Neural response to pictorial health warning labels can predict smoking behavioral change

• Published in: Social cognitive and affective neuroscience Vol. 11; no. 11; pp. 1802 - 1811
• Main Authors: Riddle, Jr, Philip J, Newman-Norlund, Roger D, Baer, Jessica, Thrasher, James F
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.11.2016
• Subjects: Adult; Amygdala - physiopathology; Biomarkers; Breath Tests; Carbon Monoxide - analysis; Female; Humans; Intention; Male; Nerve Net - physiopathology; Original; Pattern Recognition, Visual - physiology; Prefrontal Cortex - physiopathology; Product Labeling; Self Report; Smoking - physiopathology; Smoking - psychology; Smoking Cessation - psychology; Tobacco Products; fMRI; amygdala; pictorial health warning labels (HWLs); ventromedial prefrontal cortex (vmPFC); smoking
• ISSN: 1749-5016; 1749-5024
• DOI: 10.1093/scan/nsw087

In order to improve our understanding of how pictorial health warning labels (HWLs) influence smoking behavior, we examined whether brain activity helps to explain smoking behavior above and beyond self-reported effectiveness of HWLs. We measured the neural response in the ventromedial prefrontal cortex (vmPFC) and the amygdala while adult smokers viewed HWLs. Two weeks later, participants' self-reported smoking behavior and biomarkers of smoking behavior were reassessed. We compared multiple models predicting change in self-reported smoking behavior (cigarettes per day [CPD]) and change in a biomarkers of smoke exposure (expired carbon monoxide [CO]). Brain activity in the vmPFC and amygdala not only predicted changes in CO, but also accounted for outcome variance above and beyond self-report data. Neural data were most useful in predicting behavioral change as quantified by the objective biomarker (CO). This pattern of activity was significantly modulated by individuals' intention to quit. The finding that both cognitive (vmPFC) and affective (amygdala) brain areas contributed to these models supports the idea that smokers respond to HWLs in a cognitive-affective manner. Based on our findings, researchers may wish to consider using neural data from both cognitive and affective networks when attempting to predict behavioral change in certain populations (e.g. cigarette smokers).