Inter-Network FC Associations with Nicotine Dependence in Vaping as a Function of Craving State
Poster No:
1468
Submission Type:
Abstract Submission
Authors:
Somin Kim1, Jiaying Liu2, Colleen Markey1, Lawrence Sweet1
Institutions:
1University of Georgia, Athens, GA, 2University of California Santa Barbara, Santa Barabara, CA
First Author:
Co-Author(s):
Introduction:
As vaping has become the most common form of tobacco use among young adults, there is a growing need of research to understand neurobiological underpinnings of addiction in vaping. Nicotine addiction has been often associated with disruptions in large-scale networks (Weiland et al., 2014). Resting-state functional connectivity (FC) of large-brain networks has shown potential as a biomarker of addiction severity due to its ability to reflect intrinsic functional organization of the brain (Fedota et al., 2015). FC between the salience (SN), default mode (DMN) and executive control (ECN) networks appear to be disrupted as individuals experience addiction and withdrawal (Sutherland et al., 2012). To apply this theory to the vaping population, this study examined whether nicotine dependence predicts inter-network FC, depending on the intensity of their craving for vaping.
Methods:
Resting-state functional MRI (fMRI) data were obtained from 67 young adult nonsmokers who vaped on more than 15 out of the past 30 days (Mean age = 20 yrs; 46 women). 10min echoplanar resting-state scans were acquired prior to 35min of three cue reactivity fMRI paradigms. Participants completed the Penn State Nicotine Dependence Index (PSNDI; Foulds et al., 2015) and reported on a 1-10 scale how much they wanted to vape. Change in craving was calculated by subtracting pre from post cue reactivity ratings. A 3T MRI scanner with a 32 channel head coil acquired data with a temporal resolution of 2s and a spatial resolution of 3.5mm³. Preprocessing followed an afni_proc.py pipeline that included registration, censoring volumes (i.e., movement, outliers), 5mm 3D spatial smoothing, stereotaxic standardization and regression to remove effects of low frequency signal and movement. For each network, a priori seed region FC analyses were conducted using individual residual fMRI signal maps. The bilateral anterior insula, posterior cingulate gyrus, and middle frontal gyrus were used as 5mm radius seed regions for SN, DMN, and ECN (Table 1). Pearson's correlations with each seed regions' mean time series were calculated for each voxel. After r-to-z transformation, the 10 most robust >10 voxel clusters were selected by peak z-value to define target regions of interests (ROIs) for each network (Table 1). To create target ROIs, 5mm radius spheres were drawn around peak values. For each individual, mean z-values were then extracted from the 10 target ROIs of each network for each seed region map. The resulting six inter-network FC values were used to test hypotheses. Multiple regression models were used to examine the effect of nicotine dependence and the amount of craving on inter-network FC.
Results:
PSNDI was significantly associated with FC between the DMN and SN and the DMN and ECN, as a function of craving difference (Table 2). However, these effects were not observed between ECN and SN. Participants were divided into no/low and medium/high dependence groups based on their PSNDI scores (cutoff = 9) for post-hoc analyses. In high dependence group, increased craving predicted the DMN-SN coupling (β = .38, r = .45, p = .02). This was not significant in low dependence group (β = -.03, r = -.12 p = .89).
Conclusions:
Findings suggest that vaping addiction severity is associated with greater FC between DMN and SN, and DMN and ECN as craving increases. This aligns with the theory that the DMN–SN connectivity may reflect withdrawal during abstinence as vapers direct their attention to internal craving state (Craig, 2009). The results imply that FC patterns in large-brain networks observed in traditional smokers, regarding withdrawal status, can also be found in vapers. Importantly, the association between resting-state FC and nicotine dependence was actively moderated by state of craving intensity. These findings could contribute to the understanding the neural mechanisms underlying addictive vaping and may inform interventions for reducing addictive behaviors.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Addictions
ADULTS
FUNCTIONAL MRI
Other - Resting state network
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
Fedota, J. R. & Stein, E. A. (2015). Resting-state functional connectivity and nicotine addiction: prospects for biomarker development. Annals of the New York Academy of Sciences, 1349, 64-82.
Foulds, J., Veldheer, S., Yingst, J., Hrabovsky, S., Wilson, S. J., Nichols, T. T., Eissenberg, T. (2015). Development of a questionnaire for assessing dependence on electronic cigarettes among a large sample of ex-smoking E-cigarette users. Nicotine & Tobacco Research: official journal of the Society for Research on Nicotine and Tobacco,17(2), 186-192.
Sutherland, M. T., McHugh, M. J., Pariyadath, V., & Stein, E. A. (2012). Resting state functional connectivity in addiction: lessons learned and a road ahead. Neuroimage, 62, 1-15.
Weiland, B. J. Sabbineni, A., Calhoun, V. D., Welsh, R. C., & Hutchison, K. E. (2014). Reduced executive and default network functional connectivity in cigarette smokers. Human Brain Mapping, 36(3), 872-882.