Chris Camp
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Featured Publications
Test-Retest Reliability of Functional Connectivity in Adolescents With Depression
Camp C, Noble S, Scheinost D, Stringaris A, Nielson D. Test-Retest Reliability of Functional Connectivity in Adolescents With Depression. Biological Psychiatry Cognitive Neuroscience And Neuroimaging 2023, 9: 21-29. PMID: 37734478, PMCID: PMC10843837, DOI: 10.1016/j.bpsc.2023.09.002.Peer-Reviewed Original ResearchConceptsMajor depressive disorderIntraclass correlation coefficientTest-retest reliabilityPsychiatric illnessFunctional connectivityMean intraclass correlation coefficientFunctional magnetic resonance imagingMagnetic resonance imagingAverage intraclass correlation coefficientEffect sizeDepressive disorderLongitudinal cohortHealthy individualsMultivariate analysisResonance imagingSymptom severityReproducible biomarkersBrain-behavior associationsGroup differencesDepressionHealthy samplesCorrelation coefficientIllnessAdolescentsBiomarker identification
2026
External validation improves generalizability, replicability and reproducibility in predictive models for neuroimaging
Rosenblatt M, Foster M, Adkinson B, Tejavibulya L, Khaitova M, Ye J, Sun H, Rodriguez R, Camp C, Chinta A, McCusker M, Han L, Fields C, Mehta S, Scheinost D. External validation improves generalizability, replicability and reproducibility in predictive models for neuroimaging. Nature Methods 2026, 1-11. PMID: 42203860, DOI: 10.1038/s41592-026-03115-9.Peer-Reviewed Original ResearchWhat effect sizes can we expect in functional neuroimaging?
Shearer H, Rosenblatt M, Ye J, Jiang R, Tejavibulya L, Foster M, Liang Q, Dadashkarimi J, Westwater M, Cheng I, Rolison M, Peterson H, Adkinson B, Mehta S, Camp C, Fischbach A, Cravo F, Meija A, Nichols T, Curtiss J, Scheinost D, Noble S. What effect sizes can we expect in functional neuroimaging? JoCN Forum 2026 DOI: 10.21428/8e6ba8ef.af9f794c.Peer-Reviewed Original Research
2025
BrainEffeX: A Web App for Exploring fMRI Effect Sizes
Shearer H, Rosenblatt M, Ye J, Jiang R, Tejavibulya L, Foster M, Liang Q, Dadashkarimi J, Westwater M, Cahill C, Cheng I, Fischbach A, Humphries A, Baskaran A, Rolison M, Peterson H, Adkinson B, Mehta S, Camp C, Nichols T, Curtiss J, Scheinost D, Noble S. BrainEffeX: A Web App for Exploring fMRI Effect Sizes. Aperture Neuro 2025, 5: 10.52294/001c.146251. PMID: 41675933, PMCID: PMC12889895, DOI: 10.52294/001c.146251.Peer-Reviewed Original ResearchConnectome caricatures remove large-amplitude coactivation patterns in resting-state fMRI to emphasize individual differences
Rodriguez R, Noble S, Camp C, Scheinost D. Connectome caricatures remove large-amplitude coactivation patterns in resting-state fMRI to emphasize individual differences. Nature Neuroscience 2025, 28: 2601-2611. PMID: 41184631, PMCID: PMC12641774, DOI: 10.1038/s41593-025-02099-7.Peer-Reviewed Original ResearchConceptsFunctional magnetic resonance imagingResting-state functional magnetic resonance imagingResting-state dataCoactivation patternsFunctional connectivityResting-state signalsBrain's intrinsic functional architectureResting-state functional connectivityIntrinsic functional architectureTask activation patternsTask fMRI dataHigh identifiersFMRI dataLarge-scale neuroimaging datasetsConnectomeFunctional architectureNeuroimaging datasetsActivity patternsTask dataMagnetic resonance imagingTaskResonance imagingPhenotypic measurementsConnectome caricatures: large-amplitude co-activation patterns in resting-state fMRI hide sources of individual differences
Rodriguez R, Noble S, Camp C, Scheinost D. Connectome caricatures: large-amplitude co-activation patterns in resting-state fMRI hide sources of individual differences. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2025 DOI: 10.58530/2025/1347.Peer-Reviewed Original ResearchSource of individual differencesCo-activation patternsResting-state fMRIIndividual differencesFunctional connectivityBrain's intrinsic functional architectureResting-state functional connectivityResting-state signalsIntrinsic functional architectureFMRI dataCarrying informationFMRICo-activationFunctional architectureConnectome
2024
Power and reproducibility in the external validation of brain-phenotype predictions
Rosenblatt M, Tejavibulya L, Sun H, Camp C, Khaitova M, Adkinson B, Jiang R, Westwater M, Noble S, Scheinost D. Power and reproducibility in the external validation of brain-phenotype predictions. Nature Human Behaviour 2024, 8: 2018-2033. PMID: 39085406, DOI: 10.1038/s41562-024-01931-7.Peer-Reviewed Original ResearchHuman Connectome ProjectAdolescent Brain Cognitive Development StudyConnectome ProjectCognitive Development StudyPhiladelphia Neurodevelopmental CohortHealthy Brain NetworkStructural connectivity dataMatrix ReasoningWorking memoryAnxiety/depression symptomsAttention problemsNeurodevelopmental CohortBrain networksBrain-phenotype associationsEffect sizeConnectivity dataExternal validationRelated processesValidation studySample sizeBrain ProjectDevelopment studiesTraining sample sizeGeneralizability of modelsExternal samples
2023
Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for different atlases when raw data is not available
Dadashkarimi J, Karbasi A, Liang Q, Rosenblatt M, Noble S, Foster M, Rodriguez R, Adkinson B, Ye J, Sun H, Camp C, Farruggia M, Tejavibulya L, Dai W, Jiang R, Pollatou A, Scheinost D. Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for different atlases when raw data is not available. Medical Image Analysis 2023, 88: 102864. PMID: 37352650, PMCID: PMC10526726, DOI: 10.1016/j.media.2023.102864.Peer-Reviewed Original ResearchConceptsDifferent atlasesRaw data accessWeb applicationData accessOpen source dataSource codePatient privacyOptimal transportRaw dataStorage concernsLarge-scale data collection effortsOriginal counterpartsExtensive setData collection effortsProcessing effortPredictive modelNeuroimaging dataDownstream analysisPrivacyAtlasesCollection effortsComputationalTime seriesDatasetConnectome
2022
Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer
Scheinost D, Pollatou A, Dufford A, Jiang R, Farruggia M, Rosenblatt M, Peterson H, Rodriguez R, Dadashkarimi J, Liang Q, Dai W, Foster M, Camp C, Tejavibulya L, Adkinson B, Sun H, Ye J, Cheng Q, Spann M, Rolison M, Noble S, Westwater M. Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer. Biological Psychiatry 2022, 93: 893-904. PMID: 36759257, PMCID: PMC10259670, DOI: 10.1016/j.biopsych.2022.10.014.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsMood and Behaviors of Adolescents With Depression in a Longitudinal Study Before and During the COVID-19 Pandemic
Sadeghi N, Fors P, Eisner L, Taigman J, Qi K, Gorham L, Camp C, O'Callaghan G, Rodriguez D, McGuire J, Garth E, Engel C, Davis M, Towbin K, Stringaris A, Nielson D. Mood and Behaviors of Adolescents With Depression in a Longitudinal Study Before and During the COVID-19 Pandemic. Journal Of The American Academy Of Child And Adolescent Psychiatry 2022, 61: 1341-1350. PMID: 35452785, PMCID: PMC9015953, DOI: 10.1016/j.jaac.2022.04.004.Peer-Reviewed Original ResearchConceptsCoRonavIruS Health Impact SurveyAnxiety symptomsAnxiety ratingsDepressive symptomsDepression ratesDepression scoresHealthy volunteersTreatment of adolescent depressionLower anxiety symptomsPre-pandemic levelsSelf-report measuresTreatment of depressionPre-pandemicHealth Impact SurveyParent-reported behaviorSeverity of scoresAdolescent depressionLongitudinal case-control studyPre-pandemic ratesOutpatient sessionsNational InstituteCase-control studyFollow-up visitAnxietyDepression
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