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Published in final edited form as: Drug Alcohol Depend. 2021 Feb 13;221:108593. doi: 10.1016/j.drugalcdep.2021.108593

Smoking-induced craving relief relates to increased DLPFC-striatal coupling in nicotine-dependent women

Teresa R Franklin 1,*, Kanchana Jagannathan 1, Nathaniel H Spilka 1, Heather Keyser 1, Hengy Rao 2, Alice V Ely 1, Amy C Janes 3, Reagan R Wetherill 1
PMCID: PMC8026729  NIHMSID: NIHMS1678448  PMID: 33611027

Abstract

Background

Craving is a major contributor to drug-seeking and relapse. Although the ventral striatum (VS) is a primary neural correlate of craving, strategies aimed at manipulating VS function have not resulted in efficacious treatments. This incongruity may be because the VS does not influence craving in isolation. Instead, craving is likely mediated by communication between the VS and other neural substrates. Thus, we examined how striatal functional connectivity (FC) with key nodes of networks involved in addiction affects relief of craving, which is an important step in identifying viable treatment targets.

Methods

Twenty-four nicotine-dependent non-abstinent women completed two resting-state (rs) fMRI scans, one before and one following smoking a cigarette in the scanner, and provided craving ratings before and after smoking the cigarette. A seed-based approach was used to examine rsFC between the VS, putamen and germane craving-related brain regions; the dorsolateral prefrontal cortex (dlPFC), the posterior cingulate cortex, and the anterior ventral insula.

Results

Smoking a cigarette was associated with a decrease in craving. Relief of craving correlated with increases in right dlPFC- bilateral VS (r=0.57, p=0.003, corrected) as did increased right dlPFC-left putamen coupling (r=0.62, p=0.001, corrected).

Conclusions

Smoking-induced relief of craving is associated with enhanced rsFC between the dlPFC, a region that plays a pivotal role in decision making, and the striatum, the neural structure underlying motivated behavior. These findings are highly consistent with a burgeoning literature implicating dlPFC-striatal interactions as a neurobiological substrate of craving.

Keywords: craving, fMRI, cigarette smoking, ventral striatum, dorsolateral prefrontal cortex, putamen

1. Introduction

In the preclinical literature, the ventral striatum (VS) has been identified as the reward substrate of the brain (Di Chiara and Imperato, 1988) (Koob and Volkow, 2010), providing the rationale for the VS as a treatment target. Blunting neuronal activity in the VS has been suggested as a viable strategy to treat drug craving (Taylor and Robbins, 1986) (Feltenstein et al., 2007). However, such strategies have not translated well to the clinic (Pierce et al., 2012). One potential reason for this limited clinical efficacy is that the modulation of craving involves the complex interplay of multiple brain regions beyond the VS (T. Franklin et al., 2011; Gu et al., 2016; Ketcherside et al., 2020; Kober et al., 2010a, 2010b; Mondino et al., 2018; Tang et al., 2013; Volkow et al., 2010). For instance, several studies have pointed to a role of the insula in cigarette craving (Claus et al., 2013; Franklin et al., 2009, 2007; Janes et al., 2017, 2010; McBride et al., 2006); while other studies suggest a lack of top-down cortical control is important (Ketcherside et al., 2020; Kober et al., 2010a, 2010b). In addition, studies have even identified regions, such as the posterior cingulate cortex, as playing an important role in craving and relapse (Franklin et al., 2007; T. R. Franklin et al., 2011; Janes et al., 2019, 2010; Kosten et al., 2006). Given the numerous studies that have identified diverse craving correlates, it is conceivable that craving is modulated by functional connections among multiple neural networks that ultimately affect VS response. Therefore, it is plausible that craving could be effectively relieved by altering VS connectivity with germane craving-related brain regions. Identification of these circuits would be a first step toward this therapeutic goal.

The human brain is organized into synchronized functional interconnected regions (i.e., networks) and behavior reflects the interplay between the independently fluctuating networks (Fox et al., 2005). In the field of nicotine addiction, evidence suggests aberrant processing between the externally focused executive control network (ECN), the internally focused default mode network (DMN), and the salience network (SN) (Sutherland et al., 2012). The ECN is centered in the dorsolateral prefrontal cortex and the lateral posterior parietal cortex. ECN nodes show strong coactivation during cognitive tasks, specifically, externally-focused tasks that require sustained attention, working memory and response inhibition (Laird et al., 2011). The DMN is centered in the posterior cingulate cortex and contains nodes within the prefrontal cortex, angular gyri and parahippocampi. Regions of the DMN coactivate during internally-directed, self-referential mental processes (Laird et al., 2011). The SN is composed of the anterior ventral insula and anterior cingulate cortex and is involved in a wide range of behavioral tasks, and as such, the SN is considered a transitional network guiding neural network engagement to modulate behavior (Peters et al., 2016). Thus, the ECN and DMN interact with each other in a temporally anticorrelated fashion orchestrated by the SN. Nicotine appears to enhance ECN activity while suppressing DMN activity, resulting in improved performance on various tasks (Sutherland et al., 2012).

Numerous studies have examined resting state functional connectivity (rsFC) in nicotine addiction, contributing a wealth of knowledge on its neural basis (for a review, see Fedota and Stein, 2015); however, recent studies continue to improve our understanding (Avery et al., 2017; Bi et al., 2017; Perry et al., 2019; Sweitzer et al., 2016; Wang et al., 2020). For example, the anterior insula and anterior cingulate cortex (ACC) (i.e., nodes of the SN) have been shown to work in concert to enhance brain responses to smoking cues, a major relapse predictor (Janes et al., 2015). Further, the ACC/striatal circuit has been identified as predicting the strength of nicotine addiction; greater dependence severity is associated with reduced rsFC (Hong et al., 2009). Using a seed-based approach work by Falkner and colleagues found that smoking reduced craving and reduced rsFC between the anterior insula and anterior cingulate, and reductions in craving were correlated with reduced rsFC. Further, rsFC between the VS and the orbitofrontal cortex were reduced (i.e., reward and DMN nodes)(Faulkner et al., 2019). These focused seed-based approaches were conducted in smokers following overnight abstinence and contribute to our body of knowledge on nicotine dependence. However, little is known about how smoking affects rsFC in smokers who are not in withdrawal, yet this is a critical question given most smokers smoke and experience craving relief on much shorter time scales. Additionally, little is known about how all the three major networks (the DMN, SN, and ECN) interact with the reward network to reduce craving. Given our hypothesis that craving is likely mediated by multiple inputs to the VS, here we seek to examine the association between craving and rsFC between the reward network and seeds from each of the major networks within individuals who are ‘smoking as usual.’

Our work generally focuses on examinations of the mechanisms underlying drug cue-induced craving responses; however, we adopt a different approach in this study. To test hypotheses regarding coupling between multiple neural networks and craving, we sought to determine what circuits may be involved in the relief of craving. Thus, we conducted a pre- versus post-smoking rsFC craving relief study where resting-state data were acquired before and immediately after participants smoke a cigarette during the same scanning session. We used seed-based FC methodology to examine the change in rsFC coupling between the VS, a key node of the reward network, and key nodes of major networks involved in nicotine dependence. We hypothesized that rsFC strength between the VS and the PCC, a key node of the DMN; the anterior ventral insula (avInsula), a key node of the SN; and the dorsolateral prefrontal cortex (dlPFC), a key node of the ECN, will modulate relief of craving by smoking. We hypothesize that after smoking, the following relationships with relief of craving will be observed: 1) stronger rsFC coupling between the VS and the dlPFC due to greater cognitive control over motivated behavior, 2) weaker rsFC coupling between the VS and the PCC, given the role of the PCC in self-referential mental processes (e.g., craving) and the anticorrelative relationship between the DMN and the ECN, and 3) no change in rsFC coupling between the VS and the avIns, given the bidirectional role of the avIns in modulating both interoceptive emotionally-based and exteroceptive cognitively-based processes.

Until recently, the literature supporting the theory that the VS is the reward center and the final common path of all addictive drugs was generally conducted in males (Di Chiara and Imperato, 1988; Koob and Volkow, 2010; Volkow et al., 2006). However, it is now widely accepted that even basic brain chemistry differs based on sex, due to the influence of gonadal (and other) hormones during development and across the lifespan (Becker et al., 2007). Preclinical evidence suggests that the dorsal striatum (i.e., putamen) may be the reward center in females (Cummings et al., 2014). In support, a recent human study showed that when men smoke a cigarette during positron emission tomography (PET) imaging, dopamine is released within the VS of male smokers, while in women, dopamine release is more rapid and is observed in the putamen (Cosgrove et al., 2014). Others have also observed conditioned drug cue effects within the putamen (Franklin et al., 2019), but not always limited to females (Boileau et al., 2007; Redish et al., 2008; Volkow et al., 2006). Thus, we extend our hypotheses to examine functional connections between the putamen and key nodes of the major neural networks. Our goal will be met focusing on female nicotine dependent individuals for the reasons stated above and due to the clinical relevance of this question. Relative to men, women have greater difficulty quitting smoking (Scharf and Shiffman, 2004) and are thought to be more driven to smoke to mitigate withdrawal-related negative affect (Perkins and Karelitz, 2015). Thus, the current work is an initial step aimed at understanding the neurobiological consequences of smoking in women.

2. Methods and Materials

All procedures were conducted at the University of Pennsylvania Perelman School of Medicine, Center for the Studies of Addiction and approved by the University’s Institutional Review Board and conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent.

2.1. Participants

Participants were cigarette smoking women, 18–45 years of age who smoked at least five cigarettes per day for the previous six months. Interested volunteers were recruited from the Philadelphia Metropolitan Area using flyers, advertisements, and social media platforms (e.g., Facebook). Participant time and travel expenses were reimbursed. Interested volunteers completed an initial phone screen and, if eligible, an in-person screening visit. At the visit, a certified nurse practitioner obtained a structured medical history and performed a physical examination to assess general health. Blood samples (to ensure good health and probe hormonal status) and urine samples (to test for illicit drug use; pregnancy; and the presence of cotinine, the major metabolite of nicotine, to ensure smoking status) were acquired. A general demographics questionnaire assessed participant’s biological sex, age, race and years of education. A trained clinician conducted a psychological screening, which included the Mini-International Neuropsychiatric Interview (MINI) (Sheehan et al., 1998) and the Hamilton Depression (HAM-D) and Anxiety Scales (HAM-A) (Hamilton, 1960, 1959). Exclusions included current severe psychiatric symptoms, a current diagnosis of any substance use disorders other than nicotine (or mild or moderate cannabis or alcohol use disorder), current Axis 1 DSM-V disorder, inability to pass a study-specific consent quiz, use of any smoking cessation strategies or desire to quit smoking, and any MRI contraindications (Dill, 2008). To ensure hormonal influences did not confound effects, women who were naturally cycling (i.e., not on birth control or using exogenous hormones and having normal-length menstrual cycles and confirmed ovulation) and in the early follicular phase of their MC, when ovarian hormones are at their lowest levels (see Table 1), completed study procedures. HAM-D and HAM-A scores were obtained on the day of testing, except for one participant who completed these measures the following day due to time constraints. Following successful screening the Smoking History Questionnaire (SHQ) was administered to assess nicotine dependence severity and acquire smoking history characteristics. The SHQ includes the Fagerstrom Test for Cigarette Dependence [FTCD; (Fagerstrom, 2012)].

Table 1.

Demographics and Smoking Characteristics

Demographics Smoking Characteristics
Age 29.3 (± 1.2) Cigarettes per day (CPD) 11.4 (± 1.1)
Handedness (L/R) 1/23 Years Smoked 12 (± 1.4)
Education 14.7 (± 0.5) Pack Years 7.4 (± 1.4)
*FTCD 4.2 (± 0.3)
Hormones Psychological Measures
Estradiol (pg/mL) 0.55 (± 0.02) Hamilton Anxiety Scale 3.9 (± 0.7)
Progesterone (ng/mL) 19.5 (± 0.02) Hamilton Depression Scale 3.8 (± 0.9)
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*

FTCD, Fagerstrom Test for Cigarette Dependence

2.2. Study Design

This study is a sub-study of an ongoing longitudinal counterbalanced project examining the influence of ovarian hormones on brain and behavioral responses to smoking cues, thus participants are naturally cycling women. Testing for this sub-study occurred 2–5 days after the onset of menses, when hormonal influences on behavior are at their lowest (Becker et al., 2007), thus allowing for an initial analysis of nicotine’s impact on connectivity without hormonal confounds. On the day of testing, participants smoked as usual before their study appointment. Participants provide urine (for drug, pregnancy, and cotinine testing) and saliva (for hormonal assay assessment) samples. As our goal was to examine rsFC in the non-abstinent brain, approximately 90 minutes prior to the neuroimaging session, participants smoked their preferred brand of cigarette to satiety. MRI sessions began at 5:00 PM, EST, to minimize any confounding effects of time of day or effects of participants’ daily routines on study results (Byrne et al., 2017). Participants were instructed to lie still in the scanner and keep their eyes open and fixated on a white cross that was positioned on a black screen. An eye camera was used to ensure that participants followed instructions. Participants were informed that scanning would be paused halfway through the session, and they would be given the opportunity to smoke.

The functional scans acquired that are relevant to this study are two 6-minute resting-state blood oxygen level-dependent (BOLD) scans, one acquired before and one following smoking their preferred brand of a cigarette while lying supine in the scanner. This design allows direct detection of the impact of smoking on brain connectivity. Prior to smoking, a 10-minute audio/visual smoking cue task was also conducted (these data are not the focus of the current study). The Shiffman-Jarvik Withdrawal Scale (SJWS) was administered prior to and following the imaging session (Shiffman and Jarvik, 1976).

2.3. Participant Characteristics Analyses

Using SPSS version 25, means and SEMs were calculated for all continuous variables, including hormone values obtained from saliva, and analyzed by Zava Research Testing (https://www.zrtlab.com/).

2.4. Shiffman-Jarvik Withdrawal Scale (SJWS)

The SJWS is a 25-item questionnaire that consists of 5 subscales: craving, psychological discomfort, physical discomfort, stimulation/sedation, and focus/concentration (Shiffman and Jarvik, 1976). Scores for each item range from 1 to 7. Our hypotheses were constrained to the craving subscale. The craving subscale was summarized, and means and SEMs were calculated. Paired t-tests were used to compare pre- versus post-smoking scores. Analyses were conducted in SPSS version 25. The change from the pre- to post-smoking craving score was derived by simple subtraction to obtain individual relief of craving scores for correlational analyses.

2.5. Imaging Data Acquisition

Magnetic resonance images were acquired on a Siemens MAGNETOM Prisma whole-body scanner (Siemens AG, Erlangen, Germany) using a 64-channel head coil. A 14-second localizer scan was acquired to ensure correct head placement and whole-brain coverage. To co-register functional data, a T1-weighted high resolution magnetization-prepared rapid acquisition with gradient echo (MPRAGE) scan was acquired (repetition time (TR)/ echo time (TE)/ inversion time (TI), 1820/3.51/1100ms; flip angle, 9°; bandwidth, 130 HZ/Px; voxel size, 0.9×0.9×1.0mm, matrix,192×256×160; slice thickness, 1.00mm; field of view (FOV), 240mm). 420 volumes of resting-state BOLD functional data were acquired before and after smoking using gradient-echo planar imaging (EPI) (TR/TE, 800/37ms; flip angle, 52°; voxel size, 2.0×2.0×2.0; matrix, 104 × 104 × 72; slice thickness, 2mm; FOV, 208mm).

2.6. Regions of Interest

To examine whether and how FC between striatal regions and key nodes within major neural networks influence craving relief, select seeds were chosen from the DMN, SN and ECN. See Figure 1 for sagittal, axial and coronal views of each seed. Based on our previous studies (Dumais et al., 2017; Franklin et al., 2019; Wetherill et al., 2013) and a substantial literature implicating the VS as the reward center of the brain (for a review see Koob and Volkow, 2010), one anatomical seed was chosen for the medial proximal bilateral ventral striatum (VS). Based on emerging literature implicating the putamen as a reward center in females (Cummings et al., 2014) and potential lateralization of function (Cosgrove et al., 2014; Franklin et al., 2019), separate right and left anatomical seeds were chosen for the putamen. Right and left dorsolateral prefrontal cortex (dlPFC), 10 mm spherical seeds centered on coordinates ±44 38 22 (Brodmann’s area 9/46) were chosen based on multiple studies demonstrating the involvement of this region in relapse, recovery (Holla et al., 2018; Karch et al., 2019; Schluter et al., 2018; Zelle et al., 2017), and relief of craving (Ketcherside et al., 2020)]. The anterior ventral insula (avIns) anatomical seed (as described in Faillenot et al. 2017) was chosen based on studies implicating this region in cigarette craving and relapse vulnerability (T. R. Franklin et al., 2011; Franklin et al., 2015; Janes et al., 2010, 2017, 2020a; Ketcherside et al., 2020). A posterior cingulate cortex (PCC) 10 mm spherical seed centered on coordinates 0 −50 28 was chosen based on the literature identifying it as a major node of the DMN (Fox et al., 2005; Laird et al., 2011) and studies implicating it in craving (T. Franklin et al., 2011; Franklin et al., 2007; Janes et al., 2019) and relapse (Kosten et al., 2006). The primary visual cortex is a region not known to be involved in craving, and as such, we chose an anatomical seed from this region as a control. Anatomically-defined seeds were created from the Wake Forest University (WFU) Pickatlas toolbox (Maldjian et al., 2003).

Figure 1. Seed Pairs.

Figure 1

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Seed pairs used to examine whether and how functional connectivity between striatal regions and key nodes within major neural networks influence craving relief.

2.7. Imaging Data Analyses

All imaging data preprocessing and analyses were carried out using Statistical Parametric Mapping (SPM12, Wellcome Department of Cognitive Neurology, London, UK, http://www.fil.ion.ucl.ac.uk/spm) within the MATLAB R2020 environment.

2.8. Preprocessing and Data Cleaning

Data Processing Assistant for Resting-State fMRI (DPARSF) and Data Processing and Analysis of Brain Imaging (REST) toolboxes were utilized for preprocessing. Specifically, each participant’s images were slice-time corrected, realigned (each of the six rigid affine head motion parameters did not differ significantly between pre- and post-smoking scans, p > 0.05 for all parameters), and coregistered with the anatomical image of each participant. No participant’s motion exceeded 0.5mm. To reduce interference contributed by motion ≤ 0.05, we utilized the Friston 24-parameter model, which includes six head motion estimates from a specified time point (Rotation (Rt) = [X Y Z pitch roll yaw]), six head motion parameters from the preceding time point (Rt-1) and their corresponding squares (Rt 2, Rt-1 2) (Friston et al., 1996). Head micro-motion (≤ 0.2 mm) was addressed by calculating the sum of the absolute values of the differentiated head motion estimates (by backwards differences) at every time point (Power et al., 2012). White matter, cerebrospinal fluid and global mean signals were regressed out. Temporal band-pass filtering (0.01<f<0.08 Hz) and detrending were performed to reduce the effects of low-frequency drift and high-frequency noise. Finally, images were spatially normalized to the Montreal Neurological Institute (MNI) space and smoothed with a Gaussian kernel with a full-width at half-maximum (FWHM) of 6 mm. Smoothed normalized images were entered into seed-based region of interest (ROI) analyses.

2.9. Resting-State Functional Connectivity Analyses

A seed-based analysis approach was used to quantify seed pair coupling. For each data set, the average time course of the seed pairs was extracted, demeaned, hamming windowed and correlated using the rapidtide2 package implemented in Python3.6 (https://github.com/bbfrederick/rapidtide). The resulting Pearson correlation coefficients for each seed pair from each participant were converted to z-scores using Fisher’s r-to-z transformation. Change scores in coupling from post- to pre-smoking were derived by subtracting correlation coefficients. Analyses examined correlations between change in craving scores and change in coupling scores for each seed pair. To control for multiple comparisons, the Bonferonni method was applied to obtain adjusted α thresholds of p<0.0125 for VS coupling (0.05/4 seed pairs) and p<0.008 for putamen coupling (0.05/6 seed pairs).

3. Results

Table 1 provides participant demographic and smoking characteristics. Overall, women were moderately dependent on cigarettes, had low scores on the HAM-A and HAM-D, and had low estradiol and progesterone levels, indicating the early follicular phase of the menstrual cycle (i.e., 1 to 4 days following the onset of menses).

Craving scores obtained from the SJWS administered before smoking were (M ± SEM) 4.1±0.23 and 3.2±0.23 following smoking. A paired t-test showed significant reductions in craving following smoking [t23=4.31; (p<0.000)].

Analyses examining how coupling strength between key reward substrates and key nodes within major neural networks influences craving relief show that increased right dlPFC-bilateral VS coupling facilitates relief of craving (r=0.57, p=0.003) as does increased right dlPFC-left putamen coupling (r=0.62, p=0.001) (See Fig. 1). No other a priori seed pairs showed correlations (see supplementary Table). No correlations were observed between the VS and the primary visual cortex control region (r=0.25, p=0.24).

Post hoc analyses examined whether change in craving scores correlated with other subscales of the SJWS. None of the other subscales showed differences in pre versus post scores (psychological discomfort [t23=0.27; (p=0.77)], physical discomfort [t23= −0.64; (p=0.53)], stimulation/sedation [t23=1.51; (p<0.11)], and focus/concentration [t23=1.53; (p=0.14)].), and correlations were not observed. Results were unchanged when test day was included as a covariate of no interest, suggesting that effects were not related to novelty.

4. Discussion

This is the first intrasession fMRI study to examine the associations between rsFC on relief of craving by smoking a cigarette conducted in non-abstinent smokers. We use a seed-based rsFC approach to examine the change in rsFC coupling between the VS, a key node of the reward network, and key nodes of three major networks underlying nicotine addiction (i.e., ECN, DMN, and SN) (Janes et al., 2018; Sutherland et al., 2012; Weiland et al., 2015; Wetherill et al., 2019). Findings suggest that smoking-induced increase in right dlPFC-VS coupling is associated with relief of craving. Because the emerging literature suggests that the putamen may be an important reward substrate for women, we also explore rsFC between the putamen and relevant seed pairs. Analyses reveal that a smoking-induced increase in right dlPFC-left putamen coupling is associated with relief of craving. These findings suggest that relief of craving by cigarette smoking is associated with enhanced functional coupling between the dlPFC, a region thought to play a pivotal role in conscious decision making, and the striatum, considered to be the neural structure instigating reward-related motivated behavior (Redish et al., 2008). This study is unique and contributes to the literature because we examine craving relief delivered by the drug of choice rather than craving induction.

The results reported here are highly consistent with a burgeoning literature selectively implicating dlPFC-striatal interactions as a neurobiologically significant substrate of drug craving (Karch et al., 2019; Kober et al., 2010b; Zelle et al., 2017). For example, Kober and colleagues directly investigated the manipulation of craving in cigarette smokers. After ensuring that participants could regulate craving, they performed an fMRI task wherein they were asked either to think about smoking and the enjoyment received from it or to use cognitive reappraisal strategies to think about the long-term negative consequences of smoking. Kober and colleagues found a reciprocal relationship between the VS and the dlPFC. Specifically, they found that as individuals reduced their craving, VS activity was blunted and dlPFC activity was enhanced. A mediation analysis confirmed the relationship between VS, dlPFC and craving modulation (Kober et al., 2010a). Our findings directly align with Kober and colleagues and the prevailing hypotheses within the addiction field (Everitt and Robbins, 2005; Koob and Volkow, 2010; Volkow et al., 2006).

The finding that right dlPFC-striatal rsFC strength is associated with relief of craving corresponds with other recent work in our lab. We examined the effects of smoking cessation treatment with the GABA B agonist and purported anti-craving agent, baclofen, on resting baseline cerebral blood flow (rsCBF) and smoking cue (SC) neural reactivity. Participants were scanned prior to and following three weeks of medication (20 mg q.i.d. baclofen or placebo). Subjective craving reports were acquired before and after SC exposure to examine SC-elicited craving explicitly. Baclofen increased rsCBF selectively in the right dlPFC, which predicted reduced neural responses to SCs in key drug cue-reactive brain regions; the avIns and ventromedial prefrontal cortex (Ketcherside et al., 2020). Further, while both groups reported SC-elicited craving before treatment, only baclofen-treated participants showed reductions at three weeks of treatment.

The growing literature suggests that therapeutic strategies aimed at enhancing resting activity in the dlPFC can improve individuals’ ability to alleviate craving and avoid relapse. For example, there has been exponential growth in the use of transmagnetic stimulation (TMS) to reduce craving and relapse in the addictions field, and a predominant target is the dlPFC (Mondino et al., 2018) (Hanlon et al., 2018). Specifically, TMS applied to the dlPFC of cigarette smokers reduces SC-elicited craving (Li et al., 2013) and SC-induced activation of the medial orbitofrontal cortex and VS (Li et al., 2017). Similarly, TMS to the dlPFC coupled with SC exposure reduces the number of cigarettes smoked per day and increases long-term abstinence (Dinur-Klein et al., 2014). Encouragingly, informed by clinical and preclinical studies, The emerging literature utilizing TMS as a treatment for addiction (Hanlon et al., 2018) is a testament to how basic research findings are translating into viable treatment strategies.

Strengths and Limitations

The generalizability of the current findings is limited due to the modest sample size and truncated subject characteristics. All participants were relatively young females with moderate nicotine dependence. Findings are also limited in that only specific a priori hypotheses were examined, while other functional connections may also contribute. Further, when interpreting these results, the smoking state of the participants is an important consideration. Findings would likely differ if participants were in a severe state of withdrawal. However, the findings reported focus on brain changes that align with typical smoking delays individuals may experience over the course of day.

The study lacks a control condition wherein participants do not smoke a cigarette between resting-state data acquisition while other parameters are kept equal. We have repeatedly shown that craving is not relieved but actually increases over time in the scanner when smoking does not occur (Franklin et al., 2015, 2007; Ketcherside et al., 2020). Future studies can test whether the opposite correlations would be observed; one might expect increased craving to associate with less rsFC between the striatum and dlPFC.

The length of the resting state scan acquired in this study may be considered a limitation. Birn and colleagues (2013) examined the length of scan time on the reliability of resting state data and showed that a 6-minute scan falls short. We used an accelerated echo planar sequence that ensures high temporal-spatial resolution thereby giving us confidence in our results. Specifically, our design uses of a short TR (.8 sec) that provides 420 data points over the course of a 6-minute scan, which increases the signal to noise ratio. Longer TRs, such as those used by Birn and colleagues, have been shown to require much longer scan durations to acquire a similar number of data points (Birn et al., 2013). Additionally, our use of multi-band fMRI is becoming the new standard in fMRI due, in part to work conducted by the Human Connectome Project (HCP) (https://www.humanconnectome.org). While we have not employed identical measures to those presented in this work, our prior evaluation of HCP network function indicates high test-retest reliability of rsFC function (Janes et al., 2020b)

Strengths of the study include the well-characterized sample, intrasession testing that leads to improved reliability, innovative scan acquisition, testing during the early part of the menstrual cycle when any influence of hormones on responses is minimized, and investigation of both dorsal and ventral striatal functional connections and nodes from each of the 3 major networks involved in nicotine addiction.

Conclusions

This study sought to examine mechanisms underlying relief of craving provided by receipt of reward (i.e., smoking a cigarette) in smokers who were non-abstinent. In line with multiple experimental paradigms and across both preclinical and clinical studies, we identified the prefrontal/striatal pathway as a key pathway in relief of craving. The findings contribute additional evidence that modulation of the dlPFC may be a viable option for reducing craving and recidivism in addiction.

Supplementary Material

1

Figure 2. Greater Right dlPFC-Striatal Coupling is Associated with Relief of Craving.

Figure 2

Figure 2

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Graphs showing that greater right dorsolateral prefrontal cortex (Rt dlPFC)-ventral striatal (VS) (A.) and Rt dlPFC- left putamen (LP) (B.) coupling correlate with relief of craving after smoking a cigarette. Images depict axial, saggital and coronal (clockwise) functionally connected seed pairs. Right is right. Bonferroni corrected for multiple corrections.

Highlights.

  • Craving is a major contributor to drug-seeking and relapse

  • Instead of inducing craving, this study reports on relief of craving

  • Smokers participated in fMRI before and after smoking

  • Smoking-induced increases in dlPFC-striatal coupling is associated with craving relief

  • Modulation of the dlPFC is a viable treatment option to reduce craving

Acknowledgements

The authors would like to thank Melanie Maron, M.S., Wetherill Lab Manager for supervision of staff and study procedures and Joseph Hladish who designed the MRI-compatible smoking filter system.

Funding

Funding for this study was provided by an NIH NIDA grant, R01DA040670. Dr. Janes is supported by the NIDA grant, K02DA042987. Dr. Wetherill is supported by the NIAAA grant, K23AA023894. Dr. Rao is supported by the NIMH grant R01 MH107571 and NIA grant R21AG051981.

Footnotes

Disclosures

The authors declare no financial interests or conflicts of interest.

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