PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Arch Gen Psychiatry. Author manuscript; available in PMC 2010 April 1.
Published in final edited form as:
PMCID: PMC2774753
NIHMSID: NIHMS82208

Nicotine Addiction and Nicotine's Actions Are Associated with Separate Cingulate Cortex Functional Circuits

Abstract

Context

Understanding the mechanisms underlying nicotine addiction in order to develop more effective treatment is a public health priority. Research consistently shows that nicotine transiently improves multiple cognitive functions. However, using nicotine replacement to treat nicotine addiction yields generally inconsistent results. While this dichotomy is well known, the reasons are unclear. Imaging studies showed that nicotine challenges almost always involve cingulate cortex, suggesting that this loci may be a key region associated with nicotine addiction and its treatment.

Objective

To identify cingulate functional circuits that are associated with the severity of nicotine addiction and to study how nicotine affects them.

Design

Using region-specific resting-state fMRI signals to extract resting-state cingulate functional connectivity, and to study how nicotine addiction and acute nicotine administration modulate these functional pathways, in a double-blind, placebo-controlled design.

Setting

Outpatient clinics.

Participants

Nineteen healthy smokers.

Intervention(s)

Single dose (21/35mg) nicotine patch.

Main Outcome Measure(s)

Correlation of nicotine addiction severity and cingulate resting state functional connectivity, and effects of acute nicotine on connectivity strength.

Results

Clearly separated pathways that correlated with nicotine addiction vs. nicotinic action were found. The severity of nicotine addiction was associated with the strength of dorsal anterior cingulate cortex (dACC)-striatal circuits, which were not modified by nicotine patch administration. In contrast, acute nicotine enhanced cingulate-neocortical functional connectivity patterns.

Conclusions

Nicotine addiction was strongly associated with functional circuits interconnecting dACC and the striatum. Acute nicotine administration had no significant effect on these circuits. Rather, nicotine enhanced several cingulate-neocortical functional connectivity circuits that were not associated with the severity of nicotine addiction, but may play a role in nicotine's cognitive enhancing properties. Resting state dACC-striatum functional connectivity may serve as a circuit-level biomarker for nicotine addiction, and the development of new therapeutics aiming to enhance the dACC-striatum functional pathways may be effective for nicotine addiction treatment.

Introduction

Nicotine, the addictive component in tobacco, has been shown to transiently improve performance on a wide range of cognitive and neurophysiological tasks in humans, including attention 1;2, visual information processing 3, computational abilities 4, prepulse inhibition 5, vigilance 6 and working memory 7. What remains unclear is how to explain nicotine's effect on such diverse activities, which are controlled, at least in part, by different brain circuits. The seemingly pervasive behavioral effects of nicotine and the localization of nicotinic receptors on neurons of different neurotransmitter systems 8;9 suggest the interesting possibility that nicotine may exert a modulatory effect on multiple functional brain circuits independent of specific task performance. New MRI approaches to identify functional network connectivity 10;11 present the opportunity to test this hypothesis in human subjects.

A common thread running through the imaging literature is the association of the cingulate in nicotine's acute effects and nicotine addiction 9;12-15. Early autoradiographic imaging showed that the cingulate alone had increased glucose utilization following nicotine administration in rats 12. Human PET studies identified high concentrations of nicotinic binding sites in the cingulate, insula, thalamus, basal ganglia, and the frontal lobe 9. Nicotine dose-dependently increased activity in the nucleus accumbens, amygdala, cingulate, and frontal lobes in the task-free state 13. Nicotine improved sustained attention by increasing activation in posterior cingulate, bilateral parietal and occipital cortex, thalamus and caudate 3, and improved working memory by either enhancing 16 or reducing 14 activation in the cingulate and other areas. More recently, nicotine was found to improve attention by deactivating anterior and posterior cingulate and other brain regions 2, while activation in the posterior cingulate predicted the behavioral effects of nicotine 15. To summarize, of regions associated with nicotine's actions, the cingulate was the most frequently detected, suggesting that this region may be a convergent structure pivotal for the diverse CNS nicotinic effects. However, the exact location(s) of nicotine's actions within the cingulate varies across studies. Perhaps more importantly, the apparent contradiction on the valence of nicotine's action on the cingulate needs to be reconciled (both significant activation and deactivation have been reported).

Recent advances in understanding the resting-state functional connectivity of the brain 10;11;17 provide a framework to test the hypothesis that specific cingulate functional circuitry may vary both as a function of nicotine dependence and a function of acute nicotinic action, independent of any particular behavioral activation condition. Resting-state functional connectivity is task-independent, low frequency synchronized activities across brain regions 10;11. Its neural bases are under intense studies. The slow coherent fluctuations in resting-state fMRI BOLD signals may reflect suppression of neuronal activity 10;18-21. The stable spatial organization of resting-state functional connectivity maps suggests that it can be constrained by anatomical connectivity 22;23. Based upon prior knowledge of the functional and histological subdivisions of the cingulate cortex 24-29, we partitioned the entire human cingulate into anatomic subregions. The study is designed to first characterize the resting-state functional connectivity associated with each cingulate subregion, then to identify cingulate circuits that are associated with nicotine addiction and finally to determine how acute nicotine administration affects circuits that are associated with nicotine addiction.

Methods

Subject

Nineteen (5 female) healthy smokers (10 or more cigarettes per day) participated in the study. Subjects gave written informed consent approved by both University of Maryland and National Institute on Drug Abuse IRB panels. Subjects were 18−50 years of age, right handed, and were recruited through media advertisements. Major medical and neurological conditions were exclusionary criteria. Subjects have no Axis I diagnoses other than nicotine dependence as evaluated by the Structured Clinical Interview for DSM-IV (SCID) 30. The severity of nicotine addiction was assessed by the Fagerstrom Test for Nicotine Dependence (FTND) 31. FTND includes 6 items and produces a score from 0 to 10 with higher scores indicating more severe nicotine addiction. FTND marks the genetic liability of nicotine addiction 32;33. The cumulative exposure to smoking was calculated as pack-year.

Design

This was a double-blind, placebo controlled, cross-over, randomized fMRI study comparing nicotine vs. placebo patch effect on resting-state functional connectivity. Subjects were scanned on two occasions approximately one week apart, within 5−14 days. They received either a nicotine patch (Nicoderm CQ, SmithKline Beecham) or an identical placebo patch on each occasion. We used a two-dose strategy to better match the regular nicotine intakes: 21mg for individuals who smoked 10−15 cigarettes/day (n=6) and 35 mg for individuals who smoked more than 15 cigarettes/day (n=13).

Subjects were instructed to maintain their normal smoking routine prior to patch application, and then abstain for about 4.5 hours following patch administration, including 2.5 hours prior to scanning, and 2 hours inside the scanner. The 4.5 hour window was chosen to avoid confounds induced by withdrawal, which generally begins 6−12 hours after abstinence 34. In the scanner, each subject underwent 1 hour of smooth pursuit eye movement/fixation tasks (data not shown), followed by a 5-minute resting scan and an 8-minute structural scan. Resting scan data were therefore collected about 3.5 hours after patch administration, which is within the window of steady plasma nicotine levels 6. A self-reported symptom checklist and a self-reported mood change questionnaire 35 were administered before patch application and after scan. Breath CO was measured immediately prior to patch application. Plasma nicotine levels were measured at the end of each scan session.

MRI acquisition

Data were collected on a 3 Tesla Siemens Allegra scanner equipped with a quadrature volume head coil. Subjects were given a simple instruction to rest and keep their eyes open. A static neutral image (the projector's logo) was presented on the screen during the resting scan. A bite bar was used to minimize head motion. Resting-state fMRI were acquired over 39, 4 mm axial, interleaving slices using a gradient-echo EPI sequence (150 volumes, TE/TR = 27/2000 ms; FA = 80°; FOV = 220×220 mm2; image matrix = 64×64). High-resolution (1×1×1 mm3) T1-weighted MPRAGE images were acquired after each resting scan.

Data Processing

Data were analyzed in AFNI 36 and MATLAB (The MathWorks, Inc., Natick, MA). Volumes were slice-timing aligned and motion corrected to the base volume that minimally deviated from other volumes using an AFNI built-in algorithm 37. After linear detrending of the time course of each voxel, volumes were spatially normalized and resampled to Talairach space at 3×3×3 mm3, spatially smoothed (FWHM 6 mm), and temporally low-pass filtered (fcutoff= 0.1 Hz) 38-40. Correlation analyses were performed by calculating the cross-correlation coefficient (CC) between each voxel's time course and the template time course extracted by averaging time courses of all the voxels in the defined regions of interest (“seed ROI”), including the six rigid head-motion parameter time courses and the average time course in white matter as nuisance covariates 41;42. The white matter mask was generated by segmenting the high-resolution anatomical images in SPM5 43 and down-gridding the obtained white matter masks to the same resolution as the functional data. These nuisance covariates regress out fluctuations unlikely to be relevant to neuronal activities 41. CC maps were then converted to z-score maps using an AFNI built-in function.

Regions of interest (ROIs)

Each subject's cingulate was partitioned into 7 ROIs per hemisphere: 3 subregions for ACC; a middle cingulate cortex (MCC); and 3 subregions for the posterior cingulate cortex (PCC). ROIs were manually drawn on the grey matter at the coronal and sagittal planes of each subject's T1 image volume based on prior conventions when available, supplemented by histological, structural, and functional evidence (Figure 1):

  1. ACC. ACC was divided into dorsal ACC (dACC), rostral ACC (oACC), and subcallosal ACC (sACC): a) dACC was separated from the rostral ACC by drawing a coronal plane that was one slice forward from the disappearance of the juncture of the anterior corpus callosum from both hemispheres 28;29. The posterior end of dACC was the first vertical slice posterior to the anterior commissure, as proposed by Fornito et al28. This is more anterior to that defined by McCormick et al29, but is close to the division of anterior and posterior mid-cingulate cortex (MCC) that was proposed by Vogt et al25;26. The deepest points of the cingulate and callosal sulci defined the superior and inferior boundaries of dACC, respectively. When the second cingulate 28;29 was present, we traced the structure as part of the cingulate ROI following rules established by McCormick et al29; b) oACC. The posterior margin was the first plane anterior to the first dACC plane. The anterior margin was defined when the cingulate gyrus was no longer visible at the coronal slide 28;29; c) sACC was defined by the anterior dACC plane, moving posteriorly to the first coronal slice in which the caudate head and the putamen separated. The interior and superior margins were the cingulate sulcus and the corpus callosum, respectively 28. The average sizes (mean ± s.d) of dACC, oACC, and sACC are 3455 ± 851, 1360 ± 456, and 813 ± 212 mm3.
  2. MCC. MCC defined here was the posterior MCC per Vogt et al25. The anterior boundary was the posterior plane of dACC. The posterior boundary was the vertical plane on the tip of marginal ramus, which was called the posterior plane for ACC per McCormick et al. Separating MCC from ACC as a ROI is justified by cytology 25;26 and its involvement in motor control 44. The size of MCC is 2679 ± 717 mm3.
  3. PCC. PCC (Brodmann area 23 and 31) was divided into 3 ROIs 27: a) dorsal PCC (dPCC), which was posterior to the marginal ramus plane and anterior to the ventral branch of the splenial sulci; the latter defined the histological division of areas d23b and v23b, or dorsal and ventral PCC; b) Ventral PCC (vPCC), whose anterior border was dPCC, posterior-inferior border was the common trunk of the calcarine and parieto-occipital sulcus 24;27, and wrapped around the retrosplenial cortex (RSC). c) RSC was bordered by the common truck posteriorly and the callosal sulcus dorso-anteriorly 24. The anterior-lateral segment was buried within the callosal sulcus; while the posterior-ventral end was on the surface of the medial hemisphere of the isthmus of PCC 24. To segregate the 3 PCC ROIs, the RSC was drawn first using sagittal slices, which identified the RSC within the callosal sulcus. The dPCC and vPCC were then drawn using the coronal slices, where the RSC was the inferior border for dPCC and inferior and anterior border for vPCC 24;25;27. The sizes of dPCC, vPCC, and RSC are 1911 ± 573, 1207 ± 391, and 1395 ± 366 mm3.
Figure 1
Midline (2mm from midline) view of the cingulate partitions. dACC: dorsal anterior cingulate cortex. sACC: subcallosal ACC; oACC: rostral ACC; MCC: middle cingulate cortex. dPCC: dorsal posterior cingulate cortex. vPCC: ventral PCC. RSC: retrosplenial ...

Cingulate anatomical ROIs for each hemisphere were drawn separately. The structural image of the first session was used to draw the ROIs. Structural images of the two sessions were aligned and the ROIs were then applied to the structural image of the second session.

Cingulate functional connectivity map

One-sample, two-tailed t-tests were performed on each individual's z-score maps to obtain thresholded group functional connectivity maps at pcorrected < 0.05 based on Monte Carlo simulations 45. The corrected threshold corresponds to puncorrected < 0.001 46 with a minimum cluster size of 810 mm3 in both the nicotine and placebo conditions. For each seed ROI, thresholded group functional connectivity maps from both nicotine and placebo conditions were combined using “OR” operation to generate a mask, which was used to constrain the subsequent analyses.

Nicotine addiction severity by drug interaction on cingulate connectivity

We first investigated interactions between addiction severity (FTND) and nicotinic effect on functional connectivity using the regression model: V'i(diff) = ß'0 + ß'X + ε', where V' was the z score of the arithmetic difference between nicotine vs. placebo condition on the ith voxel, X was the centered FTND score, plus a random error term ε'. The t-statistics of ß' tested the effect of FTND on nicotine vs. placebo differences, i.e., drug × FTND interaction. Statistical significance of the t-statistic for this and the subsequent analyses were thresholded after correction for multiple comparisons using Monte Carlo simulations to obtain pcorrected < 0.05, corresponding to puncorrected < 0.001 with a minimum cluster size of 243 mm3 – 405 mm3. The different cluster size thresholds reflect different numbers of comparisons to be corrected for connectivity maps of different seed ROIs.

Main effect of nicotine addiction severity on cingulate connectivity

In the event of no significant interaction, we applied a second regression model: Vi(mean) = ß0 + ßX + ε, where V was the z score of the mean of nicotine and placebo conditions on the ith voxel. The t-statistics of ß tested the main effect of FTND (X). To investigate whether the effect of FTND on cingulate connectivity was secondary to covariates such as chronic smoking exposure, nicotine level, and blood pressure, exploratory regression analyses were performed to examine the relative contribution of addiction severity and each covariate. The β of FTND represented the independent contributions of FTND to the functional connectivity after controlling for the covariate.

Nicotine Effects on Cingulate Connectivity

To identify nicotinic effects on cingulate functional connectivity, paired sample t-tests were performed on z-score maps to assess placebo vs. nicotine differences for each ROI.

For clinical data, paired t-tests were used to examine nicotine vs. placebo effect on side effects, withdrawal symptoms, and nicotine and CO measures. Pearson's correlations were used to examine relationships between clinical and nicotine addiction parameters.

Results

Nicotine and demographic information

Participants were 35.7 ± 11.1 (mean ± S.D.) years of age, had 13.0 ± 1.8 years of education, started smoking at 16.9 ± 5.7 and became regular smokers at 18.9 ± 5.7 years of age. Their nicotine addiction severity, as measured by FTND, was 4.3 ± 2.4. Lifetime exposure to cigarette smoking was 15.6 ± 10.9 pack-year. Changes in withdrawal, side effect symptoms and mood were not statistically different between placebo vs. nicotine conditions (Table 1). Withdrawal symptoms assessed using time (before and after scan) and drug (nicotine vs. placebo) as repeated measures also did not show significant time × drug interaction (F1,18=0.42, p=0.52) or main effect of drug (p=0.21) or time (p=0.09). However, change in systolic blood pressure was significantly greater in the nicotine as compared to placebo condition (p=0.05). The FTND score significantly correlated to CO level prior to placebo (r=0.53, p=0.02) or nicotine patch (r=0.61, p=0.006), but not to nicotine plasma level in placebo (r=0.24, p=0.35) or nicotine (r=0.29, p=0.28) conditions, confirming that nicotine level by itself is a state measure and is not directly related to addiction severity.

Table 1
A comparison of nicotine vs. placebo related measurements. There was no significant bias on smoking level prior to each condition based on similar CO levels prior to placebo vs. nicotine patch applications. Post-scan nicotine levels confirmed robust differences ...

Cingulate functional connectivity

The connectivity maps of each cingulate “seed ROI” (pcorrected < 0.05, corresponding to puncorrected < 0.001 and a cluster size of 810 mm3) are presented in Figure 2. In general, the strongest connectivity of each cingulate ROI was adjacent to its seed region. Non-adjacent regions also showed significant connectivity associated with each cingulate subregion. A descriptive summary of the statistically corrected functional connectivity maps of each cingulate ROI is given in the legend.

Figure 2
Resting-state functional connectivity maps between each of the left (Figure 2a) and right (Figure 2b) cingulate ROIs and the rest of the brain (pcorrected <0.05). Maps for nicotine (red) and placebo (yellow) were generated separately and then ...

Cingulate connectivity and nicotine addiction severity

Voxel-wise regression analyses showed no significant interaction between FTND score and drug condition in any cingulate functional circuit. In contrast, voxel-wise regression analyses for the main effect of FTND score showed that FTND was significantly (pcorrected < 0.05, corresponding to puncorrected < 0.001 and a cluster size of 243 mm3 – 405 mm3) and negatively correlated with three co-activated circuits: between left dACC and bilateral striatum (Figure 3), and between right dACC and right striatum (Figure 4). The extent and coordinates of these findings are listed in Table 2. Note that these correlations were present and statistically significant in both placebo and nicotine conditions, indicating that nicotine did not remove the negative correlations.

Figure 3
Negative correlations between FTND and left dACC-bilateral striatum functional connectivity. Two discrete clusters (blue) had significant main effect of FTND. Scatter plot data were based on mean z values of the significant clusters. Fit lines indicate ...
Figure 4
Negative correlation between FTND and right dACC-right striatum connectivity. One cluster in the right striatum (blue) had a significant main effect of FTND based on regression analysis (r= −0.72, p<0.001). Scatter plot data were based ...
Table 2
Effects of nicotine addiction severity (FTND) on cingulate functional connectivity. Main effects of FTND were found in 3 functionally coherent circuits: between left dACC and bilateral striata (a right anteroventral cluster and a left posteriodorsal cluster); ...

To investigate whether the above effect of FTND on the dACC-striatal connectivity could be secondary to chronic exposure to smoking, rather than addiction per se, exploratory regression analyses were performed to examine the relative contribution of chronic exposure (pack-year). Analyses were carried out in placebo and nicotine conditions separately for each circuit. FTND correlations remained significant in all three dACC-striatal functional connectivity paths in the placebo (n = 19, β = −0.88 to −0.95, t = −6.14 to −7.27, all p ≤ 0.001) and nicotine (all β = −0.56 to −0.70, t = −2.50 to −3.56, p = 0.024 to 0.003) conditions, after controlling for chronic exposure.

Nicotine plasma concentration during imaging also could have influenced the contribution of FTND to the ACC-striatal path. We repeated the above analyses using nicotine level as a predictor. FTND remained negatively correlated to all three dACC-striatal functional connectivity scores in both placebo (n = 17, all β ≤ −0.78, all t ≤ −4.65, all p < 0.001) and nicotine (n = 16, all β ≤ −0.61, all t ≤ −2.84, all p ≤ 0.01) conditions after controlling for nicotine levels. FTND also remained significantly correlated to the corresponding dACC-striatal connectivity after controlling for age and gender (data not shown). Taken together, these findings do not support the conclusion that the negative correlations between addiction severity and the dACC-striatal connectivity were substantially biased by chronic smoking exposure, nicotine level during scanning, age, or gender.

Finally, we conducted exploratory regression analyses using the seven DSM-IV criteria for nicotine dependence as predictors. The DSM-IV criteria together significantly contributed to the FTND-derived dACC-striatum functional connectivities (R2 change 0.58 − 0.67 for the 3 connectivities, all p<0.001). Within the models, the functional connectivities were most closely associated with DSM-defined tolerance (standardized coefficients beta = −0.69 to −0.79 for the 3 connectivities, all p< 0.001) and to a lesser extent withdrawal (beta = −0.34 for the right dACC-striatum connectivity, p=0.02). These findings are consistent with the symptoms (tolerance and withdrawal) typically contributing to FTND scores in tobacco smokers.

Nicotinic effects on cingulate connectivity

Voxel-wise paired t-tests showed that acute nicotine administration significantly enhanced the coherence strength of several cingulate functional connectivity paths compared to the placebo condition (pcorrected < 0.05). The coordinates having significant nicotine effects are presented in Table 3. Figure 5 illustrates the increased connectivity following nicotine administration between seed ROIs and these locations. The z scores of these seven circuits were significantly different between nicotine vs. placebo conditions (paired t-tests, t= 4.1 − 5.2, p ≤ 0.001) after Bonferroni correction for fourteen ROI comparisons (0.05/14=0.003). A consistent pattern of nicotine's effect was seen in the functional connectivity between PCC ROIs and frontal midline structures, including orbitofrontal, medial superior frontal, and anterior cingulate regions (Figure 5b, 5f, and 5g). In addition, left subcallosal ACC showed significantly enhanced connectivity with medial frontal cortex (Figure 5d). Another common pattern was the cingulate connectivity with parietal regions, including connectivity between left dACC and superior parietal lobule and the postcentral gyrus, and between the right MCC and the inferior parietal lobule and the postcentral gyrus (Figure 5a and 5e). Nicotine also enhanced a relatively “local” circuit between the dPCC and vPCC (Figure 5c). Notably, and in contrast to the above, there was no indication that nicotine significantly enhanced any of the three ACC-striatal functional connectivity circuits, even with paired t-tests. Finally, all nicotine effects were in the positive (enhanced) direction; nicotine never significantly reduced synchronized activity between the cingulate and any other region of the brain.

Figure 5
Significant main effect of drug on cingulate circuitry based on paired t-tests. Seven resting-state functional connectivity pathways were statistically enhanced by nicotine administration (red) when compared to placebo. See Table 3 for the coordinates ...
Table 3
Cingulate functional connectivity circuits enhanced by nicotine

Discussion

This study identified non-task driven, resting-state functional networks associated with discrete cingulate subregions and examined the relationship between an established marker for nicotine addiction and each cingulate circuit. We report here, for the first time, that severity of nicotine addiction was inversely associated with the strength of the coherent activity between dACC and striatum, i.e., the more severe the addiction, the weaker the functional connectivity. Critically, acute nicotine challenge did not abolish these correlations. In contrast, as part of a double dissociation, acute nicotine did enhance the coherence of other cingulate-cortical circuits that were not correlated with nicotine addiction.

The dACC-striatal functional connectivity paths coincide with known “hard-wired” pathways 47-49. Baleydier and Mauguiere noted that the anterior cingulate gyrus (area 24) is connected with “the caudate nucleus, the claustrum, the lateral frontal and the posterior parietal (area 7) cortices” 47-49. Others also showed that ACC projects to the ventromedial regions of the caudate nucleus and putamen 50. These fibers are also described as the striatal fibers that are branches from the cingulate bundle 48;51; all of which provide a putative anatomical basis for the functional connectivity observations.

FTND is an established clinical and genetic trait marker of nicotine addiction and has a high heritability around 0.72 to 0.75 32;33. Its validity in marking nicotine addiction is further supported by association studies using FTND as a primary phenotype, which have identified nicotinic acetylcholine receptor variants contributing to nicotine addiction 52;53. By showing significant correlations with FTND, we can hypothesize that resting-state synchronized activity in the dACC-striatum circuits may serve as a circuitry level endophenotypic marker for nicotine addiction. Anatomically, the identified striatal clusters mainly include the putamen and its transition to the nucleus accumbens, encompassing roughly a transitional zone between dorsal and ventral striatum. The progression of addiction is thought to be associated with initial medial frontal/anterior cingulate cortex executive control over ventral striatal reinforcement mechanisms which, as the addictive processes continue, are replaced by more habit driven, dorsal striatal activity 54;55. The finding that more severe nicotine addiction is associated with weaker functional connectivity between ACC and striatum may be tied to an underlying dysfunction associated with addictive behaviors that are modulated by this circuit.

We observed that nicotine, even at high plasma levels (34.3 ± 13.1 ng/ml), did not substantially alter the negative correlations between FTND score and the ACC-striatal coherence. While there were non-significant numerical reductions in the strength of the negative correlations during nicotine administration in the post-hoc exploratory analyses, the significant negative correlations were not abolished. In other words, nicotine administration alone, at least over the short duration used in this study, did not robustly influence the circuit level abnormalities associated with nicotine addiction severity. In contrast, nicotine did significantly enhance the functional connectivity of several cingulate-cortical networks, which also seem to follow the presence of known fiber tracks interconnecting these regions. For example, one such connectivity path is between PCCs and the orbitofrontal cortex/ACC. Anatomically, PCC is linked with the medial orbito-prefrontal cortex, the medial extension of the prefrontal cortex, and the ACC by the cingulate bundle 25;47;49;51. Another example, the ACC/MCC - parietal lobe network, is a circuit with known reciprocal fiber connections 49;51. Notably, all of the observed nicotine effects were to enhance the higher-order, isocortical functional connections. Whether this cortico-cortical enhancement of functional connectivity is channeled directly via these structures cannot be determined with the present technology, although this may be suggested by the well-established cortico-cortical fibers linking these regions. That resting state connectivity strength has been shown related to performance on a working memory task also suggests the physiological relevance of our observations 56.

The apparent contrast between the effects of acute nicotine on cingulate-neocortical but not cingulate-striatal functional connectivity may be clinically relevant. While many available treatments have helped to reduce the prevalence of smoking, the efficacy of nicotine replacement therapy (NRT) has been generally unsatisfactory; for example, nicotine patch treatment yields only about a 10−15% long-term quit rate (e.g., 57). However, nicotine, even in a single dose, can induce robust, transient enhancement of diverse neurophysiological and cognitive functions (see Introduction). The mechanisms responsible for this dichotomy are unknown.

Based on the current data, we hypothesize that nicotine may improve behavioral tasks through a transient enhancement of distributed cingulate-neocortical or other cortico-cortical functional networks, regardless of task demands. The observed nicotine-related behavioral and imaging signals in a given experiment may represent the circuits taxed by the specific behavioral task performed and may be reflected by the enhanced cingulate-neocortical connectivity strengths reported herein. Depending on the behavioral tasks during imaging, cingulate activity was shown to have increased (e.g., 3;12;13;16) or decreased (e.g., 2;14;58) following nicotine administration. The present resting-state connectivity data offer a new perspective towards resolving these inconsistencies by suggesting that the cingulate involvement in nicotinic actions may not be simply local increases or decreases of activity within the cingulate proper, but may be related to enhancement in synchronized activity between cingulate and its functionally connected cortical regions. On the other hand, the FTND correlation data suggest that nicotine addiction is not primarily associated with the coherent activity of the neocortical connections, but rather is associated with a more specific alteration in dACC-striatal connectivity. Acute administration of nicotine itself does not substantially affect the strength of this connectivity circuit, which may in part help explain the low rate of smoking cessation by NRT. Our findings offer potential brain circuitry level mechanisms to explain the incongruent data of clear nicotinic effects on cognitive enhancement vs. its limited effect on treating nicotine addiction.

Task-independent, functionally coherent resting state networks are thought to represent intrinsically synchronized activity of the brain and has been termed default mode network 11;17. The most consistent regions associated with this network are PCC, ACC, and medial frontal regions 10;11, along with several frequently detected areas such as superior and inferior parietal lobule and precuneus 10;11;17;59. The default mode network is reliably detected regardless of whether a person keeps their eyes closed or open during the resting state11;17. The default mode maps identified from the PCC and ACC subregions in this study were remarkably similar to findings of the corresponding cingulate seed ROIs from previous studies 11;60. As illustrated by our data, the potential functional role of the default mode connectivity is intriguing. The default state connectivity was initially proposed to represent a “sentinel” role of the resting but awake brain to broadly evaluate information from external and internal milieu 10. Our data show that this resting-state network is also subject to pharmacological manipulations.

The steady state nicotine effect following patch administration on neocortical connectivity does not necessarily imply a similar effect during more rapid nicotine delivery as provided by cigarette smoking. The current patch design identifies the pharmacological action of nicotine on brain circuit dynamics. Replication studies using faster nicotine delivery systems, such as nasal spray or smoking itself, is warranted.

Although we did find mild elevated systolic pressure in the nicotine condition, it is likely that our findings were due to nicotine induced neuronal activity rather than secondary to nonspecific cardiovascular changes. Blood pressure changes do not correlate with cortical brain activation 61. A recent paper demonstrated no effect of nicotine on finger-tapping induced BOLD activation 62. The correlations seen between FTND and regional connectivity were similar between the nicotine and placebo conditions, which also argue against a nonspecific vascular effect.

In conclusion, this study demonstrated that nicotine increases cingulate-neocortical functional connectivity coherence strength during the ‘resting-state’. However, this short-term nicotine challenge does not significantly alter the cingulate-striatal circuitry that was associated with the severity of nicotine addiction, suggesting that nicotine replacement does not necessarily correct the network abnormalities associated with nicotine addiction. The abnormal resting-state cingulate-striatal functional connectivity may serve as an in vivo biomarker for testing new, potentially more effective, nicotine addiction therapeutics. Our study further suggests that the non-task dependent resting-state imaging approach might also be useful to characterize pharmacologically induced changes associated with nicotine and perhaps other pharmacological-imaging studies.

Acknowledgements

Support was received from National Institute on Health grants MH70644, 79172, 49826, 77852, 68580, and N01-DA-5-9909, the National Institute on Drug Abuse Intramural Research Program, Neurophysiology Core of the University of Maryland General Clinical Research Center (# M01-RR16500), and the Maryland Cigarette Restitution Fund Program – Other Tobacco-Related Diseases Research Grant.

Reference List

1. Levin ED, Rezvani AH. Development of nicotinic drug therapy for cognitive disorders. Eur J Pharmacol. 2000;393:141–146. [PubMed]
2. Hahn B, Ross TJ, Yang Y, Kim I, Huestis MA, Stein EA. Nicotine enhances visuospatial attention by deactivating areas of the resting brain default network. J Neurosci. 2007;27:3477–3489. [PMC free article] [PubMed]
3. Lawrence NS, Ross TJ, Stein EA. Cognitive mechanisms of nicotine on visual attention. Neuron. 2002;36:539–548. [PubMed]
4. Myers CS, Taylor RC, Moolchan ET, Heishman SJ. Dose-Related Enhancement of Mood and Cognition in Smokers Administered Nicotine Nasal Spray. Neuropsychopharmacology. 2007 [PubMed]
5. Kumari V, Gray JA. Smoking withdrawal, nicotine dependence and prepulse inhibition of the acoustic startle reflex. Psychopharmacology (Berl) 1999;141:11–15. [PubMed]
6. Mancuso G, Andres P, Ansseau M, Tirelli E. Effects of nicotine administered via a transdermal delivery system on vigilance: a repeated measure study. Psychopharmacology (Berl) 1999;142:18–23. [PubMed]
7. McClernon FJ, Gilbert DG, Radtke R. Effects of transdermal nicotine on lateralized identification and memory interference. Hum Psychopharmacol. 2003;18:339–343. [PubMed]
8. Swanson LW, Simmons DM, Whiting PJ, Lindstrom J. Immunohistochemical localization of neuronal nicotinic receptors in the rodent central nervous system. J Neurosci. 1987;7:3334–3342. [PubMed]
9. Nyback H, Nordberg A, Langstrom B, Halldin C, Hartvig P, Ahlin A, Swahn CG, Sedvall G. Attempts to visualize nicotinic receptors in the brain of monkey and man by positron emission tomography. Prog Brain Res. 1989;79:313–319. [PubMed]
10. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A. 2001;98:676–682. [PubMed]
11. Greicius MD, Krasnow B, Reiss AL, Menon V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A. 2003;100:253–258. [PubMed]
12. Grunwald F, Schrock H, Kuschinsky W. The effect of an acute nicotine infusion on the local cerebral glucose utilization of the awake rat. Brain Res. 1987;400:232–238. [PubMed]
13. Stein EA, Pankiewicz J, Harsch HH, Cho JK, Fuller SA, Hoffmann RG, Hawkins M, Rao SM, Bandettini PA, Bloom AS. Nicotine-induced limbic cortical activation in the human brain: a functional MRI study. Am J Psychiatry. 1998;155:1009–1015. [PubMed]
14. Ernst M, Matochik JA, Heishman SJ, Van Horn JD, Jons PH, Henningfield JE, London ED. Effect of nicotine on brain activation during performance of a working memory task. Proc Natl Acad Sci U S A. 2001;98:4728–4733. [PubMed]
15. Giessing C, Fink GR, Rosler F, Thiel CM. fMRI data predict individual differences of behavioral effects of nicotine: a partial least square analysis. J Cogn Neurosci. 2007;19:658–670. [PubMed]
16. Kumari V, Gray JA, ffytche DH, Mitterschiffthaler MT, Das M, Zachariah E, Vythelingum GN, Williams SC, Simmons A, Sharma T. Cognitive effects of nicotine in humans: an fMRI study. NeuroImage. 2003;19:1002–1013. [PubMed]
17. Fransson P. Spontaneous low-frequency BOLD signal fluctuations: an fMRI investigation of the resting-state default mode of brain function hypothesis. Hum Brain Mapp. 2005;26:15–29. [PubMed]
18. Shulman GL, Fiez JA, Corbetta M, Buckner RL, Miezin FM, Raichle ME, Petersen SE. Common Blood Flow Changes across Visual Tasks: II. Decreases in Cerebral Cortex. J Cogn Neruosci. 1997;9:648–663. [PubMed]
19. Gusnard DA, Raichle ME, Raichle ME. Searching for a baseline: functional imaging and the resting human brain. Nat Rev Neurosci. 2001;2:685–694. [PubMed]
20. Smith AT, Williams AL, Singh KD. Negative BOLD in the visual cortex: evidence against blood stealing. Hum Brain Mapp. 2004;21:213–220. [PubMed]
21. Shmuel A, Augath M, Oeltermann A, Logothetis NK. Negative functional MRI response correlates with decreases in neuronal activity in monkey visual area V1. Nat Neurosci. 2006;9:569–577. [PubMed]
22. Vincent JL, Patel GH, Fox MD, Snyder AZ, Baker JT, Van Essen DC, Zempel JM, Snyder LH, Corbetta M, Raichle ME. Intrinsic functional architecture in the anaesthetized monkey brain. Nature. 2007;447:83–86. [PubMed]
23. Johnston JM, Vaishnavi SN, Smyth MD, Zhang D, He BJ, Zempel JM, Shimony JS, Snyder AZ, Raichle ME. Loss of resting interhemispheric functional connectivity after complete section of the corpus callosum. J Neurosci. 2008;28:6453–6458. [PMC free article] [PubMed]
24. Morris R, Paxinos G, Petrides M. Architectonic analysis of the human retrosplenial cortex. J Comp Neurol. 2000;421:14–28. [PubMed]
25. Vogt BA, Vogt LJ, Perl DP, Hof PR. Cytology of human caudomedial cingulate, retrosplenial, and caudal parahippocampal cortices. J Comp Neurol. 2001;438:353–376. [PubMed]
26. Vogt BA, Vogt L. Cytology of human dorsal midcingulate and supplementary motor cortices. J Chem Neuroanat. 2003;26:301–309. [PubMed]
27. Vogt BA, Vogt L, Laureys S. Cytology and functionally correlated circuits of human posterior cingulate areas. NeuroImage. 2006;29:452–466. [PMC free article] [PubMed]
28. Fornito A, Whittle S, Wood SJ, Velakoulis D, Pantelis C, Yucel M. The influence of sulcal variability on morphometry of the human anterior cingulate and paracingulate cortex. NeuroImage. 2006;33:843–854. [PubMed]
29. McCormick LM, Ziebell S, Nopoulos P, Cassell M, Andreasen NC, Brumm M. Anterior cingulate cortex: an MRI-based parcellation method. NeuroImage. 2006;32:1167–1175. [PubMed]
30. First MB, Spitzer RL, Gibbon M, Williams JBW. Structured Clinical Interview for DSMIV Axis I Disorders. American Psychiatric Publishing, Inc; Arlington: 1997.
31. Heatherton TF, Kozlowski LT, Frecker RC, Fagerstrom KO. The Fagerstrom Test for Nicotine Dependence: a revision of the Fagerstrom Tolerance Questionnaire. Br J Addict. 1991;86:1119–1127. [PubMed]
32. Kendler KS, Neale MC, Sullivan P, Corey LA, Gardner CO, Prescott CA. A population-based twin study in women of smoking initiation and nicotine dependence. Psychol Med. 1999;29:299–308. [PubMed]
33. Vink JM, Willemsen G, Boomsma DI. Heritability of smoking initiation and nicotine dependence. Behav Genet. 2005;35:397–406. [PubMed]
34. Hughes JR, Higgins ST, Bickel WK. Nicotine withdrawal versus other drug withdrawal syndromes: similarities and dissimilarities. Addiction. 1994;89:1461–1470. [PubMed]
35. Parrott AC, Garnham NJ, Wesnes K, Pincock C. Cigarette smoking and abstinence: comparative effects upon cognitive task performance and mood state over 24 hours. Hum Psychopharmacol. 1996;11:391–400.
36. Cox RW. Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research. 1996;29:162–173. [PubMed]
37. Oakes TR, Johnstone T, Ores Walsh KS, Greischar LL, Alexander AL, Fox AS, Davidson RJ. Comparison of fMRI motion correction software tools. NeuroImage. 2005;28:529–543. [PubMed]
38. Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med. 1995;34:537–541. [PubMed]
39. Lowe MJ, Mock BJ, Sorenson JA. Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations. NeuroImage. 1998;7:119–132. [PubMed]
40. Cordes D, Haughton VM, Arfanakis K, Carew JD, Turski PA, Moritz CH, Quigley MA, Meyerand ME. Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. AJNR Am J Neuroradiol. 2001;22:1326–1333. [PubMed]
41. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A. 2005;102:9673–9678. [PubMed]
42. Lund TE, Madsen KH, Sidaros K, Luo WL, Nichols TE. Non-white noise in fMRI: does modelling have an impact? NeuroImage. 2006;29:54–66. [PubMed]
43. Ashburner J, Friston KJ. Unified segmentation. NeuroImage. 2005;26:839–851. [PubMed]
44. Turken AU, Swick D. Response selection in the human anterior cingulate cortex. Nat Neurosci. 1999;2:920–924. [PubMed]
45. Ward BD. Simultaneous Inference for FMRI Data. AFNI manual. 2000. http://afni.nimh.nih.gov/afni/doc/manual/AlphaSim.
46. Thirion B, Pinel P, Meriaux S, Roche A, Dehaene S, Poline JB. Analysis of a large fMRI cohort: Statistical and methodological issues for group analyses. NeuroImage. 2007;35:105–120. [PubMed]
47. Baleydier C, Mauguiere F. The duality of the cingulate gyrus in monkey. Neuroanatomical study and functional hypothesis. Brain. 1980;103:525–554. [PubMed]
48. Schmahmann JD, Pandya DN. Fiber pathways of the brain. Oxford University Press; New York: 2006.
49. Vogt BA, Pandya DN. Cingulate cortex of the rhesus monkey: II. Cortical afferents. J Comp Neurol. 1987;262:271–289. [PubMed]
50. Selemon LD, Goldman-Rakic PS. Longitudinal topography and interdigitation of corticostriatal projections in the rhesus monkey. J Neurosci. 1985;5:776–794. [PubMed]
51. Mufson EJ, Pandya DN. Some observations on the course and composition of the cingulum bundle in the rhesus monkey. J Comp Neurol. 1984;225:31–43. [PubMed]
52. Saccone SF, Hinrichs AL, Saccone NL, Chase GA, Konvicka K, Madden PA, Breslau N, Johnson EO, Hatsukami D, Pomerleau O, Swan GE, Goate AM, Rutter J, Bertelsen S, Fox L, Fugman D, Martin NG, Montgomery GW, Wang JC, Ballinger DG, Rice JP, Bierut LJ. Cholinergic nicotinic receptor genes implicated in a nicotine dependence association study targeting 348 candidate genes with 3713 SNPs. Hum Mol Genet. 2007;16:36–49. [PMC free article] [PubMed]
53. Thorgeirsson TE, Geller F, Sulem P, Rafnar T, Wiste A, Magnusson KP, Manolescu A, Thorleifsson G, Stefansson H, Ingason A, Stacey SN, Bergthorsson JT, Thorlacius S, Gudmundsson J, Jonsson T, Jakobsdottir M, Saemundsdottir J, Olafsdottir O, Gudmundsson LJ, Bjornsdottir G, Kristjansson K, Skuladottir H, Isaksson HJ, Gudbjartsson T, Jones GT, Mueller T, Gottsater A, Flex A, Aben KK, de Vegt F, Mulders PF, Isla D, Vidal MJ, Asin L, Saez B, Murillo L, Blondal T, Kolbeinsson H, Stefansson JG, Hansdottir I, Runarsdottir V, Pola R, Lindblad B, van Rij AM, Dieplinger B, Haltmayer M, Mayordomo JI, Kiemeney LA, Matthiasson SE, Oskarsson H, Tyrfingsson T, Gudbjartsson DF, Gulcher JR, Jonsson S, Thorsteinsdottir U, Kong A, Stefansson K. A variant associated with nicotine dependence, lung cancer and peripheral arterial disease. Nature. 2008;452:638–642. [PubMed]
54. Everitt BJ, Robbins TW. Neural systems of reinforcement for drug addiction: from actions to habits to compulsion. Nat Neurosci. 2005;8:1481–1489. [PubMed]
55. Kalivas PW, O'Brien C. Drug addiction as a pathology of staged neuroplasticity. Neuropsychopharmacology. 2008;33:166–180. [PubMed]
56. Hampson M, Driesen NR, Skudlarski P, Gore JC, Constable RT. Brain connectivity related to working memory performance. J Neurosci. 2006;26:13338–13343. [PMC free article] [PubMed]
57. Hyland A, Rezaishiraz H, Giovino G, Bauer JE, Michael CK. Over-the-counter availability of nicotine replacement therapy and smoking cessation. Nicotine Tob Res. 2005;7:547–555. [PubMed]
58. Stapleton JM, Gilson SF, Wong DF, Villemagne VL, Dannals RF, Grayson RF, Henningfield JE, London ED. Intravenous nicotine reduces cerebral glucose metabolism: a preliminary study. Neuropsychopharmacology. 2003;28:765–772. [PubMed]
59. Shulman GL, Corbetta M, Buckner RL, Raichle ME, Fiez JA, Miezin FM, Petersen SE. Top-down modulation of early sensory cortex. Cereb Cortex. 1997;7:193–206. [PubMed]
60. Margulies DS, Kelly AM, Uddin LQ, Biswal BB, Castellanos FX, Milham MP. Mapping the functional connectivity of anterior cingulate cortex. NeuroImage. 2007;37:579–588. [PubMed]
61. Grunwald F, Schrock H, Kuschinsky W. The influence of nicotine on local cerebral blood flow in rats. Neurosci Lett. 1991;124:108–110. [PubMed]
62. Murphy K, Dixon V, LaGrave K, Kaufman J, Risinger R, Bloom A, Garavan H. A validation of event-related FMRI comparisons between users of cocaine, nicotine, or cannabis and control subjects. Am J Psychiatry. 2006;163:1245–1251. [PubMed]