Toward Precision Medicine for Smoking Cessation: Developing a Neuroimaging-Based Classification Algorithm to Identify Smokers at Higher Risk for Relapse | Zendy

David W. Frank | Zendy; Paul M. Cinciripini | Zendy; Menton M. Deweese | Zendy; Maher KaramHage | Zendy; George Kypriotakis | Zendy; Caryn Lerman | Zendy; Jason D. Robinson | Zendy; Rachel F. Tyndale | Zendy; Damon J. Vidrine | Zendy; Francesco Versace | Zendy

Open Access

Toward Precision Medicine for Smoking Cessation: Developing a Neuroimaging-Based Classification Algorithm to Identify Smokers at Higher Risk for Relapse

Author(s) -

David W. Frank,

Paul M. Cinciripini,

Menton M. Deweese,

Maher KaramHage,

George Kypriotakis,

Caryn Lerman,

Jason D. Robinson,

Rachel F. Tyndale,

Damon J. Vidrine,

Francesco Versace

Publication year - 2019

Publication title -

nicotine and tobacco research

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.338

H-Index - 113

eISSN - 1469-994X

pISSN - 1462-2203

DOI - 10.1093/ntr/ntz211

Subject(s) - abstinence , neuroimaging , smoking cessation , addiction , medicine , clinical psychology , discriminant function analysis , psychological intervention , psychiatry , psychology , algorithm , machine learning , pathology , computer science

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").

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research