Brain Imaging and Behavior

, Volume 11, Issue 1, pp 155–165 | Cite as

Altered resting state functional connectivity of anterior insula in young smokers

  • Yanzhi Bi
  • Kai Yuan
  • Yanyan Guan
  • Jiadong Cheng
  • Yajuan Zhang
  • Yangding Li
  • Dahua Yu
  • Wei Qin
  • Jie Tian
Original Research


The insula has been implicated in cognitive control and craving, all of which are critical to the clinical manifestations of nicotine dependence. However, little evidence exists about the abnormalities in resting state functional connectivity (RSFC) of the insula in young smokers, which might improve our understanding of the neural mechanisms of nicotine dependence. Due to the structural and functional heterogeneity of the insula, the RSFC patterns of both left and right anterior (AI) and posterior insula (PI) were investigated in young smokers and non-smokers. Meanwhile, the relationship was assessed between the neuroimaging findings and clinical information (pack-years, FTND, and craving) as well as cognitive control deficits measured by Stroop task performance. Compared with non-smokers, young smokers showed reduced RSFC between right AI and anterior cingulate cortex (ACC), ventromedial prefrontal cortex (VMPFC), amygdala, left dorsolateral prefrontal cortex, and dorsal striatum. Additionally, left AI showed reduced RSFC with ACC. Both left and right PI network differences were not observed between two groups. Moreover, in young smokers, FTND and incongruent errors in the Stroop task were negatively correlated with the RSFC between AI and ACC. Craving scores showed a significantly negative relationship with the RSFC strength between right AI and left VMPFC. These results provide a more thorough network-level understanding the role of insula in cigarette smoking. The findings provide new insights into the roles of AI-ACC circuit in cognitive control deficits and right AI-VMPFC circuit relevant to the craving of nicotine dependence for young smokers.


Insula Resting state functional connectivity Young smokers Cognitive control Craving 


Damage to the insula disrupts smoking behavior in stroke patients (Naqvi et al. 2007), which supports a critical role for the insula in the maintenance of smoking addiction. More and more attention has been paid to reveal the mechanism underlying this phenomenon. Nicotine, the main addictive substance in cigarettes, can alter neural activity by activating nicotinic acetylcholine receptors (nAChRs) (Brody 2006; Changeux 2010). Notably, the insula has been proved to have the high density of nAChRs within the human cerebral cortex (Picard et al. 2013). Thus, rich environment of nicotinic receptor in insula may be more vulnerable to the addictive effects of nicotine. Moreover, the insula activation and its correlation with the subjects’ ratings of urge had been detected when smokers were exposed to smoking-related cues or were deprived of smoking (Brody et al. 2007; Engelmann et al. 2012; Maria et al. 2014). Recently, cortical thickness of the right anterior insula (AI) in smokers was found to be negatively correlated with cigarette dependence and the urge to smoke (Morales et al. 2014). Lower gray matter density in the left AI and its correlation with cigarettes smoked per day were also reported (Stoeckel et al. 2015). Although rapid progress had been made in understanding the role of the insula in smoking addiction, clarifying the more precise roles of the insula in smoking behaviors might help further understanding the mechanism of the nicotine dependence.

Based upon functional connectivity and cytoarchitectonic features (Deen et al. 2011; Kelly et al. 2012; Mesulam and Mufson 1982a), previous studies highlighted a crucial role of the AI in cognitive control and craving (Gu et al. 2013), and the posterior insula (PI) in the reception of primary interoceptive and exteroceptive information (Chang et al. 2012; Craig 2002). Meanwhile, the AI are structurally and functionally connected to the anterior cingulate cortex (ACC), dorsal lateral prefrontal cortex (DLPFC), ventromedial prefrontal cortex (VMPFC), amygdala, and striatum (Deen et al. 2011; Mesulam and Mufson 1982b), regions commonly implicated in craving and in the ability to control the urge to smoke (Hayashi et al. 2013; Kober et al. 2010; Li et al. 2013). Substantial evidence indicated the critical role of the insula, particularly the anterior portion, in generating the conscious urge to smoke at both the morphological and functional levels (Brody et al. 2007; Engelmann et al. 2012; Maria et al. 2014; Morales et al. 2014). In addition, the AI is a key component of the salience network (SN) (Dosenbach et al. 2007; Larson-Prior et al. 2009), which has been suggested to maintain a variety of foundational capacities fundamental to cognitive function. Taken together, dysfunction of the insula, particularly of the AI, may underlie the aberrant processing of cognitive control and craving in smokers.

Young people in the age range of late adolescence to emerging adulthood is associated with the high prevalence of cigarette smoking in China ( Some studies indicated that persons who start smoking at an early age are more likely to become life-long smokers and are more susceptible to nicotine addiction than adults (White et al. 2009). Moreover, maturational brain processes are continuing well through this age period (Paus 2005) and it has been hypothesized that tobacco use during this critical period produces neurobiological changes that promote tobacco dependence later in life (Tiffany 2008). Thus, it is extremely important to study the relationship between smoking and functional brain alterations in this age group. Circuit connectivity may inform specific neurobiological substrates underlying psychological dysfunctions associated with cognitive processing, craving and reward often observed in smokers (Sutherland et al. 2012). Resting state functional connectivity (RSFC) magnetic resonance imaging (MRI), which permits in vivo measurement of the degree of correlated activity (i.e., the strength of interaction) between macroscopic brain regions, offers a unique opportunity to examine interactions between brain regions implicated in cigarette smoking (Fox and Raichle 2007; Biswal et al. 1995). Progress has been made in revealing the critical roles of insula in neural mechanisms of smoking (Sutherland et al. 2013a; Claus et al. 2013; Sutherland et al. 2013b; Maria et al. 2014; Clewett et al. 2014; Stoeckel et al. 2015), however, with regard to young smokers, few studies had investigated the insula RSFC pattern thoroughly (AI and PI), even less is known about the relationship between neuroimaging findings and smoking behaviors (e.g., craving, pack-years, etc.). Meanwhile, chronic smoking is generally associated with cognitive control impairments and insula is associated with cognitive function. However, the implication of insula RSFC with cognitive control impairments remains unclear in young smokers.

In the present study, the RSFC patterns of both left and right AI and PI differences were assessed between young smokers and non-smokers. Moreover, behavioral-task and self-report cigarette smoking measures were carried out to estimate the cognitive control deficits and index the smoking behaviors (e.g., craving, pack-years, etc.). We hypothesized that 1) young smokers would exhibit deficient RSFC between AI and interconnected brain regions involved in cognitive control and craving. 2) RSFC between the sub-regions of the insula and the identified brain regions would be related to individual differences in cognitive control abilities and clinical information (craving, pack-years and cigarette dependence). It is hoped that our results could provide new insights into the role of the insula in pathology of cigarette smoking.

Materials and methods

Ethics statement

All the procedures were approved by the Ethical Committee of Xi’an Jiaotong University and were in accordance with the Declaration of Helsinki. Prior to this study, all participants and their guardians received a complete description about the experiment and gave written informed consent.


Forty male daily smokers and forty male non-smokers recruited from the campus were enrolled in this study. Participants were right-handed, 15 to 24 years of age, and reported no history of drug dependence (other than nicotine in smokers), neurological or psychiatric disorders, prior head injury, or any contraindications (e.g., non-removable metallic implants, etc.) for MRI scanning. Non-smokers reported no history of cigarette use with expired carbon monoxide (CO) concentrations ≤3 ppm (p.p.m) (by Smokelyzer, Bedfont Scientific, Ltd., Rochester, UK). Smokers met DSM-V criteria for nicotine dependence, reported smoking ≥10 cigarettes per day in the last 6 months and expired air CO concentrations ≥6 p.p.m. Smoking behavior was characterized by recording 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) and the brief, 10-item version of the Questionnaire of Smoking Urges (QSU-brief) was administered immediately before the scan (Tiffany and Drobes 1991). The FTND includes 6 items and produces a score from 0 to 10, with higher scores indicating more severe nicotine addiction. The Brief-QSU asks participants to indicate how strongly they agree or disagree with each item on the questionnaire using a scale from 1 (strongly disagree) to 7 (strongly agree). This study focused on the brain and behavioral differences between non-deprived young smokers and non-smokers. Therefore, participants were only asked to refrain from smoking during the 30 min immediately preceding the scan to exclude withdrawal symptoms (Franklin et al. 2007; McBride et al. 2006; Feng et al. 2015).

Behavioral measures

Cognitive control performance was measured by the Stroop color-word task using E-prime 2.0 software ( outside the scanner, which has been used widely in previous addiction investigations (Xing et al. 2014; Yuan et al. 2015; Xu et al. 2006; Feng et al. 2015; Cai et al. 2015). The task was presented using a block design with three conditions, i.e. rest, congruent and incongruent, with three Chinese words (“red”, “green”, “blue”) and three colors (red, green, blue) as stimuli. The congruent stimuli consisted of words denoting colors that matched the color in which the words were presented (e.g., “red” written in red). The conflict stimuli consisted of words denoting colors other than the color of the ink in which the words were presented (e.g., “green” written in red). 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. Subjects were instructed to respond to the displayed color as fast as possible by pressing a button on a Serial Response Box with their right hand. Button presses by the index, middle, and ring finger corresponded to red, blue, and green respectively. 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. There were seven trials in each task block, and each stimulus was presented for 1 s with an inter-stimulus interval of 2 s. Thus, each task block lasted 21 s. All rest blocks lasted 17 s, except for the first one, which lasted 19 s. Before each task block, the instruction, ‘Identify the Color’ was presented; and before each rest block, the instruction was ‘Rest’. All instructions were presented for 2 s. The entire run lasted 367 s. The participants were required to practice for the Stroop task not more than three short runs (5 min per run) before the final behavior data collection. 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 within the two or three practice runs.

MRI data acquisition

This experiment was carried out on a 3-Telsa MRI system (EXCITE; General Electric; Milwaukee; Wisc.) at the First Affiliated Hospital of the Medical College; Xi’an Jiaotong University in China. A standard birdcage head coil was used, along with restraining foam pads to minimize head motion and to diminish scanner noise. For each subject, a high-resolution structural image was acquired with the following parameters: repetition time (TR) = 8.5 ms; echo time (TE) = 3.4 ms; flip angle (FA) = 12°; in-plane matrix size =240 × 240; slices =140; field of view (FOV) = 240 × 240 mm2; slice thickness = 1 mm. Then, resting-state functional images were obtained using an echo-planar-imaging sequence (TR = 2000 ms; TE = 30 ms; FA = 90°; FOV = 240 × 240 mm2; data matrix =64 × 64) with 30 axial slices (slice thickness = 5 mm and no slice gap, total volumes =185) in one run of 6 min 10 s. During the functional scan, subjects were instructed to keep their eyes closed, keep still and not to think about anything systematically. After the scan, the subjects were asked whether or not they remained awake during the whole procedure.

Resting state data preprocessing

Data preprocessing was performed with Analysis of Functional NeuroImages (AFNI, and FMRIB Software Library (FSL, Functional data preprocessing was divided into two sections: core image processing and denoising. Core image processing consists of the following steps: slice timing correction, rigid-body head motion correction (3 displacements and 3 rotations), obliquity transformed to the structural image; affine co-registration to the skull-stripped structural image; standard spatial transform to the MNI152 template, spatial smoothing with a 6-mm kernel and intensity normalization to a whole-brain median of 1000. Previous studies had pointed out that nuisance regression and bandpass filtering alone are often insufficient to control head movement induced noise (Power et al. 2012; Patel et al. 2014). Therefore, wavelet despiking was used in the present study for the functional connectivity (Patel et al. 2014). Denoising steps included: time series despiking (wavelet domain); nuisance signal regression including the 6 motion parameters, their first order temporal derivatives, and ventricular cerebrospinal fluid signal (13-parameter regression); and a temporal Fourier filter.

Resting state functional connectivity

In order to investigate the lateralization and specificity of insula sub-regions in nicotine dependence, both left and right insula were segmented into posterior and anterior portion (Fig. 1) using the analytic scripts download via as seeds (Kelly et al. 2012). The averaged fMRI time series for total voxels of each seed was considered as the reference time series. RSFC analysis was implemented using 3dfim + (AFNI) to produce individual-level correlation maps of all voxels that were positively or negatively correlated with the seed’s time series. Finally, the resultant r value maps were transformed to approximate Gaussian distribution with a Fisher’s z transformation. One sample t tests within each group of subjects were employed to generate the functional connectivity maps for left and right insula (p < 0.05, family wise error (FWE) corrected). Group comparisons of functional connectivity were investigated using Permutation-based non-parametric testing with 5000 random permutations. The statistical procedure produced a threshold for significance of p = 0.05 using Threshold-Free Cluster Enhancement (TFCE) method with FWE correction for multiple comparisons.
Fig. 1

Schematic of the left and right insula parcellation. AI, anterior insula; PI, posterior insula

Correlation analysis

To assess whether behavioral data were related to RSFC, clusters showing abnormal RSFC between young smokers and healthy non-smokers were defined as ROIs. The RSFC value of the ROIs excluding the white matter and non-brain tissues were extracted and averaged. A series of Pearson’s correlation analyses were performed to evaluate possible relationships between the averaged ROI RSFC and the behavior indexes (i.e., Stroop task performance, craving, pack-years, FTND) in young smokers and healthy controls (Bonferroni correction, p < 0.005).


Demographic information

All the participants were age-, gender-, education-matched. More detailed demographic information was given in Table 1.
Table 1

Demographic data and Cigarette consumption of the subjects


Smoker (n = 40)

Non-smoker (n = 40)

p Value

Age (years)

19.62 ± 1.89

19.8 ± 2.041


Age range (years)




Education (years)

12.05 ± 1.319

12.25 ± 1.515


Smoking Behavior

 Initial smoking age

13.73 ± 2.418

 Cigarette per day

15.58 ± 5.533

 Smoking years

4.20 ± 1.884


3.4625 ± 2.40128


5.73 ± 2.025


19.9 ± 6.016

Values are means ± standard deviations. All variables were compared between groups with two sample t test. Pack-years = smoking years × cigarette per day/20; FTND = Fagerstrom Test for Nicotine Dependence; QSU-Brief = the brief, 10-item version of the Questionnaire of Smoking Urges

Functional connectivity results

In line with anatomical connectivity and cytoarchitectonic features of the insula (Chikama et al. 1997; Mesulam and Mufson 1982a, 1982b), one sample t test result showed that, for both groups, the AI have reciprocal connections to ‘limbic’ regions, such as the ACC, the VMPFC, the amygdala and the ventral striatum, the PI functionally connected with the thalamus, in addition to parietal, occipital and temporal association cortices (Fig. 2, FWE corrected, p < 0.05). The differences in the right AI RSFC network between the young smoker and non-smoker group were shown in Fig. 3 and Table 2, which indicated that, compared with non-smokers, the functional connectivity was significantly reduced in the ACC, left DLPFC, left VMPFC, left amygdala and the dorsal striatum in the young smokers (FWE corrected, p < 0.05). In addition, the RSFC between the left AI and ACC in the smoker group was significantly reduced compared with the non-smokers (Fig. 3, FWE corrected, p < 0.05). There is no RSFC differences detected in both left and right PI between two groups (FWE corrected, p < 0.05).
Fig. 2

One sample t test results: resting state functional connectivity (RSFC) networks of both left and right anterior and posterior insula in young smokers and non-smokers. FWE = family wise error, p < 0.05

Fig. 3

Group differences in insula sub-regions resting state functional connectivity (RSFC) network between the young smokers and non-smokers. Relative to non-smokers, the RSFC between right anterior insula (AI) and anterior cingulate cortex (ACC), left dorsolateral prefrontal cortex, left amygdala, left ventromedial prefrontal cortex and left dorsal striatum were reduced in young smokers. Additionally, the left AI showed reduced RSFC with ACC. Both left and right posterior insula networks differences were not observed between young smokers and non-smokers. FWE = family wise error, p < 0.05

Table 2

Regions exhibiting significant RSFC differences between young smokers and non-smokers


Brodmann area

Peak voxel

Volume (mm3)

Peak p value




Regions showed decreased RSFC with left AI in smokers relative to non-smokers

Anterior Cingulate Gyrus







Regions showed decreased RSFC with right AI in smokers relative to non-smokers

Anterior Cingulate Gyrus







L Medial Frontal Gyrus







L Middle/Inferior Frontal Gyrus







L Caudate







L Putamen







L Amygdala







All the coordinates are located in the talairach space. L: Left; Medial Frontal Gyrus: VMPFC; Middle/Inferior Frontal Gyrus: DLPFC; RSFC: resting state functional connectivity; AI: anterior insula

Stroop task performance

Twenty-seven non-smokers and twenty-nine smokers participated in the test. Both groups showed a significant Stroop effect, that is, longer reaction time (RT) during the incongruent than the congruent condition (smoker: 660.707 ± 93.946 vs 571.321 ± 74.056, p < 0.001; non-smoker: 658.040 ± 75.785 vs 538.388 ± 58.939, p < 0.001). The smoker group committed more errors than the non-smoker group during the incongruent condition (smoker: 11.517 ± 6.451; non-smoker: 7.48 ± 4.636, p < 0.01). The reaction delay time (incongruent RT minus congruent RT) in smoker group was significant shorter than the non-smoker group (smoker: 89.387 ± 44.129; non-smoker: 119.652 ± 52.501, p < 0.05) (Fig. 4). Besides, the RSFC between the both left and right AI and ACC showed a significantly negative correlation with the incongruent errors in young smokers (smoker, r = −0.5664, p = 0.0014; right: r = −0.5227, p = 0.0036) (Fig.5).
Fig. 4

Stroop task performance. Both groups showed a significant Stroop effect, that is, longer reaction time (RT) during the incongruent than the congruent condition. The smoker group committed more errors than the non-smoker group during the incongruent condition. The reaction delay time (incongruent RT minus congruent RT) in smoker group was significant shorter than the non-smoker group. * represents p < 0.05

Fig. 5

Correlations between the neuroimaging findings and the Stroop task performance in two groups. The resting state functional connectivity (RSFC) between anterior insula (AI) and anterior cingulate cortex (ACC) were negatively correlated with response errors during the incongruent condition in Stroop task in young smokers. Bonferroni correction, p < 0.005

Correlation analysis results

In young smokers, the RSFC between ACC and both the left and right AI showed a significant negative correlation with the FTND (left: r = −0.508, p = 0.0008; right: r = −0.5742, p = 0.0001), while a negative tendency were found with the pack-years (left: r = −0.3931, p = 0.0121; right: r = −0.4142, p = 0.0079). The craving scores were significantly negative correlated with the RSFC between the right AI and left VMPFC (r = −0.4940, p = 0.0012) (Fig. 6).
Fig. 6

Correlations between the neuroimaging findings and smoking behaviors in young smokers. Fagerstrom Test for Nicotine Dependence (FTND) was negatively correlated with the resting state functional connectivity (RSFC) between anterior cingulate cortex (ACC) and both left and right anterior insula (AI). Craving scores showed a significantly negative correlation with the RSFC strength between right AI and left ventromedial prefrontal cortex (VMPFC). Bonferroni correction, p < 0.005


Damage to the insula, a cortical region that integrates heterogeneous signals about internal states and contributes to executive functions, disrupts smoking behavior in stroke patients (Naqvi et al. 2007). This suggests a significant role for the insula in the maintenance of smoking addiction. Since then, the insula has increasingly become the focus of attention in order to reveal the neurological mechanism underlying this phenomenon. In recent years, with accumulating neuroimaging studies having been employed to investigate the role of insula in smoking addiction (Stoeckel et al. 2015; Maria et al. 2014; Morales et al. 2014; Sutherland et al. 2013b; Carroll et al. 2014; Sutherland et al. 2013a), clarifying the more precise roles of the insula in smoking behaviors becomes necessary to help further understanding the neurobiological mechanism of the nicotine dependence.

Insula-cortical RSFC differences between young smokers and non-smokers

In the present study, decreased RSFC between ACC and both left and right AI were detected in young smokers (Fig.3). In healthy controls, ACC activity was found consistently to be associated with cognitive control (MacDonald et al. 2000; Kerns et al. 2004). Notably, chronic cigarette smoking appears to be associated with deficiencies in cognitive control functions (Swan and Lessov-Schlaggar 2007), and the cognitive deficits in smokers were also detected to be correlated to the changes of the activity of ACC (Azizian et al. 2010). Consistently, reduced AI-ACC interactions which associate with decreased accuracy to name the color of incongruent words in young smokers were detected in the present study (Fig.4 and Fig.5). Furthermore, the AI and ACC form a consistently observed functional network, described as a SN (Dosenbach et al. 2007; Larson-Prior et al. 2009), which has been suggested to maintain a variety of foundational capacities fundamental to cognitive function. Disturbed structural connectivity between insula and ACC had been found to be related with impaired cognitive control in internet gaming disorder adolescents (Xing et al. 2014). Taken together, our findings of the reduced AI-ACC interactions and the decreased accuracy to name the color of incongruent words combined with the relationship between the two changes provide considerable evidence to confirm our viewpoint that the interaction between these two regions is critical for elucidating the cognitive impairments in young smokers.

Craving is an important characteristic of the cigarette-smoking behavior. A strengthening of this process promotes to smoking relapse and ongoing tobacco use. The QSU-Brief scale assesses an individual’s subjective experience of craving (Tiffany and Drobes 1991). In the present study, the overall severity of the craving to smoke has been observed to correlate with the reduced right AI - VMPFC interactions in young smokers (Fig.3 and Fig.6). The AI have reciprocal connections to VMPFC. Both regions are constituents of the neurocircuitry maintaining addiction (Koob and Volkow 2010; Naqvi and Bechara 2009). The activity in the VMPFC and insula are correlated with the subjective urges elicited by smoking-related cue exposure, suggesting that these two cortical regions function together in generating the subjective experience of urge (Brody et al. 2007; Brody et al. 2002; Wang et al. 2007). Intriguingly, when smokers were abstinent from smoking, the precise brain circuit, right AI - VMPFC circuit, has been ascribed a crucial role in tobacco craving. That is, if a smoker had a “weaker” circuit, that smoker was more likely to experience higher degrees of craving during withdrawal (Sutherland et al. 2013b; Sutherland et al. 2013a). Combined with previous findings, we suggest that, whether smokers are abstinence from smoking or not, the right AI - VMPFC circuit is “protective” against craving, and the “susceptibility circuit” potentially contribute to the perpetuation of smoking.

With respect to the DLPFC, up to date, accumulating neuroimaging studies revealed the association between cue-provoked smoking craving and changes in the activity of the DLPFC (McBride et al. 2006; Franklin et al. 2007; Brody et al. 2002; McClernon et al. 2005; Brody et al. 2007). Particularly, fMRI reactivity in the left DLPFC was assessed since high-frequency transcranial magnetic stimulation over the left DLPFC reduces cigarette craving and consumption (Amiaz et al. 2009; Pripfl et al. 2014). This indicates a crucial role of DLPFC in the initiate of the conscious feeling of urge. In addition, couples of studies have found the DLPFC involvement in disrupted cognitive control in addicts (Koob and Volkow 2010). Combined with previous findings (Janes et al. 2010), the decreased RSFC strength between the insula and the DLPFC in the current study (Fig.3) possibly reflects reduced top-down control of conscious feeling of urge.

Insula-subcortical RSFC differences between young smokers and non-smokers

Decreased RSFC between the right AI and the amygdala as well as dorsal striatum have been detected in present study (Fig.3). Although no correlations were found between these neuroimaging findings and the clinical information, these altered circuit level interactions should also receive attention for the important functions of these areas in perpetuating cigarette smoking. The involvement of the amygdala in the development of nicotine dependence had been detected by converging lines of brain imaging studies (Chase et al. 2011), such as, reduced gray matter volume (Hanlon et al. 2014), decreased activity when acute nicotine administration (Zubieta et al. 2001; Rose et al. 2014) and activation (Due et al. 2014; Franklin et al. 2007; McClernon et al. 2007) to correlate with the subjective use urges in smokers (Wang et al. 2007) following smoking-related cue presentation. Particularly, when smokers were abstinent from smoking, the RSFC strength in the amygdala-insula circuit was down-regulated by varenicline and nicotine (Sutherland et al. 2013a). Increased amygdala-insula functional coupling has been linked with elevated subjective anxiety (Paulus and Stein 2006; Baur et al. 2013) and irritability (Naqvi and Bechara 2010), which are two hallmark features of nicotine withdrawal. Therefore, the amygdala-insula circuit might provide a quantitative biometric usefully applied in the design and development of novel smoking cessation interventions. The dorsal striatum is innervated by dopaminergic neurons from the substantia nigra. Previous studies have confirmed that the dorsal striatum is an important brain structure involved in the establishment and expression of habitual behaviors (Everitt et al. 2008), as well as reward-based learning, and its activity is closely linked to drug-seeking behavior and addiction (Schultz 2006; Volkow et al. 2006; McClernon et al. 2009). Experimental evidence has shown that the dorsal striatum mediates tobacco-seeking behavior following abstinence and tobacco craving provoked by smoking cues (McClernon et al. 2009; Wang et al. 2007; Sweitzer et al. 2014). A previous study of cigarette smokers using PET also found that an index of metabolic activity in the dorsal striatum was correlated with the self-reported frequency of experiencing extreme craving (Rose et al. 2007). Taken together, we hypothesized that the decreased functional connectivity between the right AI and dorsal striatum might underlie the neural mechanism of craving and reward processing, thus, increasing the risk of nicotine dependence.

Lateralization in insula RSFC differences between smokers and non-smokers

Although differences between right and left insula were not explicitly tested in this study, results indicated that reduced functional connectivity was mostly seen between right insula and ACC, left DLPFC, left VMPFC, left amygdala and the dorsal striatum, with seed in the left insula, only the ACC showed differences between the young smoker group and the control group. Among the largest studies that have assessed the structure and function of the insula in smokers, deficits are often detected in the right hemisphere. Relative to the young smokers, right insula gray matter volume was reduced in established smokers (Hanlon et al. 2014). Also, cortical thickness of right insula (but not left) was found related to cigarette exposure, dependence, and craving in young adult smokers (Morales et al. 2014). In adult smokers, although lesions to both the right and left insula disrupt smoking behavior, a greater proportion of those with right insula lesions experience a disruption in smoking behavior (Naqvi et al. 2007). The right insula RSFC abnormalities were also detected (Stoeckel et al. 2015) related to tobacco craving (Maria et al. 2014) and alexithymia (Sutherland et al. 2013b) in adult smokers. All the findings suggested that the right insula may be particularly relevant to smoking behavior. Despite this evidence, however, the left insula was also implicated in cigarette dependence (Stoeckel et al. 2015; Gallinat et al. 2006; Zhang et al. 2011) and these findings mostly focused on the grey matter abnormalities in smokers. Thus, in the future, studies specifically designed to clarify the differential roles of right and left insula in cigarette smoking behavior are needed.


The cross-sectional study design makes it impossible to dissociate causal effects of cigarette exposure and dependence from biological susceptibility factors that promote drug use. Our sample only included males, precluding our ability to test how sex may influence the relationship between smoking and brain measures. Although altered functional connectivity between AI and its interconnected brain regions were found in current results, the dynamic interactions between insula and these brain regions to precisely describe the role of the insula in nicotine dependence are not known. Therefore, effective connectivity should be considered in the future study.


In summary, the present study provides a more thorough network-level understanding the role of insula in cigarette smoking. Young smokers demonstrated reduced RSFC between the AI (particularly the right portion) and its interconnected brain regions (e.g., ACC, VMPFC, etc.) commonly implicated in craving and in the ability to control the urge to smoke. Furthermore, in young smokers, FTND and response errors during incongruent condition in Stroop task were negatively correlated with the RSFC between AI and ACC. Craving scores showed a significantly negative relationship with the RSFC strength between right AI and left VMPFC. These findings provide new insights into the roles of the AI-ACC circuit in cognitive control deficits and right AI-VMPFC circuit relevant to the craving of nicotine dependence for young smokers.



This paper is supported by the Project for the National Key Basic Research and Development Program (973) under Grant nos. 2014CB543203, 2011CB707700, 2012CB518501, the National Natural Science Foundation of China under Grant nos. 81571751, 81571753, 61502376, 81401478, 81401488, 81271644, 81271546, 81271549, 81470816, 81471737, 81301281, the Natural Science Basic Research Plan in Shaanxi Province of China under Grant no. 2014JQ4118, and the Fundamental Research Funds for the Central Universities under the Grant nos. JB151204, JB121405, the Natural Science Foundation of Inner Mongolia under Grant no. 2014BS0610, the Innovation Fund Project of Inner Mongolia University of Science and Technology Nos. 2015QNGG03, 2014QDL002, General Financial Grant the China Post- doctoral Science Foundation under Grant no. 2014 M552416.

Compliance with ethical standards

Conflict of interest

Yanzhi Bi, Kai Yuan, Yanyan Guan, Jiadong Cheng, Yajuan Zhang, Yangding Li, Dahua Yu, Wei Qin, Jie Tian declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Ethical statements

Informed consent was obtained from all individual participants included in the study.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Yanzhi Bi
    • 1
    • 2
  • Kai Yuan
    • 1
    • 2
    • 3
  • Yanyan Guan
    • 1
    • 2
  • Jiadong Cheng
    • 1
    • 2
  • Yajuan Zhang
    • 1
    • 2
  • Yangding Li
    • 1
    • 2
  • Dahua Yu
    • 3
  • Wei Qin
    • 1
    • 2
  • Jie Tian
    • 1
    • 2
    • 4
  1. 1.School of Life Science and TechnologyXidian UniversityXi’anChina
  2. 2.Engineering Research Center of Molecular and Neuro ImagingMinistry of EducationXi’anChina
  3. 3.Information Processing Laboratory, School of Information EngineeringInner Mongolia University of Science and TechnologyBaotouChina
  4. 4.Institute of AutomationChinese Academy of SciencesBeijingChina

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