2023
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 identificationCross 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 Statements
2017
Network Analysis of Intrinsic Functional Brain Connectivity in Male and Female Adult Smokers: A Preliminary Study
Maria M, Vanderweyen D, Camp C, Zhu X, McKee SA, Cosgrove KP, Hartwell KJ, Brady KT, Joseph JE. Network Analysis of Intrinsic Functional Brain Connectivity in Male and Female Adult Smokers: A Preliminary Study. Nicotine & Tobacco Research 2017, 20: 810-818. PMID: 29059410, PMCID: PMC5991199, DOI: 10.1093/ntr/ntx206.Peer-Reviewed Original ResearchConceptsFemale smokersMale smokersAdult smokersNicotine dependenceSalience network regionsIntrinsic functional brain connectivityFemale adult smokersHigh nicotine dependenceEffects of nicotineAge-matched controlsGraph theory measuresFunctional brain connectivityIntrinsic functional connectivitySex differencesSmoking interventionsNucleus accumbensRight insulaSmokersBrain network communicationFunctional connectivityBrain connectivityBlood oxygenFunctional magnetic resonance imaging (fMRI) experimentsConnector hubsBrain networks
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