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. 2017 Sep 28;38(12):6239–6249. doi: 10.1002/hbm.23830

White matter integrity of central executive network correlates with enhanced brain reactivity to smoking cues

Yanzhi Bi 1,2,†, Kai Yuan 1,2,3,†,✉, Dahua Yu 3,†, Ruonan Wang 1,2, Min Li 1,2, Yangding Li 4, Jinquan Zhai 5, Wei Lin 6,✉, Jie Tian 1,2,7,✉
PMCID: PMC6867136  PMID: 28960762

Abstract

The attentional bias to smoking cues contributes to smoking cue reactivity and cognitive declines underlines smoking behaviors, which were probably associated with the central executive network (CEN). However, little is known about the implication of the structural connectivity of the CEN in smoking cue reactivity and cognitive control impairments in smokers. In the present study, the white matter structural connectivity of the CEN was quantified in 35 smokers and 26 non‐smokers using the diffusion tensor imaging and deterministic fiber tractography methods. Smoking cue reactivity was evaluated using cue exposure tasks, and cognitive control performance was assessed by the Stroop task. Relative to non‐smokers, smokers showed increased fractional anisotropy (FA) values of the bilateral CEN fiber tracts. The FA values of left CEN positively correlated with the smoking cue‐induced activation of the dorsolateral prefrontal cortex and right middle occipital cortex in smokers. Meanwhile, the FA values of left CEN positively correlated with the incongruent errors during Stroop task in smokers. Collectively, the present study highlighted the role of the structural connectivity of the CEN in smoking cue reactivity and cognitive control performance, which may underpin the attentional bias to smoking cues and cognitive deficits in smokers. The multimodal imaging method by forging links from brain structure to brain function extended the notion that structural connections can modulate the brain activity in specific projection target regions. Hum Brain Mapp 38:6239–6249, 2017. © 2017 Wiley Periodicals, Inc.

Keywords: central executive network, cognitive control, smoker, smoking cue, structural connectivity

INTRODUCTION

Currently, increasing evidence suggests that the brain regions act in a coordinated manner and consist of brain networks to modulate addiction‐related brain functions and behaviors [Janes et al., 2012, 2015; Li et al., 2016; Xing et al., 2014; Yuan et al., 2016a]. The central executive network (CEN) including the dorsolateral prefrontal cortex (DLPFC) and the posterior parietal cortex (PPC) is responsible for attention allocation and cognitive control in the context of goal directed behaviors [Bressler and Menon, 2010; Menon, 2011]. The critical roles of the functional coupling of the CEN in smoking behaviors had been revealed [Janes et al., 2012; Lerman et al., 2014; Weiland et al., 2015]. For example, the functional connectivity strength within CEN was lower in smokers and was negatively associated with pack years of cigarette use [Weiland et al., 2015]. The DLPFC and the PPC, two main regions of the CEN, showed significant activations in smoking cue exposure tasks [Engelmann et al., 2012]. The enhanced functional coupling between the CEN and the medial prefrontal cortex correlated with striatum cue reactivity in smokers, which indicated the involvement of the CEN in smoking cue processing [Janes et al., 2012]. Meanwhile, the CEN was associated with cognitive control by showing the activations in cognitively demanding tasks [Sridharan et al., 2008]. The dysfunctional interactions between the CEN and other large‐scale brain networks (salience network and default mode network) were critical in cognitive decline induced by abstinence in smokers [Lerman et al., 2014]. Taken together, the CEN may underlie smoking behaviors by revealing its roles in smoking cue reactivity and cognition control performances in smokers.

White matter (WM) structural connectivity is an inherent brain organization and influences brain function and behaviors by facilitating information communications between brain regions [Leong et al., 2016; Yuan et al., 2017a]. As mentioned above, most of the present studies focused on the functional abnormalities of the CEN and their association with smoking cue reactivity or smoking‐related behaviors in smokers. To date, little is known about the relationship of the CEN with the smoking‐related brain activity or behaviors in smokers with an anatomical framework. Thus, in the present study, we compared the fractional anisotropy (FA) values of the CEN fiber tracts by using the deterministic fiber tractography in smokers and non‐smokers. The whole brain regression analysis was carried out to test whether the structural connectivity of the CEN could predict the brain response to smoking related cues. We then assessed the relationship between the WM structural connectivity of the CEN and cognitive control deficits measured by the color‐word Stroop task in smokers and non‐smokers.

From the network function perspective, the CEN plays roles in the attention allocation to guide goal directed behaviors [Bressler and Menon, 2010; Menon, 2011]. In view that attentional bias to smoking cue is evident in smokers [Goldstein and Volkow, 2002; Janes et al., 2010], we speculated that the WM structural integrity of the CEN might play roles in modulating the smoking cue reactivity. Additionally, smoking has a deleterious effect on the brain's cognitive function, which is reflected by a decline in cognitive control task performances in smokers [Durazzo et al., 2010; Jacobsen et al., 2005]. The activation of the CEN during cognition tasks supported the role of the CEN in cognitive functions [Sridharan et al., 2008]. Therefore, we hypothesized that the WM structural integrity of the CEN correlated with cognitive control deficits in smokers. The current multimodal imaging study linking brain structure with brain function and behaviors by a combination of diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) may provide new insights into the role of the CEN in smoking‐related brain activity and behaviors.

MATERIALS AND METHODS

Ethics Statement

The 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. All procedures were in accordance with the Declaration of Helsinki. Prior to this study, all participants received a complete description about the experiment and gave written informed consent.

Subjects

Thirty‐six male daily smokers and twenty‐nine male non‐smokers were recruited from campus and were enrolled in this study. Smokers reported smoking ≥10 cigarettes per day in the last 6 months and had no attempt to quit smoking or attempted smoking abstinence in the past 6 months. Smoking behaviors were characterized by collecting the average number of cigarettes per day, years of smoking regularly and age of smoking initiation. Severity of cigarette dependence was assessed with the Fagerstrom Test for Nicotine Dependence (FTND) (Heatherton, et al., 1991). Non‐smokers had smoked less than five cigarettes in their lifetime and none in the past 5 years. All subjects were right‐handed as measured by the Edinburgh Handedness Inventory (Oldfield, 1971). Participants were assessed by the structured clinical interview for DSM‐IV and reported no history of drug dependence (other than nicotine in smokers), neurological or psychiatric disorders. Potential participants were excluded if they had any physical illness, prior head injury, any medications currently that may affect cognitive functioning, or any contraindications (e.g., nonremovable metallic implants, claustrophobia, etc.) for MRI scanning.

Cognitive control performance was measured in both groups by a Chinese version color‐word Stroop task using E‐prime 2.0 software (http://www.pstnet.com/eprime.cfm) outside the MRI scanner [Bi et al., 2017; Cai et al., 2016; Feng et al., 2016; Li et al., 2016; Yuan et al., 2017b]. The task was presented using a block design with three conditions: congruent, incongruent and rest. In the task block, three Chinese words (“red,” “green,” “blue”) displayed in three colors (red, green, blue) as stimuli. Participants were asked to identify the font color as quickly and accurately as possible, using a button press on a Serial Response Box with their right hand, while ignoring word meaning. Button presses by the index, middle, and ring finger corresponded to red, blue, and green respectively. In the rest blocks, a cross was displayed at the center of the screen, and subjects were required to fix their eyes on this cross without response. All events were designed into two runs with different sequences of congruent and incongruent blocks. Each run consisted of four congruent, four incongruent, and nine rest blocks. Each task block contained seven trials, and each stimulus was presented for 1 s with an inter‐stimulus interval of 2 s. Before each task block, the instruction “Identify the Color” was presented for 2 s. All rest blocks lasted 17 s, except for the first one lasted 19 s. Before each rest block, the instruction “Rest” was presented for 2 s. All the participants had normal vision without color blindness. The participants were not permitted to enter the Stroop task until they all indicated clear understanding of the task, which was supported by the 90% correction rate in the congruent condition in practice runs. The parameters of the practice run were modified from the Stroop task, only including two congruent blocks and two incongruent blocks.

Procedures

A typical event‐related cue‐reactivity paradigm was programmed using the E‐Prime software package (Psychology Software Tools, Inc., Pittsburgh), which included a total of two runs [Yuan et al., 2017a]. The order in which run appeared was randomized across participants. There were forty trials in each run, consisting of twenty smoking‐related cues and twenty neutral cues. Stimuli were standardized color pictures including smoking images of people smoking, hands holding cigarettes, or cigarettes alone, and neutral images matched for general content (faces, hands, etc.) but were devoid of smoking cues. Smoking‐related and neutral cues were randomized for presentation sequence. All images were drawn from the International Smoking Image Series [Gilbert and Rabinovich, 1999]. Image cues were projected onto a mirror system mounted on the scanner head coil and presented for 2 s with a variable 4–12 s inter‐stimulus interval (mean = 8 s) during which a crosshair was displayed. The task began with a 6 s dummy scan followed by experimental scanning and the first cue. Before the cue exposure task, all participants were instructed to focus their attention on the images during the whole procedure. Immediately after the cue exposure task, participants were asked whether they remained awake and described the pictures. Craving was assessed using the brief, 10‐item version of the Questionnaire of Smoking Urges (QSU) in smokers [Cox et al., 2001].

MRI Data Acquisition

All smokers smoked one of their own cigarettes during ∼1 h immediately preceding the MRI scan to standardize the time a cigarette was last smoked [Bi et al., 2017; Yuan et al., 2016b]. Neuroimaging data was acquired using a 3‐Tesla 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. For each subject, high‐resolution 3D T1‐weighted images were acquired with the following parameters for use of registration processing during the DTI data analyses: repetition time (TR) = 8.4 ms; echo time (TE) = 3.8 ms; in‐plane matrix size = 240 × 240; slices = 176; field of view (FOV) = 240 × 240 mm2; slice thickness = 1 mm. Then, gradient echo‐planar images were obtained during the cue exposure task with the following parameters (TR = 2,000 ms; TE = 30 ms; flip angle = 90°; FOV = 224 × 224 mm2; data matrix = 64 × 64; axial slices = 30; slice thickness = 5 mm and no slice gap, total volumes = 200). Finally, the diffusion sensitizing gradients were applied along 32 noncollinear directions (b = 1,000 s mm−2) with an acquisition without diffusion weighting (b = 0 s mm−2). The imaging parameters were 68 continuous axial slices with a slice thickness of 2 mm and no gap, FOV = 240 × 240 mm2; TR = 6800 ms; TE = 70 ms; acquisition matrix = 120 × 120.

DTI Data Preprocessing and CEN Fiber Tractography

DTI data were analyzed as in our previous studies (Bi et al., 2015) using FSL (FMRIB Software Library, http://www.fmrib.ox.ac.uk/fsl/index.html). First, correction of the diffusion data for eddy currents and head motion was performed through affine transformation to the no‐diffusion‐weighted reference volume using FMRIB's Diffusion Toolbox v2.0 (FDT). Subsequently, the corrected data were skull striped using Brain Extraction Tool v2.1 (BET) to remove non‐brain tissue and background noise. Then, the diffusion tensor was calculated and individual FA images were constructed using DTIFIT from FMRIB's Diffusion Toolbox.

The regions of interest (ROIs) of the DLPFC and PPC for each hemisphere were created based on a combination of the standard automated anatomical labeling (Tzourio‐Mazoyer et al., 2002) map numbers within parentheses: left DLPFC (3:Frontal_Sup_L, 7: Frontal_Mid_L), right DLPFC (4:Frontal_Sup_R, 8: Frontal_Mid_R) (Van Den Bos et al., 2015), left PPC (59: Parietal_Sup_L, 61: Parietal_Inf_L, 63: SupraMarginal_L, 65: Angular_L) and right PPC (60: Parietal_Sup_R, 62: Parietal_Inf_R, 64: SupraMarginal_R, 66: Angular_R). A series of spatial transformations were applied to acquire the subject‐specific ROIs (Yuan et al., 2017a) (Fig. 1a). The ROIs were dilated 2–3 mm into the WM to ensure that they were in contact with the fibers (Uddin et al., 2011). Fiber tracking was performed in DTI native space using a Diffusion Toolkit and TrackVis software (Wang et al., 2007). The fiber assignment continuous tracking algorithm was used (Mori et al., 1999). Whole brain fibers were reconstructed along the principal eigenvector of each voxel's diffusion tensor. Tracking termination criteria were angle >45° and FA < 0.2 (Mori and van Zijl, 2002) (The individual FA map derived from FSL's DTIFIT was used as the mask image in the Diffusion Toolkit). The fiber bundles connecting the DLPFC and PPC in each hemisphere were extracted from the total collection of brain fibers, which were obtained through a two‐ROI approach with logical AND concatenation. Only fibers that passed both ROIs were included in the reconstructed tract. Obviously, spurious fibers were removed from the fiber tract by using an additional avoidance ROI (logical NOT operation). Finally, the values of mean FA of remaining fiber bundles connecting each pair of ROIs were extracted.

Figure 1.

Figure 1

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Regions of interest definition of the CEN and fiber tracking analysis in both groups. (a) The DLPFC and PPC in the individual DTI native space were defined as the regions of interest of the CEN for fiber tracking analysis. In more detail, the individual DTI image was registered to its T1‐weighted structural image using FSL's Linear Image Registration Tool (FLIRT) with mutual information used as a cost function (FA_2T1 matrix). The individual T1 images were normalized into the MNI space using linear (FLIRT) and nonlinear registration FNIRT (FSL's Nonlinear 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 subsequently applied to the ROI in MNI space to obtain the ROIs in individual diffusion space. (b) Schematic representation of the fiber bundles connecting the DLPFC and the ipsilateral PPC in the individual DTI native space in both groups. DTI tractography detected that, relative to the non‐smokers, smokers showed increased fractional anisotropy of the tracts in both left and right CEN, Bonferroni corrected, P < 0.025. CEN, central executive network; DLPFC, dorsolateral prefrontal cortex; PPC, posterior parietal cortex. [Color figure can be viewed at http://wileyonlinelibrary.com]

Statistical analyses were conducted with SPSS for Windows (Statistical Package for Social Sciences, Release 18.0, Chicago: SPSS, IL). Structural connectivity differences between groups were assessed using independent sample t tests (Bonferroni corrected, P < 0.05/2). Pearson's correlation analysis was performed to assessed the possible relationship between the structural connectivity of the CEN and the incongruent errors in both groups (Bonferroni corrected, P < 0.05/4).

Smoking Cue‐induced Brain Responses

Functional data was analyzed using SPM8 (Welcome Department of Cognitive Neurology, London, UK, http://www.fil.ion.ucl.ac.uk/spm). After slice timing correction, images were realigned to estimate and modify movement parameters. Participants with head motion exceeding 1.5 mm displacements and 1.5° rotations were excluded from the analyses. Then, the functional images were spatially normalized to the echo‐planar imaging template in MNI space using a nonlinear transformation, spatially smoothed with a Gaussian kernel of 6 mm at full width half maximum and filtered using a high pass filter and cut‐off at 128 s.

Statistical analysis of individual participant imaging data was performed with 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 realignment parameters (x, y, z translation and rotation motion) included as regressors of no interest. For each subject, the contrast image of smoking and neutral stimuli was constructed for each subject. A two‐way analysis of variance (ANOVA) with cue type (smoking cue vs neutral cue) as a within‐subjects factor and group (smoker vs. non‐smoker) as a between‐subjects factor was used to assess the effect of group, cue type and the group × cue type interaction on the brain activation (false discovery rate (FDR) corrected, P < 0.05). Post hoc analyses were conducted to examine specific group differences (FDR corrected, P < 0.05). Whole brain regression analysis was performed to determine the associations between smoking cue‐induced brain response and the craving scores as well as cognitive control deficits in smokers, respectively. Then, to evaluate whether structural connectivity of the CEN influence the brain response to smoking cues, a third linear regression analysis was performed to investigate the relationships between FA values of left/right CEN and smoking cue reactivity in smokers and non‐smokers, respectively. All statistical tests were FDR corrected and the level of significance was set at P < 0.05.

RESULTS

Demographics and Smoking Behavior Measures

One smoker and three non‐smokers with excessive head motion were excluded. The remaining 35 smokers and 26 non‐smokers were well matched on gender, age, and education years. More detailed information of the smoking behavior characteristics in smokers was displayed in Table 1.

Table 1.

Demographic characteristics, smoking‐related behaviors, and cognitive control performance

Smoker (n = 35) Non‐smoker (n = 26)
Mean SD Mean SD P value
Age (years) 21.03 1.22 20.38 1.32 0.06
Education years 13.89 .63 13.58 .64 0.07
Cigarettes per day 14.83 4.46 – – –
Years of smoking 4.26 2.27 – – –
Pack years 3.27 2.56 – – –
Age of smoking onset 15.29 2.75 – – –
FTND 4.91 1.69 – – –
Craving 30.06 13.58 – – –
Stroop performance
Congruent errors (/times) 2.66 2.3 2.23 1.42 0.54
Incongruent errors (/times) 7.17 3.54 5.38 2.71 0.01
Congruent reaction time (/ms) 548.18 51.01 528.54 49.34 0.22
Incongruent reaction time (/ms) 649.82 78.28 633.53 57.53 0.31
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Pack‐years: smoking years × daily consumption/20; FTND: Fagerstrom Test for Nicotine Dependence; Craving is measured by the 10‐item version of the Questionnaire of Smoking Urges. SD, standard deviation.

White Matter Fiber Integrity of the CEN

As shown in Figure 1b, compared with non‐smokers, smokers were associated with greater FA values in left (smokers: 0.436 ± 0.030 versus non‐smokers: 0.406 ± 0.038, P = 0.001, Bonferroni corrected) and right CEN (smokers: 0.436 ± 0.033 vs. non‐smokers: 0.409 ± 0.026, P = 0.0009, Bonferroni corrected).

Brain Reactivity to Smoking and Neutral Cues in Smokers and Non‐smokers

Significant group × cue type interaction effects were observed in the bilateral DLPFC, left orbitofrontal cortex (OFC), right insula, thalamus, PPC and occipital cortex (FDR corrected, P < 0.05). Post hoc tests revealed that non‐smokers showed no significant activations (smoking vs neutral cue) (FDR corrected, P < 0.05). In contrast, the smokers showed enhanced activations in caudate, insula, DLPFC, OFC, anterior cingulate cortex, medial prefrontal cortex, thalamus, PPC, precuneus, the occipital and temporal areas (smoking vs neutral cue) (FDR corrected, P < 0.05; Fig. 2a, Table 2). Regression analysis showed that smoking cue induced brain responses in bilateral DLPFC were correlated with the smoking craving in smokers as measured by the QSU questionnaire (FDR corrected, P < 0.05; Fig. 2b, Table 3).

Figure 2.

Figure 2

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Smoking cue reactivity and its correlation with craving in smokers. (a) Smokers showed increased brain activations in the caudate, insula, DLPFC, orbitofrontal cortex, anterior cingulate cortex, medial prefrontal cortex, thalamus, PPC, precuneus, the occipital and temporal areas when they viewed smoking‐related cues compared with neutral cues, false discovery rate corrected, P < 0.05. (b) Whole brain regression results showed that the brain responses to smoking cues in bilateral DLPFC were correlated with the smoking cue‐induced craving in smokers, false discovery rate corrected, P < 0.05. DLPFC, dorsolateral prefrontal cortex; PPC, posterior parietal cortex. [Color figure can be viewed at http://wileyonlinelibrary.com]

Table 2.

Regions exhibiting significantly increased brain reactivity to smoking cues in smokers

Region Peak location Peak T value Brodmann area Cluster size (mm3)
X Y Z
L Middle Frontal Gyrus −28 −7 45 3.97 6/9/10 1,864
R Middle Frontal Gyrus 26 −7 46 3.81 6/8/9/10/11 2,128
L Medial Frontal Gyrus −10 63 12 3.43 9/10/32 176
R Medial Frontal Gyrus 8 40 −7 3.24 8/9/10/32 784
L Orbital Frontal Gyrus −4 48 −12 2.99 11 72
R Orbital Frontal Gyrus 50 37 −4 3.01 11/47 104
L Anterior Cingulate −6 20 19 4.37 24/32 1,056
R Anterior Cingulate 6 31 2 3.9 24/32 1,272
L Superior Parietal Lobule −20 −61 60 4.41 7 1,608
R Superior Parietal Lobule 34 −48 50 4.97 7 2,320
L Inferior Parietal Lobule −63 −24 25 5.95 40 3,288
R Inferior Parietal Lobule 40 −46 52 5.42 40 3,784
L Supramarginal Gyrus −59 −41 32 4.07 40 664
R Supramarginal Gyrus 61 −43 30 3.32 40 200
L Superior Temporal Gyrus −63 −26 18 4.19 22/42 1,344
R Superior Temporal Gyrus 61 −34 18 3.65 22/41/42 1,144
L Middle Temporal Gyrus −51 −73 11 4.98 19/20/21/22/37/39 2,640
R Middle Temporal Gyrus 44 −75 11 4.66 21/37/39 1,048
L Inferior Temporal Gyrus −53 −66 −2 4.81 19/37 296
R Inferior Temporal Gyrus 55 −57 −4 3.63 19/20/37 216
L Superior Occipital Gyrus −30 −80 26 4.32 19 232
L Middle Occipital Gyrus −6 −96 18 5.75 18/19/37 1,624
R Middle Occipital Gyrus 42 −75 7 5.09 18/19/37 1,104
L Inferior Occipital Gyrus −44 −74 −3 3.91 19 200
R Inferior Occipital Gyrus 44 −72 −3 4.2 19 104
L Precuneus −6 −78 43 4.79 7 4,384
R Precuneus 30 −50 50 5.35 7 4,472
L Lingual Gyrus −24 −62 1 6.34 18/19 2,296
R Lingual Gyrus 14 −66 −2 6.02 18/19 2,104
L Cuneus −8 −88 28 8.97 17/18/19/23 6,784
R Cuneus 6 −77 22 8.47 17/18/19/23 5,504
L Fusiform Gyrus −22 −61 −7 5.36 19/20/37 1,360
R Fusiform Gyrus 24 −59 −9 6.07 19/37 1,008
L Insula −30 −28 22 3.7 13 576
R Insula 57 −32 18 4.02 13 1,168
L Thalamus −14 −14 12 3.64 1,896
R Thalamus 16 −23 10 3.17 1,248
L Caudate −6 10 3 3.42 2,232
R Caudate 8 10 −1 2.6 136
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All the coordinates are located in the Talairach space. L, left; R, right.

Table 3.

Brain regions exhibiting correlations with craving in smokers

Region Peak location Peak T value Brodmann area Cluster size (mm3)
X Y Z
L Middle Frontal Gyrus −38 40 18 2.61 6/10 72
R Middle Frontal Gyrus 44 1 53 4.34 6/10 240
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All the coordinates are located in the Talairach space. L, left; R, right.

Correlations between Structural Connectivity of the CEN and Smoking Cue Reactivity

Whole‐brain regression analysis demonstrated that the FA values of the left CEN were positively correlated with the activity within bilateral DLPFC and right middle occipital gyrus in smokers (FDR corrected, P < 0.05; Fig. 3b), no smoking cue‐induced brain activations showed correlations with the FA values of right CEN in smokers (FDR corrected, P < 0.05). Additionally, no correlations were found between the structural connectivity of the CEN and the smoking cue reactivity in non‐smokers (FDR corrected, P < 0.05).

Figure 3.

Figure 3

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Correlations between the structural connectivity of the CEN and smoking cue reactivity in smokers. The structural connectivity of left CEN was positively correlated with the brain response to smoking cues in bilateral dorsolateral prefrontal cortex and right middle occipital gyrus in smokers, false discovery rate corrected, P < 0.05. CEN, central executive network. [Color figure can be viewed at http://wileyonlinelibrary.com]

Stroop Task Performance and its Correlations With the Structural Connectivity of the CEN

Thirty‐five smokers and twenty‐six non‐smokers participated in this cognitive test. Means and standard deviations for the Stroop reaction time and accuracy in two groups were shown in Table 1. Two‐way Analysis of Variance (ANOVA, groups: smoker, non‐smoker × Stroop condition: incongruent, congruent) were performed on the errors and reaction time, respectively. This analysis revealed significant main effects of the Stroop condition (F(1,118) = 61.49, P < 0.001) and group (F(1,118) = 5.12, P = 0.025) on errors, while no significant interaction effect was found (F(1,118) = 1.94, P = 0.17). Post hoc tests revealed significant differences of the errors on the incongruent condition between groups (P = 0.01). Both groups showed significant differences of the errors between the two conditions (P < 0.001). Additionally, a main effect of the Stroop condition on the reaction time was found (F(1,118) = 85.33, P < 0.001). No significant main effects of group (F(1,118) = 2.58, P = 0.11) and group × Stroop condition interaction (F(1,118) = 0.02, P = 0.88) were found. Post hoc tests showed that the reaction time between two conditions were significantly different in two groups (P < 0.001). Correlation analysis showed that the FA values of left CEN was positively correlated with the incongruent errors in smokers (r = 0.45, P = 0.007, Bonferroni corrected; Fig. 4b), the FA of left CEN showed a negative correlation trend with the incongruent errors in non‐smokers (r = −0.45, P = 0.02, Bonferroni corrected; Fig. 4b). No brain activations induced by smoking cue showed correlations with the incongruent errors during Stroop task (FDR corrected, P < 0.05).

Figure 4.

Figure 4

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Stroop task performance and its correlations with the structural connectivity of the CEN in two groups. (a) Compared with non‐smokers, smokers committed more errors during the incongruent condition in Stroop task, P < 0.05. (b) The fractional anisotropy of left CEN was positively correlated with the incongruent errors in smokers. The fractional anisotropy of left CEN showed a negative correlation trend with the incongruent errors in nonsmokers. Bonferroni corrected, P < 0.0125. CEN, central executive network.

DISCUSSION

The present study provides novel insights into the structural connectivity differences of the CEN between smokers and non‐smokers, as well as its relationships with the brain reactivity to smoking cues and the cognitive control behaviors in smokers. We showed that smokers were associated with increased structural connectivity of the bilateral CEN (Fig. 1b) and the increased structural connectivity of left CEN could predict the brain response to smoking cues in the DLPFC and right middle occipital area (Fig. 3). Furthermore, smokers showed decreased accuracy during the incongruent condition in Stroop task compared with non‐smokers (Fig. 4a), the increased structural connectivity of left CEN was correlated with the cognitive control deficits in smokers (Fig. 4b). The current multimodal imaging research enhanced our understanding of the roles of the structural connectivity of the CEN in smoking behaviors, such as cue‐induced brain responses with craving and cognitive control impairments.

In the present study, we detected increased structural connectivity of the CEN in smokers (Fig. 1b), which is consistent with the previous findings (Gogliettino et al., 2016; Yu et al., 2016). Greater WM structural connectivity promotes the transmission of functional signals and modulates the brain activity in specific projection target regions (Leong et al., 2016). For instance, the structural connectivity of the anterior insula‐nucleus accumbens tract can predict the activity of the nucleus accumbens in positively skewed gamble task (Leong et al., 2016). We recently found that the DLPFC‐caudate tract integrity could modulate activity of the caudate in smoking cue exposure task in smokers (Yuan et al., 2017a). Consistently, our current results revealed the structural connectivity of left CEN was positively correlated the activity of the DLPFC in smoking cue exposure task, which extended the links between the brain structural connections and brain activity.

The CEN is responsible for the attention allocation to guide goal‐directed behaviors. A bias to allocate attention to smoking‐related cues reflected the extent the brain responses to smoking cues, which could underpin the craving response and predict the smoking cession outcome (Janes et al., 2010; Waters et al., 2003). When taking the structural connectivity of the CEN into account of smoking cue reactivity, greater left CEN WM integrity was associated with increased cue reactivity in brain regions including the DLPFC and occipital cortex (Fig. 3b). This finding indicated that the enhanced structural connectivity of the CEN may facilitate smoking cue‐induced brain activations in the DLPFC and occipital cortex in smokers. The DLPFC is strongly implicated in the selective attention of salient stimulus that is biased towards smoking related stimuli (Silva et al., 2017). Franken et al. suggested that subjective craving to smoke and attentional bias have mutual excitatory relationships (Franken, 2003). The current positive correlations between the DLPFC activity and smoking cue‐induced craving scores in smokers supported this notion. Additionally, the observed activity of right occipital cortex provided further support of the heightened attentional bias to smoking cues (Fig. 3b). The right occipital cortex is consistently reactive to smoking cues than neutral cues in a wealth of neuroimaging evidence, which represents the enhanced attention allocation to the smoking cues (Engelmann et al., 2012). Based on the aforementioned evidences, the increased cue reactivity in DLPFC and occipital cortex were possibly associated with the increased attention allocation to smoking cue.

Smokers were associated with cognitive declines (Bi et al., 2017; Jacobsen et al., 2005; Swan and Lessov‐Schlaggar, 2007; Yuan et al., 2016b). For example, our previous studies demonstrated poorer cognitive control performance accuracy in smokers relative to non‐smokers (Bi et al., 2017; Feng et al., 2016; Yuan et al., 2016b). In the present study, we also detected the cognitive deficits in smokers by revealing the decreased response accuracy during the incongruent condition (Fig. 4a). Additionally, cognitively demanding tasks evoke activations of the brain regions involved in the CEN (Seeley et al., 2007). Converging brain imaging studies demonstrated that the CEN was critically involved in the cognitive control processes possibly by the dynamic interactions of other large‐scale brain networks (e.g., salience network and the default mode networks) (Chen et al., 2013; Sridharan et al., 2008). The dysfunction of the CEN was proposed to related to the deficiencies in cognitive control performance in smokers and may increase the smokers' difficulty in quitting smoking (Lerman et al., 2014; Weiland et al., 2015). In the current study, we found that the structural connectivity of the left CEN was correlated with the decreased response accuracy during the incongruent condition in smokers (Fig. 4b), which shed some lights on the role of the structural connectivity of the CEN in the cognitive deficits in smokers.

Several limitations of this study should be noted. Given the higher smoking rate in young males than that in young females and relatively fewer females with nicotine dependence, one limitation of the current study is that our sample only included males. Since smoking cue‐reactivity, craving (McClernon et al., 2008) and WM structure (Gur et al., 1999) differ by gender, it is unclear whether our findings can be generalized to females. Therefore, we plan to include females in future studies to investigate potential gender differences in the study outcome. An additional limitation of this research is the cross‐sectional design, it is unclear whether the differences in structural connectivity of the CEN and the cognitive deficits were pre‐existing in the smokers that predisposed individuals towards nicotine addiction or were the effects of chronic nicotine exposure. Additional longitudinal research may improve our understanding of the neuroimaging findings in the current study.

In summary, the current findings provide evidence that smokers were associated with enhanced structural connectivity of the CEN. The CEN is responsible for attention allocation and cognitive control in the context of goal directed behaviors (Bressler and Menon, 2010; Menon, 2011). The structural connectivity of left CEN correlated with the smoking cue reactivity in DLPFC and occipital cortex, which might be associated with the increased attentional allocation to smoking cues. Furthermore, the cognitive declines in smokers were observed in a Stroop task that was not related to smoking, for which the attention would not be allocated. Correlation was also found between the structural connectivity of the CEN and the cognitive declines in smokers. The attentional bias to smoking‐related cues and cognitive declines are considered two hallmarks of nicotine addiction and are critical in smoking relapse (Goldstein and Volkow, 2002; Janes et al., 2010; Swan and Lessov‐Schlaggar, 2007; Waters et al., 2003). We highlighted the implication of the CEN WM connections in smoking cue reactivity and cognitive control performance, which may enhance our understanding of the neural mechanisms of smoking. Besides, the multimodal imaging method provides a support for the links between brain structure and brain activity, which might be the new directions for physiological research.

CONFLICT OF INTEREST

The authors report no biomedical financial interests or potential conflicts of interest.

ACKNOWLEDGMENTS

The funding agencies played no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.

Contributor Information

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

Wei Lin, Email: smithlinwx@aliyun.com.

Jie Tian, Email: tian@ieee.org.

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