Changes in inhibition-related brain function and psychological flexibility during smoking abstinence predict longer time to relapse

Abstract

Despite substantial health benefits, smoking cessation attempts have high relapse rates. Neuroimaging measures can sometimes predict individual differences in substance use phenotypes – including relapse – better than behavioral metrics alone. No study to date has compared the relative prediction ability of changes in psychological processes across prolonged abstinence with corresponding changes in brain activity. Here, in a longitudinal design, measurements were made one day prior to smoking cessation, and at 1 and 4 weeks post-cessation (total n=120). Next, we tested the relative role of changes in psychosocial variables vs. task-based functional brain measures predicting time to nicotine relapse up to 12 months. Abstinence was bioverified 4-5 times during the first month. Data were analyzed with a novel machine learning approach to predict relapse. Results showed that increased electrophysiological brain activity during inhibitory control predicted longer time-to-relapse (c-index=0.56). However, reward-related brain activity was not predictive (c-index=0.45). Psychological variables, notably an increase during abstinence in psychological flexibility when experiencing negative smoking-related sensations, predicted longer time-to-relapse (c-index=0.63). A model combining psychosocial and brain data was predictive (c-index=0.68). Using a best-practice approach, we demonstrated generalizability of the combined model on a previously unseen holdout validation dataset (c-index=0.59 vs. 0.42 for a null model). These results show that changes during abstinence – increased smoking-specific psychological flexibility and increased inhibitory control brain function – are important in predicting time to relapse from smoking cessation. In the future, monitoring and augmenting changes in these variables could help improve the chances of successful nicotine smoking abstinence.

A scoping review of electroencephalographic (EEG) markers for tracking neurophysiological changes and predicting outcomes in substance use disorder treatment

“…The same study also noted increased right central P3 (failed stop trials) and decreased frontal and left posterior late P3 (correct stop trials) from T1 to T3 (4 weeks) that predicted longer time to relapse (Lespine et al, 2022 ). However, the study reported that the EEG metrics were weak predictors of time to relapse relative to psychological and affective state variables (Lespine et al, 2022 ).…”

“…The same study also showed trends for associations of smaller Pe with relapse as well as lower ERN with smoking behavior. A third report, using a stop-signal task, indicated faster left fronto-central N2 latency, higher fronto-lateral P3, and lower left central and right frontal P3 elicited by correct stop trials at T1 (pre-quit baseline) predicted longer time to relapse over a year (Lespine et al, 2022 ). The same study also noted increased right central P3 (failed stop trials) and decreased frontal and left posterior late P3 (correct stop trials) from T1 to T3 (4 weeks) that predicted longer time to relapse (Lespine et al, 2022 ).…”

“…A third report, using a stop-signal task, indicated faster left fronto-central N2 latency, higher fronto-lateral P3, and lower left central and right frontal P3 elicited by correct stop trials at T1 (pre-quit baseline) predicted longer time to relapse over a year (Lespine et al, 2022 ). The same study also noted increased right central P3 (failed stop trials) and decreased frontal and left posterior late P3 (correct stop trials) from T1 to T3 (4 weeks) that predicted longer time to relapse (Lespine et al, 2022 ). However, the study reported that the EEG metrics were weak predictors of time to relapse relative to psychological and affective state variables (Lespine et al, 2022 ).…”