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Published in final edited form as: Psychiatry Res Neuroimaging. 2020 Jul 22;304:111150. doi: 10.1016/j.pscychresns.2020.111150

Gray matter alteration in heroin-dependent men: An atlas-based magnetic resonance imaging study

Hong Shi a,#, Zifei Liang b,c,d,#, Jiajie Chen a, Wei Li a, Jia Zhu a, Yongbin Li a, Jianjun Ye a, Jiangyang Zhang b,c, Jiuhua Xue a, Wei Liu a, Fan Wang a, Wei Wang a, Qiang Li a,c,*, Xiaohai He d,*
PMCID: PMC8170872  NIHMSID: NIHMS1707477  PMID: 32717665

Abstract

Previous imaging studies on heroin addiction have reported brain morphological alterations. However, the effects of heroin exposure on gray matter volume varied among different studies due to different factors such as substitution treatment or mandatory abstinence. Meanwhile, the relationship between gray matter and heroin use history remains unknown. Thirty-three male heroin-dependent (HD) individuals who are not under any substitution treatment or mandatory abstinence and 40 male healthy controls (HC) were included in this structural magnetic resonance imaging study. With an atlas-based approach, gray matter structures up to individual functional area were delineated, and the differences in their volumes between the HD and HC groups were analyzed. In addition, the relationship between gray matter volume and duration of heroin use was explored. The HD group demonstrated significantly lower cortical volume mainly in the prefrontal cortex and mesolimbic dopaminergic regions across different parcellation levels, whereas several visual and somatosensory cortical regions in the HD group had greater volume relative to the HC group at a more detailed parcellation level. The duration of heroin use was negatively correlated with the gray matter volume of prefrontal cortex. These findings suggest that heroin addiction be related to gray matter alteration and might be related to damage/maladaption of the inhibitory control, reward, visual, and somatosensory functions of the brain, although cognitive correlates are warranted in future study. In addition, the atlas-based morphology analysis is a potential tool to help researchers search biomarkers of heroin addiction.

Keywords: Heroin addiction, Gray matter, Magnetic resonance imaging, Large deformation diffeomorphic metric mapping

1. Introduction

Addiction, the most severe form of substance use disorder, is a chronic brain disorder molded by strong biosocial factors that has devastating consequences to individuals and to society (Volkow and Boyle, 2018). Heroin addiction is a severe public health problem of the world especially in the China driven by increased populations of heroin abuse. According to Annual Report on Drug Control in China 2017 (China_Anti-Drug_Network, 2017), over 0.955 million people in China are addicted to opiate (mainly heroin). Over half a million people in the United States are addicted to heroin (NIDA, 2014; SAMHSA, 2013; Wang et al., 2015). Understanding the neurobiological characteristics of heroin addiction is the key to the development of therapeutic interventions.

As brain function is closely coupled to the stucture, there are increasing studies showing that heroin addiction is associated with alterations in brain structures. However, the results vary from different studies. Different studies demonstrated decreased volume of brain regions of HD patients relative to healthy controls, including left nucleus accumbens (Seifert et al., 2015), precuneus, cuneus (Wang et al., 2016), right posterior insular cortex (Gardini and Venneri, 2012), frontal cortex, cingulate (Liu et al., 2009), occipital regions (Wang et al., 2012), temporal and cingulate cortices (Yuan et al., 2009). The reasons for the discrepancies are likely to be multiple factors including heterogeneity of HD participants (e.g. different heroin abuse history and different treatment for heroin addiction such as methadone maintenance treatment (Liu et al., 2009; Seifert et al., 2015) or mandatory abstinence (Wang et al., 2016; Yuan et al., 2009)) and methods used to analyze (data-driven voxel-based analysis (Gardini and Venneri, 2012; Liu et al., 2009; Wang et al., 2016, 2012; Yuan et al., 2009) or region of interest based analysis (Seifert et al., 2015)).

Recently, Marchewka reported a significant effect of magnetic field strength on the assessment of brain anatomy (Marchewka et al., 2014). That is, results derived from the automatic voxel-based morphology analysis depend on the magnetic resonance field. On the other hand, the raw images with more than one million voxels are too huge to create a quantitative knowledge database of anatomical phenotypes. The sheer amount of the noisy voxel information could severely hamper people’s ability to store, search, and analyze the anatomical features (Liang et al., 2015). The method of atlas-based morphology analysis holds the potential to survive the influence of magnetic resonance filed. It includes the large deformation diffeomorphic metric mapping (LDDMM) approach and multi-atlas likelihood fusion (MALF) algorithms. The LDDMM allows shape to be uniquely encoded by the vectors normal to the outline of the template (Miller et al., 2006). This property offers the potential for more precise and sensitive analysis of anatomical structures. The MALF algorithms can weight contribution from many different atlases rather than one single atlas (Beg et al., 2005; Tang et al., 2013). This atlas-based morphology analysis has been shown to yield accurate segmentation of cortical and subcortical structures in diverse datasets (Ceritoglu et al., 2013; Faria et al., 2011; Oishi et al., 2009).

Aiming to minimize potential systematic and medication bias, we employed the atlas-based morphology analysis to explore the structural characteristics of gray matter in HD individuals who are not under any substitution treatment (e.g. methadone or buprenorphine). Correlates with their heroin use history were also investigated. As others’ and our previous studies commonly demonstrated abnormal drug-cue induced brain activity mainly in prefrontal cortex, mesolimbic dopaminergic regions and visuo-spatial attention related regions (Langleben et al., 2008; Li et al., 2014b, 2012; Wang et al., 2011; Yang et al., 2009; Zijlstra et al., 2008), we hypothesized that the volume of gray matter involved in the above regions of the HD individuals could differ from that of HC group, furtherly, the abnormality of the gray matter could be associated with the heroin use history of the HD individuals.

2. Method and materials

2.1. Participants

Seventy-three subjects (age range 19–46 years) participated in this study, including 33 male HD subjects and 40 male HC volunteers (Table 1). The HD subjects were required to be compliant with DSM-IV-TR criteria for heroin addiction for at least 1 year and without any drug substitution treatment. All the HD subjects got addicted to heroin as soon as they came into contact with heroin at the age of 25.2 ± 5.5 years old. Among them, 19 used drugs by injection, 12 by inhale and 2 by injection or inhale. They all had no stable job. They are patients who came to the hospital voluntarily for treatment and accomplished the process of detoxification with the help of families at their home. The HC subjects were required to be without any history of substance abuse except for nicotine. Exclusion criteria for all of the participants consisted of (1) history of DSM-IV Axis I psychiatric disorders or brain trauma; (2) current medical illness; (3) recent medicine use; (4) claustrophobia; and (5) presence of magnetically active objects in the body. Prior to the experiment, the HD subjects must be confirmed detoxification and without any withdraw symptom by urine drug screenings and naloxone test. All aspects of the study protocol were reviewed and approved by the ethics committee of Tangdu Hospital. All methods were performed in accordance with the relevant guidelines and regulations. All of participants provided written informed consent to participate in this study.

Table 1.

Demographic and clinical characteristics of participants.

Characteristics HC
N = 40
(mean ± SD)		 HD
N = 33
(mean ± SD)		 Group Differences
(DF = 72)
Age (years) 34.2 ± 8.2 32.3 ± 6.1 t = 1.07 P = 0.28
Years of education 10.5 ± 2.2 10.8 ± 2.7 t = −0.48 P = 0.63
Cigarettes (per day) 15.7 ± 10.0 17.9 ± 6.1 t = −1.19 P = 0.24
Duration of heroin use (months) N/A 85.2 ± 51.8 N/A N/A
Average heroin dose (g/day) N/A 0.8 ± 1.0 N/A N/A
Duration of abstinence (days) N/A 59.8 ± 131.6 N/A N/A
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HC, Healthy controls; HD, Heroin dependent; SD, Standard deviation; DF, degrees of freedom, N/A, not available. The between-group differences were analyzed with two sample t-test.

2.2. Image acquisition

All of the imaging data were acquired on a 3T MRI scanner (GE Signa Excite HD) with an 8-channel phased array head coil. Three-dimension (3D) high-resolution fast spoiled gradient echo (FSPGR) T1-weighted images were collected with the following parameters: TR = 7.8 ms, TE = 3.0 ms, FOV = 256 × 256 mm2, matrix = 256 × 256, spatial resolution = 1 × 1 × 1 mm3. The structural data were carefully checked by an experienced radiologist to assure that there was no head motion or structural abnormalities.

2.3. Image processing

Each individual image was first parcellated by an online automated brain segmentation tool “MRICloud” (https://mricloud.org/). The pipeline combines large diffeomorphic deformation metric mapping (LDDMM) for registration and multi-atlas likelihood fusion (MALF) algorithms for weights contribution from different atlases (Beg et al., 2005; Tang et al., 2013). The pre-processing of the images included skull stripping, orientation adjustment and intensity-matching and homogeneity correction. The pipeline employed three steps. In the first step, it pre-generated a population-averaged atlas created from the 23 atlases using linear transformation in the normalized space. The subject image was also linearly registered to the space. The histogram-matching and homogeneity correction by the N4 algorithm were then applied (Tustison et al., 2010). In the second step, the non-rigid transformation between the subject image and the 23 atlases was performed using LDDMM (Beg et al., 2005). Based on the transformation matrices obtained from the non-rigid registration, the structural labels were transferred from the 23 atlases to the subject. Finally, the second LDDMM was performed and the structural labels were deformed to the subject again (Fig. 1). The label of each voxel was decided by the MALF algorithm taking into account both the location (transformation) and intensity information (Tang et al., 2013). Finally, five levels (Level 1, 2, 3, 4, and 5) of segmentations, for each of which, 8, 19, 53, 125, and 286 structures were defined respectively (Fig. 2). As the structure parcellation of Level 1 and 2 is really rough, and we only focused on gray matter, we decided to perform the analyses based on high levels, such as structure parcellation of Level 3–5.

Fig. 1.

Fig. 1.

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Process map of image processing. Atlas k is one reference T1 weighted image. One subject image I is co-registered with an atlas by Linear-Normalization T and LDDMM. By inverse transform T−1 and φ−1, the pre-parcellated brain structure Parcel. Map P could be transformed into the original subject space P°φ°T.

Fig. 2.

Fig. 2.

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Brain parcellation scheme of the JHU multiple atlases. Multiple granularity levels (L1 to L5) were demonstrated. Level 5 (L5) has the highest granularity and defines 286 brain regions. An anatomy-based hierarchical relationship was established to generate larger-structures and lower-granularity parcellation, as demonstrated in L1–L4.

2.4. Statistical analysis

The volume of raw brain structures contains a large amount of cross-subject variability. In HC group, the average raw entire brain volume (n = 40) was 1370,155 ± 88,103 mm3, while it was 1344,493 ± 90,705 mm3 in HD group (n = 33). In order to minimize the influence of this variation, we normalized the volume of each structure based on the total brain volume which includes the tissue, ventricles and the sulci. For example, the average of ventricle volume for HC group was 21,643 ± 6178 mm3 before normalization, that is, the coefficient of the standard deviation in percentage was 28%. After normalization, the average volume became 1.6% ± 0.4%, and the standard deviation in percentage was 25%. Thus, the analyses were based on relative volume of the structures. The difference in normalized volumes of each gray matter structure was analyzed between the HD and HC groups with two sample t-test. The age variation was controlled with regression. The threshold of significance was set at P < 0.05, corrected for multiple comparisons with a method of Bonferroni. To explore the relationship between heroin use history and volume of gray matter, a Pearson correlation analysis was applied with age and cigarettes per day as covariant, corrected for multiple comparisons with a method of False Discovery Rate (FDR).

3. Results

3.1. Between-group differences

No differences in age, years of education and cigarettes per day were found between HD and HC groups (Table 1). As for gray matter differences, more detailed differential regions were detected by higher level of parcellation. Based on level 3, the HD group demonstrated significantly lower gray matter volume of left limbic system and right basal forebrain relative to the HC group (Table 2 and Fig. 3A). Based on level 4, the HD group demonstrated significantly lower gray matter volume of globus pallidus relative to the HC group (Table 2 and Fig. 3B). Based on level 5, the HD group demonstrated significantly lower gray matter volume of regions including prefrontal regions (bilateral superior frontal gyrus, left inferior frontal gyrus), mesolimbic dopaminergic regions (bilateral nucleus accumbens, substantia nigra, thalamus, hypothalamus and left globus pallidus), right precuneus and entorhinal cortex. However, the HD group showed greater gray matter volume of vision related regions (bilateral lingual gyrus and left fusiform) and somatosensory cortex (right postcentral gyrus) relative to HC group. (Table 2 and Fig. 4)

Table 2.

Differences in gray matter volume between HD group and HC group based on different levels.

Brain regions Hemisphere HD group
(Volume%)		 HC group
(Volume%)		 Bonferroni corrected
P value		 Original
P value
Level 3 (HD < HC)
Basal forebrain right 0.212 ± 0.010 0.221 ± 0.016 0.047 0.003
Limbic system left 2.396 ± 0.125 2.513 ± 0.163 0.017 0.001
Level 4(HD < HC)
Globus pallidus left 0.101 ± 0.008 0.111 ± 0.009 0.001 < 0.001
Level 5(HD < HC)
Superior frontal gyrus/ pole right 0.229 ± 0.020 0.281 ± 0.029 < 0.001 < 0.001
Superior frontal gyrus/ pole left 0.139 ± 0.015 0.173 ± 0.022 < 0.001 < 0.001
Superior frontal gyrus/ prefrontal cortex right 0.654 ± 0.053 0.731 ± 0.057 < 0.001 < 0.001
Superior occipital gyrus right 0.186 ± 0.022 0.210 ± 0.002 0.012 < 0.001
Inferior frontal gyrus/pars opercularis left 0.274 ± 0.027 0.305 ± 0.035 0.015 < 0.001
Nucleus accumbens right 0.050 ± 0.005 0.059 ± 0.007 < 0.001 < 0.001
Nucleus accumbens left 0.042 ± 0.005 0.047 ± 0.005 0.004 < 0.001
Substantia nigra right 0.017 ± 0.002 0.020 ± 0.002 < 0.001 < 0.001
Substantia nigra left 0.021 ± 0.003 0.026 ± 0.003 < 0.001 < 0.001
Thalamus left 0.385 ± 0.018 0.414 ± 0.02 0.001 < 0.001
Thalamus right 0.399 ± 0.021 0.423 ± 0.027 0.010 < 0.001
Hypothalamus right 0.050 ± 0.003 0.055 ± 0.005 0.004 < 0.001
Hypothalamus left 0.045 ± 0.004 0.048. ± 0.004 0.044 < 0.001
Globus Pallidus left 0.101 ± 0.007 0.110 ± 0.011 0.031 < 0.001
Precuneus right 0.523 ± 0.046 0.566 ± 0.043 0.011 < 0.001
Entorhinal cortex right 0.071 ± 0.009 0.086 ± 0.012 < 0.001 < 0.001
Level 5 (HD > HC)
Lingual gyrus left 0.584 ± 0.041 0.522 ± 0.060 < 0.001 < 0.001
Lingual gyrus right 0.645 ± 0.048 0.592 ± 0.062 0.012 < 0.001
Fusiform gyrus left 1.206 ± 0.074 1.120 ± 0.089 0.001 < 0.001
Postcentral gyrus right 0.442 ± 0.051 0.402 ± 0.041 0.046 < 0.001
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Fig. 3.

Fig. 3.

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Differences in gray matter volume between HD and HC group based on Level 3 (A) and Level 4 (B). (R, right; L, left; P < 0.05, corrected by Bonferroni method.).

Fig. 4.

Fig. 4.

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Differences in gray matter volume between HD and HC group based on Level 5. R: right; L: left. (R, right; L, left; P < 0.05, corrected by Bonferroni method.).

3.2. Correlation results

Based on level 3, the HD group demonstrated significantly negative correlation between duration of heroin use and bilateral frontal lobe volumes (left: r = −0.369, P = 0.038; right: r = −0.372, P = 0.036; FDR corrected) (Fig. 5). Based on level 4, the HD group demonstrated significantly negative correlation between duration of heroin use and right superior frontal gyrus (r = −0.512, P = 0.003, FDR corrected). We also observed a trend of negative correlation between duration of heroin use and left superior frontal gyrus (r = −0.384, P = 0.030), bilateral inferior frontal gyrus (left: r = −0.370, P = 0.037; right: r = −0.392, P = 0.026) and right supramarginal gyrus (r = −0.371, P = 0.037) (Fig. 6). Based on level 5, the HD group demonstrated significantly negative correlation between duration of heroin use and left superior frontal gyrus/prefrontal cortex (r = −0.501, P = 0.004, FDR corrected). We also observed a trend of negative correlation between duration of heroin use and right superior frontal gyrus/prefrontal cortex (r = −0.419, P = 0.017), right superior frontal gyrus (r = −0.425, P = 0.015), nucleus accumbens (r = −0.455, P = 0.009), supramarginal gyrus (r = −0.371, P = 0.037) and left inferior frontal gyrus/pars orbitalis (r = −0.376, P = 0.034) (Fig. 7). The results of Level 3 covered all the frontal results of Level4 and Level 5, which showed the consistent relationship between frontal gray matter decrease and heroin use history.

Fig. 5.

Fig. 5.

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The correlation map of gray matter volume and duration of heroin use based on level 3 in HD group. (R, right; L, left; r = correlation coefficient.).

Fig. 6.

Fig. 6.

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The correlation map of gray matter volume and duration of heroin use based on level 4 in HD group. (R, right; L, left; r = correlation coefficient.).

Fig. 7.

Fig. 7.

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The correlation map of gray matter volume and duration of heroin use based on level 5 in HD group. (R, right; L, left; r = correlation coefficient.).

4. Discussion

In this study, we employed atlas-based gray matter analyses and found that the HD group demonstrated significantly decreased gray matter volume mainly including prefrontal cortex and mesolimbic dopaminergic regions, but demonstrated increased gray matter volumes in the visual and somatosensory cortex at a more detailed parcellation level. Meanwhile, we found that the volume of prefrontal cortex was negatively correlated with the duration of heroin use among HD individuals. These findings may suggest that the alteration of gray matter is closely associated with heroin use.

As we found, most regions involved in the prefrontal cortex were smaller in the HD group when compared with the HC group, which were in line with previous studies (Liu et al., 2009; Lyoo et al., 2006; Tolomeo et al., 2016; Wang et al., 2012; Yuan et al., 2009). It is suggested that drug addiction is a complex disorder of the brain, involving different networks such as the executive control circuit (e.g. prefrontal cortex), reward circuit (e.g. nucleus accumbens), conditioning/memory circuit (e.g. hippocampus and amygdala) and motivation/drive circuit (Volkow et al., 2011).The prefrontal cortex has been associated with inhibitory control and decision-making ability as well as behavioral regulation (Garavan and Hester, 2007; Li and Sinha, 2008; Sun et al., 2016). Previous studies have shown that opioid-dependent patients have significant deficits in performance on neuropsychological tasks measuring inhibitory control, decision-making as well as impulsivity behavior (Brand et al., 2008; Fu et al., 2008; Garavan and Hester, 2007; Li and Sinha, 2008; Qiu et al., 2013; Zhang et al., 2012). Lower gray matter volume in these regions could result in high-risk behaviors seen in substance-dependent patients (Cardenas et al., 2011; Rando et al., 2011). In this study, the atrophy of prefrontal cortex (especially bilateral superior frontal and left inferior frontal gyrus) as well as negative correlation between bilateral prefrontal cortex and duration of heroin use suggested the close relationship between heroin use and damage of prefrontal cortex, and potentially, the deficits of inhibitory control and decision making.

In addition to the prefrontal cortex findings, the HD group demonstrated significantly less gray matter in the mesolimbic dopaminergic regions including bilateral nucleus accumbens, substantia nigra, thalamus hypothalamus and left globus pallidus. The finding of nucleus accumbens atrophy was in line with previous studies (Seifert et al., 2015). The nucleus accumbens, substantia nigra and globus pallidus are highly dopamine-innervated brain regions and play an important role in reward function (Adam et al., 2013; Hanssen et al., 2015). The thalamus may serve as an important center of integration of networks that underlie the ability to modulate behaviors including reward (Haber and Calzavara, 2009). The cortico-striatal-thalamic circuits work with the dopaminergic midbrain to modulate resource allocation for an effective pursuit of behavioral goals (Krebs et al., 2012). Previous studies have showed abnormally greater activity of these reward related regions when exposed to heroin related cues in HD individuals (Li et al., 2014b, 2012; Zijlstra et al., 2008). The dysregulation of hypothalamus-pituitary-adrenal axis is associated with long-term heroin addiction (Lorenzetti et al., 2010). In this study, the atrophy of mesolimbic dopaminergic regions (especially bilateral nucleus accumbens, substantia nigra, thalamus, hypothalamus and left globus pallidus) as well as the trend of negative correlation between bilateral nucleus accumbens and duration of heroin use suggested the close relationship between deficits of reward system and heroin use.

This study also found less gray matter of right precuneus and entorhinal cortex in HD group. The precuneus is a very important structure of the default mode network (Utevsky et al., 2014), which has been found to be impaired in heroin users (Li et al., 2014b; Wang et al., 2010). This region is involved mainly in processing self-reflection as well as self-awareness (Kjaer et al., 2002). Disruptions to the precuneus might potentially be related with inability to utilize self-reflective processes and impair insight, which is an ability diminished in drug addiction and other neuropsychiatric disorders (Goldstein et al., 2009). The entorhinal cortex has been implicated in contextual memory processing (Ge et al., 2017; Hunsaker et al., 2013). It is the main interface between the hippocampus and neocortex. Lower volume of entorhinal cortex might be the result derived from long-term damage of heroin use and contribute to the key factor of impaired contextual memory processing.

However, we also found that the HD group demonstrated greater volume of vision related regions (bilateral lingual gyrus and left fusiform) and somatosensory cortex (right postcentral gyrus). The finding of bilateral lingual gyrus was in line with the previous study (Li et al., 2014a). The lingual gyrus and fusiform gyrus are linked to processing vision (Naito et al., 2003). The fusiform gyrus also plays a role of high level vision process, such as face recognition (Bokde et al., 2006). The postcentral gyrus is viewed as somatosensory cortex of primates, which play a role in the visual recognition and induction of emotion (Adolphs et al., 2000). The visual cortex (the lingual gyrus and fusiform gyrus) and somatosensory cortex (right postcentral gyrus) have been associated with abnormally greater activity when exposed to heroin related cues in HD individuals (Li et al., 2014b, 2012; Wang et al., 2011). The greater gray matter volume of these regions might suggest the chronic pathological adaption to high load of heroin related salience attribution. However, more studies are needed to verify this speculation. The finding of greater volume of left fusiform in HD relative to HC group was inconsistent with two previous studies (Lyoo et al., 2006; Yuan et al., 2009). The reasons of this discrepancy likely included different age of participants, different analysis methods used and different treatment for heroin addiction such as methadone maintenance treatment (Lyoo et al., 2006) and mandatory abstinence (Yuan et al., 2009).

With the method of atlas-based morphology analysis, we are able to analyze the characteristics of gray matter of HD individuals at different parcellation levels. The similar results across different parcellation levels that the HD individuals are featured by atrophy in prefrontal cortex, proved that this atlas-based morphology analysis is a reliable tool.

There are some limitations of this study. We only restricted the experimental subjects to males because of the difficulty of data collection and the reality of few females to recruit. Therefore, whether these findings generalize to female HD individuals awaits further investigation. All of the subjects are smokers. Smoking may be a potentially confound in this study. We conducted a statistical match between the two groups in order to counterbalance the effect of nicotine. In addition, the smoking factor was regressed out when analyzing the within-group correlation for the heroin group. Although we did not perform any cognitive test in this study, future studies are warranted to explore the correlations between cognitive deficits and gray matter alterations.

To conclude, the HD group demonstrated significant less gray matter volume mainly including prefrontal cortex and mesolimbic dopaminergic regions which play a role in inhibitory control and reward, while demonstrated greater gray matter volume of some visual and somatosensory cortex. These findings provide evidence that heroin addiction might be associated damage/maladaption of inhibitory control, reward, visual and somatosensory function, although related cognitive tests are warranted. In addition, the atlas-based morphology analysis is a potential tool to help researchers search biomarkers of heroin addiction.

Acknowledgements

The authors thank Prof. Susumu Mori and Andreia V. Faria (Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA) and Prof. Hanbing Lu (Neuroimaging Research Branch, National Institute on Drug Abuse, Intramural Research Programs, National Institutes of Health, Baltimore, MD, USA) for improvement of this study. The authors also thank Mrs. Yan Meng (Department of culture and media, Shaanxi Youth Vocational College, Xi’an, Shaanxi, China) for editing the figures.

Funding information

This study was funded by National Natural Science Foundation of China (81,671,661 and 81,771,813) and Top Talent Foundation of Tangdu Hospital (2019). The funding sponsors had no role in the design and conduct of the study; data collection, analysis, and interpretation of the data; preparation, and approval of the manuscript; and decision to submit the manuscript for publication.

Footnotes

Declaration of Competing Interest

None declared.

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