen G, van Waarde J. Longitudinal resting-state network connectivity changes in electroconvulsive therapy patients compared to healthy controls. Brain Stimulation 2023, 17: 140-147. PMID: 38101469, PMCID: PMC11145948, DOI: 10.1016/j.brs.2023.12.005.
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.
Improving Explainability for Single-Channel EEG Deep Learning Classifiers via Interpretable Filters and Activation Analysis*Ellis C, Miller R, Calhoun V. Improving Explainability for Single-Channel EEG Deep Learning Classifiers via Interpretable Filters and Activation Analysis*. 2023, 00: 2474-2481. DOI: 10.1109/bibm58861.2023.10385647.
Spatial Dynamic Subspaces Encode Sex-Specific Schizophrenia Disruptions in Transient Network Overlap and its Links to Genetic RiskIraji A, Chen J, Lewis N, Faghiri A, Fu Z, Agcaoglu O, Kochunov P, Adhikari B, Mathalon D, Pearlson G, Macciardi F, Preda A, van Erp T, Bustillo J, Díaz-Caneja C, Andrés-Camazón P, Dhamala M, Adali T, Calhoun V. Spatial Dynamic Subspaces Encode Sex-Specific Schizophrenia Disruptions in Transient Network Overlap and its Links to Genetic Risk. Biological Psychiatry 2023 PMID: 38070846, PMCID: PMC11156799, DOI: 10.1016/j.biopsych.2023.12.002.
An Explainable and Robust Deep Learning Approach for Automated Electroencephalography-Based Schizophrenia DiagnosisSattiraju 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.
Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamicsEllis C, Miller R, Calhoun V. Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics. Neuroimage Reports 2023, 3: 100186. DOI: 10.1016/j.ynirp.2023.100186.
Extraction of One Time Point Dynamic Group Features via Tucker Decomposition of Multi-subject FMRI Data: Application to SchizophreniaHan Y, Lin Q, Kuang L, Hao Y, Li W, Gong X, Calhoun V. Extraction of One Time Point Dynamic Group Features via Tucker Decomposition of Multi-subject FMRI Data: Application to Schizophrenia. 2023, 1963: 518-527. DOI: 10.1007/978-981-99-8138-0_41.
Revisiting Functional Dysconnectivity: a Review of Three Model Frameworks in SchizophreniaHarikumar A, Solovyeva K, Misiura M, Iraji A, Plis S, Pearlson G, Turner J, Calhoun V. Revisiting Functional Dysconnectivity: a Review of Three Model Frameworks in Schizophrenia. Current Neurology And Neuroscience Reports 2023, 23: 937-946. PMID: 37999830, PMCID: PMC11126894, DOI: 10.1007/s11910-023-01325-8.
6 Graph Analysis of Resting State Functional Brain Networks and Associations with Cognitive Outcomes in Survivors on Pediatric Brain TumorSemmel E, Calhoun V, Hillary F, Morris R, King T. 6 Graph Analysis of Resting State Functional Brain Networks and Associations with Cognitive Outcomes in Survivors on Pediatric Brain Tumor. Journal Of The International Neuropsychological Society 2023, 29: 316-317. DOI: 10.1017/s135561772300437x.
Reproducibility in Joint Blind Source Separation: Application to fMRI AnalysisLaport F, Vu T, Yang H, Calhoun V, Adali T. Reproducibility in Joint Blind Source Separation: Application to fMRI Analysis. 2023, 00: 1448-1452. DOI: 10.1109/ieeeconf59524.2023.10477028.
Objective Assessment of the Bias Introduced by Baseline Signals in XAI Attribution MethodsDolci G, Cruciani F, Galazzo I, Calhoun V, Menegaz G. Objective Assessment of the Bias Introduced by Baseline Signals in XAI Attribution Methods. 2023, 00: 266-271. DOI: 10.1109/metroxraine58569.2023.10405708.
Age-related, multivariate associations between white matter microstructure and behavioral performance in three executive function domainsAnderson J, Calhoun V, Pearlson G, Hawkins K, Stevens M. Age-related, multivariate associations between white matter microstructure and behavioral performance in three executive function domains. Developmental Cognitive Neuroscience 2023, 64: 101318. PMID: 37875033, PMCID: PMC10618425, DOI: 10.1016/j.dcn.2023.101318.
Multimodal Fusion of Functional and Structural Data to Recognize Longitudinal Change Patterns in the Adolescent BrainSaha R, Saha D, Fu Z, Silva R, Calhoun V. Multimodal Fusion of Functional and Structural Data to Recognize Longitudinal Change Patterns in the Adolescent Brain. 2023, 00: 1-5. DOI: 10.1109/bhi58575.2023.10313489.
Dehydroepiandrosterone mediates associations between trauma‐related symptoms and anterior pituitary volume in children and adolescentsPicci G, Casagrande C, Ott L, Petro N, Christopher‐Hayes N, Johnson H, Willett M, Okelberry H, Wang Y, Stephen J, Calhoun V, Wilson T. Dehydroepiandrosterone mediates associations between trauma‐related symptoms and anterior pituitary volume in children and adolescents. Human Brain Mapping 2023, 44: 6388-6398. PMID: 37853842, PMCID: PMC10681633, DOI: 10.1002/hbm.26516.
dcSBM: A federated constrained source‐based morphometry approach for multivariate brain structure mappingSaha D, Silva R, Baker B, Saha R, Calhoun V. dcSBM: A federated constrained source‐based morphometry approach for multivariate brain structure mapping. Human Brain Mapping 2023, 44: 5892-5905. PMID: 37837630, PMCID: PMC10619413, DOI: 10.1002/hbm.26483.
Developmental changes in endogenous testosterone have sexually‐dimorphic effects on spontaneous cortical dynamicsPicci G, Ott L, Penhale S, Taylor B, Johnson H, Willett M, Okelberry H, Wang Y, Calhoun V, Stephen J, Wilson T. Developmental changes in endogenous testosterone have sexually‐dimorphic effects on spontaneous cortical dynamics. Human Brain Mapping 2023, 44: 6043-6054. PMID: 37811842, PMCID: PMC10619376, DOI: 10.1002/hbm.26496.
Addressing Global Environmental Challenges to Mental Health Using Population NeuroscienceSchumann G, Andreassen O, Banaschewski T, Calhoun V, Clinton N, Desrivieres S, Brandlistuen R, Feng J, Hese S, Hitchen E, Hoffmann P, Jia T, Jirsa V, Marquand A, Nees F, Nöthen M, Novarino G, Polemiti E, Ralser M, Rapp M, Schepanski K, Schikowski T, Slater M, Sommer P, Stahl B, Thompson P, Twardziok S, van der Meer D, Walter H, Westlye L, Heinz A, Lett T, Vaidya N, Serin E, Neidhart M, Jentsch M, Eils R, Taron U, Schütz T, Banks J, Meyer-Lindenberg A, Tost H, Holz N, Schwarz E, Stringaris A, Christmann N, Jansone K, Siehl S, Ask H, Fernández-Cabello S, Kjelkenes R, Tschorn M, Böttger S, Bernas A, Marr L, Feixas Viapiana G, Eiroa-Orosa F, Gallego J, Pastor A, Forstner A, Claus I, Miller A, Heilmann-Heimbach S, Boye M, Wilbertz J, Schmitt K, Petkoski S, Pitel S, Otten L, Athanasiadis A, Pearmund C, Spanlang B, Alvarez E, Sanchez M, Giner A, Renner P, Gong Y, Dai Y, Xia Y, Chang X, Liu J, Young A, Ogoh G. Addressing Global Environmental Challenges to Mental Health Using Population Neuroscience. JAMA Psychiatry 2023, 80: 1066-1074. PMID: 37610741, DOI: 10.1001/jamapsychiatry.2023.2996.
F71. NETWORK OF CO-METHYLATION ASSOCIATED WITH GREY MATTER MATURATION IN HUMAN ADOLESCENCEJensen D, Chen J, Turner J, Stephen J, Wang Y, Wilson T, Calhoun V, Liu J. F71. NETWORK OF CO-METHYLATION ASSOCIATED WITH GREY MATTER MATURATION IN HUMAN ADOLESCENCE. European Neuropsychopharmacology 2023, 75: s258-s259. DOI: 10.1016/j.euroneuro.2023.08.455.
Chromatic fusion: Generative multimodal neuroimaging data fusion provides multi‐informed insights into schizophreniaGeenjaar E, Lewis N, Fedorov A, Wu L, Ford J, Preda A, Plis S, Calhoun V. Chromatic fusion: Generative multimodal neuroimaging data fusion provides multi‐informed insights into schizophrenia. Human Brain Mapping 2023, 44: 5828-5845. PMID: 37753705, PMCID: PMC10619380, DOI: 10.1002/hbm.26479.
A Brainwide Risk Score for Psychiatric Disorder Evaluated in a Large Adolescent Population Reveals Increased Divergence Among Higher-Risk Groups Relative to Control ParticipantsYan 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.
Beyond the Global Brain Differences: Intraindividual Variability Differences in 1q21.1 Distal and 15q11.2 BP1-BP2 Deletion CarriersBoen R, Kaufmann T, van der Meer D, Frei O, Agartz I, Ames D, Andersson M, Armstrong N, Artiges E, Atkins J, Bauer J, Benedetti F, Boomsma D, Brodaty H, Brosch K, Buckner R, Cairns M, Calhoun V, Caspers S, Cichon S, Corvin A, Crespo-Facorro B, Dannlowski U, David F, de Geus E, de Zubicaray G, Desrivières S, Doherty J, Donohoe G, Ehrlich S, Eising E, Espeseth T, Fisher S, Forstner A, Fortaner-Uyà L, Frouin V, Fukunaga M, Ge T, Glahn D, Goltermann J, Grabe H, Green M, Groenewold N, Grotegerd D, Grøntvedt G, Hahn T, Hashimoto R, Hehir-Kwa J, Henskens F, Holmes A, Håberg A, Haavik J, Jacquemont S, Jansen A, Jockwitz C, Jönsson E, Kikuchi M, Kircher T, Kumar K, Le Hellard S, Leu C, Linden D, Liu J, Loughnan R, Mather K, McMahon K, McRae A, Medland S, Meinert S, Moreau C, Morris D, Mowry B, Mühleisen T, Nenadić I, Nöthen M, Nyberg L, Ophoff R, Owen M, Pantelis C, Paolini M, Paus T, Pausova Z, Persson K, Quidé Y, Marques T, Sachdev P, Sando S, Schall U, Scott R, Selbæk G, Shumskaya E, Silva A, Sisodiya S, Stein F, Stein D, Straube B, Streit F, Strike L, Teumer A, Teutenberg L, Thalamuthu A, Tooney P, Tordesillas-Gutierrez D, Trollor J, van ’t Ent D, van den Bree M, van Haren N, Vázquez-Bourgon J, Völzke H, Wen W, Wittfeld K, Ching C, Westlye L, Thompson P, Bearden C, Selmer K, Alnæs D, Andreassen O, Sønderby I, Group E. Beyond the Global Brain Differences: Intraindividual Variability Differences in 1q21.1 Distal and 15q11.2 BP1-BP2 Deletion Carriers. Biological Psychiatry 2023, 95: 147-160. PMID: 37661008, PMCID: PMC7615370, DOI: 10.1016/j.biopsych.2023.08.018.
Deep learning with explainability for characterizing age-related intrinsic differences in dynamic brain functional connectivityQiao C, Gao B, Liu Y, Hu X, Hu W, Calhoun V, Wang Y. Deep learning with explainability for characterizing age-related intrinsic differences in dynamic brain functional connectivity. Medical Image Analysis 2023, 90: 102941. PMID: 37683445, DOI: 10.1016/j.media.2023.102941.
Extracting functional connectivity brain networks at the resting state from pulsed arterial spin labeling dataWiseman N, Iraji A, Haacke E, Calhoun V, Kou Z. Extracting functional connectivity brain networks at the resting state from pulsed arterial spin labeling data. Meta-Radiology 2023, 1: 100023. PMID: 38298860, PMCID: PMC10830167, DOI: 10.1016/j.metrad.2023.100023.
Denoising brain networks using a fixed mathematical phase change in independent component analysis of magnitude‐only fMRI dataZhang C, Lin Q, Niu Y, Li W, Gong X, Cong F, Wang Y, Calhoun V. Denoising brain networks using a fixed mathematical phase change in independent component analysis of magnitude‐only fMRI data. Human Brain Mapping 2023, 44: 5712-5728. PMID: 37647216, PMCID: PMC10619417, DOI: 10.1002/hbm.26471.
Auditory oddball hypoactivation in schizophreniaNakahara S, Male A, Turner J, Calhoun V, Lim K, Mueller B, Bustillo J, O'Leary D, Voyvodic J, Belger A, Preda A, Mathalon D, Ford J, Guffanti G, Macciardi F, Potkin S, Van Erp T. Auditory oddball hypoactivation in schizophrenia. Psychiatry Research Neuroimaging 2023, 335: 111710. PMID: 37690161, DOI: 10.1016/j.pscychresns.2023.111710.
Transdiagnostic indicators predict developmental changes in cognitive control resting-state networks.Picci G, Petro N, Son J, Agcaoglu O, Eastman J, Wang Y, Stephen J, Calhoun V, Taylor B, Wilson T. Transdiagnostic indicators predict developmental changes in cognitive control resting-state networks. Development And Psychopathology 2023, 1-11. PMID: 37615120, PMCID: PMC11140239, DOI: 10.1017/s0954579423001013.
Metabolic syndrome and adiposity: Risk factors for decreased myelin in cognitively healthy adultsBurzynska A, Anderson C, Arciniegas D, Calhoun V, Choi I, Colmenares A, Hiner G, Kramer A, Li K, Lee J, Lee P, Oh S, Umland S, Thomas M. Metabolic syndrome and adiposity: Risk factors for decreased myelin in cognitively healthy adults. Cerebral Circulation - Cognition And Behavior 2023, 5: 100180. PMID: 38162292, PMCID: PMC10757180, DOI: 10.1016/j.cccb.2023.100180.
Developmental alterations in the neural oscillatory dynamics underlying attentional reorientingPicci G, Ott L, Petro N, Casagrande C, Killanin A, Rice D, Coutant A, Arif Y, Embury C, Okelberry H, Johnson H, Springer S, Pulliam H, Wang Y, Calhoun V, Stephen J, Heinrichs-Graham E, Taylor B, Wilson T. Developmental alterations in the neural oscillatory dynamics underlying attentional reorienting. Developmental Cognitive Neuroscience 2023, 63: 101288. PMID: 37567094, PMCID: PMC10432959, DOI: 10.1016/j.dcn.2023.101288.
Psychopathic traits and altered resting-state functional connectivity in incarcerated adolescent girlsAllen C, Maurer J, Gullapalli A, Edwards B, Aharoni E, Harenski C, Anderson N, Harenski K, Calhoun V, Kiehl K. Psychopathic traits and altered resting-state functional connectivity in incarcerated adolescent girls. Frontiers In Neuroimaging 2023, 2: 1216494. PMID: 37554634, PMCID: PMC10406221, DOI: 10.3389/fnimg.2023.1216494.
Automated Interpretation of Clinical Electroencephalograms Using Artificial IntelligenceTveit J, Aurlien H, Plis S, Calhoun V, Tatum W, Schomer D, Arntsen V, Cox F, Fahoum F, Gallentine W, Gardella E, Hahn C, Husain A, Kessler S, Kural M, Nascimento F, Tankisi H, Ulvin L, Wennberg R, Beniczky S. Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence. JAMA Neurology 2023, 80: 805-812. PMID: 37338864, PMCID: PMC10282956, DOI: 10.1001/jamaneurol.2023.1645.
Epigenetic associations with adolescent grey matter maturation and cognitive developmentJensen D, Chen J, Turner J, Stephen J, Wang Y, Wilson T, Calhoun V, Liu J. Epigenetic associations with adolescent grey matter maturation and cognitive development. Frontiers In Genetics 2023, 14: 1222619. PMID: 37529779, PMCID: PMC10390095, DOI: 10.3389/fgene.2023.1222619.
A Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging DataRokham 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.
Decentralized Parallel Independent Component Analysis for Multimodal, Multisite DataPanichvatana C, Chen J, Baker B, Thapaliya B, Calhoun V, Liu J. Decentralized Parallel Independent Component Analysis for Multimodal, Multisite Data. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083130, DOI: 10.1109/embc40787.2023.10340070.
Functional and Structural Longitudinal Change Patterns in Adolescent BrainSaha R, Saha D, Fu Z, Silva R, Calhoun V. Functional and Structural Longitudinal Change Patterns in Adolescent Brain. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38082649, DOI: 10.1109/embc40787.2023.10340079.
Towards a multimodal neuroimaging-based risk score for Alzheimer’s disease by combining clinical and large N>37000 population dataZendehrouh E, Sendi M, Calhoun V. Towards a multimodal neuroimaging-based risk score for Alzheimer’s disease by combining clinical and large N>37000 population data. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083709, DOI: 10.1109/embc40787.2023.10340414.
A Multivariate Method for Estimating and comparing whole brain functional connectomes from fMRI and PET dataSaha D, Bohsali A, Saha R, Hajjar I, Calhoun V. A Multivariate Method for Estimating and comparing whole brain functional connectomes from fMRI and PET data. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083351, DOI: 10.1109/embc40787.2023.10340631.
Network Differential in Gaussian Graphical Models from Multimodal Neuroimaging Data*Falakshahi H, Rokham H, Miller R, Liu J, Calhoun V. Network Differential in Gaussian Graphical Models from Multimodal Neuroimaging Data*. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-6. PMID: 38083176, DOI: 10.1109/embc40787.2023.10340856.
Phase and amplitude, two sides of functional connectivityWiafe S, Fu Z, Calhoun V, Faghiri A. Phase and amplitude, two sides of functional connectivity. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083120, DOI: 10.1109/embc40787.2023.10341073.
Hyperlocal Spatial Flows in BOLD fMRI Expose Novel Brain-Based Correlates of SchizophreniaMiller R, Vergara V, Calhoun V. Hyperlocal Spatial Flows in BOLD fMRI Expose Novel Brain-Based Correlates of Schizophrenia. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083298, DOI: 10.1109/embc40787.2023.10341101.
ICA-based Individualized Differential Structure Similarity Networks for Predicting Symptom Scores in Adolescents with Major Depressive DisorderLi 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.
A Novel Explainable Fuzzy Clustering Approach for fMRI Dynamic Functional Network Connectivity Analysis*Ellis C, Miller R, Calhoun V. A Novel Explainable Fuzzy Clustering Approach for fMRI Dynamic Functional Network Connectivity Analysis*. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083353, DOI: 10.1109/embc40787.2023.10340173.
How Does Aging Affect Whole-brain Functional Network Connectivity? Evidence from An ICA MethodDu Y, Guo Y, Calhoun V. How Does Aging Affect Whole-brain Functional Network Connectivity? Evidence from An ICA Method. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083384, DOI: 10.1109/embc40787.2023.10340189.
A Convolutional Autoencoder-based Explainable Clustering Approach for Resting-State EEG Analysis*Ellis C, Miller R, Calhoun V. A Convolutional Autoencoder-based Explainable Clustering Approach for Resting-State EEG Analysis*. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083554, DOI: 10.1109/embc40787.2023.10340375.
Neuropsychiatric Disorder Subtyping Via Clustered Deep Learning Classifier Explanations *Ellis C, Miller R, Calhoun V. Neuropsychiatric Disorder Subtyping Via Clustered Deep Learning Classifier Explanations *. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083012, DOI: 10.1109/embc40787.2023.10340837.
Multi-scale convolutional recurrent neural network for psychiatric disorder identification in resting-state EEGYan 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.
Elevated C-reactive protein mediates the liver-brain axis: a preliminary studyJiang R, Wu J, Rosenblatt M, Dai W, Rodriguez R, Sui J, Qi S, Liang Q, Xu B, Meng Q, Calhoun V, Scheinost D. Elevated C-reactive protein mediates the liver-brain axis: a preliminary study. EBioMedicine 2023, 93: 104679. PMID: 37356206, PMCID: PMC10320521, DOI: 10.1016/j.ebiom.2023.104679.
Constrained Independent Component Analysis Based on Entropy Bound Minimization for Subgroup Identification from Multi-subject fMRI DataYang H, Ghayem F, Gabrielson B, Akhonda M, Calhoun V, Adali T. Constrained Independent Component Analysis Based on Entropy Bound Minimization for Subgroup Identification from Multi-subject fMRI Data. 2023, 00: 1-5. DOI: 10.1109/icassp49357.2023.10095816.
Coupled CP Tensor Decomposition with Shared and Distinct Components for Multi-Task Fmri Data FusionBorsoi R, Lehmann I, Akhonda M, Calhoun V, Usevich K, Brie D, Adali T. Coupled CP Tensor Decomposition with Shared and Distinct Components for Multi-Task Fmri Data Fusion. 2023, 00: 1-5. DOI: 10.1109/icassp49357.2023.10096241.
New Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity using Dictionary LearningGhayem F, Yang H, Kantar F, Kim S, Calhoun V, Adali T. New Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity using Dictionary Learning. 2023, 00: 1-5. DOI: 10.1109/icassp49357.2023.10096473.
Independent Vector Analysis with Multivariate Gaussian Model: a Scalable Method by Multilinear RegressionGabrielson B, Sun M, Akhonda M, Calhoun V, Adali T. Independent Vector Analysis with Multivariate Gaussian Model: a Scalable Method by Multilinear Regression. 2023, 00: 1-5. DOI: 10.1109/icassp49357.2023.10096698.
Fusion of Multi-Modal Neuroimaging Data and Association With Cognitive DataLoPresto M, Akhonda M, Calhoun V, Adali T. Fusion of Multi-Modal Neuroimaging Data and Association With Cognitive Data. 2023, 00: 1-5. DOI: 10.1109/icasspw59220.2023.10193147.
Higher-Order Organization in the Human Brain From Matrix-Based Rényi’s EntropyLi Q, Yu S, Madsen K, Calhoun V, Iraji A. Higher-Order Organization in the Human Brain From Matrix-Based Rényi’s Entropy. 2023, 00: 1-5. DOI: 10.1109/icasspw59220.2023.10193346.
Multi-Modal Deep Learning on Imaging Genetics for Schizophrenia ClassificationKanyal A, Kandula S, Calhoun V, Ye D. Multi-Modal Deep Learning on Imaging Genetics for Schizophrenia Classification. 2023, 00: 1-5. DOI: 10.1109/icasspw59220.2023.10193352.
Local Spatial Flow Strengths in Bold FMRI are Strongly Impacted by SchizophreniaMiller R, Vergara V, Petropoulos H, Calhoun V. Local Spatial Flow Strengths in Bold FMRI are Strongly Impacted by Schizophrenia. 2023, 00: 1-4. DOI: 10.1109/icasspw59220.2023.10193432.
Novel Approach Explains Spatio-Spectral Interactions In Raw Electroencephalogram Deep Learning ClassifiersEllis C, Sattiraju A, Miller R, Calhoun V. Novel Approach Explains Spatio-Spectral Interactions In Raw Electroencephalogram Deep Learning Classifiers. 2023, 00: 1-5. DOI: 10.1109/icasspw59220.2023.10193605.
Deep Generative Transfer Learning Predicts Conversion To Alzheimer’S Disease From Neuroimaging Genomics DataDolci G, Rahaman M, Galazzo I, Cruciani F, Abrol A, Chen J, Fu Z, Duan K, Menegaz G, Calhoun V. Deep Generative Transfer Learning Predicts Conversion To Alzheimer’S Disease From Neuroimaging Genomics Data. 2023, 00: 1-5. DOI: 10.1109/icasspw59220.2023.10193683.
Developmental differences in functional organization of multispectral networksPetro N, Picci G, Embury C, Ott L, Penhale S, Rempe M, Johnson H, Willett M, Wang Y, Stephen J, Calhoun V, Doucet G, Wilson T. Developmental differences in functional organization of multispectral networks. Cerebral Cortex 2023, 33: 9175-9185. PMID: 37279931, PMCID: PMC10505424, DOI: 10.1093/cercor/bhad193.
Graph analysis of resting state functional brain networks and associations with cognitive outcomes in survivors of pediatric brain tumorSemmel E, Calhoun V, Hillary F, Morris R, King T. Graph analysis of resting state functional brain networks and associations with cognitive outcomes in survivors of pediatric brain tumor. Neuroimage Reports 2023, 3: 100178. DOI: 10.1016/j.ynirp.2023.100178.
Testosterone levels mediate the dynamics of motor oscillatory coding and behavior in developing youthKillanin A, Taylor B, Embury C, Picci G, Wang Y, Calhoun V, Stephen J, Heinrichs-Graham E, Wilson T. Testosterone levels mediate the dynamics of motor oscillatory coding and behavior in developing youth. Developmental Cognitive Neuroscience 2023, 61: 101257. PMID: 37236034, PMCID: PMC10232658, DOI: 10.1016/j.dcn.2023.101257.
Aging brain shows joint declines in brain within-network connectivity and between-network connectivity: a large-sample study (N > 6,000)Du Y, Guo Y, Calhoun V. Aging brain shows joint declines in brain within-network connectivity and between-network connectivity: a large-sample study (N > 6,000). Frontiers In Aging Neuroscience 2023, 15: 1159054. PMID: 37273655, PMCID: PMC10233064, DOI: 10.3389/fnagi.2023.1159054.
Multi-study evaluation of neuroimaging-based prediction of medication class in mood disordersSalman 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.
Interpretable LSTM model reveals transiently-realized patterns of dynamic brain connectivity that predict patient deterioration or recovery from very mild cognitive impairmentGao Y, Lewis N, Calhoun V, Miller R. Interpretable LSTM model reveals transiently-realized patterns of dynamic brain connectivity that predict patient deterioration or recovery from very mild cognitive impairment. Computers In Biology And Medicine 2023, 161: 107005. PMID: 37211004, PMCID: PMC10365638, DOI: 10.1016/j.compbiomed.2023.107005.
A systematic review of neuroimaging epigenetic research: calling for an increased focus on developmentWalton E, Baltramonaityte V, Calhoun V, Heijmans B, Thompson P, Cecil C. A systematic review of neuroimaging epigenetic research: calling for an increased focus on development. Molecular Psychiatry 2023, 28: 2839-2847. PMID: 37185958, PMCID: PMC10615743, DOI: 10.1038/s41380-023-02067-2.
Group 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.
The Nonlinear Brain: Towards Uncovering Hidden Brain Networks Using Explicitly Nonlinear Functional InteractionIraji A, Kazimierczak K, Chen J, Motlaghian S, Specht K, Adali T, Calhoun V. The Nonlinear Brain: Towards Uncovering Hidden Brain Networks Using Explicitly Nonlinear Functional Interaction. 2023, 00: 1-4. DOI: 10.1109/isbi53787.2023.10230347.
Any-Way Independent Component Analysis with ReferenceDuan K, Silva R, Liu J, Agcaoglu O, Calhoun V. Any-Way Independent Component Analysis with Reference. 2023, 00: 1-4. DOI: 10.1109/isbi53787.2023.10230369.
Identifying Neuropsychiatric Disorder Subtypes and Subtype-Dependent Variation in Diagnostic Deep Learning Classifier PerformanceEllis C, Miller R, Calhoun V. Identifying Neuropsychiatric Disorder Subtypes and Subtype-Dependent Variation in Diagnostic Deep Learning Classifier Performance. 2023, 00: 1-4. DOI: 10.1109/isbi53787.2023.10230384.
Evaluating Trade-Offs in IVA of Multimodal Neuroimaging using Cross-Platform Multidataset Independent Subspace AnalysisLi X, Khosravinezhad D, Calhoun V, Silva R. Evaluating Trade-Offs in IVA of Multimodal Neuroimaging using Cross-Platform Multidataset Independent Subspace Analysis. 2023, 00: 1-5. DOI: 10.1109/isbi53787.2023.10230492.
Topological Characteristics of 5d Spatially Dynamic Brain Networks in SchizophreniaSalman M, Iraji A, Lewis N, Calhoun V. Topological Characteristics of 5d Spatially Dynamic Brain Networks in Schizophrenia. 2023, 00: 1-5. DOI: 10.1109/isbi53787.2023.10230513.
Topological Correction of Subject-Level Intrinsic Connectivity NetworksLewis N, Iraji A, Miller R, Calhoun V. Topological Correction of Subject-Level Intrinsic Connectivity Networks. 2023, 00: 1-4. DOI: 10.1109/isbi53787.2023.10230598.
Multimodal Subspace Independent Vector Analysis Better Captures Hidden Relationships in Multimodal Neuroimaging DataLi X, Adali T, Silva R, Calhoun V. Multimodal Subspace Independent Vector Analysis Better Captures Hidden Relationships in Multimodal Neuroimaging Data. 2023, 00: 1-5. DOI: 10.1109/isbi53787.2023.10230605.
Federated Linear Mixed Effects Modeling for Voxel-Based MorphometryBasodi S, Raja R, Gazula H, Romero J, Panta S, Calhoun V. Federated Linear Mixed Effects Modeling for Voxel-Based Morphometry. 2023, 00: 1-4. DOI: 10.1109/isbi53787.2023.10230684.
Functional Network Connectivity Based Mental Health Category Prediction from Rest-fMRI DataAjith M, Calhoun V. Functional Network Connectivity Based Mental Health Category Prediction from Rest-fMRI Data. 2023, 00: 1-5. DOI: 10.1109/isbi53787.2023.10230721.
Effective Training Strategy for NN Models of Working Memory Classification with Limited SamplesSuresh 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.
Capturing Spatial Dynamics Using Time-Resolved Referenced-Informed Network Estimation TechniquesIraji A, Chen J, Faghiri A, Fu Z, Liu J, Bustillo J, Adali T, Dhamala M, Calhoun V. Capturing Spatial Dynamics Using Time-Resolved Referenced-Informed Network Estimation Techniques. 2023, 00: 1-4. DOI: 10.1109/isbi53787.2023.10230735.
An Adaptive Semi-Supervised Deep Clustering and Its Application to Identifying Biotypes of Psychiatric DisordersDu Y, Wu F, Niu J, Calhoun V. An Adaptive Semi-Supervised Deep Clustering and Its Application to Identifying Biotypes of Psychiatric Disorders. 2023, 00: 1-4. DOI: 10.1109/isbi53787.2023.10230805.
Symmetric data-driven fusion of diffusion tensor MRI: Age differences in white matterColmenares A, Hefner M, Calhoun V, Salerno E, Fanning J, Gothe N, McAuley E, Kramer A, Burzynska A. Symmetric data-driven fusion of diffusion tensor MRI: Age differences in white matter. Frontiers In Neurology 2023, 14: 1094313. PMID: 37139071, PMCID: PMC10149813, DOI: 10.3389/fneur.2023.1094313.
Associations of physical frailty with health outcomes and brain structure in 483 033 middle-aged and older adults: a population-based study from the UK BiobankJiang R, Noble S, Sui J, Yoo K, Rosenblatt M, Horien C, Qi S, Liang Q, Sun H, Calhoun V, Scheinost D. Associations of physical frailty with health outcomes and brain structure in 483 033 middle-aged and older adults: a population-based study from the UK Biobank. The Lancet Digital Health 2023, 5: e350-e359. PMID: 37061351, PMCID: PMC10257912, DOI: 10.1016/s2589-7500(23)00043-2.
Correction: Links between electroconvulsive therapy responsive and cognitive impairment multimodal brain networks in late-life major depressive disorderQi 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.
Large-scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortiumSchijven D, Postema M, Fukunaga M, Matsumoto J, Miura K, de Zwarte S, van Haren N, Cahn W, Pol H, Kahn R, Ayesa-Arriola R, de la Foz V, Tordesillas-Gutierrez D, Vázquez-Bourgon J, Crespo-Facorro B, Alnæs D, Dahl A, Westlye L, Agartz I, Andreassen O, Jönsson E, Kochunov P, Bruggemann J, Catts S, Michie P, Mowry B, Quidé Y, Rasser P, Schall U, Scott R, Carr V, Green M, Henskens F, Loughland C, Pantelis C, Weickert C, Weickert T, de Haan L, Brosch K, Pfarr J, Ringwald K, Stein F, Jansen A, Kircher T, Nenadić I, Krämer B, Gruber O, Satterthwaite T, Bustillo J, Mathalon D, Preda A, Calhoun V, Ford J, Potkin S, Chen J, Tan Y, Wang Z, Xiang H, Fan F, Bernardoni F, Ehrlich S, Fuentes-Claramonte P, Garcia-Leon M, Guerrero-Pedraza A, Salvador R, Sarró S, Pomarol-Clotet E, Ciullo V, Piras F, Vecchio D, Banaj N, Spalletta G, Michielse S, van Amelsvoort T, Dickie E, Voineskos A, Sim K, Ciufolini S, Dazzan P, Murray R, Kim W, Chung Y, Andreou C, Schmidt A, Borgwardt S, McIntosh A, Whalley H, Lawrie S, du Plessis S, Luckhoff H, Scheffler F, Emsley R, Grotegerd D, Lencer R, Dannlowski U, Edmond J, Rootes-Murdy K, Stephen J, Mayer A, Antonucci L, Fazio L, Pergola G, Bertolino A, Díaz-Caneja C, Janssen J, Lois N, Arango C, Tomyshev A, Lebedeva I, Cervenka S, Sellgren C, Georgiadis F, Kirschner M, Kaiser S, Hajek T, Skoch A, Spaniel F, Kim M, Bin Kwak Y, Oh S, Kwon J, James A, Bakker G, Knöchel C, Stäblein M, Oertel V, Uhlmann A, Howells F, Stein D, Temmingh H, Diaz-Zuluaga A, Pineda-Zapata J, López-Jaramillo C, Homan S, Ji E, Surbeck W, Homan P, Fisher S, Franke B, Glahn D, Gur R, Hashimoto R, Jahanshad N, Luders E, Medland S, Thompson P, Turner J, van Erp T, Francks C. Large-scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium. Proceedings Of The National Academy Of Sciences Of The United States Of America 2023, 120: e2213880120. PMID: 36976765, PMCID: PMC10083554, DOI: 10.1073/pnas.2213880120.
Novel methods for elucidating modality importance in multimodal electrophysiology classifiersEllis 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.
Data-driven multimodal fusion: approaches and applications in psychiatric researchSui J, Zhi D, Calhoun V. Data-driven multimodal fusion: approaches and applications in psychiatric research. Psychoradiology 2023, 3: kkad026. PMID: 38143530, PMCID: PMC10734907, DOI: 10.1093/psyrad/kkad026.
An active learning framework for personalized deep brain stimulationSendi M, Herron J, Miocinovic S, Dyer E, Mayberg H, Gross R, Calhoun V. An active learning framework for personalized deep brain stimulation. Brain Stimulation 2023, 16: 2. DOI: 10.1016/j.brs.2023.03.016.
Individualizing TMS treatment targets for PTSD using neuroimaging: Preliminary findings from an ongoing clinical trialvan Rooij S, Teye-Botchway L, Hinojosa C, Minton S, Job G, Riva-Posse P, Rauch S, Ressler K, Jovanovic T, Holtzheimer P, Calhoun V, Camprodon J, McDonald W. Individualizing TMS treatment targets for PTSD using neuroimaging: Preliminary findings from an ongoing clinical trial. Brain Stimulation 2023, 16: 3. DOI: 10.1016/j.brs.2023.03.020.
Joint Structural and Functional Connectivity Learning Based Independent Component AnalysisFouladivanda M, Iraji A, Wu L, Calhoun V. Joint Structural and Functional Connectivity Learning Based Independent Component Analysis. 2023, 00: 1-5. DOI: 10.1109/mlsp55844.2023.10285932.
Correction to: Multi-Subject Analysis for Brain Developmental Patterns Discovery via Tensor Decomposition of MEG DataBelyaeva I, Gabrielson B, Wang Y, Wilson T, Calhoun V, Stephen J, Adali T. Correction to: Multi-Subject Analysis for Brain Developmental Patterns Discovery via Tensor Decomposition of MEG Data. Neuroinformatics 2023, 21: 143-143. PMID: 36648690, DOI: 10.1007/s12021-023-09620-y.
Identification of neurobiology-based cognition-related biotypes using data-driven techniques and multi-scale intrinsic connectivity networks in psychotic disordersCamazón P, Ballem R, Chen J, Diaz-Caneja C, Campayo J, Calhoun V, Iraji A. Identification of neurobiology-based cognition-related biotypes using data-driven techniques and multi-scale intrinsic connectivity networks in psychotic disorders. Neuroscience Applied 2023, 2: 103003. DOI: 10.1016/j.nsa.2023.103003.
Links between electroconvulsive therapy responsive and cognitive impairment multimodal brain networks in late-life major depressive disorderQi 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. Links between electroconvulsive therapy responsive and cognitive impairment multimodal brain networks in late-life major depressive disorder. BMC Medicine 2022, 20: 477. PMID: 36482369, PMCID: PMC9733153, DOI: 10.1186/s12916-022-02678-6.
Safety and biomarker effects of candesartan in non-hypertensive adults with prodromal Alzheimer’s diseaseHajjar I, Okafor M, Wan L, Yang Z, Nye J, Bohsali A, Shaw L, Levey A, Lah J, Calhoun V, Moore R, Goldstein F. Safety and biomarker effects of candesartan in non-hypertensive adults with prodromal Alzheimer’s disease. Brain Communications 2022, 4: fcac270. PMID: 36440097, PMCID: PMC9683395, DOI: 10.1093/braincomms/fcac270.
Deep Learning From Imaging Genetics for Schizophrenia ClassificationYu H, Florian T, Calhoun V, Ye D. Deep Learning From Imaging Genetics for Schizophrenia Classification. 2022, 00: 3291-3295. DOI: 10.1109/icip46576.2022.9897977.
CAG repeat expansion in Huntington disease determines age at onset in a fully dominant fashionLee J, Ramos E, Lee J, Gillis T, Mysore J, Hayden M, Warby S, Morrison P, Nance M, Ross C, Margolis R, Squitieri F, Orobello S, Di Donato S, Gomez-Tortosa E, Ayuso C, Suchowersky O, Trent R, McCusker E, Novelletto A, Frontali M, Jones R, Ashizawa T, Frank S, Saint-Hilaire M, Hersch S, Rosas H, Lucente D, Harrison M, Zanko A, Abramson R, Marder K, Sequeiros J, Paulsen J, Landwehrmeyer G, Myers R, MacDonald M, Gusella J, Durr A, Rosenblatt A, Frati L, Perlman S, Conneally P, Klimek M, Diggin M, Hadzi T, Duckett A, Ahmed A, Allen P, Ames D, Anderson C, Anderson K, Anderson K, Andrews T, Ashburner J, Axelson E, Aylward E, Barker R, Barth K, Barton S, Baynes K, Bea A, Beall E, Beg M, Beglinger L, Biglan K, Bjork K, Blanchard S, Bockholt J, Bommu S, Brossman B, Burrows M, Calhoun V, Carlozzi N, Chesire A, Chiu E, Chua P, Connell R, Connor C, Corey-Bloom J, Craufurd D, Cross S, Cysique L, Santos R, 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CAG repeat expansion in Huntington disease determines age at onset in a fully dominant fashion. Neurology 2012, 78: 690-695. PMID: 22323755, PMCID: PMC3306163, DOI: 10.1212/wnl.0b013e318249f683.

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