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Electrophysiological signature of the interplay between habits and inhibition in response to smoking-related cues in individuals with a smoking habit: an Event-Related Potential study.

Julien DampuréPaola Agudelo-OrjuelaMaartje van der MeijDavid BelinHoracio A Barber
Published in: The European journal of neuroscience (2023)
The rigid, stimulus-bound nature of drug seeking that characterizes substance use disorder (SUD) has been related to a dysregulation of motivational and early attentional reflexive and inhibitory reflective systems. However, the mechanisms by which these systems are engaged by drug-paired conditioned stimuli CSs) when they promote the enactment of seeking habits in individuals with a SUD have not been elucidated. The present study aimed behaviorally and electrophysiologically to characterize the nature of the interaction between the reflexive and reflective systems recruited by CSs in individuals with a smoking habit. We measured the behavioral performance and associated Event Related Potentials (ERPs) of 20 individuals with a smoking habit and 20 controls, who never smoked regularly, in a modified Go/NoGo task during which smoking-related CSs, appetitive, and neutral pictures, presented either in first-person or as a third-person visual perspective were displayed 250 ms before the Go/NoGo cue. We show that smoking-related cues selectively influence early incentive motivation-related attentional bias (N2 after picture onset), motor readiness and behavioral inhibition (Go-P3, NoGo-P3 and Pc) of individuals with a smoking habit only when presented from a first-person perspective. These data together identify the neural signature of the aberrant engagement of the reflexive and reflective systems during the recruitment of an incentive habit by CSs presented as if they had been response-produced, i.e., as conditioned reinforcers.
Keyphrases
  • smoking cessation
  • mental health
  • ms ms
  • emergency department
  • multiple sclerosis
  • drug induced
  • climate change
  • social media
  • electronic health record
  • artificial intelligence