Brain Resting-State Salience and Executive Network Connectivity Predictors of Smoking Progression, Nicotine-Enhanced Reward Sensitivity, and Depression,

Author

Matthew Phillip Gunn, Southern Illinois University CarbondaleFollow

Date of Award

8-1-2024

Degree Name

Doctor of Philosophy

Department

Psychology

First Advisor

Habib, Reza

Second Advisor

Gilbert, David

Abstract

The study’s objective was to assess whether resting-state regional functional connectivity and current source density (CSD) measured during smoking abstinence predict smoking progression across 18 months, depressive traits, and nicotine-enhanced reward sensitivity (NERS) in young light-nicotine (NIC) smokers using low-resolution brain electromagnetic tomography analysis (LORETA). A secondary goal was to assess whether depressive traits moderate the ability of connectivity and regional CSD to predict NERS. Brain regions of interest (ROIs) hypothesized to predict smoking progression, NERS, and depressive traits include structures with high-density nicotinic acetylcholine receptors (nAChRs) and reward-related areas. A total of N=108, 14-hour NIC-deprived young (age 18-24) light (5-35 NIC uses/week) smokers underwent electroencephalogram (EEG) recording while at rest (i.e., viewed a white crosshair on a black background) for 8 minutes then completed the PRT, an assessment of reward sensitivity, after smoking a placebo (0.05 mg NIC) and NIC (0.8 mg NIC) cigarette using a within-subjects design allowing for the assessment of NIC-induced changes in reward sensitivity. All EEG power and LORETA activity bands underwent regression analysis to discover if EEG-assessed brain activity can predict smoking progression, depressive traits, NERS, and their potential interaction. Localized brain regions include 1) reward-related structures, 2) depressive trait-related structures, and 3) large-scale neural (e.g., salience network (SN), default mode network (DMN), executive control network (ECN)) and substance use disorder networks (e.g., orbital frontal cortex (OFC), insula, dorsal lateral prefrontal cortex (dlPFC) anterior cingulate cortex (ACC)). Weaker resting-state connectivity (rsC) between the insula and ACC (i.e., SN) predicted greater smoking progression at 18 months (theta1 and theta2) and greater depressive traits (delta and theta1), while greater rsC within the SN predicted greater NERS (alpha2 and beta 2/3[23.19 – 25.14 Hz]). Greater NERS was also predicted by greater alpha2 connectivity between the 1) ACC and posterior cingulate cortex (PCC) and 2) ACC and left dlPFC. Greater depressive traits were also predicted by 1) weaker delta and theta2 connectivity between the bilateral insula, 2) weaker delta, theta1, and theta2 between the insula and dlPFC, 3) weaker delta and theta1 between the insula and subgenual cortex, 4) greater theta2 in the right vs. left default mode, and 5) greater delta (2.44 – 3.41 Hz) in the left vs. right default mode network. Both greater depressive traits and greater NERS were predicted by weaker 1) theta2/alpha1 (6.59 – 9.52 Hz) between the insula and dlPFC and 2) alpha1 (7.5 – 9.5 Hz) between the left orbital frontal cortex and right dlPFC. These findings provide the first evidence that differences in EEG-assessed brain connectivity in young light smokers are associated with nicotine-enhanced reward sensitivity, depressive traits, and smoking progression. Notably, weaker low-frequency rsC within the salience network predicted depressive traits and smoking progression, while greater high-frequency rsC predicted greater nicotine-enhanced reward sensitivity. These findings suggest that salience network rsC and drug-enhanced reward sensitivity may be useful tools and potential endophenotypes for reward sensitivity and drug-dependence research.