Electrophysiological responses to emotional and cocaine cues reveal individual neuroaffective profiles in cocaine users.
Heather E SoderConstanza de DiosMargaret C WardleRobert SuchtingCharles E GreenJoy M SchmitzScott D LaneFrancesco VersacePublished in: Experimental and clinical psychopharmacology (2021)
Smokers with stronger neuroaffective responses to drug-related cues compared to nondrug-related pleasant images (C > P) are more vulnerable to compulsive smoking than individuals with the opposite brain reactivity profile (P > C). However, it is unknown if these neurobehavioral profiles exist in individuals abusing other drugs. We tested whether individuals with cocaine use disorder (CUD) show similar neuroaffective profiles to smokers. We also monitored eye movements to assess attentional bias toward cues and we further performed exploratory analyses on demographics, personality, and drug use between profiles. Participants with CUD (n = 43) viewed pleasant, unpleasant, cocaine, and neutral images while we recorded electroencephalogram. For each picture category, we computed the amplitude of the late positive potential (LPP), an event-related potential component that reflects motivational relevance. k-means clustering classified participants based on their LPP responses. In line with what has been observed in smokers, clustering participants using LPP responses revealed the presence of two groups: one with larger LPPs to pleasant images compared to cocaine images (P > C) and one group with larger LPPs to cocaine images compared to pleasant images (C > P). Individuals with the C > P reactivity profile also had higher attentional bias toward drug cues. The two groups did not differ on demographic and drug use characteristics, however individuals with the C > P profile reported lower distress tolerance, higher anhedonia, and higher posttraumatic stress symptoms compared to the P > C group. This is the first study to report the presence of these neuroaffective profiles in individuals with CUD, indicating that this pattern may cut across addiction populations. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Keyphrases
- deep learning
- convolutional neural network
- optical coherence tomography
- smoking cessation
- prefrontal cortex
- working memory
- magnetic resonance imaging
- drug induced
- physical activity
- machine learning
- white matter
- risk assessment
- rna seq
- computed tomography
- genetic diversity
- depressive symptoms
- sleep quality
- human health