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Changes in distinct brain systems identified with fMRI during smoking cessation treatment with varenicline: a review.

Psychopharmacology 2024 March 3
BACKGROUND: Varenicline is considered one of the most effective treatment options for smoking cessation. Nonetheless, it is only modestly effective. A deeper comprehension of the effects of varenicline by means of the in-depth review of relevant fMRI studies may assist in paving the development of more targeted and effective treatments.

METHODOLOGY: A search of PubMed and Google Scholar databases was conducted with the keywords "functional magnetic resonance imaging" or "fMRI", and "varenicline". All peer-reviewed articles regarding the assessment of smokers with fMRI while undergoing treatment with varenicline and meeting the predefined criteria were included.

RESULTS: Several studies utilizing different methodologies and targeting different aspects of brain function were identified. During nicotine withdrawal, decreased mesocorticolimbic activity and increased amygdala activity, as well as elevated amygdala-insula and insula-default-mode-network functional connectivity are alleviated by varenicline under specific testing conditions. However, other nicotine withdrawal-induced changes, including the decreased reward responsivity of the ventral striatum, the bilateral dorsal striatum and the anterior cingulate cortex are not influenced by varenicline suggesting a task-dependent divergence in neurocircuitry activation. Under satiety, varenicline treatment is associated with diminished cue-induced activation of the ventral striatum and medial orbitofrontal cortex concomitant with reduced cravings; during the resting state, varenicline induces activation of the lateral orbitofrontal cortex and suppression of the right amygdala.

CONCLUSIONS: The current review provides important clues with regard to the neurobiological mechanism of action of varenicline and highlights promising research opportunities regarding the development of more selective and effective treatments and predictive biomarkers for treatment efficacy.

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