White matter integrity alternations associated with cocaine dependence and long-term abstinence: Preliminary findings

White matter integrity alternations associated with cocaine dependence and long-term abstinence: Preliminary findings

Journal Pre-proof White matter integrity alternations associated with cocaine dependence and long-term abstinence: Preliminary findings Qinghua He (Met...

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Journal Pre-proof White matter integrity alternations associated with cocaine dependence and long-term abstinence: Preliminary findings Qinghua He (Methodology) (Software) (Formal analysis) (Investigation) (Data curation) (Writing - original draft) (Writing review and editing) (Visualization), Dandan Li (Writing - original draft) (Writing - review and editing) (Visualization), Ofir Turel (Writing - review and editing), Antoine Bechara (Conceptualization) (Supervision) (Project administration), Yih-Ing Hser (Conceptualization) (Resources) (Supervision) (Funding acquisition)

PII:

S0166-4328(19)31242-2

DOI:

https://doi.org/10.1016/j.bbr.2019.112388

Reference:

BBR 112388

To appear in:

Behavioural Brain Research

Received Date:

11 August 2019

Revised Date:

21 November 2019

Accepted Date:

25 November 2019

Please cite this article as: He Q, Li D, Turel O, Bechara A, Hser Y-Ing, White matter integrity alternations associated with cocaine dependence and long-term abstinence: Preliminary findings, Behavioural Brain Research (2019), doi: https://doi.org/10.1016/j.bbr.2019.112388

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White matter integrity alternations associated with cocaine dependence and long-term abstinence: Preliminary findings Qinghua He1, 2*, Dandan Li1, Ofir Turel2,3, Antoine Bechara2, Yih-Ing Hser4

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of Psychology, Southwest University, Beibei, Chongqing, CHINA

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1 Faculty

Brain and Creativity Institute and Department of Psychology, University of Southern

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California, Los Angeles, CA, USA

Systems and Decision Sciences, California State University, Fullerton, CA,

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Center for Advancing Longitudinal Drug Abuse Research, University of California, Los

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Angeles, CA, USA

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Running Head: DTI AND COCAINE ABSTINENCE

*Correspondence should be sent to: Dr. Qinghua He

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Faculty of Psychology, Southwest University Beibei, Chongqing, 400715, CHINA Tel: +86-13647691390 Email: [email protected] Word Count Abstract: 235; Introduction: 881; Discussion: 981; Tables: 3; Figures: 2.

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Highlights: Cocaine dependence has been associated with deficits in white matter integrity.



The control and abstinence groups had higher FA in WM tracks than cocaine users.



Duration of abstinence was not associated with white matter recovery.

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Abstract Cocaine dependence has been associated with deficits in white matter (WM) integrity. Nevertheless, what happens to WM integrity after long-term abstinence is not fully understood. To bridge this gap, changes in WM integrity were examined with diffusion tensor imaging (DTI) applied to 39 participants: 12 participants who used cocaine in the

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last year (CURRENT USERS), 20 who were at different stages of cocaine abstinence (ABSTINENCE) [five with 1–5 years of abstinence (ABS1), five with 6–10 years of

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abstinence (ABS2), and 10 with over 10 years of abstinence (ABS3)], and 7 healthy controls (CONTROLS). The CONTROL group had higher fractional anisotropy (FA)

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compared to CURRENT USERS in frontal cortex tracts, including the bilateral corpus

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callosum, bilateral superior longitudinal fasciculus, bilateral inferior fronto-occipital fasciculus, left internal capsule, left middle cingulum, and left ventral and dorsal medial

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frontal regions. The ABSTINENCE group also had higher FA compared to CURRENT USERS in frontal cortex tracts, such as the bilateral corpus callosum, bilateral superior

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longitudinal fasciculus, left inferior longitudinal fasciculus, left uncinate fasciculus, left inferior fronto-occipital fasciculus, and the left ventral and dorsal medial frontal regions. Tractography analysis showed (1) deficits in terms of number of fibers and fiber length in these regions, and that (2) while there was some recovery of white matter in dorsolateral

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regions during abstinence, duration of abstinence was not associated with such recovery. The results identified WM differences among cocaine users, cocaine abstinent participants, and controls. These preliminary findings point to WM tracts that recover, and some that do not, after long-term abstinence from cocaine. Keywords: Cocaine; DTI; Abstinence; FA; Tractography; White matter recovery.

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Background Cocaine dependence

is a debilitating chronically relapsing disorder that is

characterized by persistent and compulsive drug-seeking despite harmful consequences [1, 2]. It is a significant public health problem, particularly among adult males [3]. In the United States, the National Survey on Drug Use and Health, administered by the National

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Institute on Drug Addiction (NIDA) has estimated the 2013 prevalence of lifetime cocaine use to be 11.6% in 18 to 25 year-olds, and 16.5% in 26 year-olds and over Cocaine

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(http://www.drugabuse.gov/national-survey-drug-use-health).

use

has

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increasingly attracted the attention of both researchers and the public because of its adverse impacts on individuals and societies. For example, cocaine use, along with other

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illicit drugs, is estimated to cost $181 billion per year in the United States through direct adverse effects, associated healthcare costs, and productivity loss.

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Five lines of research suggest that cocaine use could alter the structure and function of different brain systems. First, studies from molecular biology suggest that

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cocaine blocks the dopamine recycle process from synapses, causing excessive amounts of dopamine between neurons [4]. Repeated use causes long-term molecular changes in relevant brain systems, especially in the reward system, which may eventually form

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cocaine dependence [5]. Second, compared with healthy controls, cocaine-dependent individuals show reduced glutamate metabolism in both the reward system and the anterior cingulate cortex, a brain region critical for response/impulse inhibition [6-9]. Third, repeated cocaine exposure is associated with changes in regional gray matter [10, 11]. For example, Ersche et al. [11] found that cocaine-dependents had decreased gray matter volume in the orbitofrontal, cingulate, insular, temporoparietal, and cerebellar

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cortex, and increased gray matter volume in the basal ganglia. Fourth, white matter (WM) is impaired with cocaine use [10, 12-15]. For example, Romeroet al. [12] found that cocaine-dependent participants present higher fractional anisotropy (FA) values in the anterior cingulate and lower FA in the anterior-posterior commissure plane [16]. Last, functional magnetic resonance imaging (fMRI) studies demonstrated different brain resting state and response activity in cocaine dependent participants compared to

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controls [17-19]. For example, in a recent study, Hu et al. [18] found that cocaine use was

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associated with increased resting state functional connectivity in striatal-frontal circuits, and decreased resting state functional connectivity between the striatum and cingulate,

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striatal, temporal, hippocampal/amygdalar, and insular regions.

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Further illuminating the brain underpinnings of cocaine use and recovery, our previous results [5] showed that there are differences in both brain structure (gray matter

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volume) and function between CURRENT cocaine USERS and CONTROLS, with CURRENT USERS showing plausible relative strengthening in neural systems for

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processing reward and craving, and relative weakening in neural systems involved in inhibitory control and decision-making. Moreover, some prefrontal regions showed no recovery, even after years of abstinence. Here, we seek to extend the examination of brain recovery after abstinence, from

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the gray matter volume and functional domains, to the WM domain. To do so, we employed diffusion tensor imaging (DTI). DTI provides information about WM integrity based upon the flow of water molecules through WM tissue using FA as the dependent variable [20]. Prefrontal and striatal regions have been implicated in reward learning and cognitive control processes, both of which are pivotal to addiction formation and

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maintenance [21]. However, the function of these regions is also determined by ability to communicate over their WM inputs and outputs [22]. Thus, WM studies can lead to a better understanding of brain deficits associated with cocaine dependence. Previous studies using DTI found FA reductions in cocaine-dependent individuals within the genu and the rostral body of the corpus callosum, as well as in frontal, frontallimbic and parietal regions [23, 24], and lower whole brain WM integrity associations with

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the number of substances used [25]. Several studies have also examined WM recovery

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following abstinence. For instance, WM differences in cocaine-dependent patients at different stages of abstinence have been demonstrated, and pointed to WM deficits in

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current cocaine users, as well as to evidence of early WM recovery in abstinent cocaine

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users [23].

Given the findings of reduced FA in cocaine-dependent individuals compared to

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healthy controls, and the findings of at least some FA recovery in abstinent cocaine users, the present study further examines these WM associations, but over longer periods of

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abstinence. Specifically, we examined a cohort of abstinent cocaine patients who varied in the duration of their abstinence. Comparisons between healthy control, cocainedependent, and abstinent groups (abstinent were split into three sub-groups based on the duration of abstinence) allowed us to test for WM integrity differences related to cocaine

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use and to assess if these differences changed with abstinence duration. We hypothesized that cocaine users will show lower WM integrity (as measured by FA) compared to healthy controls and abstinent participants. We also hypothesized that there would be negative associations between patterns of cocaine use and WM integrity, and

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positive associations between duration of abstinence and WM integrity in large portions of the frontal cortex.

Methods Participants Thirty-nine males from the greater Los Angeles area were recruited to participate

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in this study. These participants were a subsample of a long-term cohort study of male

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military veterans, investigating the longitudinal patterns and consequences of cocaine use and dependence (Hser et al., 2006). Details of the sample could be found in our

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previous publications (He et al., 2018; Hser et al., 2006). The healthy control participants

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were individuals of similar ages and ethnicity/race who responded to flyers posted in local Veterans Affairs facilities. A screening process was conducted in 2013-2014 to exclude

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candidates who were: (1) suffering from Axis 1 psychiatric conditions other than cocaine dependence, including PTSD and alcohol dependence; 2) had medical conditions that

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might have interfered with safe study participation; and /or 3) were currently taking medications for psychiatric or medical conditions. A urine test verified self-reported cocaine use. The mean age for these participants was 56.5 ± 5.4 (Std. Deviation) years. They were in different stages of cocaine use and abstinence without treatment: 12 of them

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had relapsed within the last year (CURRENT USERS); 20 others were at different stages of abstinence from cocaine (ABSTINENCE) [5 relapsed 1–5 years ago (ABS1); 5 relapsed 6-10 years ago (ABS2); and 10 had their last cocaine over 10 years ago (ABS3)]; and the other seven participants were controls (CONTROLS). Controls had matched age, sex, education, race, and marital status (Table 1), had never tried cocaine, but lived in

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the same neighborhood as CURRENT and ABSTINENCE participants. All participants were right handed and had normal or corrected-to-normal vision. All participants gave informed consent to the study procedures, which were approved by Institutional Review Board of both the University of Southern California (reference number UP-11-00097) and the University of California, Los Angeles (reference number IRB#11-000396). Procedures

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Participants were asked to come to the Dana and David Dornsife Cognitive

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Neuroscience Imaging Center for behavioral interviews and MRI scans. They were first asked to read and sign the consent form, and then complete the behavioral interview.

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Prior to the MRI scan, they provided a urine sample. The MRI scan took about 30 minutes

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to finish. During the scan, images for one high-resolution structural (MRI) scan, one DTI scan, and one session of cue task (fMRI) were acquired with short resting periods in

Behavioral Interviews

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between. After the scan, participants were asked to rate their urge to use cocaine.

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Measures collected include history of drug use, alcohol and tobacco use, mental health (e.g., Beck Depression, SCL-58), quality of health (e.g., SF36), sensation-seeking, and self-efficacy. The current study updated drug use (e.g., years of cocaine abstinence)

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and other measures (e.g., current physical health and mental health) from past study results [5].

MRI Protocol

Brain images were acquired using a 3 Tesla Siemens MAGNETOM Tim/Trio scanner at the Dana and David Dornsife Cognitive Neuroscience Imaging Center at the University of Southern California. Participants were scanned in the supine position on the

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scanner bed. Foam pads were used to minimize head motion.

Participants were

instructed to rest and keep their head very still. Structural images were acquired using T1-weighted 3D-Magnetization Prepared RApid Gradient Echo (MPRAGE) sequence, covering the whole brain with the following scanning parameters: TR/TE = 2530/3.39 ms, flip angel = 7°, matrix = 256 x 256, number of slices = 128, and slice thickness = 1.33 mm. The diffusion-tensor data for each participant was acquired using a diffusion-weighted,

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single-shot, spin-echo, Echo-Planar Imaging (EPI) sequence (TR/TE = 7200/104 ms,

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matrix = 128 x 128, 49 axial slices, 2.5 mm slice thickness, b-value = 1000 s/mm2) in 64 directions. A dual spin-echo technique combined with bipolar gradients was employed to

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minimize the geometric distortion induced by eddy currents.

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Statistical Analysis

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First, participants were divided into three major groups: CURRENT USERS (relapsed to using cocaine within the last one year, N = 12), ABSTINENCE (at different

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stages of cocaine abstinence, N = 20) and CONTROLS (matched non-user participants, N = 7). The group ABSTINENCE was further divided into three subgroups: ABS1 (relapsed 1–5 years ago, N = 5), ABS2 (relapsed 6–10 years ago, N = 5), ABS3 (last used cocaine over 10 years ago, N = 10). Group difference were first compared between 1)

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CURRENT USERS and CONTROLS, 2) USERS and ABSTINENCE, and 3) ABSTINENCE and CONTROLS. To delve deeper, two additional comparisons were performed: 1) between CURRENT USERS and ABS1, to explore differences between current users and recently abstinent; 2) between CONTROLS and ABS3, to explore differences between controls and long-term abstinent participants. Next, the years of abstinence were calculated for each participant, except for the controls, and the brain

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data were correlated with both the years of abstinence and the log10 transformation of years of abstinence. We used Log10 transformation of years of abstinence because it was skewed (skewness = 0.68). Nevertheless, it can be easier to interpret years of abstinence directly. We hence report both analyses. Lastly, brain data were extracted for both major groups and subgroups to perform region of interest (ROI) analyses, and differences were compared across subgroups. For all correlational analyses, robust regression was used

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to minimize the impact of outliers in the behavioral data, using iteratively reweighted least

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squares implemented using the robustfit command in MATLAB [26]. Reported R-values reflect (non-robust) Pearson product-moment correlation values, whereas the reported p-

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values and regression lines are based on the robust regression results [26].

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TBSS Analysis

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The DTI data were processed by FMRIB's Diffusion Toolbox (FDT) from the FMRIB's Software Library (FSL). This method has been widely used to identify white

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matter integrity deficits in addiction [27-30] and other related disease [31-34] from a voxelwise statistic perspective (i.e., performing statistics for each voxel in the brain). Diffusion data were corrected for eddy currents and possible head motion. Images were then skullstripped [35], aligned to MNI space using FNIRT [36, 37], and resampled to 1 mm3.

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Fractional anisotropy (FA) was reconstructed by fitting a diffusion tensor model at each voxel. Voxel-wise statistical analysis of the FA data was carried out using Tract-Based Spatial Statistics (TBSS) [38] in FSL. The mean FA image was created and thinned to define a mean FA skeleton that represented the centers of all tracts common to the group. Each participant's aligned FA data were then projected onto this skeleton and the resulting data were fed into voxel-wise cross-subject statistics. Lastly, group differences

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and correlations between the resulting skeletonized FA images and years of abstinence were computed using non-parametric permutation methods [Randomise v2.1 in FSL, 39]. The null distribution of each voxel was constructed using 10,000 random permutations of the data. Threshold-Free Cluster Enhancement (TFCE) was used to correct for multiple comparisons across the whole brain. The mean FA value in each significant cluster was

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then extracted for each ROI to show correlation patterns.

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Tractography Analysis

To better understand the WM bundles that pass through the significant regions

Diffusion

Toolkit

(version

0.6.4.1)

and

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identified in the TBSS analysis, deterministic fiber tractography was performed using TrackVis

(version

0.6.1)

software

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(http://www.trackvis.org) on the data from each participant. Detailed description of fiber

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tracking can be found in He, Chen, Dong, Xue, Chen, Lu and Bechara [34]. In brief, the three regions that showed differences in the TBSS analysis were extracted as binary

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masks. The masks were dilated 2 mm to ensure that these regions would cover the targeted white matter region for each individual [27, 34, 40]. The tractography was performed at the individual space so these masks were registered to the diffusion space of each participant. Deterministic fiber tracking reconstructed tracts along the principal

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eigenvector of each voxel's diffusion tensor. The termination criteria were set as angles > 35 degrees and/or FA < 0.2. The length criterion was set as > 5 mm to remove very short fibers which might be an artifact of noise. The number of fibers and average length of fibers for each tract in their diffusion space were extracted to serve as indices for further analysis. Paired sample t-tests were performed to examine differences between groups. Correlation coefficients were calculated to examine the association between WM

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tractography and (log) years of abstinence. False discovery rate was applied to correct family-related errors when applicable. Each participant’s WM tracts were projected to the MNI space for illustration purposes. False discovery rate (FDR) correction was applied to correct for family wise error. Results

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Fractional Anisotropy and Cocaine Abstinence There were large FA differences between USERS and CONTROLS (Table 2).

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Compared with USERS, CONTROLS had higher FA in large portions of the frontal cortex,

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including the bilateral corpus callosum, bilateral superior longitudinal fasciculus, bilateral inferior fronto-occipital fasciculus, left internal capsule, left middle cingulum, and in WM

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tracts in the left ventral and dorsal medial frontal regions. In contrast, CURRENT USERS had no regions with higher FA compared to CONTROLS. The ABSTINENCE group was

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compared with both CURRENT USERS and CONTROLS (Table 2). Results showed that abstinent users’ WM tended to become like that of CONTROLS, as suggested by the lack

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of differences between the CONTROLS and ABSTINENCE groups. The ABSTINENCE group had higher FA than CURRENT USERS in large portions of the frontal cortex, including the bilateral corpus callosum, bilateral superior longitudinal fasciculus, left

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inferior longitudinal fasciculus, left uncinate fasciculus, left inferior fronto-occipital fasciculus, and in WM tracts in the left ventral and dorsal medial frontal regions. No region showed higher FA in the CURRENT USERS group compared to the ABSTINENCE group. Specifically, the most recent abstinence group, ABS1, had higher FA values compared to CURRENT USERS in the orbitofrontal region; while there were no regions with higher FA

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in the USERS group compared with the ABS1 group. There were also no differences between the CONTROL group and the longest abstinence group, ABS3. Lastly, we analyzed the correlation between FA and both years of abstinence and log years of abstinence (Table 3). This analysis was restricted to those regions which showed differences between USERS and CONTROLS in a liberal (p < 0.05, uncorrected) statistical map. Results showed that FA in the left orbital frontal region and ventromedial

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frontal region were each positively correlated with abstinence duration.

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The above results suggest that there are three key regions that demonstrated diffusion differences and trajectories in the process of abstinence. The first one was the

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left orbital frontal region (Figure 1A), which showed lower FA in CURRENT USERS than

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in CONTROLS or ABSTINENCE. This region even differed between CURRENT USERS and ABS1, a group of individuals who recently (1-5 years) abstained from cocaine. The

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FA in the left orbital frontal region was also associated with years of abstinence. The second region demonstrating a diffusion difference and trajectory was the left

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ventromedial frontal region (Figure 1B), which showed lower FA in CURRENT USERS than in CONTROLS. The FA in the left ventromedial frontal region was also associated with years of abstinence. The FA in both the left orbital frontal and left ventromedial frontal regions were also positively correlated with (log) years of abstinence. The third region

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demonstrating a diffusion difference, but not a difference in trajectory, was the left dorsolateral frontal region (Figure 1C); it showed lower FA in CURRENT USERS than in CONTROLS and in ABSTINENCE groups. It even differed between CURRENT USERS and ABS1, a group of users who recently (1-5 years ago) quit using cocaine. However, there was no correlation between FA and years of abstinence. This suggests that the WM

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in this region likely recovered in the early stages of abstinence (first years) and reached a level like that of CONTROLS early on during abstinence. Hence, its recovery was relatively rapid and reached a plateau of healthy levels relatively quickly. To examine if the deficits in WM integrity existed only in the aforementioned areas, or if there were also deficits in the tracts across these regions, tractography analysis was performed for each participant, and the left orbital frontal region, left ventromedial

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prefrontal region, and left dorsolateral prefrontal region served as predefined regions for

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the tracts. Tracts of a typical participants are illustrated in Figure 2 (see movies in supplemental materials). Results suggest that all these regions had deficits when

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comparing CURRENT USERS with CONTROLS (see Table 3 for details). Furthermore,

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the number of fibers and mean length was positively correlated with (log) years of abstinence in the left orbital frontal cortex and left ventromedial prefrontal cortex, but not

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Discussion

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in left dorsolateral prefrontal cortex region.

The results point to significant structural brain differences between abstinent users (with cocaine abstinence duration ranging from 1 year to 30 years), CONTROLS, and

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CURRENT USERS. Differences among the three groups were observed in the left orbital frontal region, left ventromedial frontal region and the left dorsolateral frontal region. The findings suggest that specific WM differences persist throughout the abstinence period such that people in the ABSTINENCE sub-group had some consistent differences from CURRENT USERS. CONTROLS and ABSTINENCE groups had higher FA compared to

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CURRENT USERS in large portions of the frontal cortex, including the bilateral corpus callosum, bilateral superior longitudinal fasciculus, left inferior fronto-occipital fasciculus, left internal capsule, left middle cingulum, and in WM tracts in the left ventral and dorsal medial frontal regions. Moreover, we also found positive correlations between years of abstinence and FA in both the left orbital frontal region and ventromedial frontal region. Current cocaine use is associated with lowered FA, which can account for decreased

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cognitive functioning, such as working memory [14, 41]. Therefore, higher FA values with

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longer abstinence would be consistent with the restoration of WM integrity [23]. This suggests that it is plausible that abstinence is linked to progressive recovery in these

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tracts. However, the white matter in the left dorsolateral regions was not correlated with

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abstinence duration. We assume that this region presented fast WM recovery, which plateaued after reaching normal levels. This recovery is clinically significant, because

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mounting evidence suggests that the dorsolateral prefrontal cortex (DLPFC) plays an important role in decision making [42]. For example, activation of the left DLPFC has been

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linked to delay discounting in the intertemporal choice task [42, 43].The observed neural changes after abstinence suggested that presumed recovery can occur mostly in neural systems related to reward, craving, and inhibitory control, but to a lesser extent in neural

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systems related to decision-making, though such systems’ WM recovers too [5].

Moreover, these results were predominantly in the left hemisphere .It can be argued that the hemispheric “imbalance” between the left and right PFC that characterizes the participants’ decisions, can stem from left-hemisphere imbalance [44]. While the left PFC has been implicated in approach-related and reward-related motivations and emotions, the right PFC was found to be involved in withdrawal-related motivations and emotions

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[45].In addition, one study of participants with substance use disorder explored the impact of cortical frontal asymmetry (left lateralization effect) in a decisional task (Iowa Gambling Task) [44]. Participants who had substance use disorder had difficulty making advantageous decisions that favor longer-term larger-reward outcomes, over immediate, smaller rewards [44]. Another potential explanation for the lateralization of our findings is that all participants were right handed. We call for future research to examine more

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specifically lateralization issues by recruiting left-handed participants and comparing

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them to right handed ones.

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Neuroimaging studies of currently cocaine-dependent individuals have shown differences in brain function [46-49], gray matter volume [50-53], and FA (as a measure

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of WM) between cocaine-dependent individuals and healthy controls [54, 55]. With

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regards to WM, structural MRI studies found an increased number of WM hyperintensities in cocaine-dependent individuals compared to healthy controls [56, 57]. Examination of possible neural changes after abstinence can provide initial evidence for WM deficits in

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currently cocaine-dependent participants and illuminate WM recovery trajectories in abstinent cocaine users. To date, several studies examined WM differences between cocaine-dependent patients at different stages of abstinence [13, 14, 16, 58-61]. For

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example, Xu et al. (2010) found that self-reported days of cocaine abstinence were positively correlated with FA values in the right superior longitudinal fasciculus, right body of the corpus callosum, right posterior limb of the internal capsule and the left cerebellum measured before entering a treatment program for cocaine dependence. Furthermore, recovery of FA in these regions of cocaine abstinence people is in line with findings of improved functions after sustained cocaine abstinence, including in short-term memory,

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visuospatial skills and flexibility [62, 63] and sustained attention and working memory after MDMA abstinence [64, 65]. Similarly, Paulus et al. (2005) found different activation of the posterior cingulate, insular, middle frontal, and temporal cortices in abstinent methamphetamine-dependent participants compared to relapsing participants [66]. Moreover, activation of the left posterior cingulate cortex was predictive of worse treatment outcome in treatment-seeking cocaine-dependent patients [67]. Because both

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the posterior cingulate and the uncinate fasciculus are important for communication [66]

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between cortical regions and the limbic system [68], they are involved in a variety of emotional processes; these processes include emotional memory and social cognition

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[69, 70], which are both relevant to drug abstinence.

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Similar findings were obtained regarding other substance use disorders, such as participants diagnosed with alcohol use disorder [71-73], and methamphetamine abuse

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[74]. For example, studies report volume reductions in the frontal WM of polysubstance abusers [75], in the cerebellar WM of cocaine users [50], and FA decreases in inferior

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frontal WM of cocaine addicts [58, 61] and methamphetamine abusers [76], as well as in the frontal and anterior cingulate WM of alcoholics [77, 78]. DTI identified that alcoholic individuals are more likely to resume drinking six months following initial evaluation based on lower FA and higher diffusivity in frontal WM at baseline [79]. Therefore, increases in

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FA and decreases in diffusivity have been interpreted as evidence for white matter recovery during abstinence [16, 23]. Substantiation for recovery has been shown in the genu and body of the corpus callosum when one year of abstinence was compared with two weeks of abstinence [80], and in frontal WM at one month of abstinence compared with one week of abstinence, in non-smoking, sober alcoholics [81]. A DTI investigation

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of brain WM in adult multiple sclerosis participants revealed modest (3–12%) decrements in FA in multiple regions, including right prefrontal WM above the anterior commissureposterior commissure (AC–PC) plane, bilateral superior corona radiata, genu corpus callosum, and right prefrontal path; they further showed that those MA participants with more intact WM (higher FA) may be more aware of their own illness and/or be more willing to endorse their symptoms [82].

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Interestingly, this study suggested that the recovery of brain WM was to a lesser extent

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in the prefrontal cortex, and as such was to a lesser extent related to cognitive decision making process [83, 84]. This was consistent with our previous report suggesting that

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presumed recovery of gray matter and function can occur mostly in neural systems

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related to reward, craving, and inhibitory control, but to a lesser extent in neural systems related to decision-making [5]. This was also consistent with many observations that

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prefrontal deficits can be difficult to fix [85] and that these brain regions are less prone to morphological changes compared to sub-cortical region [86]. While recovery of

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neuropsychological functions are expected after a long period of abstinence [87, 88], the persistence of decision-making impairments has been intriguing [5]. These results might suggest that even with multiple years of abstinence, cocaine users may find it difficult to recover their decision-making abilities.

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In conclusion, there is much evidence that cocaine abuse can be associated with

altered structure and function of brain regions involved in impulsion, cravings, inhibitory control and decision making [5, 89]. Specifically, cocaine abuse can be associated with structural and functional abnormalities in the striatum, which is known to play a key role in cocaine reward; the lateral prefrontal cortex (especially the dorsolateral PFC), which is

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known to play a major role in inhibitory control; the insula, which is implicated in craving; and the medial orbitofrontal (OFC) and ventromedial prefrontal cortex (VMPFC), which are involved in integrative and reflective thought leading to decision-making [5]. Functional brain imaging demonstrated that cocaine users show more activation of the striatum, insula, and lateral prefrontal cortex, combined with lower activation in the VMPFC/OFC [90-92]. Also, structural brain imaging demonstrated that repeated cocaine

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exposure changes regional gray matter and white matter: less GMV in the ventromedial

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orbitofrontal, anterior cingulate, anteroventral insular, and superior temporal cortices [51, 93, 94], premotor cortex bilaterally, right orbitofrontal cortex, bilateral temporal cortex, left

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thalamus, and bilateral cerebellum, as well as lower right cerebellar white matter volume

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[95, 96]. Neural recovery in the abovementioned systems is reflected in improvements in the opposite direction. Such changes can manifest in reduced impulsions and craving to

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use cocaine, and/or improved ability to reflect on cocaine use and its consequences, and inhibit impulsions to use cocaine. Through these reversed-changes, the brain can resort

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to pre-abuse normal functioning. Considering neural recovery after long-term cocaine abstinence, it was found that the GMV and activation of some of the some neural regions (namely, striatum, insula, and dorsolateral prefrontal cortex) correlated positively with the duration of cocaine abstinence [5]. Additionally, another study found positive correlations

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between self-reported duration of abstinence and activation in the left posterior cingulate cortex, left ventral medial prefrontal cortex and right putamen [23, 97]. Abstinent cocaine dependent participants also have shown reduced gray matter volume in the prefrontal cortex[98], medial orbitofrontal cortex [99] and lateral orbitofrontal cortex and right cingulate gyrus [98] when compared against non-using controls.

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Overall, our findings provide interesting insights regarding cocaine addiction and possible recovery from a WM perspective. They specifically extend prior research by showing possible long-term WM changes associated with cocaine abstinence. However, there are several limitations of the present study that should be acknowledged. First, the sample size was relatively small and the participants were all male veterans in their mid50s. Although we had a large cohort of participants in the behavioral screening phase,

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the relatively strict exclusion criteria, other co-morbidities, the time scope of the study,

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and body condition disqualified many of them from being scanned, thus reducing the sample size. The sample size was lower than the recommendation with G*Power (version

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3.1.9.2) with large effect size estimation (r = 0.5), and power analysis results suggested

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that a sample of 23 was enough for getting a statistical power lager than 0.80. We have 20 participants for doing the correlation analysis, so the results should be treated as

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preliminary and be interpreted with caution. Second, this was a cross-sectional and correlational study, which limits causality arguments. Longitudinal studies should be

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conducted in the future to replicate and extend the findings from this study. Despite these limitations, the patterns of FA differences across abstinence durations may provide initial evidence for dynamic patterns of WM recovery during cocaine abstinence. These patterns may reflect changing reliance on different psychological processes as drug users escape

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their drug dependence; and the brain’s adaptation to support such changes.

Author Contribution Statement

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Qinghua He: Methodology, Software, Formal Analysis, Investigation, Data Curation, WritingOriginal draft preparation, Writing- Review & Editing, Visualization.

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Dandan Li: Writing- Original draft preparation, Writing - Review & Editing, Visualization.

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Ofir Turel: Writing - Review & Editing.

Antoine Bechara: Conceptualization, Supervision, Project administration.

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Yi-Ing Hser: Conceptualization, Resources, Supervision, Funding acquisition.

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Conflict of Interest

Acknowledgments

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The authors declare no conflict of interest.

YH was supported by National Institute on Drug Abuse (NIDA), Grants

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R03DA032542-01A1 and P30DA016383. QH was supported by research grants from the National Natural Science Foundation of China (31972906), Entrepreneurship and Innovation Program for Chongqing Overseas Returned Scholars (cx2017049), Open

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Research Fund of the Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences (KLMH2019K05), and the High-end Foreign Expert Introduction Program (G20190022029). We would also like to thank Alexandra Hollihan and Stephanie Castillo who helped with the data collection.

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References

Jo

ur na

lP

re

-p

ro

of

[1] C.C. Bell, DSM-IV: Diagnostic and Statistical Manual of Mental Disorders, 1994. [2] L.M. Hulka, M. Vonmoos, K.H. Preller, M.R. Baumgartner, E. Seifritz, A. Gamma, B.B. Quednow, Changes in cocaine consumption are associated with fluctuations in self-reported impulsivity and gambling decision-making, Psychological Medicine -1(14) (2015) 1-14. [3] R. Agabio, I. Campesi, C. Pisanu, G.L. Gessa, F. Franconi, Sex differences in substance use disorders: focus on side effects, Addiction Biology 21(5) (2016) 1030-1042. [4] M.J. Kuhar, M.C. Ritz, J.W. Boja, The dopamine hypothesis of the reinforcing properties of cocaine, Trends in Neurosciences 14(7) (1991) 299-302. [5] Q. He, X. Huang, O. Turel, M. Schulte, D. Huang, A. Thames, A. Bechara, Y.I. Hser, Presumed structural and functional neural recovery after long-term abstinence from cocaine in male military veterans, Progress in Neuro-Psychopharmacology and Biological Psychiatry 84(Pt A) (2018) S0278584617306656. [6] S. Yang, B.J. Salmeron, T.J. Ross, Z.-X. Xi, E.A. Stein, Y. Yang, Lower glutamate levels in rostral anterior cingulate of chronic cocaine users — A 1H-MRS study using TE-averaged PRESS at 3 T with an optimized quantification strategy, Psychiatry Research: Neuroimaging 174(3) (2009) 171-176. [7] L. Schmaal, D.J. Veltman, A. Nederveen, W. van den Brink, A.E. Goudriaan, N-Acetylcysteine Normalizes Glutamate Levels in Cocaine-Dependent Patients: A Randomized Crossover Magnetic Resonance Spectroscopy Study, Neuropsychopharmacology 37(9) (2012) 2143-2152. [8] W. Sun, G.V. Rebec, Repeated Cocaine Self-Administration Alters Processing of Cocaine-Related Information in Rat Prefrontal Cortex, The Journal of Neuroscience 26(30) (2006) 8004-8008. [9] D.A. Baker, K. McFarland, R.W. Lake, H. Shen, X.-C. Tang, S. Toda, P.W. Kalivas, Neuroadaptations in cystine-glutamate exchange underlie cocaine relapse, Nat Neurosci 6(7) (2003) 743-749. [10] K.O. Lim, J.R. Wozniak, B.A. Mueller, D.T. Franc, S.M. Specker, C.P. Rodriguez, A.B. Silverman, J.P. Rotrosen, Brain macrostructural and microstructural abnormalities in cocaine dependence, Drug Alcohol Depend 92(1-3) (2008) 164-72. [11] K.D. Ersche, A. Barnes, P.S. Jones, S. Morein-Zamir, T.W. Robbins, E.T. Bullmore, Abnormal structure of frontostriatal brain systems is associated with aspects of impulsivity and compulsivity in cocaine dependence, Brain 134(7) (2011) 2013-2024. [12] M.J. Romero, S. Asensio, C. Palau, A. Sanchez, F.J. Romero, Cocaine addiction: diffusion tensor imaging study of the inferior frontal and anterior cingulate white matter, Psychiatry Res 181(1) (2010) 57-63. [13] J. Xu, E.E. DeVito, P.D. Worhunsky, K.M. Carroll, B.J. Rounsaville, M.N. Potenza, White matter integrity is associated with treatment outcome measures in cocaine dependence, Neuropsychopharmacology 35(7) (2010) 1541-9. [14] S.D. Lane, J.L. Steinberg, L. Ma, K.M. Hasan, L.A. Kramer, E.A. Zuniga, P.A. Narayana, F.G. Moeller, Diffusion Tensor Imaging and Decision Making in Cocaine Dependence, PloS one 5(7) (2010) e11591. [15] F.G. Moeller, J.L. Steinberg, J.M. Schmitz, L. Ma, S. Liu, K.L. Kjome, N. Rathnayaka, L.A. Kramer, P.A. Narayana, Working memory fMRI activation in cocaine-dependent subjects: association with treatment response, Psychiatry Res 181(3) (2010) 174-82. [16] M.J. Romero, S. Asensio, C. Palau, A. Sanchez, F.J. Romero, Cocaine addiction: Diffusion tensor imaging study of the inferior frontal and anterior cingulate white matter, Psychiatry Research 181(1) (2010) 57-63. [17] D. Tomasi, R.Z. Goldstein, F. Telang, T. Maloney, N. Alia-Klein, E.C. Caparelli, N.D. Volkow, Thalamocortical dysfunction in cocaine abusers: implications in attention and perception, Psychiatry Res 155(3) (2007) 189-201.

He et al.

23

Jo

ur na

lP

re

-p

ro

of

[18] Y. Hu, B. Salmeron, H. Gu, E.A. Stein, Y. Yang, IMpaired functional connectivity within and between frontostriatal circuits and its association with compulsive drug use and trait impulsivity in cocaine addiction, JAMA Psychiatry (2015). [19] B. Adinoff, H. Gu, C. Merrick, M. McHugh, H. Jeon-Slaughter, H. Lu, Y. Yang, E.A. Stein, Basal Hippocampal Activity and Its Functional Connectivity Predicts Cocaine Relapse, Biol Psychiatry 78(7) (2015) 496-504. [20] C. Beaulieu, The basis of anisotropic water diffusion in the nervous system - a technical review, Nmr in Biomedicine 15(8) (2002) 435-455. [21] T. Kahnt, S.Q. Park, M.X. Cohen, A. Beck, A. Heinz, J. Wrase, Dorsal striatal-midbrain connectivity in humans predicts how reinforcements are used to guide decisions, J Cogn Neurosci 21(7) (2009) 1332-1345. [22] R.E. Passingham, K.E. Stephan, R. Kötter, The anatomical basis of functional localization in the cortex, Nature Reiview Neuroscience 3(8) (2002) 606-616. [23] R.P. Bell, J.J. Foxe, J. Nierenberg, M.J. Hoptman, H. Garavan, Assessing White Matter Integrity as a Function of Abstinence Duration in Former Cocaine-Dependent Individuals, Drug & Alcohol Dependence 114(2) (2011) 159-168. [24] L. Ma, K.M. Hasan, J.L. Steinberg, P.A. Narayana, S.D. Lane, E.A. Zuniga, L.A. Kramer, F.G. Moeller, Diffusion tensor imaging in cocaine dependence: regional effects of cocaine on corpus callosum and effect of cocaine administration route, Drug & Alcohol Dependence 104(3) (2009) 262-267. [25] A.M. Kaag, G.A. van Wingen, M.W. Caan, J.R. Homberg, d.B.W. Van, L. Reneman, White matter alterations in cocaine users are negatively related to the number of additionally (ab)used substances, Addiction Biology 22(4) (2017) 1048-1056. [26] S.M. Tom, C.R. Fox, C. Trepel, R.A. Poldrack, The neural basis of loss aversion in decision-making under risk, Science 315(5811) (2007) 515-518. [27] K. Yuan, W. Qin, G. Wang, F. Zeng, L. Zhao, X. Yang, P. Liu, J. Liu, J. Sun, K.M. von Deneen, Microstructure abnormalities in adolescents with internet addiction disorder, PloS one 6(6) (2011) e20708. [28] E. Bora, M. Yücel, A. Fornito, C. Pantelis, B.J. Harrison, L. Cocchi, G. Pell, D.I. Lubman, White matter microstructure in opiate addiction, Addiction biology 17(1) (2012) 141-148. [29] Y. Qiu, G. Jiang, H. Su, X. Lv, X. Zhang, J. Tian, F. Zhuo, Progressive white matter microstructure damage in male chronic heroin dependent individuals: a DTI and TBSS study, PLoS One 8(5) (2013) e63212. [30] C.-B. Weng, R.-B. Qian, X.-M. Fu, B. Lin, X.-P. Han, C.-S. Niu, Y.-H. Wang, Gray matter and white matter abnormalities in online game addiction, European journal of radiology 82(8) (2013) 1308-1312. [31] B. Bosch, E.M. Arenaza-Urquijo, L. Rami, R. Sala-Llonch, C. Junqué, C. Solé-Padullés, C. Peña-Gómez, N. Bargalló, J.L. Molinuevo, D. Bartrés-Faz, Multiple DTI index analysis in normal aging, amnestic MCI and AD. Relationship with neuropsychological performance, Neurobiology of aging 33(1) (2012) 61-74. [32] S.J. Colloby, M.J. Firbank, A.J. Thomas, A. Vasudev, S.W. Parry, J.T. O'Brien, White matter changes in late-life depression: a diffusion tensor imaging study, Journal of affective disorders 135(1-3) (2011) 216220. [33] Y. Shen, L. Bai, Y. Gao, F. Cui, Z. Tan, Y. Tao, C. Sun, L. Zhou, Depressive symptoms in multiple sclerosis from an in vivo study with TBSS, BioMed research international 2014 (2014). [34] Q. He, C. Chen, Q. Dong, G. Xue, C. Chen, Z. Lu, A. Bechara, Gray and White Matter Structures in the Midcingulate Cortex Region Contribute to Body Mass Index in Chinese Young Adults, Brain Struct Funct 220(1) (2015) 319-329. [35] S. Smith, Fast robust automated brain extraction, Human Brain Mapping 17(3) (2002) 143-155. [36] J.L.R. Andersson, M. Jenkinson, S. Smith, Non-linear registration, aka Spatial normalisation, FMRIB technical report TR07JA2 from www.fmrib.ox.ac.uk/analysis/techrep (2007). [37] J.L.R. Andersson, M. Jenkinson, S. Smith, Non-linear optimisation, FMRIB technical report TR07JA1 from www.fmrib.ox.ac.uk/analysis/techrep (2007).

He et al.

24

Jo

ur na

lP

re

-p

ro

of

[38] S. Smith, M. Jenkinson, H. Johansen-Berg, D. Rueckert, T. Nichols, C. Mackay, K. Watkins, O. Ciccarelli, M. Cader, P. Matthews, Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data, Neuroimage 31(4) (2006) 1487-1505. [39] T. Nichols, A. Holmes, Nonparametric permutation tests for functional neuroimaging: a primer with examples, Human Brain Mapping 15(1) (2002) 1-25. [40] A. Rajagopal, J.S. Shimony, R.C. McKinstry, M. Altaye, T. Maloney, F.T. Mangano, D.D. Limbrick, S.K. Holland, B.V. Jones, S. Simpson, White matter microstructural abnormality in children with hydrocephalus detected by probabilistic diffusion tractography, American Journal of Neuroradiology 34(12) (2013) 23792385. [41] M. Frederick Gerard, K.M. Hasan, J.L. Steinberg, L.A. Kramer, D.M. Dougherty, R.M. Santos, V. Ignacio, A.C. Swann, E.S. Barratt, P.A. Narayana, Reduced anterior corpus callosum white matter integrity is related to increased impulsivity and reduced discriminability in cocaine-dependent subjects: diffusion tensor imaging, Neuropsychopharmacology Official Publication of the American College of Neuropsychopharmacology 30(3) (2005) 610. [42] Q. He, M. Chen, C. Chen, G. Xue, T. Feng, A. Bechara, Anodal Stimulation of the Left DLPFC Increases IGT Scores and Decreases Delay Discounting Rate in Healthy Males, Frontiers in psychology 7 (2016) 1-4. [43] B. Weber, S. Huettel, The neural substrates of probabilistic and intertemporal decision making, Brain Research 1234(3) (2008) 104-115. [44] M. Balconi, R. Finocchiaro, Decisional impairments in cocaine addiction, reward bias, and cortical oscillation “unbalance”, Neuropsychiatric Disease & Treatment 11(default) (2015) 777-786. [45] M. Balconi, G. Mazza, Lateralisation effect in comprehension of emotional facial expression: a comparison between EEG alpha band power and behavioural inhibition (BIS) and activation (BAS) systems, Laterality 15(3) (2010) 361-384. [46] C.A. Hanlon, M.J. Wesley, L.J. Porrino, Loss of Functional Specificity in the Dorsal Striatum of Chronic Cocaine Users, Drug & Alcohol Dependence 102(1) (2009) 88-94. [47] J.N. Kaufman, T.J. Ross, E.A. Stein, H. Garavan, Cingulate Hypoactivity in Cocaine Users During a GONOGO Task as Revealed by Event-Related Functional Magnetic Resonance Imaging, Journal of Neuroscience the Official Journal of the Society for Neuroscience 23(21) (2003) 7839-43. [48] H. Garavan, J.N. Kaufman, R. Hester, Acute effects of cocaine on the neurobiology of cognitive control, Philos Trans R Soc Lond B Biol Sci 363(1507) (2008) 3267-3276. [49] C.R. Li, C. Huang, P. Yan, Z. Bhagwagar, V. Milivojevic, R. Sinha, Neural Correlates of Impulse Control During Stop Signal Inhibition in Cocaine-Dependent Men, Neuropsychopharmacology Official Publication of the American College of Neuropsychopharmacology 33(8) (2011) 1798-806. [50] M.E. Sim, I.K. Lyoo, C.C. Streeter, J. Covell, O. Saridsegal, D.A. Ciraulo, M.J. Kim, M.J. Kaufman, D.A. Yurgeluntodd, P.F. Renshaw, Cerebellar Gray Matter Volume Correlates with Duration of Cocaine Use in Cocaine-Dependent Subjects, Neuropsychopharmacology 32(10) (2007) 2229-2237. [51] T.R. Franklin, P.D. Acton, J.A. Maldjian, J.D. Gray, J.R. Croft, C.A. Dackis, C.P. O'Brien, A.R. Childress, Decreased gray matter concentration in the insular, orbitofrontal, cingulate, and temporal cortices of cocaine patients, Biological Psychiatry 51(2) (2002) 134-142. [52] K. Rando, T.M. Chaplin, M.N. Potenza, L. Mayes, R. Sinha, Prenatal Cocaine Exposure and Gray Matter Volume in Adolescent Boys and Girls: Relationship to Substance Use Initiation, Biological Psychiatry 74(7) (2013) 482-489. [53] L. Vaquero, E. Cámara, F. Sampedro, d.L.C.J. Pérez, F. Batlle, J.M. Fabregas, J.A. Sales, M. Cervantes, X. Ferrer, G. Lazcano, Cocaine addiction is associated with abnormal prefrontal function, increased striatal connectivity and sensitivity to monetary incentives, and decreased connectivity outside the human reward circuit, Addiction Biology 22(3) (2017) n/a-n/a.

He et al.

25

Jo

ur na

lP

re

-p

ro

of

[54] L. Ma, J.L. Steinberg, L. Keysermarcus, D. Ramesh, P.A. Narayana, R.E. Merchant, F.G. Moeller, D.X. Cifu, Altered white matter in cocaine-dependent subjects with traumatic brain injury: A diffusion tensor imaging study, Drug & Alcohol Dependence 151 (2015) 128-134. [55] C.A. Hanlon, T.J. Beveridge, L.J. Porrino, Recovering from cocaine: insights from clinical and preclinical investigations, Neurosci Biobehav Rev 37(9) (2013) 2037-2046. [56] G. Bartzokis, M. Beckson, D.B. Hance, P.H. Lu, J.A. Foster, J. Mintz, W. Ling, P. Bridge, Magnetic resonance imaging evidence of "silent" cerebrovascular toxicity in cocaine dependence, Biological Psychiatry 45(9) (1999) 1203-11. [57] L. In Kyoon, C.C. Streeter, A. Kyung Heup, L. Ho Kyu, M.H. Pollack, M.M. Silveri, N. Leanne, J.M. Levin, S.S. Ofra, D.A. Ciraulo, White matter hyperintensities in subjects with cocaine and opiate dependence and healthy comparison subjects, Psychiatry Research Neuroimaging 131(2) (2004) 135-145. [58] K.O. Lim, S.J. Choi, N. Pomara, A. Wolkin, J.P. Rotrosen, Reduced frontal white matter integrity in cocaine dependence: a controlled diffusion tensor imaging study, Biological Psychiatry 51(11) (2002) 890895. [59] J. O'Neill, V.A. Cardenas, D.J. Meyerhoff, Separate and interactive effects of cocaine and alcohol dependence on brain structures and metabolites: quantitative MRI and proton MR spectroscopic imaging, Addiction Biology 6(4) (2010) 347-361. [60] C.A. Hanlon, D.L. Dufault, M.J. Wesley, L.J. Porrino, Elevated gray and white matter densities in cocaine abstainers compared to current users, Psychopharmacology 218(4) (2011) 681-692. [61] K.O. Lim, J.R. Wozniak, B.A. Mueller, D.T. Franc, S.M. Specker, C.P. Rodriguez, A.B. Silverman, J.P. Rotrosen, Brain macrostructural and microstructural abnormalities in cocaine dependence, Drug & Alcohol Dependence 92(1) (2008) 164-172. [62] V.D. Sclafani, M. Toloushams, L.J. Price, G. Fein, Neuropsychological performance of individuals dependent on crack–cocaine, or crack–cocaine and alcohol, at 6 weeks and 6 months of abstinence, Drug & Alcohol Dependence 66(2) (2002) 161-171. [63] E.F. Paceschott, P.T. Morgan, R.T. Malison, C.L. Hart, C. Edgar, M. Walker, R. Stickgold, Cocaine Users Differ from Normals on Cognitive Tasks Which Show Poorer Performance During Drug Abstinence, American Journal of Drug & Alcohol Abuse 34(1) (2008) 109-121. [64] J.E. Iudicello, S.P. Woods, O. Vigil, J.C. Scott, M. Cherner, R.K. Heaton, J.H. Atkinson, I. Grant, Longer Term Improvement in Neurocognitive Functioning and Affective Distress Among Methamphetamine Users Who Achieve Stable Abstinence, Journal of Clinical & Experimental Neuropsychology 32(7) (2010) 704-718. [65] M.H. Schulte, J. Cousijn, T.E. den Uyl, A.E. Goudriaan, d.B.W. Van, D.J. Veltman, T. Schilt, R.W. Wiers, Recovery of neurocognitive functions following sustained abstinence after substance dependence and implications for treatment, Clinical Psychology Review 34(7) (2014) 531-550. [66] M.P. Paulus, S.F. Tapert, M.A. Schuckit, Neural Activation Patterns of Methamphetamine-Dependent Subjects During Decision Making Predict Relapse, Arch Gen Psychiatry 62(7) (2005) 761-768. [67] T.R. Kosten, B.E. Scanley, K.A. Tucker, A. Oliveto, C. Prince, R. Sinha, M.N. Potenza, P. Skudlarski, B.E. Wexler, Cue-Induced Brain Activity Changes and Relapse in Cocaine-Dependent Patients, Neuropsychopharmacology Official Publication of the American College of Neuropsychopharmacology 31(3) (2006) 644. [68] B.A. Vogt, L. Vogt, S. Laureys, Cytology and functionally correlated circuits of human posterior cingulate areas, Neuroimage 29(2) (2006) 452-466. [69] L.E. Downey, C.J. Mahoney, A.H. Buckley, H.L. Golden, S.M. Henley, N. Schmitz, J.M. Schott, I.J. Simpson, S. Ourselin, N.C. Fox, White matter tract signatures of impaired social cognition in frontotemporal lobar degeneration, Neuroimage Clinical 8(C) (2015) 640-651.

He et al.

26

Jo

ur na

lP

re

-p

ro

of

[70] M. Jalbrzikowski, J.E. Villalonreina, K.H. Karlsgodt, D. Senturk, C. Chow, P.M. Thompson, C.E. Bearden, Altered white matter microstructure is associated with social cognition and psychotic symptoms in 22q11.2 microdeletion syndrome, Frontiers in Behavioral Neuroscience 8 (2014) 393. [71] A. Pfefferbaum, M.J. Rosenbloom, R. Fama, S.A. Sassoon, E.V. Sullivan, Transcallosal White Matter Degradation Detected With Quantitative Fiber Tracking in Alcoholic Men and Women: Selective Relations to Dissociable Functions, Alcoholism Clinical & Experimental Research 34(7) (2010) 1201-1211. [72] C.A. Durkee, J.E. Sarlls, D.W. Hommer, R. Momenan, White Matter Microstructure Alterations: A Study of Alcoholics with and without Post-Traumatic Stress Disorder, PloS one 8(11) (2013) e80952e80952. [73] N.M. Zahr, Structural and microstructral imaging of the brain in alcohol use disorders, Handbook of Clinical Neurology 125(125C) (2014) 275-290. [74] M.C. Tobias, J. O'Neill, M. Hudkins, G. Bartzokis, A.C. Dean, E.D. London, White-matter abnormalities in brain during early abstinence from methamphetamine abuse, Psychopharmacology 209(1) (2010) 1324. [75] T.E. Schlaepfer, E. Lancaster, R. Heidbreder, E.C. Strain, M. Kosel, H.U. Fisch, G.D. Pearlson, Decreased frontal white-matter volume in chronic substance abuse, International Journal of Neuropsychopharmacology 9(2) (2006) 147. [76] A. Chung, I.K. Lyoo, S.J. Kim, J. Hwang, S.C. Bae, Y.H. Sung, M.E. Sim, I.C. Song, J. Kim, K.H. Chang, Decreased frontal white-matter integrity in abstinent methamphetamine abusers, Int J Neuropsychopharmacol 10(6) (2007) 765-775. [77] G.J. Harris, S.K. Jaffin, S.M. Hodge, D. Kennedy, V.S. Caviness, K. Marinkovic, G.M. Papadimitriou, N. Makris, M. Oscar ‐ Berman, Frontal white matter and cingulum diffusion tensor imaging deficits in alcoholism, Alcoholism Clinical & Experimental Research 32(6) (2010) 1001-1013. [78] A. Pfefferbaum, M. Rosenbloom, T. Rohlfing, E.V. Sullivan, Degradation of Association and Projection White Matter Systems in Alcoholism Detected with Quantitative Fiber Tracking, Biological Psychiatry 65(8) (2009) 680-690. [79] A. Pfefferbaum, M.J. Rosenbloom, W. Chu, S.A. Sassoon, T. Rohlfing, K.M. Pohl, N.M. Zahr, E.V. Sullivan, White matter microstructural recovery with abstinence and decline with relapse in alcohol dependence interacts with normal ageing: a controlled longitudinal DTI study, Lancet Psychiatry 1(3) (2014) 202-212. [80] O.M. Alhassoon, S.F. Sorg, M.J. Taylor, R.A. Stephan, B.C. Schweinsburg, N.H. Stricker, A. Gongvatana, I. Grant, Callosal white matter microstructural recovery in abstinent alcoholics: a longitudinal diffusion tensor imaging study, Alcoholism Clinical & Experimental Research 36(11) (2012) 1922–1931. [81] S. Gazdzinski, T.C. Durazzo, A. Mon, P.H. Yeh, D.J. Meyerhoff, Cerebral white matter recovery in abstinent alcoholics—a multimodality magnetic resonance study, Brain 133(Pt 4) (2010) 1043-1053. [82] E. Tirotta, K.S. Carbajal, C.S. Schaumburg, L. Whitman, T.E. Lane, Cell replacement therapies to promote remyelination in a viral model of demyelination, Journal of Neuroimmunology 224(1) (2010) 101107. [83] C. Lv, Q. Wang, C. Chen, J. Qiu, G. Xue, Q. He, The regional homogeneity patterns of the dorsal medial prefrontal cortex predict individual differences in decision impulsivity, Neuroimage 200 (2019) 556-561. [84] X. Huang, H. Zhang, C. Chen, G. Xue, Q. He, The neuroanatomical basis of the Gambler's fallacy: A univariate and multivariate morphometric study, Human brain mapping 40(3) (2019) 967-975. [85] A. Bechara, Neurobiology of decision-making: risk and reward, Seminars in Clinical Neuropsychiatry 6(3) (2001) 205. [86] Q. He, O. Turel, A. Bechara, Brain anatomy alterations associated with Social Networking Site (SNS) addiction, Scientific Reports 7 (2017) 45064. [87] R.P. Bell, J.J. Foxe, L.A. Ross, H. Garavan, Intact inhibitory control processes in abstinent drug abusers (I): A functional neuroimaging study in former cocaine addicts, Neuropharmacology 82(1) (2014) 143-150.

He et al.

27

Jo

ur na

lP

re

-p

ro

of

[88] F.J. Mcclernon, F.B. Hiott, S.A. Huettel, J.E. Rose, Abstinence-Induced Changes in Self-Report Craving Correlate with Event-Related fMRI Responses to Smoking Cues, Neuropsychopharmacology Official Publication of the American College of Neuropsychopharmacology 30(10) (2005) 1940. [89] X. Noël, D. Brevers, A. Bechara, A neurocognitive approach to understanding the neurobiology of addiction, Current Opinion in Neurobiology 23(4) (2013) 632-638. [90] N.D. Volkow, G.J. Wang, J.S. Fowler, D. Tomasi, Addiction Circuitry in the Human Brain, Annual Review of Pharmacology & Toxicology 52(3) (2012) 321. [91] R.Z. Goldstein, N.D. Volkow, Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications, Nature Reviews Neuroscience 12(11) (2011) 652-669. [92] P.W. Kalivas, N.D. Volkow, The Neural Basis of Addiction: A Pathology of Motivation and Choice, Am J Psychiatry 162(8) (2005) 1403-1413. [93] M.F. Weaver, S.H. Schnoll, Stimulants: Amphetamines and Cocaine, 2006. [94] J.S. Ide, Z. Sheng, S. Hu, R. Sinha, C.M. Mazure, C.R. Li, Cerebral gray matter volumes and lowfrequency fluctuation of BOLD signals in cocaine dependence: duration of use and gender difference, Drug & Alcohol Dependence 134(134) (2014) 51-62. [95] M.E. Sim, I.K. Lyoo, C.C. Streeter, J. Covell, O. Sarid-Segal, D.A. Ciraulo, M.J. Kim, M.J. Kaufman, D.A. Yurgelun-Todd, P.F. Renshaw, Cerebellar gray matter volume correlates with duration of cocaine use in cocaine-dependent subjects, Neuropsychopharmacology 32(10) (2007) 2229-2237. [96] L. Morenolópez, J.C. Perales, S.D. Van, N. Albeinurios, C. Sorianomas, J.M. Martinezgonzalez, R.W. Wiers, A. Verdejogarcía, Cocaine use severity and cerebellar gray matter are associated with reversal learning deficits in cocaine-dependent individuals, Addiction Biology 20(3) (2015) 546-556. [97] J.A. Brewer, P.D. Worhunsky, K.M. Carroll, B.J. Rounsaville, M.N. Potenza, Pre-Treatment Brain Activation During Stroop Task is Associated with Outcomes in Cocaine Dependent Patients, Biological Psychiatry 64(11) (2008) 998-1004. [98] J.A. Matochik, E.D. London, D.A. Eldreth, J.L. Cadet, K.I. Bolla, Frontal cortical tissue composition in abstinent cocaine abusers: A magnetic resonance imaging study, Neuroimage 19(3) (2003) 1095-1102. [99] J. Tanabe, J.R. Tregellas, M. Dalwani, L. Thompson, E. Owens, T. Crowley, M. Banich, Medial orbitofrontal cortex gray matter is reduced in abstinent substance-dependent individuals, Biological psychiatry 65(2) (2009) 160-164.

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Tables Table 1: Demographic information of participants Years of Abstinence Current Users

Controls

Total

N (%) (N = 12)

1-5 years

6-10 years

11+ years

(N = 5)

(N = 5)

(N = 10)

(N = 7)

(N = 39)

48-50

2 (50 .0)

2 (50 .0)

0

1 (20.0)

0

4 (11.1)

51-60

2 (50.0)

2 (50.0)

4 (80.0)

2 (40.0)

8 (80.0)

21 (58.3)

61 +

0

0

1 (20.0)

2 (40.0)

2 (20.0)

11 (30.6)

Mean (SD)

51.5 (3.0)

51.5 (3.0)

57.4 (5.4)

56.2 (5.5)

55.8 (4.6)

56.5 (5.4)

White

1 (8.3)

0

1 (20.0)

1 (20.0)

0

3 (7.7)

Black

11 (91.7)

4 (57.1)

4 (80.0)

4 (80.0)

9 (90.0)

32 (82.1)

Hispanic

0

0

0

Other

0

3 (42.9)

0

1 (8.3)

0

0

1 (8.3)

3 (42.9)

0

Some college

4 (33.3)

4 (57.1)

College

6 (50.0)

0

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Race/ethnicity

0

0

0

0

1 (10.0)

4 (10.2)

0

1 (10.0)

2 (5.1)

1 (20.0)

3 (30.0)

8 (20.5)

3 (60.0)

3 (60.0)

3 (30.0)

17 (43.6)

2 (40.0)

1 (20.0)

3 (30.0)

12 (30.8)

1 (14.3)

1 (20.0)

3 (60.0)

2 (20.0)

9 (23.1)

5 (71.4)

3 (60.0)

1 (20.0)

4 (40.0)

20 (51.3)

1 (20.0)

1 (20.0)

4 (40.0)

10 (25.6)

school

Married

or 2 (16.7)

living together Divorced, Marital Status 7 (58.3)

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separated,

lP

or GED

re

HS completed

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Less than high

Education

of

Age

widowed

Never married

3 (25.0)

1 (14.3)

Numbers in parenthesis represent percentages.

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Table 2: Summary of TBSS results

L/R

Brain region

TFCE MNI

MNI

MNI

Voxels

corrected x

y

z p

CONTROLS > CURRENT USERS

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29 Corpus Callosum / Superior Longitudinal

L/R

Fasciculus

/

Inferior

Fronto-Occipital

4156

46

25

10

2662

-53

3

14

679

-8

33

-18

465

55

5

24

203

-18

-2

<.001

Fasciculus

L

Internal Capsule / Superior Longitudinal

<.001

Fasciculus

L R

Ventral and Dorsal Medial Frontal Region Superior Longitudinal Fasciculus (Lateral

<.001 <.001

L

Middle Cingulum

-p

NONE ABSTINENCE > CURRENT USERS

L

Longitudinal

fasciculus/Inferior

Fasciculus/Uncinate

re

Inferior

Fronto-Occipital

L/R R

Corpus Callosum

Superior Longitudinal Fasciculus (Lateral

ur na

Frontal Region)

5594

<.001

<.001

-29

5

-11

1355

2

10

22

862

42

30

-2

lP

Fasciculus

40

ro

CURRENT USERS > CONTROLS

of

Frontal Region)

<.001 <.001

L

Dorsomedial Frontal Region

345

-10

56

-11

<.001

L

Ventromedial Frontal Region

263

-15

42

30

<.001

CURRENT USERS > ABSTINENCE

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NONE

ABSTINENCE > CONTROLS NONE

CONTROLS > ABSTINENCE NONE

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CURRENT USER > ABS1 NONE ABS1 >CURRENT USER L

Lateral Frontal Region

364

-31

2

53

<.001

L

Orbital Frontal Region

177

-38

29

-7

.001

of

ABS3 > CONTROL NONE

ro

CONTROL > ABS3 NONE

Orbital Frontal Region

181

L

Ventromedial Frontal Region

-7

re

L

-p

POSITIVE CORRELATION WITH YEARS OF ABSTINENCE

137

-31

46

-19

<.001

20

-17

.002

lP

NEGATIVE CORRELATION WITH YEARS OF ABSTINENCE NONE

ur na

POSITIVE CORRELATION WITH LOG10 YEARS OF ABSTINENCE L

Orbital Frontal Region

317

-8

38

-18

<.001

L

Ventromedial Frontal Region

208

-31

20

-17

<.001

NEGATIVE CORRELATION WITH LOG10 YEARS OF ABSTINENCE

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NONE

L: Left; R: Right.

Note: The voxel size in the TBSS analysis was 1 mm × 1 mm × 1 mm = 1 mm3.

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Table 3: Summary of the tractography results CONTROLS Regions

Indexes

USERS (M ± SD)

with

Correlation

with

log

years of abstinence

years of abstinence

No. of tracks

183.0 ± 17.4

343.9 ± 19.7

t(17) = 18.5, p < 0.001

r(20) = 0.54, p < 0.001

r(20) = 0.56, p < 0.001

Mean Length

31.4 ± 3.0

40.6 ± 7.4

t(17) = 3.8, p = 0.001

r(20) = 0.57, p < 0.001

r(20) = 0.53, p < 0.001

Left Ventromedial Prefrontal

No. of tracks

868.4 ± 37.9

932.9 ± 38.0

t(17) = 3.6, p = 0.002

r(20) = 0.48, p < 0.001

r(20) = 0.44, p < 0.001

Cortex

Mean Length

30.8 ± 4.2

40.4 ± 6.7

t(17) = 3.9, p = 0.001

r(20) = 0.51, p < 0.001

r(20) = 0.47, p < 0.001

Left Dorsolateral Prefrontal

No. of tracks

Cortex

Mean Length

lP

re

Left Orbital Frontal Cortex

759.8 ± 12.7

884.3 ± 11.8

t(17) = 21.1, p < 0.001

r(20) = 0.27, p = 0.25

r(20) = 0.23, p = 0.32

29.8 ± 5.0

35.9 ± 5.6

t(17) = 2.5, p = 0.03a

r(20) = 0.21, p = 0.37

r(20) = 0.12, p = 0.61

ur na

not significant after FDR correction for family wise error.

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a

Correlation

Group difference

-p

(M ± SD)

ro

statistics

CURRENT

of

He et al.

He et al.

32

lP

re

-p

ro

of

Figure Captions

Figure 1: FA in three key brain regions (red yellow region) and their relationship with

ur na

years of abstinence. The standard brain was laid out in the bottom with gray scale. Green color represented the white matter skeleton with FA > 0.2. A) The FA in left orbital frontal region was lower in USERS than in CONTROLS and in ABSTINENCE. It even differed between CURRENT USERS and ABS1, who recently (less than 5 years ago) quit using

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cocaine. The FA in this region was associated with the years of abstinence that participants achieved. B) The FA in left ventromedial frontal region was lower in USERS than in CONTROLS. The FA in this region was associated with the years of abstinence that participants achieved. C) The FA in left dorsolateral frontal region was lower in

He et al.

33

CURRENT USERS than in CONTROLS and in ABSTINENCE. However, it was not

of

associated with years of abstinence. R indicates the right hemisphere (side) of the brain.

ro

Figure 2: Tracts of a typical participant. The solid green area represents the ventromedial

-p

prefrontal cortex region, the solid red area represents the orbital frontal cortex region, and the solid yellow area represent the dorsolateral prefrontal cortex region. Lines represent

re

fibers through each region (Red lines represents the main direction of the fiber is from left

lP

to right or from right to left; Green lines represents the main direction of the fiber is from anterior to posterior or from posterior to anterior; Blue lines represents the main direction of the fiber is from inferior to superior or from superior to inferior). To get a better view of

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the fiber bundle please refer to the supplemental materials for video demonstration.