Classifying and characterizing nicotine use disorder with high accuracy using machine learning and resting-state fMRI.

ABSTRACT: Cigarette smoking continues to be a leading cause of preventable morbidity and mortality. Although the majority of smokers report making a quit attempt in the past year, smoking cessation rates remain modest. Thus, developing accurate, data-driven methods that can classify and characterize the neural features of nicotine use disorder (NUD) would be a powerful clinical tool that could aid in optimizing treatment development and guide treatment modifications. This investigation applied support vector machine-based classification to resting-state functional connectivity (rsFC) data from individuals diagnosed with NUD (n = 108; 63 male) and matched nonsmoking controls (n = 108; 63 male) and multi-dimensional scaling to visualize the heterogeneity of NUD in individual smokers based on rsFC meas

SUBMITTER: Wetherill RR 

PROVIDER: S-EPMC6310107 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature