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Accepted Manuscript
Abnormal frontostriatal tracts in young male tobacco smokers
Kai Yuan, Dahua Yu, Meng Zhao, Min Li, Ruonan Wang, Yangding Li, Peter Manza,
Ehsan Shokri-Kojori, Corinde E. Wiers, Gene-Jack Wang, Jie Tian
PII:
S1053-8119(18)30744-4
DOI:
10.1016/j.neuroimage.2018.08.046
Reference:
YNIMG 15207
To appear in:
NeuroImage
Received Date: 4 May 2018
Revised Date:
16 August 2018
Accepted Date: 17 August 2018
Please cite this article as: Yuan, K., Yu, D., Zhao, M., Li, M., Wang, R., Li, Y., Manza, P., Shokri-Kojori,
E., Wiers, C.E., Wang, G.-J., Tian, J., Abnormal frontostriatal tracts in young male tobacco smokers,
NeuroImage (2018), doi: 10.1016/j.neuroimage.2018.08.046.
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ACCEPTED MANUSCRIPT
Abnormal Frontostriatal Tracts in Young Male Tobacco Smokers
Kai Yuan1,2,3,4,5#*, Dahua Yu4#, Meng Zhao1,3, Min Li1,3, Ruonan Wang1,3, Yangding Li5, Peter
Manza2, Ehsan Shokri-Kojori2, Corinde E. Wiers2, Gene-Jack Wang2, Jie Tian1,3,6*
1
School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, Peoples
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R China
2
Laboratory of Neuroimaging, National Institute on Alcoholism and Alcohol Abuse,
Bethesda, MD 20892, USA.
3
Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, Xi'an
Shaanxi 710071, Peoples R China
Information Processing Laboratory, School of Information Engineering, Inner Mongolia
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University of Science and Technology, Baotou, Inner Mongolia 014010, Peoples R China
Guangxi Key Laboratory of Multi-Source Information Mining and Security, Guangxi
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5
Normal University, Guilin, Guangxi 541004, Peoples R China
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, Peoples R China
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# Equal contribution to this work
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*Corresponding author:
Kai Yuan, Ph. D.
School of Life Science and Technology,
Xidian University,
Xi’an, Shaanxi, Peoples R China
Email: kyuan@xidian.edu.cn
or
Jie Tian, Ph. D.
School of Life Science and Technology,
Xidian University,
Xi’an, Shaanxi, Peoples R China
Email: tian@ieee.org
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Abstract
Dysfunctions in frontostriatal circuits have been associated with craving and cognitive control
in smokers. However, the relevance of white matter (WM) diffusion properties of the ventral
and dorsal frontostriatal tracts for behaviors associated with smoking remains relatively
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unknown, especially in young adulthood, a critical time period for the development and
maintenance of addiction. Here, diffusion tensor imaging (DTI) and probabilistic tractography
were used to investigate the WM tracts of the ventral and dorsal frontostriatal circuits in two
independent studies (Study1: 36 male smokers (21.3 ± 1.3 years) vs. 35 male nonsmokers
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(21.2 ± 1.3 years); Study2: 29 male smokers (21.4 ± 1.1 years) vs. 25 male nonsmokers
(21.0±1.4 years)). Subjective craving was measured by the Questionnaire on Smoking Urges
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(QSU) and cognitive control ability was assessed with the Stroop task. In both studies,
smokers committed more response errors than nonsmokers during the incongruent condition
of the Stroop task. Relative to controls, smokers showed lower fractional anisotropy (FA) and
higher radial diffusivity in left medial orbitofrontal cortex-to-nucleus accumbens fiber tracts
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(ventral frontostriatal path) and also lower FA in right dorsolateral prefrontal cortex-tocaudate fiber tracts (dorsal frontostriatal path). The FA values of the right dorsal fibers were
negatively correlated with incongruent response Stroop errors in smokers, whereas the mean
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diffusivity values of the left ventral fibers were positively correlated with craving in smokers.
Thus, WM diffusion properties of the dorsal and ventral frontostriatal tracts were associated
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with cognitive control and craving, respectively, in young male tobacco smokers. These data
highlight the importance of studying WM in relation to neuropsychological changes
underlying smoking.
Key words: white matter; smoker; diffusion tensor imaging; craving; cognitive control
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1. INTRODUCTION
Tobacco smoking has been recognized as one of the leading risk factors for early
death and disability worldwide in recent decades, and its contribution to overall disease
burden is growing (Collaborators, 2017). Despite global decreases, the prevalence of smoking
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among individuals aged 15-19 years remains high in China (10.6% for male) (Collaborators,
2017; Yuan et al., 2016). Smoking during adolescence and young adulthood produces
neurophysiological and brain structural changes that may promote nicotine dependence later
in life (Yuan et al., 2015). Despite the acknowledged importance of this critical time period,
younger adults, a primary goal of the current study.
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however, little attention has been paid to identifying neuroimaging markers of smoking in
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Acute tobacco smoking induces an increase in dopamine (DA) release in the striatum,
whereas chronic tobacco smoking is associated with decreased striatal DA transporter and
D2/3 receptor availability in smokers (Barrett et al., 2004; Brody et al., 2004b; Fehr et al.,
2008; Wiers et al., 2017). Subsequently, DA system dysfunctions in drug abuse including
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nicotine, methamphetamine and cocaine triggers neuroplasticity of the frontostriatal circuits,
which have been associated with craving and cognitive control (Huang et al., 2017; Kober et
al., 2010; Volkow et al., 2016; Volkow et al., 2011; Volkow et al., 2013). These neuroplastic
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changes should be reflected by changes in functional and structural characteristics of brain
networks. For example, the functional interactions within frontostriatal circuits during resting
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state (Sutherland et al., 2012; Yuan et al., 2016) and during smoking cue reactivity tasks
(Kober et al., 2010; Yuan et al., 2017a) have been linked with craving regulation and
cognitive control abnormalities in smokers. However, the functional repertoire of any system
is ultimately determined by its structural composition (Bonnelle et al., 2012; Leong et al.,
2016). We recently documented gray matter changes, including reduced prefrontal cortical
thickness and increased dorsal striatal volume, in young smokers relative to non-smokers (Li
et al., 2015; Yuan et al., 2016). However, white matter (WM) structures also play a prominent
role in regulating brain activity and mediating the functional coupling between brain regions
and behavior (Bi et al., 2017; Bonnelle et al., 2012; Leong et al., 2016; Yuan et al., 2017a).
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To date, the differences in WM diffusion properties of the frontostriatal tracts between young
smokers and nonsmokers as well as the behavioral implications of such differences remain
largely unclear.
In general, the frontostriatal circuits related to addiction are divided into two
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pathways (Tomasi and Volkow, 2013): (1) a ventral valuation pathway (orbitofrontal cortex
(OFC) projecting to nucleus accumbens (NAc) involved in reward processing (representing
the incentive value of the different options) (Haber et al., 2006); and (2) a dorsal control
pathway (dorsolateral prefrontal cortex (DLPFC) projecting to caudate) involved in cognitive
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control (maintaining future goals and inhibiting prepotent responses) (Kelly and Strick,
2004). Based on this a priori knowledge, in the current study, we chose the NAc and the
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caudate for the striatal regions of interest (ROIs) and employed diffusion tensor imaging
(DTI) and probabilistic tractography to study differences in structural connections of the
dorsal and ventral frontostriatal tracts between young smokers (n=36) and nonsmokers
(n=35). To specify the white matter changes, multiple diffusion metrics were calculated,
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including fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean
diffusivity (MD). To study the potential implications of the WM findings in smokers, tobacco
craving was assessed with the 10-item brief Questionnaire on Smoking Urges (QSU) (Cox et
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al., 2001). The color word Stroop task was used to assess the differences of cognitive control
performances between smokers and nonsmokers similar with our previous studies (Bi et al.,
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2017; Yuan et al., 2016). Based on previous studies (Goldstein and Volkow, 2011; Tomasi
and Volkow, 2013; Volkow and Morales, 2015), we hypothesized that (1) WM diffusion
properties of the dorsal pathway would be associated with impairments in cognitive control
and (2) WM diffusion properties of the ventral pathway would covary with craving scores in
smokers. To test the robustness of the results, an extra independent cohort (smokers (n=29)
and nonsmokers (n=25)) were included.
2. MATERIALS AND METHODS
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In this paper, we conducted two independent studies to investigate the different WM diffusion
properties of dorsal and ventral frontostriatal circuits in young smokers and nonsmokers. We
refer to these as Study1 for discovery of effects and Study2 for replication effects throughout.
2.1 Ethics Statement
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Study1 was performed at the First Affiliated Hospital of Baotou Medical College, Inner
Mongolia University of Science and Technology, Baotou, China. Study1 was approved by the
ethics committee of medical research in First Affiliated Hospital of Baotou Medical College,
Inner Mongolia University of Science and Technology, Baotou, China. As a replication study,
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Study2 was carried out at the First Affiliated Hospital of the Medical College, Xi’an Jiaotong
University, China. Study2 was approved by the Medical Ethics Committee of the First
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Affiliated Hospital of the Medical College, Xi’an Jiaotong University. After the study
procedure was fully explained, all participants gave written informed consent. All
experimental procedures followed the guidelines of human medical research (Declaration of
Helsinki). The two studies were performed using the same procedure and protocols.
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2.2 Participants
Nicotine-dependent cigarette smokers (DSM-IV) were enrolled in local universities. All
young smokers had no attempt to quit or smoking abstinence in the past 6 months. Nicotine
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dependence severity was assessed with the Fagerström Test for Nicotine Dependence
(FTND). Pack_years of smoking was calculated by multiplying the average number of packs
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of cigarettes smoked per day by the number of years the participant smoked. A timeline
follow-back method (TLFB) was used to assess the average number of packs of cigarettes
smoked per day for the prior 90 days. For smokers, the minimum number of cigarettes per
day was 10; minimum length of smoking was 3 years. The age-, education- and gendermatched healthy nonsmokers were also enrolled. Nonsmokers were defined as individuals
who have never smoked in their life. Expiratory carbon monoxide (CO) levels of all
participants were measured using the Micro+ Smokerlyzer (Bedfont Scientific, Ltd,
Rochester, UK). CO level in exhaled air was verified as ≥8 ppm in smokers and ≤3 ppm in
nonsmokers.
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Exclusion criteria for both groups were: (1) physical illness such as chronic pain, asthma,
renal and cardiac illness according to clinical evaluations and medical records; (2) Axis I
psychiatric disorder including drug and alcohol abuse or dependence by the Structured
Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV);
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(3) urine test demonstrating current substance use other than nicotine dependence; (4) harmful
alcohol drinking measured by Alcohol Use Disorders Identification Test (AUDIT) with a cutoff score of 8 (Vergara et al., 2018; Weiland et al., 2014a); (5) current use of medications that
may affect cognitive functioning. All participants were right-handed as measured by the
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Edinburgh Handedness Inventory. The self-rating anxiety scale (SAS) (Zung, 1971) and selfrating depression scale (SDS) (Zung, 1965) were also collected for both groups, which has
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been validated with adequate internal consistency.
In Study1, 36 young male smokers (aged 18–26 years, mean age = 21.3 ± 1.3SD years) and
35 age, gender matched nonsmokers (aged 18–24 years, mean age = 21.2 ± 1.3SD years)
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were recruited (Table1). Detailed demographical analysis showed no significant differences
in the distributions of age or years of education between the two groups (p > 0.05). For the
replication, 25 nonsmokers (aged 18–24 years, mean age = 21.0 ± 1.4 SD years) and 29
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smokers (aged 18–24 years, mean age = 21.4 ± 1.1 SD years) were enrolled in Study2.
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(Insert Table1 here)
2.3 Behavioral measurements
Cognitive control performance was measured by the Stroop color-word test outside of the
MRI scanner. The paradigm has been described in our previous studies (Yuan et al., 2016;
Yuan et al., 2017b). Participants were instructed to respond to the displayed color as fast as
possible by pressing a button on a Serial Response BoxTM with their right hand. Participants
were not permitted to start the Stroop task until they all indicated clear understanding of the
task and demonstrated ≥ 90% accuracy in the congruent condition in practice runs. After the
Stroop task was completed, smokers filled out the QSU to assess craving 10 minutes before
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the MRI scanning.
2.4 MRI Data Acquisition
A previous study found that 24 hours abstinence could influence FA values of frontal fiber
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tracts (Savjani et al., 2014). However, the primary purpose of the current study was to assess
the effect of chronic smoking, rather than acute abstinence, on frontostriatal circuits in young
male smokers. Therefore, participants were only asked to refrain from smoking during about
60 min immediately preceding the scan (average duration of abstinence before scan:
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40.6±14.2 min). Study1 was performed on a 3T Philips scanner (Achieva; Philips Medical
Systems, Best, The Netherlands) at the First Affiliated Hospital of Baotou Medical College,
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Inner Mongolia University of Science and Technology, Baotou, China. The 3D-T1 weighted
images were acquired using a magnetization prepared rapid acquisition gradient echo
(MPRAGE) pulse sequence with a voxel size of 1mm3 (repetition time (TR) = 8.4 ms; echo
time (TE) = 3.8 ms; data matrix = 240×240; slices= 176; field of view (FOV) = 240×240
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mm2). The DTI data were collected with a single-shot echo-planar imaging sequence (68
continuous axial slices with a slice thickness of 2 mm, TR = 6800 ms, TE = 70 ms, data
matrix = 120×120, FOV = 240×240 mm2). The 32 non-collinear directions (b = 1000 s/mm2)
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were applied with an acquisition without diffusion weighting (b = 0 s/mm2). Participants with
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any clinically silent lesions were excluded.
2.5 Preprocessing of DTI images and Definition of the ROIs in diffusion space
DTI images were processed with FMRIB’s Diffusion Toolbox 3.0 in FSL 5.0.9
(https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/). Eddy current distortion, head motion and B0
inhomogeneity distortion were applied to the DTI images by using dti_preprocess
(https://github.com/RIKEN-BCIL/dti_preprocess). The diffusion tensor was then calculated
for each voxel. Diffusion maps were derived in individual diffusion space for each subject.
The DLPFC projects to caudate, whereas the mOFC projects to ventral striatum, which has
been documented in anatomical studies in non-human primates and rodents (Tomasi and
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Volkow, 2013). Thus, both the bilateral dorsal (DLPFC-caudate) and the ventral (mOFCNAc) frontostriatal tracts were examined in the current study. In detail, the bilateral cortical
ROIs were extracted in the automated anatomical labeling (AAL) map (mOFC
(Frontal_Sup_Orb, Frontal_Med_Orb, Rectus), DLPFC (Frontal_Mid, Frontal_Sup)) (Van et
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al., 2015). The bilateral ROIs of the caudate and NAc were generated from the HarvardOxford subcortical structural atlas in standard space. All ROIs were transformed into native
diffusion space (Figure1.a). In detail, the raw DTI images were registered to the individual T1
image using FSL’s Linear Image Registration Tool (FLIRT) with mutual information used as
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a cost function (FA_2T1 matrix). The individual T1 images were normalized into MNI space
using linear (FLIRT) and nonlinear registration FNIRT (FSL’s Non-linear Image Registration
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Tool). The co-registered DTI images in structural space were then warped using the
transformation field derived from T1 to MNI normalization. The transformation matrix
(FA_2T1) and warp-fields (T1_2MNI warp) were inverted using convert_xfm and invwarp
command respectively, which were subsequently applied to the ROI in MNI space to obtain
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the ROIs in individual diffusion space (Figure 1.a).
2.6 Probabilistic tractography of the Frontostriatal tracts
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Based on the probability density function, probabilistic tractography reconstructs white matter
tracts and enhances the robustness and sensitivity of the tractography, especially for crossing
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fibers (King et al., 2012; Leong et al., 2016). FA can reflect the structure of axonal cell
membranes and myelin sheaths and a decrease of AD suggests axonal loss or loss of bundle
coherence, and an increase in RD suggests disrupted myelination. MD is a measure of the
average molecular motion, independent of tissue directionality (Ashtari et al., 2007). These
supplement measurements, together with FA, can provide more detailed information about
WM integrity than FA alone (Benedetti et al., 2011). The seed-based probabilistic approach
was employed to track the bilateral dorsal (DLPFC-caudate) and the ventral (mOFC-NAc)
tracts. The distributions of fiber orientations at each voxel were calculated with BEDPOSTX
(Bayesian Estimation of Diffusion Parameters Obtained using Sampling Techniques). The
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probabilistic tractography from each voxel within the seed ROI was initiated with the
probtrackx2 command and the following parameters: streamlines=25,000; step length=0.5
mm; curvature threshold=0.2. To ensure that only WM tracks were kept for calculating, each
fiber-track was conducted twice, e.g., once using the DLPFC as a seed mask, the ipsilateral
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caudate as a termination mask and the ipsilateral mOFC as an exclusion mask
(DLPFC_2caudate tracts), and vice versa (i.e., caudate_2DLPFC tracts: caudate=seed mask;
the ipsilateral DLPFC=termination mask, the ipsilateral mOFC=exclusion mask). We set the
threshold of all tracts based on the individual maximum connectivity value within a tract, i.e.,
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more than 5% of the maximum connectivity value (Yuan et al., 2017a). For each subject, the
DLPFC_2caudate and caudate_2DLPFC tracts were binarized and then overlapped in
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individual diffusion space (Figure 1.b). The binarized maps were then transformed to standard
space and combined. The final tract mask for each frontostriatal circuit was generated by
keeping the voxels with values larger than 40% of the total number of the subjects in each
group (Figure 1.d). The mean diffusion values (FA, AD, RD, MD) of identified tracts were
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extracted for statistical analysis. The right ventral frontostriatal tracts were tracked similarly
(mOFC-NAc) (Figure 1.c,e). The reconstruction of the left tracts is shown in Figure S1 of the
supplementary materials.
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(Insert Figure1 here)
2.7 Statistical analyses
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Two-way repeated measures Analysis of covariance (ANCOVA) with a between-subjects
factor comparing the group (smoker vs. non-smoker) and a within-subjects factor comparing
the condition type (congruent vs. incongruent) was performed on the reaction time and
response errors of the Stroop task, including the SAS and SDS as covariates. Regarding the
DTI results, the mean FA, MD, AD, RD values of the bilateral dorsal and ventral tracts were
imported into SPSS 20.0 (SPSS Statistics, IBM, Armonk, NY). Multivariate ANCOVA was
used to detect the differences in mean diffusion measurements of the four pathways between
smokers and nonsmokers, including the SAS and SDS as covariates. Bonferroni procedure for
multiple comparisons (4 tracts × 4 diffusion measurements) was applied and the level of
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significance was set at p < 0.003 (0.05/4×4). Furthermore, partial correlation was carried out
to assess the relationship between WM diffusion properties of fiber tracts and behavioral data
in smokers (i.e., cognitive control task performance, as indexed by reaction time and errors of
the congruent and incongruent conditions, and reaction delay) and smoking characteristics
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(FTND, pack years, age of onset and craving), controlling for SAS and SDS. Since there were
144 correlation analyses (9 (behavioral variables) * 16 (imaging variables) in smokers, we
chose a standard and relatively liberal method (FDR) for the multi-comparison corrections for
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the correlation analysis instead of Bonferroni correction (p<0.00035).
2.8 Replication analysis (Study2)
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Data collection and data processing were also similar to Study1 (see Supplementary
information). Furthermore, a 2×2 ANOVA of study (Study1, Study2) × group (smoker,
nonsmoker) was used to test the robustness of the WM findings.
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3. RESULTS
3.1 Behavioral characteristics and Stroop performance of smokers and nonsmokers
In Study1, smokers reported smoking 15.3 ± 5.5 cigarettes per day and the mean FTND was
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4.5 ± 2.0 (Table1). Smokers had higher anxiety (t(69) = 4.22, p < 0.001) and depression
scores (t(69) = 2.73, p = 0.008) than controls (Table 1). There was a main effect of condition
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for Stroop response errors (F(1,67)=4.23, p=0.044) and RT (F(1,67)=15.17, p<0.001;
controlled for SAS and SDS), which demonstrated a significant Stroop effect. The main effect
of group was detected for the incongruent response errors (F(1,67)=5.10, p=0.027) but not for
the RT (F(1,67)=1.27, p=0.27). An interaction of group × condition was detected for the
response errors (F(1,67)=5.36, p=0.024) but not for RT (F(1,67)=0.71, p=0.40). Post-hoc
analysis demonstrated that smokers committed more errors than nonsmokers during the
incongruent condition (F(1, 67)=6.13, p=0.016). Similar results were detected in Study2. That
is, smokers had higher self-reported anxiety (t(52)=3.6, p<0.001) and depression scores
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(t(52)=2.7, p=0.01) compared with nonsmokers. For Stroop measures, there was an
interaction effect of group × condition for response errors (F(1,50)=5.79, p=0.02), but not for
RT (F(1,50)=0.19, p=0.69). Post-hoc analysis demonstrated that smokers committed more
errors than nonsmokers during the incongruent (F(1, 50)=9.51, p=0.003). The results of the
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Stroop task are reported in Figure S2 of the supplementary materials.
(Insert Table1 here)
3.2 FA of the frontostriatal fibers between smokers and nonsmokers
In Study1, the reconstructed fibers displayed in Figure 2.a demonstrated that our approach
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was able to reliably trace the dorsal and ventral frontostriatal tracts when we chose 40% as the
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final group mask of frontostriatal tracts. A main effect of group was found (Θ=0.87,
F(16,52)=2.83, p=0.002). Smokers showed lower FA values of the left mOFC-NAc fiber
tracts (F(1, 67)= 14.70, p<0.001) and right DLPFC-caudate fiber tracts (F(1, 67)= 11.13,
p=0.001) (Bonferroni corrected) as well as the increased RD of the left mOFC-NAc fiber
tracts (F(1, 67)= 8.13, p=0.006) compared with nonsmokers while controlling for SAS and
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SDS (Figure 2.b and Table 2). No other significant group differences in other fiber tracts were
found (Figure S3 of the supplementary materials). With regard to Study2 (Figure S4 of the
supplementary materials), while controlling for SAS and SDS, smokers showed reduced FA
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of the left mOFC-NAc fiber tracts (F(1, 50) = 12.22, p =0.001) (Bonferroni corrected) and
right DLPFC-caudate fiber tracts (F(1, 50) = 8.07, p =0.006) compared with nonsmokers
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(Figure 2.c).
3.3 The association of the diffusion findings with behavior
In Study1, the FA values of the right DLPFC-caudate fiber tracts were negatively correlated
with the response errors during incongruent condition of Stroop task in the smokers (r=-0.63,
p<0.001) (Figure 3.a) and nonsmokers (r=-0.50, p=0.002) (FDR p<0.05). The MD values of
the left mOFC-NAc fiber tracts were positively correlated with the QSU of the smokers
(r=0.51, p=0.0016) (FDR p<0.05) (Figure3.b). For Study2, FA values of the left DLPFCcaudate fiber tracts were negatively correlated with the response errors during the incongruent
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Stroop condition in both smokers (r=-0.48, p=0.008) and nonsmokers (r=-0.44, p=0.028)
(Figure 3.a). In smokers, the MD values of the left mOFC-NAc fiber tracts were positively
correlated with the QSU craving scores (r=0.51, p=0.005) (Figure 3.b). To investigate the
possible effect of SAS and SDS for the correlation analysis, we employed partial correlation
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analysis by including SAS, SDS as the covariates. Similar results were observed to those in
study1: the FA values of the right DLPFC-caudate fiber tracts were negatively correlated with
incongruent Stroop response errors in the smokers (r=-0.65, p<0.001). The MD values of the
left mOFC-NAc fiber tracts were positively correlated with the QSU of the smokers (r=0.50,
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p=0.003). In Study2, the partial correlation analysis also revealed similar results: (r=-0.508,
p=0.007) for the dorsal fibers and (r=-0.503, p=0.007) for the ventral fibers in smokers. These
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associations between fiber integrity and behavior were region-specific: no significant
correlations were detected when the variables were switched. That is, the FA values of dorsal
circuits were not correlated with craving in the smokers (r=-0.06, p=0.37), and the FA values
of ventral circuits were not correlated with Stroop task performances in the smokers (r=-0.18,
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p=0.14).
(Insert Figure2, 3 and Table2 here)
3.4 Study × Group ANOVA analysis
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Although findings between the two studies were generally similar, we assessed whether there
were differences between the two studies in age or education that could have impacted results
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by conducting 2 × 2 ANOVAs with study and group as factors. Neither a main effect (study
(F(1,121)=0, p=0.98), group (F(1,121)=0.85, p=0.36)) nor an interaction effect
(F(1,121)=0.69, p=0.41) was found for age. Similarly, neither main effect study
((F(1,121)=0.4, p=0.53), group (F(1,121)=0.48, p=0.49)) nor interaction effect
(F(1,121)=0.30, p=0.59)) were detected for education. With regard to the DTI results, there
were no main effects of study on WM values and no interaction effect of study × group.
However, as expected based on the results from each individual study, there was a main effect
of group for FA values of the left DLPFC-caudate (F(1,121)=21.2, p<0.0001) and mOFC-
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NAc tracts (F(1,121)=36.8, p<0.0001), with lower FA in smokers versus nonsmokers.
3.5 The effect of threshold for the group mask
To investigate the possible effect of the threshold for the group mask of frontostriatal tracts,
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we chose 50% to verify the results in Study1. Similar results were detected compared to the
original 40% threshold (Figure. S5 and Table. S1 in supplementary materials), i.e., the
reduced FA of right DLPFC-caudate (F(1, 67)= 9.57, p=0.003) and left mOFC-NAc (F(1,
67)= 11.33, p=0.001) fiber tracts in smokers (Figure 2.c). The correlations remained
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significant for the dorsal tract with response errors (r=-0.34, p=0.045) and ventral tract with
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QSU (r=0.39, p=0.02) in smokers.
4. DISCUSSION
In the current study, we examined the dorsal and ventral frontostriatal tracts in young male
smokers by using probabilistic tractography (Figure1). We found lower FA values of the right
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DLPFC-caudate and the left mOFC-NAc tracts in smokers relative to nonsmokers in two
studies. Although FA is thought to reflect axonal cell membrane structures and myelin sheath
changes, it is not very specific to the exact types of changes (Mori and Zhang, 2006).
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Therefore, we calculated complementary measures that provide a more complete picture of
WM integrity than FA alone. Specifically, MD is a measure of the average molecular motion,
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independent of tissue directionality, reduced AD suggests axonal loss or loss of bundle
coherence, an increased RD suggests disrupted myelination. These supplement
measurements, together with FA, may provide more specific information about the structure
of axonal cell membranes and myelin sheaths (Benedetti et al., 2011). Since we observed
lower FA and higher RD of the left mOFC-NAc tracts without AD differences in smokers,
this pattern of findings may reflect disrupted myelination without axonal loss for the ventral
striatal circuits. In addition, we observed lower FA, but not MD, RD, or AD in right DLPFCcaudate tracts in smokers, which could reflect axonal cell membrane and/or myelin sheath
changes for the dorsal striatal circuits. These findings are consistent with a previous human
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study that reported lower FA and higher RD in tracts projecting to the prefrontal cortex from
NAc (Savjani et al., 2014). Moreover, rodent studies revealed gestational nicotine exposure
decreased myelin gene expression in both adolescent and adult rats in frontal cortex and
dorsal/ventral striatum (Cao et al., 2013a; Cao et al., 2013b). Taken together with the current
frontostriatal circuits between smokers and nonsmokers.
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studies, these findings provide evidence for differences in white matter characteristics of the
Previous studies suggest craving and cognitive control impairments that are
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characteristic of addiction might stem from frontal cortical and striatal dysfunction (Goldstein
and Volkow, 2011; Volkow and Morales, 2015; Volkow et al., 2016). Indeed, neuroimaging
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studies have consistently shown frontostriatal impairments in smokers, such as abnormal
volume, dysfunction, metabolism and functional connectivity (Brody et al., 2004a; Li et al.,
2015; Newberg et al., 2007; Salokangas et al., 2000; Yuan et al., 2016; Yuan et al., 2017a).
The present results are in line with these findings by showing altered WM structural
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connectivity in young smokers (Leong et al., 2016; Yuan et al., 2017a). Moreover, it has been
suggested that the ventral pathway is mainly involved in craving whereas the dorsal pathway
is involved in cognitive control (Tomasi and Volkow, 2013). Although both pathways are
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important for the neural mechanisms of smoking (Brody et al., 2004a; Li et al., 2015;
Newberg et al., 2007; Salokangas et al., 2000; Yuan et al., 2016; Yuan et al., 2017a), the
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association of their WM diffusion properties with craving and cognition control deficits
remains unknown in smokers. The brain-behavior correlation analysis clearly provided
evidence for separable associations with behavior between dorsal and ventral striatal circuits.
Specifically, the reduced FA of the right DLPFC-caudate pathway correlated with impaired
cognitive control on the Stroop task in smokers. The MD values of the left mOFC-NAc
pathway were correlated with the craving score measured by QSU in smokers. These results
were still significant when we chose a more stringent threshold to identify tracts in Study1
(FigureS5 in the supplementary materials). More importantly, the main findings of Study1
were replicated in an independent study (Study2), indicating the robustness of our WM results
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(Figure3).
4.1 The DLPFC-caudate pathway and cognitive control deficits in smokers
Emerging evidence has demonstrated that nicotine is highly toxic to the developing brain and
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chronic smoking might cause cognitive deficits through its neurotoxic effects (Collaborators,
2017; Jasinska et al., 2014). Thus, it is anticipated that chronic smoking is associated with
impaired cognitive control (Jasinska et al., 2014; Yuan et al., 2016). Consistently, we found
more incongruent response errors on the Stroop task smokers versus nonsmokers, indicating
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cognitive deficits in smokers. Cognitive control is in large part regulated by frontostriatal
circuits (Casey et al., 2007; Liston et al., 2006), and developmental improvements in
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cognitive control are paralleled by increases in activation and functional connectivity of
frontostriatal circuits (Vink et al., 2014). It has been reported that greater WM coherence
facilitates the transmission of functional information (Bonnelle et al., 2012; Leong et al.,
2016), suggesting that WM structural integrity of the frontostriatal tracts may be associated
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with cognitive control deficits in smokers. Several studies demonstrated that the coherence of
WM projections from the PFC to the striatum could account for individual differences in
cognitive control in healthy subjects (Casey et al., 2007; Liston et al., 2006) and in
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individuals with addiction (Hanlon et al., 2011; Morein-Zamir and Robbins, 2014). Here, we
found an association of WM diffusion properties of the right DLPFC-caudate tract and Stroop
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task performance in both smokers and nonsmokers. Taken together, these correlational
findings in both studies further emphasize the contribution of the right DLPFC-caudate tract
in cognitive control deficits in smokers.
4.2 The mOFC-NAc pathway and subjective craving in smokers
In the current study, lower FA and higher RD values of the left mOFC-NAc pathway were
also observed in smokers compared with nonsmokers. We found significant correlations
between MD of the right mOFC-NAc tract and subjective craving in smokers. Our findings
are consistent with previous human and rat studies (Savjani et al., 2014). Intravenous nicotine
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administration in rats induced dopamine release and stimulated energy metabolism in NAc
(Pontieri et al., 1996), which has a central role in the reinforcing effects of addictive
compounds. Similarly, human studies demonstrated that nicotine induced a dose-dependent
increase in neuronal activity in the NAc and the mOFC and the signal was proportional to
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subjective craving (Brody et al., 2002; Hayashi et al., 2013; Salokangas et al., 2000).
Moreover, smoking cues can acquire incentive salience via activation of midbrain
dopaminergic projections, such as ventral striatum and mOFC (Engelmann et al., 2012;
Hayashi et al., 2013). The mOFC is thought to contribute to goal-directed behavior by
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assessing the motivational significance of stimuli and selecting behaviors to obtain desired
outcomes (Wilson et al., 2004). The mOFC also has extensive connections with the striatum
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and, as a result, is well situated to integrate the activity of the subcortical areas associated
with motivational behavior and reward processing (Wilson et al., 2004). Previous smoking
studies revealed the capacity of mOFC in regulating smoking cue induced craving by
interacting with ventral striatum in smokers (Kober et al., 2010). Our results provide evidence
craving in smokers.
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for the possible role of the mOFC-NAc tract as a potential neuroimaging marker for nicotine
The PFC works in tandem with striatal regions via frontostriatal networks, which are
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modulated by dopamine (Volkow et al., 2011). Decreased striatal dopamine transporter and
receptor availability has been detected in smokers compared with nonsmokers, which
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suggests a dysfunctional striatal dopaminergic system (Newberg et al., 2007). Moreover,
decreases in striatal dopamine D2 receptor availability have been associated with reduced
baseline glucose metabolism in PFC in substance abusers (Tomasi and Volkow, 2013;
Volkow et al., 2011). These correlations may suggest improper DA regulation between PFC
and striatum (Volkow et al., 2011). Although the ventral striatum is well known to mediate
the reinforcing effects of drugs, the dorsal striatum is thought to become an important player
as drug seeking transitions from voluntary to habitual behavior (Everitt and Robbins, 2005).
Further study of the serial communications between dorsal and ventral striatal circuits may be
critical for understanding the neural mechanisms of reinforcement for drug addiction. In
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addition, human neuroimaging studies with more comprehensive designs (e.g., using
combined functional MRI-PET) are needed to reveal the link between dopaminergic
transmission and dorsal vs. ventral frontostriatal function in smokers.
Addiction studies have repeatedly shown altered frontostriatal function that is related
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to reward and cognition (Everitt and Robbins, 2005). Our results in the present study largely
corroborate these findings by showing that structural connectivity within frontostriatal circuits
is also affected by chronic smoking. However, some prior exploratory studies did not find
significant functional group differences within this circuit (Weiland et al., 2014a; Weiland et
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al., 2014b), and in fact, a recent dynamic functional connectivity study instead reported
dysfunction between the caudate and postcentral gyrus in smokers (Vergara et al., 2018). The
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divergent findings could have emerged because NAc was not included as an a priori ROI in
those studies, or it could be due to differences in statistical approaches to correct for many
comparisons (i.e., FDR vs. Bonferroni). Future studies with a larger sample size and a more
sophisticated investigation of between cortical regions and striatum may be necessary to
4.3 Limitations
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resolve these issues.
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There are several limitations to address. First, although DTI tractography can illuminate
structural connections, it cannot provide information about chemical transmission (Leong et
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al., 2016). Our motivation to focus on the frontostriatal circuits was based on the extensive
evidence of DA dysfunction in these regions across different drugs including nicotine, heroin,
cocaine and alcohol (Volkow et al., 2016). However, DTI data do not directly speak to
dopaminergic mechanisms, and therefore multimodal imaging studies including PET imaging
will be necessary to address whether these findings are indeed related to dopaminergic
signaling differences in smokers. Second, the cortical areas for tractography were defined
based on the AAL atlas, and these regions are quite large and functionally heterogeneous. In
the future, ROIs based on task activations or other functional gradients that can delineate
cortical boundaries in a more refined way (e.g., Glasser et al., 2016) should be taken into
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consideration to expand on these findings. Third, the cross-sectional nature of this study
cannot demonstrate whether smoking causes WM abnormalities or whether WM differences
were preexisting. Longitudinal studies should be employed in the future to address these
questions. Fourth, since there were 144 correlation analyses and the results might not survive
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Bonferroni correction, we chose different multiple comparisons correction methods for
detecting differences of DTI and correlations between DTI and behaviors. In future studies, a
larger sample size will be necessary to solve this. Finally, we did not include females in our
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study sample, so the results here may only generalize to males.
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5. Conclusion
Here we identified reliable differences in diffusion measurements of the right dorsal and left
ventral frontostriatal tracts between young male smokers and nonsmokers in two independent
cohorts. Moreover, we observed correlations between diffusion measures in dorsal circuits
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with cognitive control impairments and diffusion measures in ventral circuits with subjective
craving in smokers. Thus, WM integrity of dorsal and ventral frontostriatal circuits
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contributes to distinct aspects of smoking-related behaviors.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China under
Grant Nos. 81871426, 81871430, 81571751, 81571753, 61771266, 31800926 and
81701780, the Fundamental Research Funds for the Central Universities under the
Grant No. JB151204, the program for Young Talents of Science and Technology in
Universities of Inner Mongolia Autonomous Region NJYT-17-B11, the Natural
Science Foundation of Inner Mongolia under Grant No. 2017MS(LH)0814, the
program of Science and Technology in Universities of Inner Mongolia Autonomous
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Region NJZY17262, the Innovation Fund Project of Inner Mongolia University of
Science and Technology No. 2015QNGG03, National Natural Science Foundation of
Shaanxi Province under Grant no. 2018JM7075. US National Institutes of Health,
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The authors declare no conflict of interest.
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Intramural Research program Y1AA3009.
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Figure Legends
Figure 1. Probabilistic tractography of the frontosriatal tracts in right hemisphere.
(a). The regions of interest (ROIs) definitions in diffusion space. The bilateral cortical ROIs
were extracted in the automated anatomical labeling (AAL) map (mOFC (Frontal_Sup_Orb,
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Frontal_Med_Orb, Rectus), DLPFC (Frontal_Mid, Frontal_Sup)). The bilateral ROIs of the
caudate and NAc were generated from Harvard-Oxford subcortical structural atlas in standard
space. All ROIs were transformed into native diffusion space. First, the DTI images was
registered to the individual T1 image using FSL’s Linear Image Registration Tool (FLIRT)
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with mutual information used as a cost function (FA_2T1 matrix). The individual T1 image
was normalized into MNI space using linear (FLIRT) and nonlinear registration FNIRT
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(FSL’s Non-linear Image Registration Tool). The co-registered DTI image in structural space
was then warped using the transformation field derived from T1 to MNI normalization. The
transformation matrix (FA_2T1) and warp-fields (T1_2MNI warp) were inverted using
convert_xfm and invwarp command respectively, which were subsequently applied to the
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ROI in MNI space to obtain the ROIs in individual diffusion space.
(b) and (c). The fiber tracking of the right dorsal and ventral frontostriatal tracts. The seedbased probabilistic approach was employed to track the dorsal (DLPFC-caudate) and the
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ventral (mOFC-NAc) tracts. The distributions of fiber orientations at each voxel were
calculated by using the BEDPOSTX (Bayesian Estimation of Diffusion Parameters Obtained
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using Sampling Techniques). The probabilistic tractography from each voxel within the seed
ROI was initiated with the probtrackx2 command and the following parameters:
streamlines=25,000; step length=0.5 mm; curvature threshold=0.2. To ensure that only the
WM part were kept for calculating, each fiber-tracking were conducted twice, e.g., once using
the DLPFC as a seed mask, the ipsilateral caudate as a termination mask and the ipsilateral
mOFC as a exclusion mask (DLPFC_2caudate tracts), and vice versa (i.e., caudate_2DLPFC
tracts: caudate=seed mask; the ipsilateral DLPFC=termination mask, the ipsilateral
mOFC=exclusion mask). We set the threshold of all tracks based on the individual maximum
connectivity value within a tract, i.e., more than 5% of the maximum connectivity value. For
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each subject, the DLPFC_2caudate and caudate_2DLPFC tracts were binarized and then
overlapped in individual diffusion space.
(d) and (e). The group mask of the right dorsal and ventral frontostriatal tracts. (e). The
ventral frontostriatal tracts were extracted similarly (mOFC-NAc). The binarized maps were
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then transformed to standard space and added up. The final group tract masks of the dorsal
and ventral frontostriatal tracts were generated by show the voxels common to 40% of the
total number of the subjects in each group.
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Figure 2. Reduced FA of the frontostriatal tracts in smokers of Study1.
(a). The fiber tracking results of the bilateral frontostriatal tracts in smokers. The derived
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fibers were shown in 3D manner by using the BrainNet Viewer (www.nitrc.org/projects/bnv/)
(Red: caudate; Blue: DLPFC; Yellow: NAc; Purple: mOFC; Green: dorsal frontostriatal
tracts; Cyan: ventral frontostriatal tracts).
(b). The reduced FA of the left mOFC-NAc fiber tracts (F(1, 67)= 14.70, p<0.001) and right
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DLPFC-caudate fiber tracts (F(1, 67)= 11.13, p=0.001)were detected between the smokers
and nonsmokers (Bonferroni corrected).
(c). Reduced FA of the frontostriatal tracts in smokers (50% threshold for the group mask).
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The reduced FA of right DLPFC-caudate (F(1, 67)= 9.57, p=0.003) and left mOFC-NAc
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(F(1, 67)= 11.33, p=0.001) fiber tracts in smokers.
Figure 3. The correlation between the diffusion properties of the frontostriatal tracts
and behavior in smokers.
(a). The FA values of the right DLPFC-caudate fiber tracts were negatively correlated with
the response errors during incongruent condition of Stroop task in the smokers in both
studies.
(b). The MD values of the left mOFC-NAc fiber tracts were positively correlated with the
QSU of the smokers in both studies.
25
ACCEPTED MANUSCRIPT
Table1. Demographics and Baseline Self-Report Measures.
Study1
Study2
RI
PT
Clinical details
Smoker (n=36)
Nonsmoker (n=35)
Smoker (n=29)
Nonsmoker (n=25)
Age (years)
21.3 ± 1.3
21.2±1.3
21.4 ± 1.1
21.0±1.4
Education Years
14.1±0.14 (13-16)
14.2±0.2
14.0±0.12 (13-16)
SAS**
35.1±6.0
36.4±7.4
9.1±2.7 (8-22)
15.3±5.5 (10-26)
4.5±2.0 (2-8)
4.6±2.7 (1-10)
3.8±3.3 (1-13)
14.9±2.9 (8-20)
29.4±5.8
31.9±6.4
1.9±0.6 (1-3)
n/a
n/a
n/a
n/a
n/a
35.4±6.0
36.9±7.1
8.7±3.7 (8-21)
15.4±5.7 (10-26)
4.6±2.1 (2-8)
4.9±2.9 (1-10)
4.1±3.6 (1-13)
14.7±3.0 (8-20)
Cigarettes Per Day (CPD)
FTND
Years of Smoking
Pack_Years
Initial smoking age
SC
CO (ppm)**
29.7±5.7
32.1±5.6
2.0±0.6 (1-3)
n/a
n/a
n/a
n/a
n/a
M
AN
U
SDS*
14.1±0.2
**: p<0.005
EP
*: p<0.05
AC
C
×daily consumption/20.
TE
D
SD: Standard Derivation; SAS: Self-Rating Anxiety Scale; SDS: Self-Rating Depression Scale; FTND: Fagerstrom Test for Nicotine Dependence; Pack_years: smoking years
ACCEPTED MANUSCRIPT
Study1
Right
Left
mOFC_NAc
Right
Frontostriatal circuits
DLPFC_caudate
Hemisphere
Left
Fiber
measurements
FA
AD
SC
Nonsmoker (n=35)
Mean
SD
0.3282
0.01837
0.0012
0.00011
0.0007
0.00009
0.0009
0.0001
0.3228
0.01522
0.0011
0.00007
0.0007
0.00007
0.0009
0.00007
0.3171
0.02195
0.0012
0.00009
0.0007
0.00007
0.0009
0.00008
0.3499
0.01961
0.0012
0.00008
0.0007
0.00005
0.0009
0.00006
M
AN
U
DLPFC_caudate
Smoker (n=36)
Mean
SD
0.3207
0.01953
0.0012
0.00008
0.0007
0.00007
0.0009
0.00007
0.3094
0.01496
0.0012
0.00008
0.0007
0.00007
0.0009
0.00007
0.2962
0.01906
0.0012
0.00006
0.0008
0.00005
0.0009
0.00005
0.3478
0.01449
0.0012
0.00005
0.0007
0.00004
0.0009
0.00004
TE
D
Left
Fiber
measurements
FA
AD
RD
MD
FA
AD
RD
MD
FA
AD
RD
MD
FA
AD
RD
MD
EP
Hemisphere
AC
C
Frontostriatal circuits
RI
PT
Table2. The fiber diffusion measurements differences of frontostriatal circuits between smokers and nonsmokers.
F value
p value
3.14
0.005
0.16
0.06
11.13
0.52
2.94
1.91
14.70
2.50
8.13
5.93
0.60
0.03
0
0.007
0.08
0.94
0.69
0.81
0.001*
0.47
0.09
0.17
<0.001*
0.12
0.006*
0.018
0.44
0.86
1
0.93
Study2
Smoker (n=29)
Mean
SD
0.3203
0.01396
0.0012
0.0001
Nonsmoker (n=25)
Mean
SD
0.3278
0.00985
0.0012
0.00008
F value
p value
3.05
0.57
0.087
0.45
ACCEPTED MANUSCRIPT
mOFC_NAc
Right
AC
C
EP
Multivariate analysis of covariance after controlling the SAS and SDS.
0.00007
0.00007
0.00968
0.00008
0.00007
0.00007
0.01807
0.00008
0.00004
0.00005
0.01444
0.00006
0.00003
0.00004
RI
PT
0.0007
0.0009
0.3181
0.0011
0.0007
0.0008
0.3181
0.0012
0.0007
0.0009
0.3426
0.0012
0.0007
0.0009
SC
0.00009
0.00009
0.01152
0.00011
0.0001
0.00011
0.01769
0.0001
0.00009
0.00009
0.01272
0.00008
0.00006
0.00007
M
AN
U
Left
0.0007
0.0009
0.3092
0.0012
0.0008
0.0009
0.2963
0.0012
0.0008
0.0009
0.3439
0.0012
0.0007
0.0009
TE
D
Right
RD
MD
FA
AD
RD
MD
FA
AD
RD
MD
FA
AD
RD
MD
0.06
0.19
8.07
0.40
2.16
1.41
12.22
0.96
3.91
2.77
1.80
1.06
0.29
0.58
0.81
0.09
0.006*
0.53
0.15
0.67
0.001*
0.33
0.054
0.10
0.19
0.31
0.59
0.45
AC
C
EP
TE
D
M
AN
U
SC
RI
PT
ACCEPTED MANUSCRIPT
AC
C
EP
TE
D
M
AN
U
SC
RI
PT
ACCEPTED MANUSCRIPT
AC
C
EP
TE
D
M
AN
U
SC
RI
PT
ACCEPTED MANUSCRIPT
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