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Published in final edited form as: Drug Alcohol Depend. 2023 Jan 8;244:109766. doi: 10.1016/j.drugalcdep.2023.109766

Associations Between Right Inferior Frontal Gyrus Morphometry and Inhibitory Control in Individuals with Nicotine Dependence

Alexander A Brown a,b,c, Spencer Upton b, Stephen Craig a,b,c, Brett Froeliger a,b,c
PMCID: PMC9974751  NIHMSID: NIHMS1865135  PMID: 36640686

Abstract

Background:

The hyperdirect pathway - a circuit involved in executing inhibitory control (IC) - is dysregulated among individuals with nicotine dependence. The right inferior frontal gyrus (rIFG), a cortical input to the hyperdirect circuit, has been shown to be functionally and structurally altered among nicotine-dependent people who smoke. The rIFG is composed of 3 cytoarchitecturally distinct subregions: The pars opercularis, pars triangularis, and pars orbitalis. The present study assessed the relationship between rIFG subregion morphometry and inhibitory control among individuals with nicotine dependence.

Methods:

Behavioral and magnetic resonance brain imaging (MRI) data from 127 nicotine-dependent adults who smoke (MFTND = 5.4, ±1.9; MCPD = 18.3, ±7.0; Myears = 25.04, ±11.97) (Mage = 42.9, ±11.1) were assessed. Brain morphometry was assessed from T1-weighted MRIs using Freesurfer. IC was assessed with a response-inhibition Go/Go/No-Go (GGNG) task and a smoking relapse analog task (SRT).

Results and conclusions:

Vertex-wise analyses revealed that GGNG task scores were positively associated with cortical thickness and volume in the right pars triangularis (cluster-wise p = 0.006, 90% CI = 0.003 – 0.009; cluster-wise p = 0.040, 90% CI = 0.032 – 0.048), and the ability to inhibit ad lib smoking during the SRT was positively associated with cortical thickness in the right pars orbitalis (cluster-wise p = 0.011, 90% CI = 0.007 – 0.015). Our results indicate that cortical thickness of distinct rIFG subregions may serve as biomarkers for unique forms of IC deficits.

Keywords: Addiction, inhibitory control, magnetic resonance imaging, nicotine, smoking cessation

1. Introduction

Cigarette smoking continues to be the leading cause of preventable disease and death (CDC, 2022). Most people who smoke report the desire to quit smoking and nearly half report a quit-attempt during the previous 12 months (CDC, 2009); however, most quit attempts are unsuccessful. Therefore, research investigating the neurobiological mechanisms underpinning nicotine addiction and unsuccessful cessation attempts is of critical importance, as findings may help guide new treatments and reduce the public health burden nicotine addiction and cigarette consumption imposes on society.

Regarding the effects of smoking on brain structure, chronic cigarette smoking has been linked to regional brain atrophy, decreased cortical grey matter volume (Durazzo et al., 2007; Gallinat et al., 2006; Pan et al., 2013; Sutherland et al., 2016), and accelerated cortical thinning (Durazzo et al., 2018, 2013; Karama et al., 2015; Kühn et al., 2010). Additionally, substance use disorders such as nicotine addiction are associated with dysregulated cognitive control (Hester et al., 2010).

Inhibitory control (IC), one specific form of cognitive control, is the ability to voluntarily stop a prepotent response in favor of goal-oriented behavior. The hyperdirect pathway – a corticothalamic circuit involved in executing IC – has been shown to be dysregulated among individuals with nicotine dependence (Bell and Froeliger, 2021; Froeliger et al., 2012a, 2012b, 2013, 2017; Kozink et al., 2010b). The hyperdirect pathway consists of the right inferior frontal gyrus (rIFG), pre-supplementary motor area, thalamus, and subthalamic nucleus (Jahanshahi et al., 2015; Swann et al., 2012). Literature implicates the rIFG as the key cortical input node to the hyperdirect circuit and the locus of inhibitory control (Aron et al., 2014). The rIFG is composed of 3 cytoarchitecturally distinct subregions: The pars opercularis, pars triangularis, and pars orbitalis. It has been suggested that the opercularis is the main subregion for inhibition (Aron et al., 2014) based on lesion studies (Aron et al., 2003; Clark et al., 2007), transcranial magnetic stimulation (TMS) studies (Chambers et al., 2006; Verbruggen et al., 2010), and fMRI studies (Levy and Wagner, 2011). However the triangularis and orbitalis are also commonly involved during IC tasks (Levy and Wagner, 2011).

Previous research has shown the rIFG to be structurally and functionally altered among people who smoke. Compared to people who do not smoke, people who smoke exhibit less rIFG grey matter volume (Brody et al., 2004; Fritz et al., 2014; Gallinat et al., 2006) and greater rIFG BOLD response during IC task probes (Froeliger et al., 2013, 2012b). Additionally, acute smoking abstinence increases rIFG BOLD response during IC tasks (Kozink et al., 2010a). In relation to smoking cessation, rIFG BOLD response measured during an IC Go/Go/No-Go (GGNG) task has shown to be predictive of a return to smoking during a quit attempt and the ability to inhibit ad lib smoking during a smoking relapse analog task (SRT) (Froeliger et al., 2017).

The majority of previous studies investigating inhibitory control and rIFG morphometry used voxel-based morphometry (VBM). Although VBM is a well-established method, it can only assess volume and density. The present study utilized advanced, surface-based morphometric analysis allowing for the examination of volume and its components – thickness and area. The present study was designed to improve our understanding of the rIFG structure - function relationship and how it is associated with IC in individuals with nicotine dependence. We hypothesized that rIFG morphometry (thickness, volume, area) would be positively associated with IC task scores and the ability to inhibit ad lib smoking during a smoking relapse analog task.

2. MATERIAL AND METHODS

2.1. Participants

The data analyzed in this study was compiled from 3 studies conducted at the University of Missouri-Columbia and the Medical University of South Carolina (MUSC). All studies were approved by the University of Missouri-Columbia and/or MUSC Internal Review Boards and were completed in accordance with the provisions of the World Medical Association Declaration of Helsinki. Informed consent was received from all participants prior to participation. Inclusion criteria were being aged 18-65 years; nicotine dependent and currently smoking ≥ 5 cigarettes per day (CPD) for ≥ 2 years; stable mental and physical health; and agreeing to refrain from other forms of tobacco and/or nicotine products for the duration of the study. Exclusion criteria were contraindication to MRI; history of psychosis; use of smoking cessation medications in the last month; electroconvulsive therapy within 6 months; history or MRI evidence of a neurological disorder that would lead to brain lesions or significant impairment; positive pregnancy test or currently breast-feeding; or breath alcohol content > 0.Participants (n = 127) were recruited from the local communities at both MUSC (n = 82) and the University of Missouri (n = 45). Behavioral data from 127 adults who smoke (48% women; Mage = 42.9, ±11.1, age range = 21- 63 years old) at least 5 cigarettes per day (CPD) (MCPD = 18.3, ±7.0) for at least 2 years (Myears = 25.04, ±11.97) and demonstrating moderate or greater nicotine dependence as measured by the Fagerstrom Test for Nicotine Dependence (FTND) (MFTND = 5.4, ±1.9) were assessed (see Table 1).

Table 1.

Sample Characteristics

Demographics
Sample Size 127
Sex 61 F, 66 M
Age - mean (SD) 42.9 (11.1)
Education - mean (SD) 14.1 (2.3)
Clinical Measures & Task Performance - Mean (SD)
Nicotine Dependence (FTND score) 5.4 (1.9)
Expired Carbon Monoxide Level (ppm) 23 (12.3)
Years Smoking 25 (12)
Cigarettes Per Day (30-day average) 18.3 (7)
Pack Years 23.8 (15.4)
IC GGNG Task Score (% correct on NoGo trials) 54.9 (18.7)
SRT Task Score (number of blocks completed) 7.6 (3.1)
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2.2. MRI data acquisition

3T MRI scanners (Siemens Prisma Fit – University of Missouri and MUSC [n = 106]; Siemens Tim Trio – MUSC [n = 21]), were used to acquire sets of high-resolution (1mm3) T1-weighted structural brain images. Images were collected using standard T1-weighted magnetization prepared – rapid gradient echo (MPRAGE) pulse sequences (TR = 2300 ms [1900ms on Trio], TE = 2.26 ms, flip angle = 9°, 192 slices, 1 mm3 voxels, FOV = 256 mm).

2.3. Behavioral data acquisition

To assess IC, participants performed a Go/Go/No-Go (GGNG) task (Chikazoe et al., 2009; Froeliger et al., 2017; Newman-Norlund et al., 2020) during an fMRI scan. Participants smoked a cigarette approximately 30 minutes before their MRI scan. The GGNG task consists of randomly presented colored circles for three trial types: Frequent grey (Go, 75.4%; n = 388 trials), rare yellow (Rare Go, 12.3%; n = 65 trials), and rare blue (No Go, 12.3%; n = 65 trials). Participants were instructed to press a button with their right index finger as quicky as possible when “Go” and “Rare Go” trials appeared, and to withhold any response to “No Go” trials. Each trial was presented for 400ms separated by a 400ms blank screen (800ms intertrial interval). Percent correct on “No Go” trials was the outcome measure for IC task performance. To control for lapses in sustained attention, only correctly omitted “No Go” trials in which the participant responded to the preceding “Go” trial were counted as a correct omission.

To assess the ability to inhibit ad lib smoking, participants performed a smoking relapse analog task (SRT) immediately following their MRI scan, approximately 1.5 hours after a participant had smoked their last cigarette. The SRT is a laboratory analog of a return to smoking during a cessation attempt as described in a previous publication from our lab (Froeliger et al., 2017) and is similar to the laboratory smoking lapse task developed by Dr. McKee and colleagues (McKee et al., 2012; Oberleitner et al., 2018). During the SRT, 6-minute blocks of negative, positive, and neutral pictures from the International Affective Picture System were presented to participants. After each 6-minute block, participants were asked if they would like to stop the task and smoke a cigarette or continue the task. Participants were awarded $1 for each block they completed without smoking for up to 10 blocks (60 min). For participant engagement purposes, participants were asked to rate their mood after each image.

2.4. Data processing & analysis

Acquired T1s were visually inspected for quality assurance before being used as input for Freesurfer’s (version 6.0.0) cortical reconstruction and volumetric segmentation pipeline. The technical details of these procedures are described in prior publications (Dale et al., 1999; Dale and Sereno, 1993; Fischl and Dale, 2000; Fischl et al., 2001, 2002, 2004a, 1999a, 1999b, 2004b; Han et al., 2006; Jovicich et al., 2006; Ségonne et al., 2004). Once the cortical models were complete, a number of deformable procedures were performed for further data processing and analysis including surface inflation (Fischl et al., 1999a), registration to a spherical atlas which is based on individual cortical folding patterns to match cortical geometry across subjects (Fischl et al., 1999b), parcellation of the cerebral cortex into units with respect to gyral and sulcal structure from the Desikan-Killiany (DK) atlas (Desikan et al., 2006; Fischl et al., 2004b), and creation of a variety of surface-based data including maps of curvature and sulcal depth. All Freesurfer-generated segmentations and cortical reconstructions were inspected, and manual edits were made when necessary to ensure accurate segmentations.

The potential relationships between brain morphometry and scores from the IC and SRT tasks were examined using Freesurfer’s surface-based, vertex-wise general linear model analyses and hierarchical linear regression models in SPSS (version 28.0). For all analyses, age, education, and sex were entered as nuisance variables as previous studies have shown grey matter volume, thickness, and area to be associated with aging (Lemaitre et al., 2012) and cognitive ability (Cox et al., 2019; Schnack et al., 2015). Additionally, previous studies have observed sex differences in grey matter volume, thickness, and area (Ritchie et al., 2018). Estimated total intracranial volume (eTIV) was entered as an additional nuisance variable for cortical volume and area analyses to account for inter-individual variance related to overall brain size. Cortical surface reconstructions for each participant were registered to Freesurfer’s template brain – fsaverage – and were smoothed with a 10-millimeter full-width half-maximum (FWHM) gaussian spatial smoothing kernel. Based on previous findings and apriori hypotheses, an rIFG mask was used to reduce search space and increase sensitivity of analyses by reducing the number of multiple statistical comparisons. The rIFG mask was generated by combing the right opercularis, triangularis, and orbitalis from the DK atlas. Clusters were corrected for multiple comparisons via permutation simulation (n =1000) with a cluster forming threshold of p < 0.01. Only surviving clusters with a cluster-wise p < 0.05 were considered significant.

2.5. Post-hoc mediation model

A post-hoc mediation model assessing if cortical thickness mediated the association between GGNG task performance and SRT performance was performed using the PROCESS macro (Hayes, 2022) in SPSS.

3. Results

3.1. Associations between GGNG task performance and rIFG morphometry

Vertex-wise cluster analysis revealed that GGNG task scores were positively associated with both cortical thickness and volume (figure 1) in the right triangularis (Thickness: cluster-wise p = 0.006, size = 268.09 mm2, cluster-wise p-value 90% CI = 0.003 – 0.009; Volume: cluster-wise p = .040, size = 153.81 mm2, cluster-wise p-value 90% CI = 0.032 – 0.048). Participant’s cortical thickness and volume values from the resultant clusters were extracted and further analyzed in SPSS via hierarchical regression. Average cluster cortical thickness values and total cluster volumes were positively associated with GGNG task performance (Thickness: ß = 0.003, t [122] = 4.501, r = 0.377, p < 0.001, 95% ß-CI = 0.002 - 0.004; Volume: ß = 1.547, t [122] = 3.737, r = 0.322, p < 0.001, 95% ß-CI = 0.727 - 2.366). No significant associations between GGNG task performance and rIFG area were observed.

Figure 1. GGNG task performance and rIFG morphometry.

Figure 1.

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Figure 1. A. Corrected cluster results (yellow) overlaid on the Freesurfer template (fsaverage) inflated surface displaying a positive association between inhibitory control Go/Go/No-Go (GGNG) task performance and cortical thickness in the right triangularis (cluster-wise p = 0.006, size = 268.0 mm2). rIFG subregions are outlined in white (opercularis, triangularis, orbitalis, from left to right) B. Corrected cluster results displaying a positive association between inhibitory control task scores and cortical volume in the right triangularis (cluster-wise p = 0.040, size = 153.81 mm2). Scatter plots of residual cluster volumes, thicknesses, and IC task scores are shown adjacent to brain surfaces. C. Overlapping region of the volume and thickness cluster analysis results.

Each participant’s thickness, volume, and area values from the overlapping region of the volume and thickness clusters (figure 1C) were extracted and analyzed. Thickness, volume, and area in the overlapping region were positively associated with GGNG task scores (Thickness: ß = 0.005, t [122] = 3.687, r = 0.317, p < 0.001, 95% ß-CI = 0.002 - 0.007; Volume: ß = 1.862, t [122] = 3.125, r = 0.273, p = 0.002, 95% ß-CI = 0.683 - 3.042; Area: ß = 0.198, t [122] = 2.047, r = 0.183, p = 0.043, 95% ß-CI = 0.007 – 0.390).

3.2. Associations between SRT performance and rIFG morphometry

The relationship the between number of blocks completed during the SRT and rIFG cortical thickness (figure 2) showed a positive association in the anterior orbitalis (cluster-wise p = 0.011, size = 211.89 mm2, cluster-wise p-value 90% CI = 0.007 – 0.015). Regression analysis of extracted average cluster cortical thickness values were positively associated with the number of blocks completed during the SRT (ß = 0.022, t [121] = 3.249, r = 0.283, p = 0.001, 95% ß-CI = 0.009 - 0.035). No significant associations were observed between SRT performance and rIFG volume or area.

Figure 2. Smoking relapse task performance and rIFG cortical thickness.

Figure 2.

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Figure 2. Corrected cluster results (yellow) displaying a positive association between number of blocks completed during the smoking relapse task (SRT) and cortical thickness in the right orbitalis (cluster-wise p = 0.011, size = 153.81 mm2). rIFG subregions are outlined in white (opercularis, triangularis, orbitalis, from left to right). Scatter plot of residual average cluster thicknesses and number of blocks completed during the SRT is shown adjacent to brain surface.

3.3. Associations between SRT and GGNG task performance

Post-hoc analyses revealed that GGNG task performance was associated with inhibiting ad lib smoking during the SRT (ß = 0.032, t [121] = 2.264, r = 0.202, p = 0.025, 95% ß-CI = 0.004 - 0.061). A mediation model was assessed to see if rIFG cortical thickness mediated the association between GGNG and SRT performance, but no mediation was observed.

4. Discussion

Previous studies have assessed rIFG morphometry in the context of inhibitory control, but to our knowledge this is the first study to assess the relationship between rIFG cortical thickness, inhibitory control, and the ability to inhibit ad lib smoking in adults who smoke. The present study found that right triangularis thickness and volume was predictive of GGNG task performance, and right orbitalis thickness was predictive of the ability to inhibit ad lib smoking during the SRT.

4.1. Inhibitory control and rIFG morphometry

The present study’s findings of associations between rIFG morphometry and IC support findings from previous studies. For example, Aron et al. (2003) reported that individuals with damage to the rIFG exhibited disrupted response inhibition during a stop-signal reaction time task (SSRT) compared to individuals with frontal cortex lesions outside of the rIFG. Additional evidence for a relationship between rIFG morphometry and IC can be seen in studies assessing neuropsychiatric disorders known to exhibit IC deficits such as attention-deficit/hyperactive disorder (ADHD) and obsessive-compulsive disorder (OCD). For example, Depue et al. (2010) assessed response inhibition in young adults with ADHD and found that poor performance on IC tasks was predictive of less grey matter volume in the rIFG. Additionally, a meta-analysis of the relationship between IC and brain morphometry in OCD patients demonstrated that OCD patients exhibited thinner rIFG cortices compared to controls (Fouche et al., 2017).

As mentioned previously, nicotine dependent individuals who smoke exhibit structural and functional abnormalities in the rIFG such as less rIFG volume (Brody et al., 2004; Fritz et al., 2014; Gallinat et al., 2006) and greater rIFG BOLD response during IC task probes (Froeliger et al., 2013, 2012b) in comparison to their non-smoking counterparts. The present study’s findings in conjunction with extant literature suggest that low levels of rIFG cortical thickness or volume may serve as a biomarker of IC deficits as demonstrated in substance use disorders and other neuropsychiatric disorders.

Although training effects on IC and brain morphometry are mixed, studies investigating rIFG plasticity and IC training have shown positive associations between rIFG morphometry and IC training. A study conducted by Kühn et al. (2017) found that individuals who underwent 2 months of response inhibition training demonstrated shorter stop-signal response times than the active and passive control groups. The improvement in IC was accompanied by cortical thickness growth in the right triangularis compared to pre-training. The increase in triangularis thickness was associated with time spent on the training task and predicted successful inhibition during the stop-signal task.

Although more research is needed to replicate findings on IC training, the studies provide promise that IC can be improved and that the improvement is accompanied by morphometric changes in the rIFG. Through a better understanding of the structural and functional segregation of the rIFG, the potential to design novel treatments and therapies for substance use disorders such as nicotine addiction and IC-related neuropsychological disorders warrants further investigation.

4.2. Ad lib smoking inhibition and rIFG morphometry

Quitting smoking can be a daunting task for people who are addicted to nicotine and smoke. As mentioned previously, most quit attempts are unsuccessful. A previous smoking cessation study in our lab (Froeliger et al., 2017) used VBM to assess brain morphometry and found that individuals who returned to smoking following a quit attempt exhibited less rIFG volume than those who remained abstinent. The present study, using surface-based morphometric analysis, examined volume and the two components of volume - thickness and area - as previous studies have shown cortical thickness to be genetically and phenotypically distinct from volume (Panizzon et al., 2009) and to be more sensitive to neurodegenerative processes than volume (Hutton et al., 2009). Utilizing this approach, we observed that anterior orbitalis cortical thickness, but not volume or area, was positively associated with the ability to inhibit ad lib smoking during the SRT.

While the GGNG task is dominantly a motor response inhibition task, the SRT exposes people who smoke to smoking cues such as a cigarette of the participant’s preference, a lighter, and ash tray all within arm’s reach. The SRT is also monetarily incentivized, goal-oriented, and promotes delayed gratification as the longer a participant continues the task without smoking the more money they can receive. The increased demand of cognitive-related inhibitory control and decreased demand for motor response inhibition for the SRT compared to the GGNG task may explain why cortical thickness of the orbitalis, the most anterior subregion of the rIFG, exhibited a significant association with the ability to inhibit ad lib smoking while the triangularis and opercularis did not. Indeed, a meta-analysis of rIFG functional connectivity conducted by Hartwigsen et al. (2019) proposed a posterior-to-anterior functional organization of the rIFG, with the action-related functions on the posterior end (opercularis) and cognition-related functions on the anterior end (orbitalis). In support of this proposed posterior-to-anterior structure-function gradient, a recent study by Boen et al. (2022) investigated the structural connectivity of the rIFG subregions via diffusion tensor imaging and reported stark differences between subregions regarding the number and extent of connections seeding from the rIFG to other brain regions. The most posterior region of the rIFG (opercularis) showed the least number of connections to other brain regions while the most anterior region (orbitalis) showed the greatest number and most widespread connections.

Findings from the current study in conjunction with extant literature suggest the most frontal subregion of the rIFG, the orbitalis, is more involved in higher-level cognitive-related IC than the triangularis and opercularis, and orbitalis thickness may serve as a biomarker for IC deficits over ad lib smoking.

4.3. Association between GGNG and SRT performance

Post-hoc analysis revealed an association between SRT and GGNG task performance. This association is not surprising as both tasks demand a level of IC, and both were associated with cortical thickness in the rIFG. Additionally, previous morphometric analysis analyzing the rIFG without subregion resolution found that rIFG morphometry is associated with both SRT and GGNG task performance.

4.4. Limitations

The present study’s data were compiled from 3 studies conducted at two different sites, MUSC (n = 82) and the University of Missouri (n = 45). Additionally, multiple Siemens 3T MRI scanners were used for data collection (Tim Trio and Prisma Fit). A slightly different repetition time (TR) was used for the T1 sequence on the Tim Trio (1900ms) in comparison to the Prisma Fit (2300ms). However, vertex-wise cluster analysis of the right hemisphere and rIFG revealed no significant differences in cortical volume, thickness, or area between site or scanner. The IC and SRT tasks were administered the same across studies, but environmental variation due to different lab settings was present.

Addiction studies can be difficult to assess provided the heterogeneity of individuals with addiction and the commonality of psychiatric comorbidities among individuals with addiction. The present study did not conduct structured psychiatric interviews but did administer the Center for Epidemiological Studies-Depression (CES-D) and Beck Anxiety Inventory (BAI). Vertex-wise analysis revealed no significant associations between rIFG morphometry and the CES-D or BAI. However, the potential effects of other psychiatric comorbidities on the present study’s findings are a limitation.

5. Conclusions

In conclusion, our results provide additional support to the breadth of literature implicating the rIFG as a locus of inhibitory control. By using advanced neuroimaging analytical techniques, we observed a heterogeneity of the rIFG subregions based on associations between rIFG morphometry, inhibitory control GGNG task performance, and the ability to inhibit ad lib smoking during the SRT. The posterior-to-anterior rIFG functional gradient proposed by Hartwigsen et al. (2019) and others coincides with our morphometric findings and the structural connectivity findings of Boen et al. (2022), as GGNG task performance was associated with cortical thickness and volume in the middle and posterior region of the triangularis, while the ability to inhibit ad lib smoking during the SRT was associated with cortical thickness in the anterior orbitalis. Future studies assessing rIFG morphometry in people who smoke may provide more details for the structure-function relationship of the rIFG. Combined with extant literature, present and future findings may lead to more precise treatment for individuals with nicotine dependence and better smoking cessation outcomes.

Highlights.

  • Inhibitory control is associated with right inferior frontal gyrus morphometry

  • Pars orbitalis thickness is associated with inhibition of ad lib smoking

  • Pars triangularis thickness & volume is associated with motor response inhibition

  • rIFG subregion morphometry shows heterogeneity based on associations with different forms of inhibitory control

Role of Funding Source

This Project was funded by research grants from the National Institute of Health (NIH) / National Institute on Drug Abuse (NIDA) grants R01DA048094 [BF], R01DA038700 [BF], UG3DA048510 [BF]. The funding sources had no involvement in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Conflict of Interest

All authors declare no conflict of interest

Declarations of interest: None

ETHICS APPROVAL AND PATIENT CONSENT STATEMENT

The present study was approved by the University of Missouri and Medical University of South Carolina Internal Review Boards and was carried out in accordance with the provisions of the World Medical Association Declaration of Helsinki. Informed consent was obtained for all individuals prior to participation.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study may be available from the corresponding author (BF), upon reasonable request. The data are not publicly available due to restrictions related to internal review board policies and informed consent limitations under which the data was originally collected.

REFERENCES

  1. Aron AR, Fletcher PC, Bullmore ET, Sahakian BJ, Robbins TW, 2003. Stop-signal inhibition disrupted by damage to right inferior frontal gyrus in humans. Nat. Neurosci 6, 115–116. 10.1038/nn1003 [DOI] [PubMed] [Google Scholar]
  2. Aron AR, Robbins TW, Poldrack RA, 2014. Inhibition and the right inferior frontal cortex: one decade on. Trends Cogn. Sci 18, 177–185. 10.1016/j.tics.2013.12.003 [DOI] [PubMed] [Google Scholar]
  3. Bell S, Froeliger B, 2021. Associations Between Smoking Abstinence, Inhibitory Control, and Smoking Behavior: An fMRI Study. Front. Psychiatry 12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Boen R, Raud L, Huster RJ, 2022. Inhibitory Control and the Structural Parcelation of the Right Inferior Frontal Gyrus. Front. Hum. Neurosci 16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Brody AL, Mandelkem MA, Jarvik ME, Lee GS, Smith EC, Huang JC, Bota RG, Bartzokis G, London ED, 2004. Differences between smokers and nonsmokers in regional gray matter volumes and densities. Biol. Psychiatry 55, 77–84. 10.1016/s0006-3223(03)00610-3 [DOI] [PubMed] [Google Scholar]
  6. CDC, 2022. Current Cigarette Smoking Among Adults in the United States [WWW Document]. Cent. Dis. Control Prev URL https://www.cdc.gov/tobacco/data_statistics/fact_sheets/adult_data/cig_smoking/index.htm (accessed 8.17.22). [Google Scholar]
  7. Centers for Disease Control and Prevention (CDC), 2009. Cigarette smoking among adults and trends in smoking cessation - United States, 2008. MMWR Morb. Mortal. Wkly. Rep 58, 1227–1232. [PubMed] [Google Scholar]
  8. Chambers CD, Bellgrove MA, Stokes MG, Henderson TR, Garavan H, Robertson IH, Morris AP, Mattingley JB, 2006. Executive “brake failure” following deactivation of human frontal lobe. J. Cogn. Neurosci 18, 444–455. 10.1162/089892906775990606 [DOI] [PubMed] [Google Scholar]
  9. Chikazoe J, Jimura K, Asari T, Yamashita K, Morimoto H, Hirose S, Miyashita Y, Konishi S, 2009. Functional Dissociation in Right Inferior Frontal Cortex during Performance of Go/No-Go Task. Cereb. Cortex 19, 146–152. 10.1093/cercor/bhn065 [DOI] [PubMed] [Google Scholar]
  10. Clark L, Blackwell AD, Aron AR, Turner DC, Dowson J, Robbins TW, Sahakian BJ, 2007. Association Between Response Inhibition and Working Memory in Adult ADHD: A Link to Right Frontal Cortex Pathology? Biol. Psychiatry, Advances in the Neurobiology of ADHD 61, 1395–1401. 10.1016/j.biopsych.2006.07.020 [DOI] [PubMed] [Google Scholar]
  11. Cox SR, Ritchie SJ, Fawns-Ritchie C, Tucker-Drob EM, Deary IJ, 2019. Structural brain imaging correlates of general intelligence in UK Biobank. Intelligence 76, 101376. 10.1016/j.intell.2019.101376 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Dale AM, Fischl B, Sereno MI, 1999. Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction. NeuroImage 9, 179–194. 10.1006/nimg.1998.0395 [DOI] [PubMed] [Google Scholar]
  13. Dale AM, Sereno MI, 1993. Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach. J. Cogn. Neurosci 5, 162–176. 10.1162/jocn.1993.5.2.162 [DOI] [PubMed] [Google Scholar]
  14. Depue BE, Burgess GC, Bidwell LC, Willcutt EG, Banich MT, 2010. Behavioral performance predicts grey matter reductions in the right inferior frontal gyrus in young adults with combined type ADHD. Psychiatry Res. Neuroimaging 182, 231–237. 10.1016/j.pscychresns.2010.01.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ, 2006. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31, 968–980. 10.1016/j.neuroimage.2006.01.021 [DOI] [PubMed] [Google Scholar]
  16. Durazzo TC, Cardenas VA, Studholme C, Weiner MW, Meyerhoff DJ, 2007. Non-treatment-seeking heavy drinkers: Effects of chronic cigarette smoking on brain structure. Drug Alcohol Depend. 87, 76–82. 10.1016/j.drugalcdep.2006.08.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Durazzo TC, Meyerhoff DJ, Yoder KK, 2018. Cigarette smoking is associated with cortical thinning in anterior frontal regions, insula and regions showing atrophy in early Alzheimer’s Disease. Drug Alcohol Depend. 192, 277–284. 10.1016/j.drugalcdep.2018.08.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Durazzo TC, Mon A, Gazdzinski S, Meyerhoff DJ, 2013. Chronic cigarette smoking in alcohol dependence: associations with cortical thickness and N-acetylaspartate levels in the extended brain reward system. Addict. Biol 18, 379–391. 10.1111/j.1369-1600.2011.00407.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Fischl B, Dale AM, 2000. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci 97, 11050–11055. 10.1073/pnas.200033797 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Fischl B, Liu A, Dale AM, 2001. Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE Trans. Med. Imaging 20, 70–80. 10.1109/42.906426 [DOI] [PubMed] [Google Scholar]
  21. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM, 2002. Whole Brain Segmentation: Automated Labeling of Neuroanatomical Structures in the Human Brain. Neuron 33, 341–355. 10.1016/S0896-6273(02)00569-X [DOI] [PubMed] [Google Scholar]
  22. Fischl B, Salat DH, van der Kouwe AJW, Makris N, Ségonne F, Quinn BT, Dale AM, 2004a. Sequence-independent segmentation of magnetic resonance images. NeuroImage, Mathematics in Brain Imaging 23, S69–S84. 10.1016/j.neuroimage.2004.07.016 [DOI] [PubMed] [Google Scholar]
  23. Fischl B, Sereno MI, Dale AM, 1999a. Cortical Surface-Based Analysis: II: Inflation, Flattening, and a Surface-Based Coordinate System. NeuroImage 9, 195–207. 10.1006/nimg.1998.0396 [DOI] [PubMed] [Google Scholar]
  24. Fischl B, Sereno MI, Tootell RBH, Dale AM, 1999b. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp 8, 272–284. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Fischl B, van der Kouwe A, Destrieux C, Halgren E, Ségonne F, Salat DH, Busa E, Seidman LJ, Goldstein J, Kennedy D, Caviness V, Makris N, Rosen B, Dale AM, 2004b. Automatically Parcellating the Human Cerebral Cortex. Cereb. Cortex 14, 11–22. 10.1093/cercor/bhg087 [DOI] [PubMed] [Google Scholar]
  26. Fouche J-P, Plessis S. du, Hattingh C, Roos A, Lochner C, Soriano-Mas C, Sato JR, Nakamae T, Nishida S, Kwon JS, Jung WH, Mataix-Cols D, Hoexter MQ, Alonso P Consortium, O.B.I, Wit S.J. de, Veltman DJ, Stein DJ, Heuvel O.A. van den, 2017. Cortical thickness in obsessive–compulsive disorder: Multisite mega-analysis of 780 brain scans from six centres. Br. J. Psychiatry 210, 67–74. 10.1192/bjp.bp.115.164020 [DOI] [PubMed] [Google Scholar]
  27. Fritz H-C, Wittfeld K, Schmidt CO, Domin M, Grabe HJ, Hegenscheid K, Hosten N, Lotze M, 2014. Current Smoking and Reduced Gray Matter Volume—a Voxel-Based Morphometry Study. Neuropsychopharmacology 39, 2594–2600. 10.1038/npp.2014.112 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Froeliger B, McConnell PA, Bell S, Sweitzer M, Kozink RV, Eichberg C, Hallyburton M, Kaiser N, Gray KM, McClernon FJ, 2017. Association Between Baseline Corticothalamic-Mediated Inhibitory Control and Smoking Relapse Vulnerability. JAMA Psychiatry 74, 379–386. 10.1001/jamapsychiatry.2017.0017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Froeliger B, Modlin L, Wang L, Kozink RV, McClernon FJ, 2012a. Nicotine withdrawal modulates frontal brain function during an affective Stroop task. Psychopharmacology (Berk) 220, 707–718. 10.1007/s00213-011-2522-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Froeliger B, Modlin LA, Kozink RV, Wang L, Garland EL, Addicott MA, McClernon FJ, 2013. Frontoparietal Attentional Network Activation Differs Between Smokers and Nonsmokers during Affective Cognition. Psychiatry Res. 211, 57–63. 10.1016/j.pscychresns.2012.05.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Froeliger B, Modlin LA, Kozink RV, Wang L, McClemon FJ, 2012b. Smoking abstinence and depressive symptoms modulate the executive control system during emotional information processing. Addict. Biol 17, 668–679. 10.1111/j.1369-1600.2011.00410.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Gallinat J, Meisenzahl E, Jacobsen LK, Kalus P, Bierbrauer J, Kienast T, Witthaus FL, Leopold K, Seifert F, Schubert F, Staedtgen M, 2006. Smoking and structural brain deficits: a volumetric MR investigation. Eur. J. Neurosci 24, 1744–1750. 10.1111/j.1460-9568.2006.05050.X [DOI] [PubMed] [Google Scholar]
  33. Han X, Jovicich J, Salat D, van derKouwe A, Quinn B, Czanner S, Busa E, Pacheco J, Albert M, Killiany R, Maguire P, Rosas D, Makris N, Dale A, Dickerson B, Fischl B, 2006. Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer. NeuroImage 32, 180–194. 10.1016/j.neuroimage.2006.02.051 [DOI] [PubMed] [Google Scholar]
  34. Hartwigsen G, Neef NE, Camilleri JA, Margulies DS, Eickhoff SB, 2019. Functional Segregation of the Right Inferior Frontal Gyrus: Evidence From Coactivation-Based Parcellation. Cereb. Cortex 29, 1532–1546. 10.1093/cercor/bhy049 [DOI] [PubMed] [Google Scholar]
  35. Hester R, Lubman DI, Yücel M, 2010. The Role of Executive Control in Human Drug Addiction, in: Self DW, Staley Gottschalk JK (Eds.), Behavioral Neuroscience of Drug Addiction, Current Topics in Behavioral Neurosciences. Springer, Berlin, Heidelberg, pp. 301–318. 10.1007/7854_2009_28 [DOI] [PubMed] [Google Scholar]
  36. Hutton C, Draganski B, Ashburner J, Weiskopf N, 2009. A comparison between voxel-based cortical thickness and voxel-based morphometry in normal aging. Neuroimage 48, 371–380. 10.1016/j.neuroimage.2009.06.043 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Jahanshahi M, Obeso L, Rothwell JC, Obeso JA, 2015. A fronto–striato–subthalamic–pallidal network for goal-directed and habitual inhibition. Nat. Rev. Neurosci 16, 719–732. 10.1038/nrn4038 [DOI] [PubMed] [Google Scholar]
  38. Jovicich J, Czanner S, Greve D, Haley E, van der Kouwe A, Gollub R, Kennedy D, Schmitt F, Brown G, MacFall J, Fischl B, Dale A, 2006. Reliability in multi-site structural MRI studies: Effects of gradient non-linearity correction on phantom and human data. NeuroImage 30, 436–443. 10.1016/j.neuroimage.2005.09.046 [DOI] [PubMed] [Google Scholar]
  39. Karama S, Ducharme S, Corley J, Chouinard-Decorte F, Starr JM, Wardlaw JM, Bastin ME, Deary IJ, 2015. Cigarette smoking and thinning of the brain’s cortex. Mol. Psychiatry 20, 778–785. 10.1038/mp.2014.187 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Kozink RV, Kollins SH, McClernon FJ, 2010a. Smoking Withdrawal Modulates Right Inferior Frontal Cortex but not Presupplementary Motor Area Activation During Inhibitory Control. Neuropsychopharmacology 35, 2600–2606. 10.1038/npp.2010.154 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Kozink RV, Lutz AM, Rose JE, Froeliger B, McClernon FJ, 2010b. Smoking withdrawal shifts the spatiotemporal dynamics of neurocognition. Addict. Biol 15, 480–490. 10.1111/j.1369-1600.2010.00252.X [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Kuhn S, Lorenz RC, Weichenberger M, Becker M, Haesner M, O’Sullivan J, Steinert A, Steinhagen-Thiessen E, Brandhorst S, Bremer T, Gallinat J, 2017. Taking control! Structural and behavioural plasticity in response to game-based inhibition training in older adults. NeuroImage 156, 199–206. 10.1016/j.neuroimage.2017.05.026 [DOI] [PubMed] [Google Scholar]
  43. Kuhn S, Schubert F, Gallinat J, 2010. Reduced Thickness of Medial Orbitofrontal Cortex in Smokers. Biol. Psychiatry, Neuroplasticity in Anxiety Disorders 68, 1061–1065. 10.1016/j.biopsych.2010.08.004 [DOI] [PubMed] [Google Scholar]
  44. Lemaitre H, Goldman AL, Sambataro F, Verchinski BA, Meyer-Lindenberg A, Weinberger DR, Mattay VS, 2012. Normal age-related brain morphometric changes: nonuniformity across cortical thickness, surface area and gray matter volume? Neurobiol. Aging 33, 617.e1–617.e9. 10.1016/j.neurobiolaging.2010.07.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Levy BJ, Wagner AD, 2011. Cognitive control and right ventrolateral prefrontal cortex reflexive reorienting, motor inhibition, and action updating. Ann. N. Y. Acad. Sci 1224, 40–62. 10.1111/j.1749-6632.2011.05958.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. McKee SA, Weinberger AH, Shi J, Tetrault J, Coppola S, 2012. Developing and Validating a Human Laboratory Model to Screen Medications for Smoking Cessation. Nicotine Tob. Res 14, 1362–1371. 10.1093/ntr/nts090 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Newman-Norlund RD, Gibson M, McConnell PA, Froeliger B, 2020. Dissociable Effects of Theta-Burst Repeated Transcranial Magnetic Stimulation to the Inferior Frontal Gyrus on Inhibitory Control in Nicotine Addiction. Front. Psychiatry 11 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Oberleitner LMS, Moore KE, Verplaetse T, Roberts W, McKee SA, 2018. Developing a laboratory model of smoking lapse targeting stress and brief nicotine-deprivation. Exp. Clin. Psychopharmacol 26, 244–250. 10.1037/pha0000187 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Pan P, Shi H, Zhong J, Xiao P, Shen Y, Wu L, Song Y, He G, 2013. Chronic smoking and brain gray matter changes: evidence from meta-analysis of voxel-based morphometry studies. Neurol. Sci. Off. J. Ital. Neurol. Soc. Ital. Soc. Clin. Neurophysiol 34, 813–817. 10.1007/s10072-012-1256-x [DOI] [PubMed] [Google Scholar]
  50. Panizzon MS, Fennema-Notestine C, Eyler LT, Jernigan TL, Prom-Wormley E, Neale M, Jacobson K, Lyons MJ, Grant MD, Franz CE, Xian FL, Tsuang M, Fischl B, Seidman L, Dale A, Kremen WS, 2009. Distinct Genetic Influences on Cortical Surface Area and Cortical Thickness. Cereb. Cortex 19, 2728–2735. 10.1093/cercor/bhp026 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Ritchie SJ, Cox SR, Shen X, Lombardo MV, Reus LM, Alloza C, Harris MA, Alderson HL, Hunter S, Neilson E, Liewald DCM, Auyeung B, Whalley HC, Lawrie SM, Gale CR, Bastin ME, McIntosh AM, Deary IJ, 2018. Sex Differences in the Adult Human Brain: Evidence from 5216 UK Biobank Participants. Cereb. Cortex 28, 2959–2975. 10.1093/cercor/bhy109 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Schnack HG, van Haren NEM, Brouwer RM, Evans A, Durston S, Boomsma DI, Kahn RS, Hulshoff Pol HE, 2015. Changes in Thickness and Surface Area of the Human Cortex and Their Relationship with Intelligence. Cereb. Cortex 25, 1608–1617. 10.1093/cercor/bht357 [DOI] [PubMed] [Google Scholar]
  53. Ségonne F, Dale AM, Busa E, Glessner M, Salat D, Hahn HK, Fischl B, 2004. A hybrid approach to the skull stripping problem in MRI. NeuroImage 22, 1060–1075. 10.1016/j.neuroimage.2004.03.032 [DOI] [PubMed] [Google Scholar]
  54. Sutherland MT, Riedel MC, Flannery JS, Yanes JA, Fox PT, Stein EA, Laird AR, 2016. Chronic cigarette smoking is linked with structural alterations in brain regions showing acute nicotinic drug-induced functional modulations. Behav. Brain Funct 12, 16. 10.1186/s12993-016-0100-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Swann NC, Cai W, Conner CR, Pieters TA, Claffey MP, George JS, Aron AR, Tandon N, 2012. Roles for the pre-supplementary motor area and the right inferior frontal gyrus in stopping action: electrophysiological responses and functional and structural connectivity. NeuroImage 59, 2860–2870. 10.1016/j.neuroimage.2011.09.049 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Verbruggen F, Aron AR, Stevens MA, Chambers CD, 2010. Theta burst stimulation dissociates attention and action updating in human inferior frontal cortex. Proc. Natl. Acad. Sci 107, 13966–13971. 10.1073/pnas.1001957107 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study may be available from the corresponding author (BF), upon reasonable request. The data are not publicly available due to restrictions related to internal review board policies and informed consent limitations under which the data was originally collected.

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