Toward Precision Medicine for Smoking Cessation: Developing a Neuroimaging-Based Classification Algorithm to Identify Smokers at Higher Risk for Relapse.
Introduction: By improving our understanding of the neurobiological mechanisms underlying addiction, neuroimaging research is helping to identify new targets for personalized treatment interventions. When trying to quit, smokers with larger electrophysiological responses to cigarette-related, compar...
| Publicado en: | Nicotine & Tobacco Research Vol. 22; no. 8; pp. 1277 - 1285 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Aug2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144614072&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144614072 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14622203 57O jtl: Nicotine & Tobacco Research issn: 14622203 maglogo: N pubinfo: dt: Aug2020 vid: 22 iid: 8 pid: 622 pub: Oxford University Press / USA artinfo: ui: 144614072 144614072 NLM31724052 144614072 10.1093/ntr/ntz211 NLM31724052 144614072 ppf: 1277 ppct: 8 formats: tig: atl: Toward Precision Medicine for Smoking Cessation: Developing a Neuroimaging-Based Classification Algorithm to Identify Smokers at Higher Risk for Relapse. aug: au: Frank, David W Cinciripini, Paul M Deweese, Menton M Karam-Hage, Maher Kypriotakis, George Lerman, Caryn Robinson, Jason D Tyndale, Rachel F Vidrine, Damon J Versace, Francesco affil: Department of Behavioral Science, University of Texas MD Anderson Cancer Center , Houston, TX sug: subj: Algorithms Smoking Cessation Neuroradiography Methods Substance Use Disorders Diagnosis Male Female Recurrence Human United States Substance Use Disorders Psychosocial Factors Substance Use Disorders Epidemiology Middle Age Validation Studies Comparative Studies Evaluation Research Multicenter Studies Scales Middle Aged: 45-64 years Male Female ab: Introduction: By improving our understanding of the neurobiological mechanisms underlying addiction, neuroimaging research is helping to identify new targets for personalized treatment interventions. When trying to quit, smokers with larger electrophysiological responses to cigarette-related, compared with pleasant, stimuli ("C > P") are more likely to relapse than smokers with the opposite brain reactivity profile ("P > C").Aim and Method: The goal was to (1) build a classification algorithm to identify smokers characterized by P > C or C > P neuroaffective profiles and (2) validate the algorithm's classification outcomes in an independent data set where we assessed both smokers' electrophysiological responses at baseline and smoking abstinence during a quit attempt. We built the classification algorithm applying discriminant function analysis on the event-related potentials evoked by emotional images in 180 smokers.Results: The predictive validity of the classifier showed promise in an independent data set that included new data from 177 smokers interested in quitting; the algorithm classified 111 smokers as P > C and 66 as C > P. The overall abstinence rate was low; 15 individuals (8.5% of the sample) achieved CO-verified 12-month abstinence. Although individuals classified as P > C were nearly 2.5 times more likely to be abstinent than smokers classified as C > P (12 vs. 3, or 11% vs. 4.5%), this result was nonsignificant, preliminary, and in need of confirmation in larger trials.Conclusion: These results suggest that psychophysiological techniques have the potential to advance our knowledge of the neurobiological underpinnings of nicotine addiction and improve clinical applications. However, larger sample sizes are necessary to reliably assess the predictive ability of our algorithm.Implications: We assessed the clinical relevance of a neuroimaging-based classification algorithm on an independent sample of smokers enrolled in a smoking cessation trial and found those with the tendency to attribute more relevance to rewards than cues were nearly 2.5 times more likely to be abstinent than smokers with the opposite brain reactivity profile (11% vs. 4.5%). Although this result was not statistically significant, it suggests our neuroimaging-based classification algorithm can potentially contribute to the development of new precision medicine interventions aimed at treating substance use disorders. Regardless, these findings are still preliminary and in need of confirmation in larger trials. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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