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 ResearchMeSH KeywordsAdultCentral Nervous System StimulantsCerebral CortexCorpus StriatumFemaleGyrus CinguliHumansMachine LearningMagnetic Resonance ImagingMaleMiddle AgedNeural PathwaysSubstance-Related DisordersConceptsFunctional 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