2017
Machine Learning of Functional Magnetic Resonance Imaging Network Connectivity Predicts Substance Abuse Treatment Completion
Steele VR, Maurer JM, Arbabshirani MR, Claus ED, Fink BC, Rao V, Calhoun VD, Kiehl KA. Machine Learning of Functional Magnetic Resonance Imaging Network Connectivity Predicts Substance Abuse Treatment Completion. Biological Psychiatry Cognitive Neuroscience And Neuroimaging 2017, 3: 141-149. PMID: 29529409, PMCID: PMC5851466, DOI: 10.1016/j.bpsc.2017.07.003.Peer-Reviewed Original ResearchConceptsFunctional magnetic resonanceSubstance abuse treatment programsAnterior cingulate cortexSubstance abuse treatmentSubstance abuse treatment completionCingulate cortexAbuse treatmentSubstance abuse treatment outcomesSubstance useTreatment programFNC analysisTreatment completionLong-term outcomesResponse inhibitionNeural network connectionsNetwork connectivity measuresClinical assessment measuresPositive outcomesSubstance abusersPattern classification modelAssessment measuresIllicit drug useIncarcerated participantsDepressive symptomatologyTreatment interventions
2015
Machine learning of structural magnetic resonance imaging predicts psychopathic traits in adolescent offenders
Steele VR, Rao V, Calhoun VD, Kiehl KA. Machine learning of structural magnetic resonance imaging predicts psychopathic traits in adolescent offenders. NeuroImage 2015, 145: 265-273. PMID: 26690808, PMCID: PMC4903946, DOI: 10.1016/j.neuroimage.2015.12.013.Peer-Reviewed Original ResearchConceptsLow psychopathic traitsPsychopathic traitsPersonality traitsNeural measuresElevated psychopathic traitsHigh psychopathic traitsVoxel-based morphometry dataNon-incarcerated youthSupport vector machine (SVM) learning modelStructural magnetic resonance imagingNeural correlatesAdolescent offendersAdolescent participantsGroup membershipClinical groupsHealthy controlsParalimbic systemNuanced modelsPersonality disorderFuture behaviorIncarcerated individualsYouthPsychopathyMachine learning modelsLearning model