2023
Multiple visual objects are represented differently in the human brain and convolutional neural networks
Mocz V, Jeong S, Chun M, Xu Y. Multiple visual objects are represented differently in the human brain and convolutional neural networks. Scientific Reports 2023, 13: 9088. PMID: 37277406, PMCID: PMC10241785, DOI: 10.1038/s41598-023-36029-z.Peer-Reviewed Original ResearchConceptsObject representationsConvolutional neural networkMultiple visual objectsHuman brainRecurrent processingVisual objectsProcessing regionsSingle-unit levelObject pairsIT neuronsFMRI voxelsSingle objectConstituent objectsPrimate brainBrain dataNeural networkDifferent contextsResponse patternsObject classificationReal worldObjectsRepresentationUnit levelBrainSuch distortions
2022
A generalizable connectome-based marker of in-scan sustained attention in neurodiverse youth
Horien C, Greene A, Shen X, Fortes D, Brennan-Wydra E, Banarjee C, Foster R, Donthireddy V, Butler M, Powell K, Vernetti A, Mandino F, O'Connor D, Lake E, McPartland J, Volkmar F, Chun M, Chawarska K, Rosenberg M, Scheinost D, Constable R. A generalizable connectome-based marker of in-scan sustained attention in neurodiverse youth. Cerebral Cortex 2022, 33: 6320-6334. PMID: 36573438, PMCID: PMC10183743, DOI: 10.1093/cercor/bhac506.Peer-Reviewed Original ResearchConceptsAttention taskAttentional stateConnectome-based predictive modelingNeurodiverse conditionsSustained attention taskAttention network modelSample of youthNeurotypical participantsSustained attentionBrain correlatesNeurobiological correlatesAttention networkIndividual participantsSeparate samplesYouthParticipantsHead motionTaskCorrelatesAttentionAutismConfoundsNetwork modelGeneralizesHealthcare settingsDifferences in the functional brain architecture of sustained attention and working memory in youth and adults
Kardan O, Stier A, Cardenas-Iniguez C, Schertz K, Pruin J, Deng Y, Chamberlain T, Meredith W, Zhang X, Bowman J, Lakhtakia T, Tindel L, Avery E, Lin Q, Yoo K, Chun M, Berman M, Rosenberg M. Differences in the functional brain architecture of sustained attention and working memory in youth and adults. PLOS Biology 2022, 20: e3001938. PMID: 36542658, PMCID: PMC9815648, DOI: 10.1371/journal.pbio.3001938.Peer-Reviewed Original ResearchConceptsSustained attentionFunctional brain architectureWM performanceIndividual differencesFunctional connectivityBrain architectureLater recognition memoryRecognition memoryWM modelBrain networksWMMemoryYouthAge groupsFunctional connectionsAdultsChildrenNetwork predictorAttentionPerformancePredictorsDifferencesConnectivityAbilityFunctional connectome stability and optimality are markers of cognitive performance
Corriveau A, Yoo K, Kwon Y, Chun M, Rosenberg M. Functional connectome stability and optimality are markers of cognitive performance. Cerebral Cortex 2022, 33: 5025-5041. PMID: 36408606, PMCID: PMC10110430, DOI: 10.1093/cercor/bhac396.Peer-Reviewed Original ResearchConceptsFunctional connectivityAspects of attentionConnectivity patternsFunctional connectivity patternsAttentional abilitiesMemory taskCognitive tasksIndividual differencesCognitive performanceInitial evidenceCognitionIndependent samplesTaskFunctional connectionsConnectomeIndividualsConnectivityAttentionDistinctive patternsOptimal patternAbilityPrevious workPatterns
2015
A neuromarker of sustained attention from whole-brain functional connectivity
Rosenberg MD, Finn ES, Scheinost D, Papademetris X, Shen X, Constable RT, Chun MM. A neuromarker of sustained attention from whole-brain functional connectivity. Nature Neuroscience 2015, 19: 165-171. PMID: 26595653, PMCID: PMC4696892, DOI: 10.1038/nn.4179.Peer-Reviewed Original ResearchFunctional connectome fingerprinting: identifying individuals using patterns of brain connectivity
Finn ES, Shen X, Scheinost D, Rosenberg MD, Huang J, Chun MM, Papademetris X, Constable RT. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nature Neuroscience 2015, 18: 1664-1671. PMID: 26457551, PMCID: PMC5008686, DOI: 10.1038/nn.4135.Peer-Reviewed Original Research
2014
Neural portraits of perception: Reconstructing face images from evoked brain activity
Cowen AS, Chun MM, Kuhl BA. Neural portraits of perception: Reconstructing face images from evoked brain activity. NeuroImage 2014, 94: 12-22. PMID: 24650597, PMCID: PMC4028096, DOI: 10.1016/j.neuroimage.2014.03.018.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultBiometryBrainBrain MappingComputer SimulationData Interpretation, StatisticalEvoked Potentials, VisualFaceFemaleHumansImage Interpretation, Computer-AssistedMagnetic Resonance ImagingMaleModels, NeurologicalModels, StatisticalNerve NetPattern Recognition, VisualReproducibility of ResultsSensitivity and SpecificityStatistics as TopicConceptsBrain activityHigher-level cortical areasOccipital cortexHigher-level cortical regionsEarly visual cortexFace imagesTraining facesIndividual face imagesFace perceptionPerceptual informationTest faceVisual experienceVisual inputDistributed patternNeural activityCortical networksRecent neuroimaging advancesCortical regionsVisual cortexCortical areasNeuroimaging advancesRetinotopic mappingNeural reconstructionValidation measuresCortex
2002
The dark side of visual attention
Chun M, Marois R. The dark side of visual attention. Current Opinion In Neurobiology 2002, 12: 184-189. PMID: 12015235, DOI: 10.1016/s0959-4388(02)00309-4.Peer-Reviewed Original ResearchConceptsVisual attentionNon-visual factorsDark sideAttentional deploymentEmotional salienceNeural correlatesVisual eventsNeural processingVisual processingUnattended eventsNeural mechanismsFocal processingNumber of objectsBehavioral setsBright sidePast experienceFunctional blindnessProcessingAttentionSalienceLimited capacityRecent workCorrelatesExperienceObjects
1996
Functional imaging of human visual recognition
Kanwisher N, Chun M, McDermott J, Ledden P. Functional imaging of human visual recognition. Brain Research 1996, 5: 55-67. PMID: 9049071, DOI: 10.1016/s0926-6410(96)00041-9.Peer-Reviewed Original Research