Altered subcallosal and posterior cingulate cortex-based functional connectivity during smoking cue and mental simulation processing in smokers

Progress in Neuropsychopharmacology & Biological Psychiatry 97 (2020) 109772

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Altered subcallosal and posterior cingulate cortex-based functional connectivity during smoking cue and mental simulation processing in smokers

T

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Joong Il Kima,b, Jong Doo Leec, Hee-Jin Hwangd, Seon Wan Kie, Il Ho Parke, , Tae-Yong Parkf a

Institute of Bio-Medical Convergence, Catholic Kwandong University International St. Mary's Hospital, Incheon, Republic of Korea Future Medicine Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea c Department of Diagnostic Radiology, Catholic Kwandong University International St. Mary's Hospital, Incheon, Republic of Korea d Department of Family Medicine, Catholic Kwandong University International St. Mary's Hospital, Incheon, Republic of Korea e Department of Psychiatry and Behavioral Neurosciences, Catholic Kwandong University International St. Mary's Hospital, Incheon, Republic of Korea f Department of Korean Traditional Medicine, Catholic Kwandong University International St. Mary's Hospital, Incheon, Republic of Korea b

A R T I C LE I N FO

A B S T R A C T

Keywords: Nicotine Addiction Functional connectivity Subcallosal cingulate cortex Posterior cingulate cortex Prefrontal cortex

Background: Long-term cigarette smoking induces sensitization of incentive salience and conditioning of contextual cues which involves brain function alteration across multiple regions. Understanding how nicotine affects hub-based functional connectivities involved in affective and cognitive function can help us determine the treatment strategy for nicotine dependence. Method: Functional MRI was conducted on 30 smokers and 30 non-smokers while mentally simulating neutral and smoking hand movements. Smoking cue and mental simulation processing-related changes in functional connectivity strengths of the subcallosal and posterior cingulate cortex (SCC and PCC) with major brain network nodes were examined. Results: Compared to non-smokers, smokers showed cue-induced SCC functional connectivities which were enhanced with the intraparietal sulcus and reduced with the medial prefrontal cortex. The PCC activation and functional connectivity enhancements with the anterior insula cortex and rostro-lateral prefrontal cortex was found during smoking mental simulation. The PCC-lateral prefrontal cortex functional connectivity correlated with nicotine dependence severity. Conclusion: The present results demonstrate that smokers can be identified by cue-induced SCC functional connectivity strength decline and increment in the default mode and dorsal attention network nodes. However, nicotine dependence was associated with smoking mental simulation-related PCC-lateral prefrontal cortex functional connectivity strength, suggesting that the development of nicotine dependence may depend on the strength of coupling between the default mode network and the central executive network at the cognitive level.

1. Introduction Long term cigarette smoking leads to nicotine dependence which involves habitual use of cigarettes due to craving. Enhancement and neuroadaptation of the dopamine system by repeated exposure to nicotine induce sensitization of the incentive salience processing which may underlie craving. Contextual cues that are associated with cigarette smoking trigger craving and craving increases the likelihood of smoking behavior (Shiffman et al., 2013). The brain regions and neural networks involved in addiction have

been extensively studied by functional neuroimaging research. Visual cue-induced anticipation of smoking is associated with neural activations in the arousal and attention network (McBride et al., 2006). The ventromedial prefrontal cortex (VMPFC), including the subcallosal cingulate cortex (SCC), has been implicated with drug-seeking behaviors (Kalivas et al., 2005). Specifically, the VMPFC monitors and engages in suppressing previously learned behavior, and its failure in inhibitory control results in impulsive and uncontrolled behaviors related to addiction (Goldstein and Volkow, 2011). Striatum plays an important role in addiction-related plasticity and behavior. Within the

⁎ Corresponding author at: Department of Psychiatry and Behavioral Neurosciences, Catholic Kwandong University, International St. Mary's Hospital, 25, Simgokro 100beon-gil, Seo-gu, Incheon 227111, Republic of Korea. E-mail address: [email protected] (I.H. Park).

https://doi.org/10.1016/j.pnpbp.2019.109772 Received 3 February 2019; Received in revised form 27 August 2019; Accepted 2 October 2019 Available online 22 October 2019 0278-5846/ © 2019 Elsevier Inc. All rights reserved.

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assessed using the Korean versions of the Beck Depression Inventory (BDI-II) and Alcohol Use Disorder Identification Test (AUDIT) (Kim et al., 2014; Song et al., 2012). Severity of smoking and its characteristics were assessed using the Korean versions of the Fagerström Test for Nicotine Dependence (FTND), Minnesota Nicotine Withdrawal Scale (MNWS) and the short form of the Tobacco Craving Questionnaire (TCQ-SF) (Ahn et al., 2002; Choi et al., 2008; Heishman et al., 2008; Kim et al., 2007). The participant's cigarette smoking statuses were confirmed by conducting urine cotinine tests which showed urine cotinine values of > 31.5 ng/mL (Goniewicz et al., 2011). This study included smoking cessation treatments in which all smokers participated after functional magnetic resonance imaging (MRI) scanning.

striatum, the nucleus accumbens (NAc) is involved in processing conditioned and unconditioned stimuli and is activated by visual smoking cues in smokers (David et al., 2007; Yager et al., 2015). Smoking cueinduced putamen reactivity is consistently reported in fMRI studies and can be potentiated after abstinence (Engelmann et al., 2012; McClernon et al., 2009). Both the nucleus accumbens and the subcallosal cingulate cortex activities are decreased when craving is cognitively downregulated (Kober et al., 2010). Also, brain lesion and volumetric MRI studies have suggested that anterior insula cortex (AIC) plays a central role in nicotine addiction (Naqvi et al., 2007; Zhang et al., 2011). Interoceptive experiences of cigarette smoking are represented in the insula and may influence how smokers feel, remember, and decide (Naqvi and Bechara, 2010). Abstinence-induced craving is likewise associated with increases in cerebral blood flow in brain regions comprising the reward, salience and visual networks (Wang et al., 2007). Exploring how smoking affects the brain response at a network level may help us understand how addiction-related experiences emerge and identify addiction at the brain functioning level. However, the complexity of the brain functioning through interconnections between brain regions and interactions between different networks make it difficult to determine treatment targets in the addicted brain. With recent advances in functional network analysis, regions which are widely coupled within and across functional networks have been identified as brain hubs. High centrality makes brain hubs reasonable targets for understanding the dynamics between networks and dysfunction in various brain disorders (van den Heuvel and Sporns, 2013). The SCC and the posterior cingulate cortex are both structural and functional hubs. The SCC has been implicated in conditioning and assigning incentive salience and modulating the physiological response associated with emotion (Palomero-Gallagher et al., 2015; Roy et al., 2012). Its connections with the striatum and anterior insula constitute the reward and interoceptive network which are relevant to craving (Hamani et al., 2011; Goldstein and Volkow, 2011). The PCC is a major node of the default mode network and its sub-region is functionally connected with the fronto-parietal network (Leech et al., 2012). As a functional hub for several networks, it has recently been suggested to regulate metastability and synchrony between internal to external and broad to narrow attentional state (Leech and Sharp, 2014). Moreover, the enhancing effect of nicotine on visuospatial attention is mediated through the PCC (Lawrence et al., 2002; Hahn et al., 2007). Thus, the SCC and PCC are key regions involved in affective and cognitive functions that are particularly influenced by nicotine. To understand how neural pathophysiology of nicotine dependence affects the brain network functioning, this study examines the hubbased functional connectivity changes associated with smoking cue and mental simulation processing. We hypothesized that the SCC and PCC would show functional connectivity changes with the salience and reward network nodes by smoking-cue induced attentional state transition in smokers Moreover, the clinical characteristics of nicotine addiction would be associated with these hub-based functional connectivity strengths.

2.2. Functional MRI data acquisition MRI scanning was conducted on all participants before the beginning of smoking cessation. Smokers were instructed to abstain from smoking after midnight and scanned in the morning. We informed smokers that they were allowed to smoke immediately after scanning to enhance craving and related response to smoking cues by inducing expectation of smoking as reported in prior studies (Carter and Tiffany, 2001; Dols et al., 2002). All MRI images were obtained using a SIEMENS 3.0T scanner (MAGNETOM Skyra, SIEMENS, Germany). A high-resolution T1weighted image was acquired using a 3D magnetization prepared rapid acquisition gradient echo (MP-RAGE) sequence with the following parameters: field of view = 256 mm, voxel size = 1.0 × 1.0 × 1.0 mm3, slice gap = 0.5 mm, TR = 2000 ms, TE = 2.26 ms, flip angle = 8°. The functional MRI images were acquired using T2*-weighted single-shot echo-planar imaging (EPI) sequence with the following parameters: field of view = 224 mm, voxel size = 3.5 × 3.5 × 3.5 mm3, number of slices = 35, no slice gap, TR = 2000 ms, TE = 30 ms, flip angle = 90°. During MRI scanning, foam pads were placed around the head to reduce participants' head motion. 2.3. Mental simulation task and scanning procedure We selected 18 smoking cue pictures showing a hand holding a cigarette at different positions and angles and 18 neutral cue pictures consisting of 16 images with a hand holding an object unrelated to smoking (e.g. pen, pencil, brush, blank card, block) and 2 hand movements (i.e. a partially open hand, a hand with the thumb touching the 5th finger). The functional MRI scanning session included 5 neutral and 5 smoking cue blocks with 10 in-between visual fixation blocks which were repeatedly presented in the order of visual fixation, neutral cue, visual fixation, and smoking cue. In a neutral or smoking cue block, 3 cue pictures were randomly non-repeatedly imported from the corresponding cue picture set and consecutively presented for 8 s per picture. In the visual fixation block, a fixation crosshair was presented for 16 s. Before scanning, participants were instructed to imagine that they were acting according to the pictures of hand movements during the neutral and smoking cue blocks and to not think about anything in particular while staring at the crosshair in the visual fixation block. (Fig. 1).

2. Methods 2.1. Subjects

2.4. Imaging data analyses of task activation

Thirty-three male cigarette smokers and 30 healthy male nonsmokers volunteered in this study approved by the Institutional Review Board of International St. Mary's Hospital. Participants who were 19–65 years of age, smokers who did not abstain from smoking for 3 months or more in the previous year and non-smokers who never smoked before were included. Individuals with a history of neurologic disorders, alcohol or substance use disorders, intellectual disability, mental disorders, severe medical conditions, implantation of a cardiac pacemaker or any metal devices or on any medications within 1 month were excluded. Current depressive and alcohol use disorders were

Image preprocessing of all acquired MRI images and activation analyses of task images were performed using the Statistical Parametric Mapping (SPM12, http://www.fil.ion.ucl.ac.uk/spm, Wellcome Department of Cognitive Neurology, London, UK). The first four volumes of each functional time series were discarded to ensure the magnetic field stabilization. Differences of all image volumes in slice acquisition time from the interleaved sequence were adjusted, and realignment to the first volume to correct the artifacts caused by head 2

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Fig. 1. Schematic description of the functional MRI scanning order and the mental simulation task.

(MPFC), lateral parietal cortex (LP), posterior cingulate cortex (PCC)), fronto-parietal/central executive network (i.e. lateral prefrontal cortex (LPFC), posterior parietal cortex (PPC)), dorsal attention network (i.e. frontal eye field, intraparietal sulcus (IPS)), and the visual network (i.e. medial, lateral and posterior occipital cortex (MO, LO and PO)). All cortical ROIs were selected from the ROIs defined by the CONN's Independent Component Analysis of Human Connectome Project dataset. Other ROIs, including the SCC, NAc, and putamen, were selected from the Harvard-Oxford atlas. In the 2nd level analyses, the functional connectivity changes associated with the smoking cue and smoking mental simulation were identified by a priori defined SCC-to-ROI and PCC-to-ROI analyses in smokers. The smoking cue-related changes (i.e. smoking cue effect) were examined by subtracting the neutral from the smoking mental simulation condition thereby canceling out the effect of mentalization processing. The changes related to smoking mental simulation (i.e. smoking mental simulation effect) were examined by subtracting the visual fixation from the smoking mental simulation condition. Smokerspecific functional connectivities were established by conducting a between-group comparison in the SCC- and PCC-to-ROI analyses of the smoking mental simulation condition with age and the AUDIT score included as covariates. ROI-to-ROI analyses in all subjects by group average during visual fixation and the smoking mental simulation and within-group of the smoking cue and mental simulation effects were conducted to explore other possible hub nodes among the ROIs. All results were considered statistically significant at an FDR-P < .05. Functional connectivity values (i.e. Fisher-transformed correlation coefficients) were calculated from SCC or PCC connections showing significant smoking cue or mental simulation-related functional connectivity changes. Correlation analyses between these values and FTND, MNWS, or TCQ scores were conducted at a Bonferroni-corrected statistical level of P < .017.

movement. After realignment, T1-weighted anatomical image was coregistered to the realigned images of the same individual, and spatially normalized to standard stereotactic space of the Montreal Neurological Institute (MNI) template. Subsequently, they were spatially smoothed with a Gaussian kernel of 6 mm full width at half maximum (FWHM). A high-pass filter with a cut-off frequency of 1/128 Hz was applied to functional data to remove low-frequency signal drifts. In the first-level analyses, the fixation, neutral and smoking cue conditions were modeled by creating a single boxcar function convolved with the canonical hemodynamic response function. Six rigidbody realignment parameters for each individual were entered as regressors to control for the movement-related variance. Individual contrast maps representing each condition and smoking versus neutral cue condition were generated. In the 2nd-level analyses, regional brain activity changes related to smoking cue effect during the mental simulation was explored by one sample t-test in smokers using smoking cue-minus-neutral cue condition contrasts. Smoker-specific regional brain activity during the smoking mental simulation was examined by two-sample t-test of the smoking cue blocks. The AUDIT scores were included as covariates in both tests since they were higher in smokers than non-smokers. An ROI image, including the SCC and PCC, was generated at 95% probability threshold from the Harvard-Oxford atlas using the FMRIB Software Library (FSL v6.0, https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL). Using the ROI image, small volume corrections on t-tests of the SPM were conducted as ROI analyses. The results were considered statistically significant at peaklevel family-wise error rate-corrected P (FWE-P) < .05. 2.5. Imaging data analyses of functional connectivity Functional brain connectivity analyses were carried out using the CONN toolbox (version 18a, https://www.nitrc.org/projects/conn/). The white matter, cerebrospinal fluid and realignment parameters were entered as confounds for individual data. A component-based noise correction method (CompCor) was used for denoising and reduction of head motion-related artifact (Behzadi et al., 2007). At the first level General Linear Modeling, the visual fixation, neutral and smoking cue conditions were defined, a temporal band-pass filter of equal or > 0.01 Hz was applied, and detrending and despiking were conducted. The nodes of major brain networks were selected as ROIs; These included the nodes of the reward network (i.e. SCC, nucleus accumbens (NAc), putamen), salience network (i.e. anterior cingulate cortex (ACC), anterior insular cortex (AIC), rostral prefrontal cortex (RPFC), supramarginal gyrus), default mode network (i.e. medial prefrontal cortex

3. Results 3.1. Smoker and non-smoker characteristics Smokers consumed an average of 16.8 cigarettes/day (SD = 7.0) and showed a mean FTND score of 4.3 (SD = 2.0) and MNWS score of 14.0 (SD = 7.6). Smokers were older than non-smokers, whereas nonsmokers were more educated than smokers. Smokers had a greater tendency for alcohol use disorder than non-smokers (Table 1).

3

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3.2. Smoking mental simulation activation

Table 1 Demographic and clinical data.

Age (years) Years of education BDI-II AUDIT Cigarettes/day Years of smoking Pack-year FTND MNWS F1. Craving F2. Impatience TCQ-SF

Non-smoker (n = 30)

Smoker (n = 30)

F(P)/T(P)

36.0 (6.1) 17.7 (2.1) 6.5 (5.2) N = 29 6.5 (4.0)

41.2 (9.3) 15.3 (2.3) 8.8 (5.4) 9.6 (5.2) 16.8 (7.0) 21.6 (9.5) 20.3 (14.2) 4.3 (2.0) 14.0 (7.6) 9.7 (4.7) 4.3 (3.8) 52.7 (12.9)

2.56 3.95 1.66 2.58

ROI analyses showed significant smoking cue-related activity changes in the PCC only in the smokers (x = 2, y = −48, z = 28; t = 4.00, FWE-P = .038). The PCC activities were significantly greater in smokers than non-smokers during the smoking mental simulation (x = 0, y = −44, z = 32; t = 3.96, FWE-P = .020) (Fig. 2). No significant smoking cue effects in smokers or between-group differences were found in the SCC. Exploratory whole-brain analysis in the smokers showed smoking cue-induced regional activity increases in the occipital gyrus and the PCC which were statistically significant (cluster-level FWE-P < .05) and at a trend level of significance (cluster-level FWE-P < .1), respectively. The PCC activities during the smoking mental simulation were greater in smokers than non-smokers (cluster-level FWE-P < .05) (Table 2).

(.01) (< .001) (.10) (.01)

Means (standard deviations) are presented. Beck Depression Inventory-II, BDIII; Alcohol Use Disorder Identification Test, AUDIT; Fagerström Test for Nicotine Dependence, FTND; Minnesota Nicotine Withdrawal Scale, MNWS; Short form of the Tobacco Craving Questionnaire, TCQ-SF.

3.3. Functional connectivities related to cue and mental simulation processing of smoking The exploratory ROI-to-ROI functional connectivity analyses of the

Fig. 2. The regions of interest (ROI) in the posterior cingulate cortex (PCC) and subcallosal cingulate cortex were generated from the Harvard-Oxford human brain atlas at 95% probability (A). ROI analyses showed significant PCC activities related to smoking cue effect in smokers (B) and between-group difference during smoking mental simulation (C). Clusters within the ROI with greater or equal to 10 voxels with peak-level uncorrected P < .001 are presented. Bar graphs of the mean beta values and their standard errors in the PCC are shown at the bottom row. 4

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3.4. Smoker-specific functional connectivity during smoking mental simulation

Table 2 Exploratory t-test results of smoking mental simulation task. Region

Voxels

Coordinates (x, y, z)

One sample t-test (smokers: smoking cue > neutral cue) Occipital lobe (BA18) 167 −24, −96, −6 Posterior cingulate cortex 98 6, −48, 28 (BA23) −4, −46, 24 Medial prefrontal cortex 15 2, 56, 16 (BA9) Retrosplenial cingulate 12 −14, −50, 6 cortex (BA30) Occipital lobe (BA17) 13 22, −94, −8 Anterior cingulate cortex 11 0, 36, 12 (BA24) Two sample t-test (smoking cue: smokers > non-smokers) Posterior cingulate cortex 115 −6, −44, 16 (BA23) 4, −44, 30 −10, −48, 26 Medial prefrontal cortex 21 −10, 48, 4 (BA10) Anterior cingulate cortex 30 6, 26, 32 (BA32) 2, 32, 28

T

Cluster-level FWE-P

4.94 4.76

.007 .067

4.06

.974

4.12

.990

3.87 3.69

.986 .993

4.16

.045

4.05

.916

3.65

.775

In the exploratory ROI-to-ROI analysis, between-group differences in connectivity strength were found from the reward network nodes to the IPS of the dorsal attention network, the ACC of the salience network, the MO of the visual network, and the MPFC of the default mode network. Among the reward network nodes, the SCC had the greatest number of functional connectivities, thus confirming the SCC as a hub node. Significant between-group differences in smoking mental simulation-related functional connectivities were found arising from the SCC, but not from the PCC (SCC-to-ROI, F(12,45) = 2.67, FDR-P = .02). The connectivity strengths of the SCC were stronger with the left and right IPS of the DAN, and weaker with the MPFC of the DMN in the smoker group (t = 3.92, df = 56, FDR-P = .007; t = 3.51, df = 56, FDRP = .01; t = −3.03, df = 56, FDR-P = .03, respectively) (Fig. 5B). The SCC-to-ROI and PCC-to-ROI functional connectivities during the neutral mental simulation and visual fixation did not show any significant between-group differences. 3.5. Functional connectivities-related to severity of nicotine dependence, withdrawal, and craving

Results are thresholded at peak-level uncorrected P < .001, cluster-level ≥ 10 voxels. BA, Brodmann Area.

A significant inverse correlation was found between the FTND scores and the PCC-right LPFC functional connectivity strength during the smoking mental simulation (Fig. 6). The correlations of the FTND scores with the PCC-left RPFC, the MNWS scores with the SCC-right IPS or the PCC-right AIC functional connectivity strengths showed trends toward significance (r = −0.38, df = 28, P = .04; r = −0.38, df = 28, P = .04; r = 0.39, df = 29, P = .03; respectively). None of the a priori seed-to-network functional connectivities correlated with the TCQ scores. Age inversely correlated with smoking mental simulation-related PCC-right LPFC connectivity strengths in smokers but not in non-smokers (r = −0.49, df = 28, P = .006; r = −0.16, df = 28, P = .40). BDIII scores showed significant positive correlations with SMS-related SCCMPFC connectivity strength in both groups (non-smokers, r = 0.45, df = 27, P = .01; smokers, r = 0.38, df = 28, P = .04). Years of education and AUDIT-K scores did not show significant correlations with functional connectivity strength in any of the above networks.

group average showed that intra-network nodal connectivities during visual fixation and the smoking mental simulation. Moreover, the number of inter-network nodal connectivities increased from visual fixation to smoking mental simulation between the putamen of the reward network and the ACC of the salience network, SCC and the MPFC of the default mode network, and the nodes of fronto-parietal network and the default mode network (Supplementary Fig. 1). In the exploratory within-group ROI-to-ROI analyses, the smoking cue effect was associated with connectivity strength changes from the IPS which was decreased with the RPFC of the salience network in nonsmokers and increased with the SCC of the reward network in smokers. The smoking mental simulation effect-related functional connectivity strength changes were observed between inter-network nodes; Connectivity strengths were increased between the MPFC of the default mode network, LPFC of the fronto-parietal network and the ACC of the salience network and decreased between nodes of the salience, dorsal attention, and the visual networks in non-smokers. Whereas smokers showed increased PCC functional connectivities with the LPFC of the fronto-parietal network and the salience network nodes and increased SCC functional connectivities with the nodes of the dorsal attention and visual network. The nodes with the greatest number of functional connectivities were the SCC and PCC in smokers and the MPFC in nonsmokers (Fig. 3). In the SCC/PCC-to-ROI analysis, the smoking cue effect-related functional connectivity were found significantly increased between the SCC and nodes of the reward, dorsal attention and the visual networks in smokers (F(6,24) = 3.15, seed-level FDR-P = .04). These nodes include the left putamen, left and right IPS, LO and MO (t = 2.90, df = 29, FDR-P = .04; t = 3.24, df = 29, FDR-P = .04; t = 3.08, df = 29, FDR-P = .04; t = 3.20, df = 29, FDR-P = .04; t = 3.01, df = 29, FDR-P = .04; respectively) (Fig. 4A). The smoking mental simulation effect-related functional connectivity changes of the PCC with nodes in the salience, fronto-parietal and the visual networks were significant in smokers (F(6,24) = 4.29, FDR-P = .01). The PCC connectivities increased in the nodes including the right AIC, left RPFC, right LPFC, right PPC and decreased in the right LO (t = 3.60, df = 29, FDR-P = .02; t = 3.18, df = 29, FDR-P = .03; t = 3.06, df = 29, FDRP = .03; t = 2.85, df = 29, FDR-P = .04; t = −3.81, df = 29, FDRP = .02, respectively) (Fig. 4B).

4. Discussion We examined activations and functional connectivity changes related to smoking mental simulation in the brain hubs of the affective and cognitive functions (i.e. the SCC and PCC) in overnight abstinent smokers. Only the PCC activation was induced by smoking cue while both SCC and PCC showed distinct patterns of functional connectivity changes associated with smoking cues and mental simulation. Smoking cue-induced functional coupling between the SCC and the nodes of reward network (i.e. putamen), dorsal attention (i.e. IPS), and visual network (i.e.MO and LO). Mental transition to smoking simulation was associated with enhanced connectivities of the PCC with the AIC and RPFC of the salience network, and LPFC and PPC of the fronto-parietal network. As compared to non-smokers, smokers showed functional connectivity strength differences only originating from the SCC where its connectivities were stronger with the right IPS of the DAN and weaker with the MPFC of the DMN. Among smoker-specific inter-network nodal couplings, weaker PCC-right LPFC connectivity was associated with greater severity of nicotine dependence. These results suggest that the reward network hub linking with the nodes involved in exteroceptive attention processing reflect functional plasticity associated with smoking. Moreover, individuals with weaker prefrontal control of the salience network via the default mode network hub may be vulnerable to nicotine dependence. 5

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Fig. 3. The exploratory within-group ROI-to-ROI functional connectivity analysis results of the effects of smoking cue (i.e. smoking MS – neutral MS) and smoking mental simulation (i.e. smoking MS – visual fixation) in 6 brain networks and 26 ROIs are shown at a statistical threshold of FDR-corrected P < .05. Brain networks consisted of the default mode network (DMN), dorsal attention network (DAN), fronto-parietal network (FPN), reward network (RW), salience network (SN), and the visual network (VN). The ROIs included the rostral prefrontal cortex (RPFC), medial prefrontal cortex (MPFC), lateral prefrontal cortex (LPFC), anterior cingulate cortex (ACC), subcallosal cingulate cortex (SCC), anterior insula cortex (AIC), nucleus accumbens (NAc), putamen (Pt), supramaginal gyrus (SMG), intraparietal sulcus (IPS), posterior parietal cortex (PPC), posterior cingulate cortex (PCC), lateral occipital cortex (LO), medial occipital cortex (MO), and the posterior occipital cortex (PO).

addiction. Our result of weaker SCC functional connectivity with the MPFC in smokers may reflect disengagement of top-down processing of emotion in smokers. The SCC is involved in reward processing and valuation in decision-making (Laxton et al., 2013; Rogers et al., 2004). It also plays an important role in addiction as indicated in prior studies that show SCC activation associated with imagery-induced craving and its cueinduced activation predicting subsequent relapse (Kilts et al., 2001; Li et al., 2015). Emotion regulation and decision-making studies suggest that MPFC is involved in value reappraisal and temporal extension of value associated with voluntary persistence (Goldin et al., 2008; McGuire and Kable, 2015). Moreover, nicotine has been previously demonstrated to directly enhance the SCC-MPFC function connectivity

4.1. Functional connectivity alteration induced by smoking cue Smoking cue-induced functional connectivity increase was found between the SCC of the reward network and the IPS of the dorsal attention network. In addition to its role in attention, The IPS is one of the nodes of the action observation network involved in imitating actions of others and activated during watching another person smoking (Wagner et al., 2011). It has been suggested that reinforced smoking behavior causes conditioning of the sensorimotor association area function which underlies action representation triggered smoking behavior (Tiffany, 1990). Thus enhanced IPS-SCC functional connectivity may reflect valuation processing of smoking-related action thereby provides further neurofunctional evidence for habit induced sensation associated with 6

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4.2. Functional connectivity alteration during the smoking mental simulation Smoking mental simulation-related functional connectivities of the PCC to the right LPFC and, to some extent, the left RPFC and right AIC were associated with nicotine use problems. The PCC and prefrontal cortex have been implicated in nicotine use and craving in several studies. Franklin et al. (2007) found smoking cue-induced activations in the PCC and dorsolateral prefrontal cortex correlated with craving severity independent of withdrawal. Whereas, Hong et al. (2009) reported that nicotine-induced enhancement of the resting-state functional connectivities from the PCC to frontal midline regions which were not associated addiction. Given that PCC activity was greater in smokers and PCC-right LPFC connectivity correlated with nicotine dependence severity, our results suggest that weaker default mode and central executive inter-network functioning are associated with nicotine addiction in smokers. Interestingly, smokers with older age were particularly associated with weaker PCC-right LPFC connectivity, suggesting that long-term nicotine exposure may contribute to the change in functional connectivity. Studies of transcranial magnetic stimulation suggest that regulating craving and cigarette consumption can be reduced by inactivating the dorsolateral prefrontal cortex (Amiaz et al., 2009; Hayashi et al., 2013). In contrast, our result suggests that stronger PCC-LPFC connectivity may have a protective role in nicotine addiction as the interaction between the default mode network and executive function has been suggested to be involved in goal-directed mental simulation (Gerlach et al., 2011). The functional connectivities of the PCC-to-salience network nodes were associated with smoking mental simulation, but were not specifically greater in smokers. Also, the PCC-left RPFC and right AIC showed association tendencies with the severity of nicotine dependence and withdrawal. One reason why these results show insufficient statistical power is because not all smokers had nicotine dependence in this study. The RPFC has not been well studied in relation to nicotine use, but it has interesting implications with its supervisory role of attending to environmental stimuli and one's thought (Burgess et al., 2007). The PCC is involved in regulating attention by modulating between cognitive control and default mode network and considered one of the central hubs of self-awareness (Leech et al., 2011; Herwig et al., 2012; Peer et al., 2015). Taken together we speculate that PCC-RPFC connectivity represents intrinsic processing of metacognition during mental simulation. The enhancement of PCC-AIC functioning connectivity during the smoking mental simulation may represent internal attention to visceral sensations. Its association tendency with nicotine withdrawal supports the network dynamic hypothesis of addiction (Sutherland et al., 2012) which proposed that representation of internal bodily sensations of subjectively experienced withdrawal symptoms form through the strengthening of functional interaction between the insula and default mode network during abstinence. One important limitation of our study is the exclusion of females. Prior evidence suggests that gender difference in nicotine addiction can be found in the intake of nicotine, sensitivity to specific nicotine effect, association with depression, response to environmental cues, cessation, brain response to smoking cue and resting-state brain function (Benowitz and Hatsukami, 1998; Smith et al., 2016; Beltz et al., 2015; Wetherill et al., 2013). Therefore, our results cannot be generalized to females and further studies on gender difference are warranted.

Fig. 4. Within-group a priori defined SCC-to-ROI and PCC-to-ROI functional connectivity analysis results of the smoking cue and smoking mental simulation effect in smokers. (A) The smoking cue effect (i.e. smoking – neutral mental simulation (MS))-related function connectivity strength from the subcallosal cingulate cortex (SCC) to the intraparietal sulcus (IPS), medial and lateral occipital cortex (MO, LO), and the putamen (Pt) were significantly increased. (B) Smoking mental simulation effect (i.e. smoking MS – visual fixation)-related posterior cingulate cortex (PCC) functional connectivity strengths were significantly increased with the rostral and lateral prefrontal cortex (RPFC, LPFC), anterior insula cortex (AIC), and the posterior parietal cortex (PPC) and decreased with the lateral occipital cortex (LO). These results were statistically significant at FDR-corrected P < .05. The corresponding brain networks of each ROI are shown in square brackets which include the reward network (RN), dorsal attention network (DAN), visual network (VN), salience network (SN), default mode network (DMN), fronto-parietal network (FPN).

in smokers (Hong et al., 2009). Since smokers were scanned after overnight abstinence, we speculate that our result of weakened functional connectivity reflects the functional effect of nicotinic receptor upregulation (Govind et al., 2009). Taken together, the weakened SCCMPFC connectivity may reflect reduced cognitive control over cue-induced automatic reward processing following repeated exposure to nicotine. Interestingly, we also found that lower depression score was associated with weaker SCC-MPFC connectivity. The SCC is considered a treatment target of major depression (Hamani et al., 2011) and its activity has been shown to mediate the relationship between MPFC activity and depressive symptoms (Yoshimura et al., 2010). We suggest that cigarette smoking alleviate depressive symptoms by strengthening the weakened SCC-MPFC functional connections. This may help us understand why high comorbidity and relapse of nicotine dependence are observed in patients with major depression (Zvolensky et al., 2015).

5. Conclusion The present findings have two clinical implications. First, smoking cue-induced shift in SCC functional connectivities showing increment with the dorsal attention network node (i.e. IPS) and decline with the default mode network node (i.e. MPFC) can be used as a biomarker for identifying the change in brain function related to nicotine use. Secondly, the enhanced functional connectivities with fronto-parietal/ 7

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Fig. 5. Between-group comparison results in functional connectivity strength during smoking mental simulation. (A) Exploratory ROI-to-ROI analyses in all 24 ROIs in 6 brain networks show differences between the subcallosal cingulate cortex (SCC), nucleus accumbens (NAc), putamen (Pt) in the reward network (RN), intraparietal sulcus (IPS) in the dorsal attention network (DAN), anterior cingulate cortex (ACC) in the salience network (SN), medial prefrontal cortex (MPFC) in the default mode network (DMN), and the medial occipital cortex (MO) in the visual network. (B) The SCC-to-ROI analysis show significant between-group differences with the IPS and MPFC among the 24 ROIs. The t-value color bar show that the ROI-to-ROI lines closer to red and blue color represent stronger and weaker functional connectivity strength respectively in smokers as compared to non-smokers. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Declaration of competing interest None. Acknowledgment Funding: This work was supported by the research fund of Catholic Kwandong University International St. Mary's Hospital (CKURF201407070001) in the Repulic of Korea. References Ahn, H.K., Lee, H.J., Jung, D.S., Lee, S.Y., Kim, S.W., Kang, J.H., 2002. The reliability and validity of Korean version of questionnaire for nicotine dependence. J. Korean Acad. Fam. Med. 23, 999–1008. Amiaz, R., Levy, D., Vainiger, D., Grunhaus, L., Zangen, A., 2009. Repeated high-frequency transcranial magnetic stimulation over the dorsolateral prefrontal cortex reduces cigarette craving and consumption. Addiction 104, 653–660. https://doi.org/ 10.1111/j.1360-0443.2008.02448.x. Behzadi, Y., Restom, K., Liau, J., Liu, T.T., 2007. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage 37, 90–101. https://doi.org/10.1016/j.neuroimage.2007.04.042. Beltz, A.M., Berenbaum, S.A., Wilson, S.J., 2015. Sex differences in resting state brain function of cigarette smokers and links to nicotine dependence. Exp. Clin. Psychopharmacol. 23, 247–254. https://doi.org/10.1037/pha0000033. Benowitz, N.L., Hatsukami, D., 1998. Gender differences in the pharmacology of nicotine addiction. Addict. Biol. 3, 383–404. https://doi.org/10.1080/13556219871930. Burgess, P.W., Dumontheil, I., Gilbert, S.J., 2007. The gateway hypothesis of rostral prefrontal cortex (area 10) function. Trends Cogn. Sci. 11, 290–298. https://doi.org/ 10.1016/j.tics.2007.05.004. Carter, B.L., Tiffany, S.T., 2001. The cue-availability paradigm: the effects of cigarette availability on cue reactivity in smokers. Exp. Clin. Psychopharmacol. 9, 183–190. Choi, K.S., Lee, C.H., Yu, J.C., Kim, S.J., Choi, H.J., Jeong, B.S., 2008. The reliability and validity of the Korean version of tobacco craving questionnaire. J. Korean Neuropsychiatr. Assoc. 47, 161–167. David, S.P., Munafò, M.R., Johansen-Berg, H., Mackillop, J., Sweet, L.H., Cohen, R.A.,

Fig. 6. Scatter plot between the Fagerström Test for Nicotine Dependence (FTND) scores and the posterior cingulate cortex (PCC) – right lateral prefrontal cortex (LPFC) in smokers. Significant at P < .017.

central executive network (i.e. LPFC) and the salience network node (i.e. AIC) in the smoking mental simulation-related PCC activation and its association with nicotine dependence highlight the importance of the PCC as an inter-network hub in nicotine use disorder. Further studies may validate whether intervention strategies enhancing the default mode and central executive inter-network connectivity can treat nicotine addiction. Supplementary data to this article can be found online at https:// doi.org/10.1016/j.pnpbp.2019.109772. 8

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