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. 2017 Jun 27;38(9):4644–4656. doi: 10.1002/hbm.23690

The left dorsolateral prefrontal cortex and caudate pathway: New evidence for cue‐induced craving of smokers

Kai Yuan 1,2,3,✉, Dahua Yu 3, Yanzhi Bi 1,2, Ruonan Wang 1,2, Min Li 1,2, Yajuan Zhang 1,2, Minghao Dong 1,2, Jinquan Zhai 4, Yangding Li 5, Xiaoqi Lu 3,✉, Jie Tian 1,2,6
PMCID: PMC6866730  PMID: 28653791

Abstract

Although the activation of the prefrontal cortex (PFC) and the striatum had been found in smoking cue induced craving task, whether and how the functional interactions and white matter integrity between these brain regions contribute to craving processing during smoking cue exposure remains unknown. Twenty‐five young male smokers and 26 age‐ and gender‐matched nonsmokers participated in the smoking cue‐reactivity task. Craving related brain activation was extracted and psychophysiological interactions (PPI) analysis was used to specify the PFC‐efferent pathways contributed to smoking cue‐induced craving. Diffusion tensor imaging (DTI) and probabilistic tractography was used to explore whether the fiber connectivity strength facilitated functional coupling of the circuit with the smoking cue‐induced craving. The PPI analysis revealed the negative functional coupling of the left dorsolateral prefrontal cortex (DLPFC) and the caudate during smoking cue induced craving task, which positively correlated with the craving score. Neither significant activation nor functional connectivity in smoking cue exposure task was detected in nonsmokers. DTI analyses revealed that fiber tract integrity negatively correlated with functional coupling in the DLPFC‐caudate pathway and activation of the caudate induced by smoking cue in smokers. Moreover, the relationship between the fiber connectivity integrity of the left DLPFC‐caudate and smoking cue induced caudate activation can be fully mediated by functional coupling strength of this circuit in smokers. The present study highlighted the left DLPFC‐caudate pathway in smoking cue‐induced craving in smokers, which may reflect top‐down prefrontal modulation of striatal reward processing in smoking cue induced craving processing. Hum Brain Mapp 38:4644–4656, 2017. © 2017 Wiley Periodicals, Inc.

Keywords: dorsolateral prefrontal cortex, caudate, smoker, craving, diffusion tensor imaging, psychophysiological interactions

INTRODUCTION

Cigarette smoking is the leading cause of preventable death for human beings [Benowitz, 2010]. Six million smokers died each year and more than 5 million death is directly related to tobacco use [Munro, 2015]. Cue induced craving perpetuates substance abuse and/or trigger relapse [Shiffman et al., 2007; Xue et al., 2012]. Targeting the reactivity to substance‐related cues is therefore critical for addiction intervention [Salling and Martinez, 2016; Shen et al., 2016; Wang et al., 2016]. Smoking cues evoke brain response from control (e.g., dorsolateral prefrontal cortex [DLPFC]), reward (e.g., striatum), memory (e.g., hippocampus, amygdala), and motivation (e.g., orbital frontal cortex [OFC]) circuits [Engelmann et al., 2012], suggesting that response of individual brain regions is inadequate for cue‐induced craving in smokers [Volkow et al., 2011]. Therefore, more attention should be shifted from single brain regions to the connectivity among these brain regions [Clewett et al., 2014; Janes et al., 2015; Lerman et al., 2014; Sutherland et al., 2012; Yuan et al., 2016].

Generally, cue induced craving is a complicated phenomenon involving the cognitive and affective processes [Kober et al., 2010]. The conflict between the momentary affective salience and cognitive decision to act to obtain the reward and/or to resist their urge to smoke exists in the presence of the cues [Engelmann et al., 2012]. Dual‐systems models of brain function provide a useful framework for conceptualizing how such activation patterns may relate to cue induced craving in smokers [Pfeifer and Allen, 2012; Volkow and Baler, 2012]. Broadly, dual‐systems models suggest that two distinct but interacting neural systems influence our behavior and account for atypical brain function in addiction [Bechara, 2005]: the affective system mainly comprised of striatum that is driven by affective, reward‐related, and visceral influences, and a cognitive system mainly comprised of the PFC that supports regulation of the ventral affective system's reactions through inhibitory control [McClure and Bickel, 2014; Pfeifer and Allen, 2012]. Although the activation of the PFC and striatum had been found in smoking cue induced craving task [Engelmann et al., 2012] and resting state functional connectivity between them had been associated with addiction behaviors [Motzkin et al., 2014; Yuan et al., 2016], whether and how the interactions between these brain regions contribute to craving processing during smoking cue exposure task remains unknown. Psychophysiological interactions (PPI) measures whether a psychological context alters how one brain region (a “seed region”) contributes to another brain region (a “target region”) by explicitly testing whether a significant interaction between psychological context and the seed region is expressed in the target region [Smith et al., 2016]. The PPI methods should shed novel insights into the craving mechanisms from functional integration perspective.

Recently, investigators have used diffusion tensor imaging (DTI) to illuminate the structural connections between brain regions, for example, the descending projections from the PFC to the striatum had been validated [King et al., 2012; Liston et al., 2006]. Fractional anisotropy (FA) have been associated with diffusion properties, such as white‐matter density, alignment, and diameter [Mori and Zhang, 2006]. Assuming that greater white matter coherence facilitate the transmission of functional information, higher coherence might regulate brain activity in projection target regions and even their functional couplings associated with specific behavior [Leong et al., 2016]. Consistently, a couple of studies had demonstrated that the coherence of white matter projections from the PFC to the striatum can account for individual differences in cognitive control [Casey et al., 2007; Liston et al., 2006] and inhibition control in addiction [Hanlon et al., 2011; Morein‐Zamir and Robbins, 2015]. However, the implication of white matter coherence between the PFC and the striatum remains unclear in cue induced craving in smokers.

Therefore, classical cue reactivity paradigm was used to extract the brain activation related with craving in smokers. Then, we determined how smoking cue induced craving relate to the functional connectivity of the PFC using PPI analysis. Next, based on the PPI findings, we used DTI to characterize the identified circuit associated with smoking cue induced craving. We further explored whether the fiber connectivity strength facilitated functional coupling of the circuit and accounted for the brain activity associated with cue induced craving in smokers. We hoped that the current multimodal imaging study could improve our understanding of the accurate roles of the frontostriatal circuits in smoking cue induced craving.

MATERIALS AND METHODS

Ethics Statement

This study was approved by the ethic committee of medical research in First Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou, China. After the study procedure was fully explained, all participants gave the written informed consent. All experimental procedures followed the guidelines of human medical research (Declaration of Helsinki).

Participants

Nicotine‐dependent cigarette smokers (DSM‐IV) (≥10 cigarettes/day) with no attempt to quit or experience of smoking abstinence in the past 6 months were recruited for present study. The age‐, education‐, and gender‐matched nonsmokers were also enrolled (smoked no more than 5 cigarettes in their lifetime). All participants were right‐handed as measured by the Edinburgh Handedness Inventory [Oldfield, 1971]. The subjects were asked to abstain from smoking overnight (average duration of abstinence before scan: 17.4 ± 0.7 h), which was confirmed by CO level in expired air <10 ppm in smokers measured by Smokerlyzer System (Bedfont Scientific, Rochester, UK). The exclusion criteria were: (1) any physical illness (brain tumor, obstructive lung disease, hepatitis, or epilepsy); (2) current drug abuse (except nicotine) by the structured clinical interview for DSM‐IV and urine test; (3) alcohol use disorders measured by Alcohol Use Disorders Identification Test; (4) any medications currently that may affect cognitive functioning; (5) IQ score < 90 (measured by Wechsler intelligence Scale).

Smoking Cue Exposure Task

There were a total of two runs programmed by the E‐Prime software package (Psychology Software Tools, Pittsburgh). The order in which run appeared was randomized for each participant. Cigarette craving score was assessed by responding to a subjective zero to 100 craving scale immediately after the two runs (0 as not at all, and 100 as very likely to use). During each run, an event‐related cue‐reactivity paradigm with 40 trials (20 smoking‐related images and 20 neutral images) was used when the functional scanning was carried out. The smoking and neutral images were drawn from the International Smoking Image Series [Gilbert and Rabinovich, 1999] and randomized for presentation sequence. Each image cue was presented for 2 s and with an average 8 s inter‐stimulus interval (4–12 s). The task began with a 6 s dummy scan followed by experimental scanning.

MRI Data Acquisition

The experiment was carried out on a 3T Philips scanner (Achieva; Philips Medical Systems, Best, The Netherlands) at the First Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou, China. Foam pads were used to restrain head motion and earplugs were used to reduce scanner noise during the scan session. Prior to the resting state fMRI run, T1 weighted images were acquired using a magnetization prepared rapid acquisition gradient echo pulse sequence with a voxel size of 1mm3 (repetition time [TR] = 8.4 ms; echo time [TE] = 3.8 ms; data matrix = 240 × 240; slices= 176; field of view [FOV] = 240 × 240 mm2). The functional images were obtained during the cue‐exposure task, with an echo‐planar imaging (EPI) sequence (30 contiguous slices with slice thickness = 5 mm, TR = 2,000 ms, TE = 30 ms, flip angle = 90°, FOV = 224 × 224 mm2, data matrix = 64 ×64, and total volumes = 200). Finally, the DTI data were acquired with a single‐shot EPI sequence (68 continuous axial slices with a slice thickness of 2 mm, TR = 6,800 ms, TE = 70 ms, data matrix = 120 × 120, FOV = 240 × 240 mm2). The diffusion sensitizing gradients were applied along 32 non‐collinear directions (b = 1,000 s/mm2) together with an acquisition without diffusion weighting (b = 0 s/mm2). After the scan, all of participants were asked whether they remained awake and described the pictures during the whole procedure. Two expert radiologists examined the images of all participants to exclude any clinically silent lesions.

Brain Activation Extraction and Psychophysiological Interaction Analysis

Functional data were analyzed using SPM8 (Welcome Department of Cognitive Neurology, London, UK, http://www.fil.ion.ucl.ac.uk/spm) and the generalized PPI toolbox (gPPI; http://www.nitrc.org/projects/gppi) [McLaren et al., 2012]. The detailed preprocessing included slice timing correction (participants with head motion exceeding 1.5 mm or 1.5° were excluded), spatially normalization to the EPI template (resolution 3 × 3 × 3 mm3), smooth with a Gaussian kernel of 6 mm at full width half maximum and filtered using a high pass filter and cut‐off at 128s. Statistical analysis of individual participant imaging data was performed using first‐level fixed‐effects analyses using a general linear model. The regressors (smoking and neutral stimuli) were convolved with the canonical hemodynamic response function with the realignment parameters (x, y, z translation and rotation motion) included as regressors of no interest. Brain activations by smoking cue and neutral cue were then extracted.

Whole‐brain wise PPI analyses were then performed to examine how PFC‐centered functional connectivity pattern is modulated by smoking cue, which is a specific form of moderated multiple regression model. The predictors of this regression model include the time course of the experiment (psychological factor), the time course of a seed region (physiological factor), an interaction term of both, and covariates. Significant interaction effects between the psychological and physiological factors reveal how activity in a brain region covariates with activity in the seed region in response to the experimental manipulation. In this manner, the psychological factors, that is, three task conditions (fixation, smoking cue, neutral cue) of the cue reactivity paradigm, were represented by three separate regressors in the PPI model. We extracted the mean BOLD time series from the voxels within a 6‐mm radius sphere surrounding the activation peaks that fell within PFC derived from smoking cue induced activation. These spheres were combined with individual gray matter masks to ensure analyses did not include signals from non‐brain or white matter voxels. Variance associated with the six motion regressors was removed from the extracted time series, which was carried out to control the possible effect of head motion on the PPI results [Ries et al., 2012; Takaya et al., 2016]. For each seed mask, the time courses were then de‐convolved based on the model for the canonical hemodynamic response to construct a time series of neural activity, which was the physiological factor. Interaction terms were calculated separately for each experimental condition, as the product between the vector of the condition and the physiological factor. The PPI terms were also been convolved with the hemodynamic response function. Then, we revealed how smoking cue induced functional connectivity change to the corresponding seed region compared with neutral cue.

Fiber Tracking ROI Definitions

Based on PPI findings, we extract the ROIs from standard space, which were transformed into native diffusion space with the following steps: First, the DTI images was registered to the individual T1 image using FSL's Linear Image Registration Tool (FLIRT) with mutual information used as a cost function (FA_2T1 matrix). The individual T1 image was normalized into MNI space using linear (FLIRT) and nonlinear registration FNIRT (FSL's Non‐linear Image Registration Tool). The co‐registered DTI image in structural space was then warped using the transformation field derived from T1 to MNI normalization. The transformation matrix (FA_2T1) and warp‐fields (T1_2MNI warp) were inverted using convert_xfm and invwarp command respectively, which were subsequently applied to the ROI in MNI space to obtain the ROIs in individual diffusion space.

Probabilistic Tractography

FSL was used for eddy current correction and correction of head motion of the diffusion weighted images. FSL's diffusion toolbox was used for tensor fitting to obtain FA maps. We used a seed‐based probabilistic approach to track the specific circuits associated with craving derived from PPI analysis results (i.e., the left DLPFC‐caudate). According to a previous study [Hirsiger et al., 2016], we used the Bayesian Estimation of Diffusion Parameters Obtained using Sampling Techniques (BEDPOSTX) command to calculate the distribution of fiber orientations at each brain voxel. The probtrackx2 command was then used to initiate probabilistic tractography from each voxel within the seed ROI with the following parameters: streamlines = 25,000; step length = 0.5 mm; curvature threshold = 0.2. To ensure that only the white matter fibers were tracked, tractography for each hemisphere was conducted twice, once using the left DLPFC ROI as a seed mask and the left caudate as a termination mask, and vice versa (i.e., the left caudate = seed mask; the left DLPFC = termination mask, the left caudate‐DLPFC tract). All tracts were thresholded based on the individual maximum connectivity value within a tract [Bennett and Madden, 2013; Hirsiger et al., 2016]. The maximum connectivity value was obtained with fslstats and voxels which had values of more than 5% of the maximum connectivity value were kept in the analysis. Furthermore, tracts in diffusion space were binarized and combined by overlapping operation (overlapped‐tract) for each subject. Finally, we masked diffusion maps with the binarized overlapped‐tract to compute the mean value of FA within the final‐tract mask in diffusion space. The detailed processing is depicted in Figure 1.

Figure 1.

Figure 1

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Regions of interest definition in diffusion space and DTI Probabilistic tractography of left DLPFC‐caudate fiber. The ROIs from MNI space were transformed into native diffusion space. First, the DTI images was rigidly registered to the individual T1 image using FSL's Linear Image Registration Tool (FLIRT) with mutual information used as a cost function (FA_2T1 matrix). The individual T1 image was normalized into MNI space using linear (FLIRT) and nonlinear registration FNIRT (FSL's Non‐linear Image Registration Tool). The co‐registered DTI image in structural space was then warped using the transformation field derived from T1 to MNI normalization. The transformation matrix (FA_2T1) and warp‐fields (T1_2MNI warp) were inverted using inverse and invwarp command respectively, which were subsequently applied to the ROI in MNI space to obtain the ROIs in native diffusion space. We used the BEDPOSTX (Bayesian Estimation of Diffusion Parameters Obtained using Sampling Techniques) to calculate the distribution of fiber orientations at each brain voxel. To ensure that only the WM fibers were tracked, tractography for each hemisphere was conducted twice, once using the PFC ROI as a seed mask and the striatum as a termination mask (frontal‐striatum tract), and vice versa (i.e., striatum = seed mask; PFC = termination mask, striatum‐frontal tract). All tracts were thresholded based on the individual maximum connectivity value within a tract (more than 5% of the maximum connectivity value). Furthermore, tracts in diffusion space were binarized and combined by overlapping operation (overlapped‐tract) for each subject. Finally, we masked diffusion maps with the binarized overlapped‐tract to compute the mean value of FA within the final‐tract mask in diffusion space. [Color figure can be viewed at http://wileyonlinelibrary.com]

Statistical Analyses

The 2‐way analysis of variance with a between‐subjects factor comparing the group (smoker vs. nonsmoker) and a within‐subjects factor comparing the cue type (smoking vs. neutral cue) was performed on the brain responses and PPI analysis (false discovery rate [FDR] corrected, P < 0.05). To determine the relationship between smoking cue‐induced brain responses and the craving elicited by smoking cue, a multiple regression model with craving as the regressor was used in the smokers (FDR P < 0.05). To investigate the relationship between addiction cue‐induced functional connectivity changes and craving score, we performed a multiple regression analysis on PPI results with cigarette craving score as main predictor (FDR P < 0.05).

To assess whether the structural connectivity contribute to the functional coupling within frontostriatal circuits and smoking cue induced activation, correlations was performed between diffusion measures and PPI results as well as smoking induced brain activation in the smokers. Subsequently, mediation analyses test whether the relationship between any two correlated variables (i.e., diffusion measures of frontostriatal circuits and craving related activation) can be explained by the values from a third variable (i.e., PPI values within frontostriatal circuits) [Kober et al., 2010]. According to standard convention [Kober et al., 2010; Yuan et al., 2016], “a” refers to relationship between diffusion measures and function coupling of frontostriatal circuits effect, “b” refers to function coupling of frontostriatal circuits and cue induced striatum activation, and “c” refers to the direct diffusion measures of frontostriatal circuits and craving related striatum activation, controlling for the mediator functional coupling of frontostriatal circuits during craving task. The product “a*b” tests the significance of the direct mediator. As is customary, we used a bootstrapping test for the statistical significance of the product “a*b.” All the mediation analysis processing was finished according to the SPSS macros downloaded from the Psychonomic Society's Web archive at http://www.psychonomic.org/archive/ [Preacher and Hayes, 2004].

RESULTS

Demographic Characteristics of Participants

Twenty‐five young male smokers (aged 19–23 years, mean ± SE: 21 ± 0.21 years) and 26 nonsmokers (aged 19–23 years, mean ± SE: 20.8 ± 0.16) participated in our study. The smokers had 3.3 ± 0.48 pack‐years of cigarette use, reported smoking >10 cigarettes per day over the past 6 months. Fagerström Test for Nicotine Dependence (FTND) was used to assess the nicotine dependence severity (5.1 ± 0.34) [Heatherton et al., 1991]. The clinical and demographic characteristics of the participants were shown in Table 1.

Table 1.

Clinical details of the smokers and nonsmokers

Clinical details Smokers (n = 25) Nonsmokers (n = 26)
Mean ± SE (range) Mean ± SE (range)
Age (years) 21.0 ± 0.21 (19–23) 20.8 ± 0.16 (19–23)
Education years 14.1 ± 0.14 (13–16) 14.2 ± 0.19 (13–16)
Cigarettes per day (CPD) 14.9 ± 0.94 (10–26) n/a
Years of smoking 4.3 ± 0.4 (1–10) n/a
Pack years 3.3 ± 0.48 (1–13) n/a
FTND 5.1 ± 0.34 (2–8) n/a
Initial smoking age 15.1 ± 0.63 (8–20) n/a
Abstinence (h) 17.4 ± 0.74 (13–23.5) n/a
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Pack‐years: smoking years × daily consumption/20; FTND: Fagerstrom Test for Nicotine Dependence; QSU‐brief: 10‐item version of the Questionnaire of Smoking Urges; SE: Standard Error.

Cue Induced Activation in Smokers

The interaction effects (group*cue) of brain responses were detected in the left DLPFC, the right OFC and the left caudate (FDR P < 0.05). Consistent with previous findings [Due et al., 2002; Engelmann et al., 2012], the post hoc analysis revealed that nonsmokers showed no significant activations (smoking vs. neutral cue) (FDR P < 0.05). In contrast, the smokers showed enhanced activation of the left DLPFC, ACC, insula, right OFC and bilateral PCC/precuneus (smoking cue vs. neutral cue) (FDR P < 0.05) (Fig. 2a). Whole brain multiple regression analysis revealed that the activation levels of left caudate (r = 0.4857, P = 0.01) and right OFC (r = 0.711, P = 0.0001) were positively correlated with cigarette craving score (Fig. 2b). No significant responses were detected for the opposite examination (neutral cue > smoking cue) (FDR P < 0.05). The correlation analysis between the neural responses and clinical scales (pack_years and FTND) revealed no significant results in the smokers. The correlation of neural responses and clinical scales (pack_years and FTND) revealed no significant results in smokers.

Figure 2.

Figure 2

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Brain activation of smoking cue vs. neutral cue in smokers. The left DLPFC, ACC, insula, right OFC, and bilateral PCC/precuneus showed enhanced activation under smoking cue exposure, when compared with the neutral cue condition (FDR P < 0.05). In addition, cue induced activation was detected in bilateral temporal lobe and posterior parietal cortex areas. The activation level of left caudate (r = 0.4875, P = 0.01) and OFC (r = 0.711, P = 0.0001) positively correlated with cigarette craving score. [Color figure can be viewed at http://wileyonlinelibrary.com]

PPI Analysis Results in Smokers

Taking left DLPFC (Talairach coordinate: −26, 48, 25, radium: 3mm) as a seed region, we used PPI analysis to detect functional connectivity changes induced by the cue task. The interaction effects (group*cue) of the PPI results were detected in the left caudate (FDR P < 0.05). The post hoc analysis revealed no significant functional connectivity of the DLPFC in smoking cue exposure task for nonsmokers (FDR P < 0.05). In contrast, the left DLPFC showed negative coupling with the left caudate and positive coupling with the left insula and the right inferior frontal gyrus in smokers (smoking vs. neutral cue) (FDR P < 0.05) (Fig. 3a). The left caudate‐based PPI analysis also revealed the negative connectivity with left DLPFC in smokers (Supporting Information). Moreover, the PPI values of the left caudate correlated with cigarette craving score (r = 0.588, P = 0.002) (Fig. 3b), and the cue induced activation in caudate (r = 0.6766, P = 0.0002) (Fig. 3b). Conversely, the PPI analysis using right OFC as seed region did not detect any significant results in smokers (FDR P < 0.05).

Figure 3.

Figure 3

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Psychophysiological interaction analysis of left dorsolateral prefrontal cortex (DLPFC) in smokers. Taking left DLPFC (–26, 48, 25) as a seed region detected negative coupling with left caudate and positive coupling with left insula and right inferior frontal gyrus (FDR P < 0.05). The PPI values of the left caudate were well correlated with cigarette craving score (r = 0.588, P = 0.002) and the cue induced activation in caudate (r = 0.6766, P = 0.0002). Conversely, the PPI analysis using right OFC as seed region did not detect any significant results in smokers (FDR P < 0.05). [Color figure can be viewed at http://wileyonlinelibrary.com]

DTI Results in Smokers

DTI analysis demonstrated that the FA of left DLPFC‐caudate pathway was correlated with the strength of functional coupling of left DLPFC‐caudate during smoking cue exposure task (r = −0.5266, P = 0.0068) and cue induced caudate activation (r = −0.5392, P = 0.0054) in smokers (Fig. 4a). Mediation analysis was applicable among the fiber connectivity strength, functional coupling of left DLPFC‐caudate, cue induced caudate activation in smokers (Fig. 4a). The results demonstrated that the relationship between the fiber connectivity strength of left DLPFC‐caudate and cue induced caudate activation can be fully mediated by functional coupling of this circuit in smokers (c = −18.2 ± 5.9, P = 0.005; c′ = −8.56 ± 5.9, P = 0.166) (Fig. 4b).

Figure 4.

Figure 4

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Mediation analysis among structural connectivity, functional coupling of left DLPFC‐caudate pathway and caudate activation. DTI analysis demonstrated that the FA of left DLPFC‐caudate pathway was correlated with the strength of functional coupling of left DLPFC‐caudate (r = −0.5266, P = 0.0068), and cue induced caudate activation (r = −0.5392, P = 0.0054) in smokers. Mediation analysis was applicable among the fiber connectivity strength, functional coupling of left DLPFC‐caudate, cue induced caudate activation in smokers. The results demonstrated that the relationship between the fiber connectivity strength of left DLPFC‐caudate and cue induced caudate activation can be fully mediated by functional coupling of this circuit in smokers (c = −18.2 ± 5.9, P = 0.005; c′ = −8.56 ± 5.9, P = 0.166). [Color figure can be viewed at http://wileyonlinelibrary.com]

DISCUSSION

The frontostriatal circuits have been implicated in cue induced craving in addiction studies [Bu et al., 2016; Cai et al., 2016; Feng et al., 2016; Kober et al., 2010; Li et al., 2015, 2017; Tomasi and Volkow, 2013; Yu et al., 2016; Yuan et al., 2017]. The activation of PFC (e.g., DLPFC, OFC) and striatum induced by smoking cue and their association with smoking craving had been detected [Barrett et al., 2004; Brody et al., 2004, 2006, 2006]. However, it is unclear whether and how will they interact in the perspective of functional integration perspective and contribute to increased smoking craving during smoking cue exposure. In the current study, we found left DLPFC had a significant change in connectivity with left caudate during smoking cue exposure task. The negative functional coupling between left DLPFC and caudate was positively associated with craving score and cue‐induced activation in caudate. In addition, the fiber integrity of left DLPFC‐caudate pathway negatively correlated with cue‐induced activation in caudate. Using multimodal neuroimaging approaches, we integrated isolated findings of functional and structural connectivity, that is, the relationship between the fiber connectivity strength of left DLPFC‐caudate path and cue‐induced activation in caudate can be fully mediated by functional coupling in smokers during cue induced craving task.

Extraction of Smoking Cue Induced Brain Activation

Smoking cue induced craving is affected by several factors associated with reward, cognitive control, decision‐making and memory [Engelmann et al., 2012; Wilson et al., 2004]. The saliency value of the cue was influenced by previous individual experience and memory [Barrett et al., 2004; Brody et al., 2004; David et al., 2005]. The stronger the saliency value of the cue, the higher the motivational drive to smoking becomes. In line with previous studies, we revealed enhanced activation of the right OFC, left DLPFC, ACC, insula, and bilateral PCC/precuneus under smoking cue compared with neutral cue in smokers (Fig. 2). The activation of the left caudate and the right OFC was significant correlated with the craving score in smokers. As the key node of the nigrostriatal dopamine (DA) circuits, the caudate is critical for craving [Volkow et al., 2006] and reward processing [Delgado et al., 2000]. Smoking induced DA release in the left caudate [Barrett et al., 2004], which was significantly associated with the self‐reports of craving [Brody et al., 2004]. The caudate mediates tobacco‐seeking behavior following abstinence [McClernon et al., 2009; Wang et al., 2007] and smoking cues can provoke tobacco craving [Engelmann et al., 2012]. With regards to the OFC, the activation induced by smoking cues had been revealed in smokers [Engelmann et al., 2012]. Anatomically, the OFC has extensive connections with the striatum and limbic regions (such as the amygdala). It integrates function of the limbic and subcortical regions with reward processing [Schoenbaum et al., 2006]. The OFC contributes to goal‐directed behavior through assessing motivational significance of stimuli and the selection of behavior to obtain desired outcomes [Wilson et al., 2004]. In contrast, the DLPFC regions are important structures in the expression of executive functions, such as the exertion of inhibitory control over behavior [Kober et al., 2010; Volkow et al., 2011]. Reactivity of these areas to drug cues might reflect a process that the smokers were resisting their urge to smoke in the presence of the cues [Engelmann et al., 2012]. Rationally, the correlation analysis of this study showed the association of craving score with activation in the OFC and the caudate, but not the DLPFC, in smokers.

Negative Functional Coupling of the Left DLPFC‐Caudate in Smokers

Smoking cue induced activation of the DLPFC, the OFC, and the caudate for craving in the current study is consistent with previous findings [Engelmann et al., 2012]. To investigate functional coupling of the PFC regions in cue induced craving task, we used functionally determined cortical ROIs (the left DLPFC and the right OFC) as the seed regions in the PPI analysis. We revealed that the left DLPFC had a significant change in connectivity with left caudate during smoking cue versus neutral cue in smokers (Fig. 3), which was confirmed by the left caudate‐based PPI analysis in smokers (Supporting Information). That is, there was an increase of negative coupling between the left DLPFC and the left caudate during the cue induced craving task, which suggested the increase in the left DLPFC activity was associated with the reduced caudate activity during smoking cue versus neutral cue in smokers. The functional coupling strength of the left DLPFC–caudate pathway was correlated with craving scores (Fig. 3), which demonstrated that increasingly positive functional coupling is associated with higher craving or the caudate activation (Fig. 3b). The caudate (associated the reward processing) and the DLPFC (implicated with cognitive control) were crucial to the smoking behaviors [Sutherland et al., 2012; Yuan et al., 2016], which are inter‐modulated via frontostriatal circuits modulated by dopamine (DA) [Tomasi and Volkow, 2013]. The decreased striatal DA transporter and increased DA activity demonstrated DA system dysfunction in smokers [Fehr et al., 2008; Newberg et al., 2007]. Therefore, the interactions between the DLPFC and the caudate may be abnormal and associated with the underlying neural mechanisms of smoking behaviors. In addition, previous studies found that the impaired DA function in the caudate correlated with reduced baseline glucose metabolism in the DLPFC in addicts [Tomasi and Volkow, 2013], that is, the improper regulation by DA of reward regions in addicted subjects probably modulate the function of the DLPFC. Recently, cognitive strategies reduced craving in smokers by enhancing the activation of the DLPFC to suppress the striatal activation [Kober et al., 2010]. As the subjects could not smoke a cigarette in response to the cues while in the MRI scanner, this result possibly suggested the involvement of the DLPFC in cognitive decision to obtain the reward and/or to resist their urge to smoke in the presence of the cues [Engelmann et al., 2012; Wilson et al., 2004]. Based on all the information mentioned above, we suggested that our results might reflect top‐down prefrontal modulation of the striatal reward processing in smoking cue induced craving processing. Our results extended previous understanding of cue induced craving by revealing the negative coupling between the left DLPFC and the caudate during the cue induced craving task in smokers. The functional coupling findings also demonstrated that the smoking cue induced craving was not only associated with regional changes of the reward and cognitive control regions in smokers but also depended on the strength of connections among these regions, notably between the left DLPFC and the caudate.

Structural Connectivity and Functional Coupling of Left DLPFC‐Caudate Pathway

Anatomically, nonhuman studies had revealed that neurons of the PFC are known to project to the caudate [Rolls et al., 1983]. In addition to the functional coupling findings, we showed that the fiber integrity of the left DLPFC‐caudate pathway were associated with caudate activation during cue induced craving task in smokers (Fig. 4a). We found that the enhanced caudate activity reflected increased cigarette craving score in smokers (Fig. 2b). The fiber integrity of the left DLPFC‐caudate pathway was correlated with functional coupling between the left DLPFC and the caudate in smokers (Fig. 4a). We hypothesized that the fiber tracts possibly facilitated the functional connectivity of the left DLPFC‐caudate involved in smoking cue induced craving. Specifically, increased structural connectivity may allow for increased negative coupling between the left DLPFC and the caudate, which in turn may facilitate the activation of caudate. We explored this hypothesis more formally using a mediation analysis among the structural connectivity, functional connectivity of the left DLPFC‐caudate pathway, and the caudate activation in smokers (Fig. 4b). The results supported our hypotheses that an increase in left DLPFC‐caudate tract integrity leads to increased negative functional coupling between the left DLPFC and the caudate during smoking cue induced craving task, which finally leads to less activation of the left caudate.

The Implication of the Left DLPFC‐Caudate Pathway in Cue Induced Craving

Cooperation of the caudate, area involves in reward processing and the DLPFC implicates with cognitive control in mediating smoking behaviors has been observed in smokers [Hayashi et al., 2013; Janes and Nickerson, 2012; Kober et al., 2010; Yuan et al., 2016] and addiction studies [Tomasi and Volkow, 2013; Yuan et al., 2017]. As the final projections of mesolimbic system, the DLPFC plays crucial roles in smoking craving by integrating motivational or affective (e.g., current desires in the initiation of drug‐seeking behavior) and cognitive information (e.g., the exertion of inhibitory control over behavior) [Goldstein and Volkow, 2002; Kober et al., 2010; Morein‐Zamir and Robbins, 2015; Yuan et al., 2016; Yuan et al., 2017]. Previous study suggested that cognitive strategies effectively regulated craving in smokers involved the PFC–striatum pathway [Kober et al., 2010]. It is via the control mechanism of the DLPFC on the striatum, that is, decreases in craving correlated with decreases in the striatum activity and increases in the DLPFC activity, with striatum activity fully mediating the relationship between the DLPFC and craving scores [Kober et al., 2010]. Our results demonstrated that the negative coupling between the left DLPFC and the caudate correlated with cue induced caudate activation and craving scores, which may reflect top‐down PFC modulation of the striatal reward processing in smoking cue induced craving processing. Several human studies have applied this modulation processes to reduce cravings and cigarettes consumption in smokers, that is, using low‐ or high‐frequency repetitive transcranial magnetic stimulation (rTMS) on left DLPFC [Dunlop et al., 2016; Pripfl et al., 2014], transcranial direct current stimulation (tDCS) in bilateral DLPFC [Dunlop et al., 2016]. Thus, the left DLPFC‐caudate pathway can be the neuroimaging marker for the potential mechanism of noninvasive brain stimulation intervention for smoking.

Limitation

It is likely using both probabilistic tractography and PPI cannot resolve the direction of connectivity. However, animal tracing [Paus, 2010] and human smoking craving [Kober et al., 2010] studies reveal the interactions between the striatum and prefrontal regions are afferent (i.e., the prefrontal region provides input to the striatum), which suggest the feasibility of top‐down modulation of the DLPFC on striatal reward processing in response to smoking craving. Further studies using rTMS or tDCS in smokers might help to infer directionality [Shen et al., 2016]. Although the OFC has extensive anatomical connections with the striatum and limbic region, we did not observe any OFC derived target regions in PPI analysis. This may be due to small sample size and only male smokers were enrolled. Further study with larger sample sizes including males and females may help to address more related questions.

Conclusion

Our study used multimodal neuroimaging approaches to assess interaction among structural connectivity, functional coupling and cue induced activation in smokers. The findings revealed the implication of the left DLPFC‐caudate pathway in smoking cue induced craving in smokers. Moreover, the functional coupling of the left DLPFC‐caudate mediates the relationship between fiber tract integrity and smoking cue induced caudate activation in smokers.

Disclosures

The authors report no biomedical financial interests or potential conflicts of interest. The funder had no role in the study design, data collection, data analysis, decision to publish, or preparation of the manuscript. All listed authors approved the final manuscript submission. Experimental procedures have been conducted in conformance with the policies and principles contained in the Federal Policy for the Protection of Human Subjects and in the Declaration of Helsinki.

Supporting information

Supporting Information Figure 1.

Click here for additional data file. (174.3KB, tif)

ACKNOWLEDGMENTS

The authors would like to thank Shaoping Su for assistance with data collection and MRI operations.

K.Y., D.Y., and Y.B. have equally contributed to this work.

Contributor Information

Kai Yuan, Email: kyuan@xidian.edu.cn.

Xiaoqi Lu, Email: lxiaoqi@imust.cn.

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