structural deficits in the emotion circuit and cerebellum are associated with depression, anxiety...
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Psychiatry Research: Neuroimaging 201 (2012) 89–97
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Psychiatry Research: Neuroimaging
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Structural deficits in the emotion circuit and cerebellum are associated withdepression, anxiety and cognitive dysfunction in methadone maintenancepatients: A voxel-based morphometric study
Wei-Che Lin a,b, Kun-Hsien Chou c, Hsiu-Ling Chen a,b, Chu-Chung Huang b, Cheng-Hsien Lu d,Shau-Hsuan Li e, Ya-Ling Wang f, Yu-Fan Cheng a, Ching-Po Lin b,g,⁎,1, Chien-Chih Chen f,⁎⁎,1
a Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwanb Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwanc Institute of Biomedical engineering, National Yang-Ming University, Taipei, Taiwand Department of Neurology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwane Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwanf Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwang Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan
⁎ Correspondence to: C.-P. Lin, Department of BiomeSciences, Institute of Neuroscience, National Yang-MinSec. 2, Peitou, Taipei, Taiwan. Tel.: +886 2 28267338; f⁎⁎ Correspondence to: C.-C. Chen, Department of PsycMemorial Hospital and Chang Gung University CollegeNiao-Sung Hsiang, Kaohsiung 83305, Taiwan. Tel.: +8867326817.
E-mail addresses: [email protected] (C.-P. Lin), chen(C.-C. Chen).
1 Equally contributed to the work and share the corre
0925-4927/$ – see front matter © 2011 Elsevier Irelanddoi:10.1016/j.pscychresns.2011.05.009
a b s t r a c t
a r t i c l e i n f oArticle history:Received 12 January 2011Received in revised form 18 May 2011Accepted 23 May 2011
Keywords:Cingulate gyrusDependenceHeroinMagnetic resonance imagingMood disorderPre-frontal cortex
Heroin users on methadone maintenance treatment (MMT) have elevated rates of co-morbid depression andare associated with have higher relapse rates for substance abuse. Structural abnormalities in MMT patientshave been reported, but their impact on clinical performance is unknown. We investigated differences in graymatter volume (GMV) between 27 MMT patients and 23 healthy controls with voxel-based morphometry,and we correlated findings in the patients with Beck Depression Inventory scores, Beck Anxiety Inventoryscores, and diminished cognitive functioning. MMT patients exhibited higher emotional deficits than healthysubjects. There was significantly smaller GMV in multiple cortices, especially in the left inferior frontal gyrusand left cerebellar vermis in the MMT group. The smaller GMV in the pre-frontal cortices, left sub-callosalcingulate gyrus, left post-central gyrus, left insula, and right cerebellar declive correlated with higherdepression scores. The smaller GMV in the pre-frontal cortices, left sub-callosal cingulate gyrus, and leftpostcentral gyrus also correlated with higher anxiety scores, while smaller GMV in the cerebellum andbilateral insula was associated with impaired performance on tests of executive function. These results revealthat MMT patients have low GMV in brain regions that are hypothesized to influence cognition and emotion,and the GMV findings might be involved comorbid disorders in the MMT group.
dical Imaging and Radiologicalg University, 155 Li-Nong St.,ax: +886 2 28262285.hiatry, Kaohsiung Chang Gungof Medicine, 123 Ta-Pei Road,7 7317123x8753; fax: +886 7
sponding authorship.
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1. Introduction
Cognitive impairment associated with heroin use has beenconsistently reported (Miller, 1985; Mintzer et al., 2005). Subjectswith heroin abuse have deficits in decision-making that may be due tocognitive impulsivity, delayed aversion, hyper-sensitivity to reward,hypo-sensitivity to punishment, or sensation seeking (Vassileva et al.,
2007). Higher impulsivity during problem-solving (Lee and Pau, 2002)and increased errors of commission when performing response-suppression tasks have also been observed (Lee et al., 2005). Most ofthese findings are associated with alterations in the reward circuitcharacteristic of drug dependence (Madden et al., 1997).
Aside from cognitive dysfunction, heroin dependence can causefeelings of depression, which may last for weeks during drugwithdrawal. Methadone maintenance treatment, initially developedin the 1960s (Dole and Nyswander, 1965; Freedman and Senay, 1973),is now widely employed throughout the world and is the mosteffective known treatment for heroin dependence. However, regularuse of methadone also causes physical dependence. In addition tomethadone-related cognitive impairment (Mintzer and Stitzer, 2002),studies of heroin users on methadone maintenance treatment (MMT)show elevated rates of co-morbid depression that far exceed estimatesfor the general population (Brienza et al., 2000; Alys et al., 2006).
This high prevalence of depression among subjects receiving MMTis of clinical importance because it affects morbidity, mortality, and
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treatment outcome. A depressive disorder without sufficient treat-ment may result in residual symptoms that can lead to worseoutcomes as exemplified by higher relapse rates, suicidal tendencies(Trivedi et al., 2008), and diminished quality of life and psycho-socialfunctioning (Kennedy and Paykel, 2004). Heroin-dependent individ-uals have depression associatedwith elevated suicide risk (Chathametal., 1995; Darke and Ross, 2001) and poor physical health (Darke andRoss, 2001; Teesson et al., 2005). Furthermore, longitudinal studieshave documented a relationship between depression at entry intotreatment and greater use of illicit substances on follow-up (Comptonet al., 2003) and higher rates of relapse following treatment(Hatsukami and Pickens, 1982). It is apparent that depression inheroin-dependent individuals influences long-term treatment out-comes (Rounsaville and Kleber, 1985).
Reports on structural alterations in the brain and depressionsymptoms associatedwith heroin dependence are limited. In previousanatomical studies, heroin-dependent subjects have heen reported tohave low GM volume in the bilateral pre-frontal cortex, bilateralcingulate cortices, bilateral insular gyrus, left supplementary motorcortex, temporal lobe and thalamus, and right cerebellum (Lyoo et al.,2006; Reid et al., 2008; Liu et al., 2009; Yuan et al., 2009). It has alsobeen suggested that the duration of heroin use is a critical factorleading to brain damage (Yuan et al., 2009). However, the impact ofstructural abnormalities on clinical state, including cognition, depres-sion, and anxiety, remains unclear.
The neural circuitry thatmediatesmood is incompletely understood.In depressive disorders, several regions of graymatter abnormality havebeen correlated with disease severity using voxel-based morphometry(VBM) analysis (Botteron et al., 2002; Taki et al., 2005; Yuan et al., 2008;Bergouignan et al., 2009; Li et al., 2010). In addition, depressed patientswith structural deficits in certain brain areas like the dorso-lateral pre-frontal cortex (Brodmann areas (BA) 9, 10 and 46) may show poorerresponse to anti-depressants because of abnormal metabolism (Brodyet al., 2001;Drevets et al., 2002). Intriguingly, themain anatomicdeficitsare largely overlapping in depression and heroin dependence (Taki etal., 2005; Tang et al., 2007; Yuan et al., 2008; Leung et al., 2009; Li et al.,2010; Peng et al., 2011).Whether these or not these particular anatomicchanges also relate to depression and influence treatment outcome inheroin-dependent subjects is not known.
The present study targeted morphometric brain characteristicsand depression in a sample of patients with heroin dependence whohad been on MMT for 6 months to 4 years. The effects of substanceexposure on the brain were investigated using VBM and whether ornot neuro-anatomical volumes were related to the severity ofdepression, anxiety, and cognitive impairment. Relationships be-tween GMV deficits and the effects of drugs, including duration andintensity, in heroin abuse and methadone treatment were alsodetermined.
2. Materials and methods
2.1. Participants
From March 2009 to February 2010, 27 patients with heroindependence (26 males, 1 female, median age 37 years, range 23–53 years) attending the psychiatric department of a tertiary referralcenter hospital for methadone maintenance therapy (MMT) wereprospectively enrolled. Twenty-three sex- and age-matched healthyvolunteers (22 males, 1 female, median age 34 years, range 21–55 years) were the control subjects and recruited through advertisingwithin the hospital. None of the control subjects had a history ofneurologic or psychiatric illness, alcohol or substance abuse, or headinjury. Each participant provided written informed consent and theprotocol conformed to the ethical guidelines of the 1975 Declarationof Helsinki. The Chang Gung Memorial Hospital human researchcommittee approved the study protocol.
2.1.1. Inclusion criteria of MMTThe MMT participants met the DSM-IV criteria for opiate
dependence within 2 years before the study, had been on a stabledose of methadone within half a year preceding the study, and hadbeen illicit drug-free for the last 12 months. They were also requiredto have negative urine toxicology screening tests (excluding meth-adone) as reported in their treatment programs.
2.1.2. Exclusion criteriaThe exclusionary criteria included current or lifetime history of
any Axis I diagnosis (other than opiate dependence and nicotine use),current alcohol intake greater than 15 drinks (1.5 oz liquor, 12 ozbeer, or 5 oz wine equivalents) per week, history of head trauma orneurologic disease, and HIV seropositivity. Heroin-induced depressivesyndrome was the only depressive disorder history allowed in thepatients. Nine patients exhibited axis I depressive illness beforeheroin use and were excluded. Cognitive impairment induced byacute doses of benzodiazepines was well documented, so potentialparticipants with a history of recent benzodiazepine use or a currentdiagnosis of sedative/hypnotic dependence (based on self-reports andclinical records) were also excluded.
Exclusion criteria for control subjects included current or remotehistory of significant drug abuse. However, moderate use of caffeine(b600 mg of caffeine per day based on self-reports) was acceptable.Control subjects with a history of recent benzodiazepine use andalcohol intake (more than 15 drinks per week) were also excluded.
2.1.3. Survey of participantsAll MMT subjects underwent physical examination, routine screen-
ing tests, and urine drug screening examination for the deterction ofamphetamines,methadone, and opiates. Negative urine drug-screeningtest results (exceptmethadone)were a condition forparticipation in theMMT program. Questionnaires about years of heroin abuse, approxi-mate dollars spent daily on heroin, years of methadone use, and typicalmethadone dose were collected.
2.2. Measures Neuropsychological (NP) tests
2.2.1. Self-report questionnairesThe Beck Depression Inventory II (BDI) and the Beck Anxiety
Inventory (BAI) are 21-item self-report questionnaires used toevaluate the severity of depression and anxiety symptoms, respec-tively (Beck et al., 1996). Both tools have been well establish as highlyreliable and valid devices for different patient studies, and both wereused in the MMT and control participants.
2.2.2. Cognitive testingA comprehensive battery of tests was used to assess the cognitive
strength of all participants. The MMT group completed the neuro-psychological (NP) testing in the afternoon before daily methadonedosing while the healthy volunteers completed theirs in theafternoon. Participants were asked not to consume alcohol orbenzodiazepines for 24 h prior to the NP tests. All participantscompleted their NP testing either before or within 2 days after themagnetic resonance imaging (MRI) studies.
For memory functions, verbal and non-verbal episodic memorywere assessed with the Six-Object Memory Test after a 10-min delay(Delis et al., 2000), theWord Sequence Learning Test (Hua, 1986b), andan irregular geometric memory task derived from the Benton VisualRetention Test (Benton, 1974). Verbal semantic memory was assessedby the Remote and Recent Life Event Test (Hua, 1986a) and the SemanticAssociation of Verbal Fluency (number of fruits, vegetables, and fishnamed in 1 min) (Hua et al., 1997). Executive functions were assessedby digit forward span, backward span, arithmetic, digit symbol andsimilarity sub-tests of the WAIS-R (Wechsler, 1981), and Proverbs(Reitan and Wolfson, 1996).
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Language screening included Object Naming and the Token Test ofthe NCCEA (Benton and Hamsher, 1978) whereas visual constructionalpraxis was assessed by performing the Three-Dimensional BlockConstruction-Model (Benton, 1968) and the block design and objectassembly subtests of the WAIS-R. All tests were administered to bothpatients and controls for statistical comparison.
2.3. MRI data acquisition
High resolution T1-weighted images were obtained in 50 subjectsusing the three-dimensional fluid-attenuated inversion-recovery fastspoiled gradient recalled echo (3D Inversion Recovery-FSPGR) pulsesequence with the following parameters: TR=9.5 ms; TE=3.9 ms;TI=450 ms; flip angle=20 degrees; field of view (FOV)=256×256 mm; matrix size=512×512; number of slices=110 and1.3 mm slice thickness (Mike et al., 2011). All MR scanning wasperformedon a3.0 TGESignaMRI scanner (Milwaukee,Wis., USA)withan eight-channel head coil. Each subject's head was immobilized withcushions inside the head coil in order to avoid movement problems.During scanning, if motion artifacts appeared in the phase-encodingdirection of T1 images, additional scans were performed to obtainvisibly acceptable images until the participant could tolerate the wholescanning time.
2.4. DARTEL-based T1 VBM analysis
A DARTEL-based T1 VBM approachwas used for pre-processing andsubsequent analysis of whole brain T1-weighted volumetric images(Ashburner et al., 2000; Ashburner, 2007). Individual T1-weightedvolumetric images were analyzed using Gaser's toolbox VBM5 (http://dbm.neuro.uni-jena.de/vbm/) with SPM5 (Statistical Parametric Map-ping. Wellcome Department of Imaging Neuroscience, London, UK;available online at http://www.fil.ion.ucl.ac.uk/spm) implemented inMatlab 7.3 (MathWorks, Natick, MA, USA). The image-processingprocedures of T1 DARTEL-based VBM used in this study included thefollowing steps: All images were carefully checked to be without anyscanner artifacts, motion problems, or gross anatomic abnormalities foreach participant by an experienced neuro-radiologist. The semi-automatic approach (Blumenthal et al., 2002) was used to evaluatethe quality of structural images, specifically for motion problems.
Motion artifact was evaluated on individual tissue segment imagesand assigned a rating of none, mild, moderate, or severe. In order toreduce bias during VBM processing, participants' images wereexcluded if their tissue segment images were given a rating ofmoderate or severe. The anterior commissure was set as the origin ofimaging for each participant. Whole brain native space T1-weightedimages were normalized and bias field corrected and segmented togray matter (GM), white matter (WM) and cerebrospinal fluid (CSF)partitions based on the same generative model (Ashburner andFriston, 2005). Unified segmentation involved alternating betweensegmentation, bias field correction, and normalization to obtain localoptimal solutions for each process. In order to improve the quality oftissue segmentation, this procedure was further refined by applyingan iterative hidden Markov field (HMRF) model (Cuadra et al., 2005)to minimize the influence of noise level during segmentation. Toachieve higher accuracy of registration across subjects, the nativespace GM,WM and CSF segments were imported into a rigidly alignedspace and iteratively registered to group-specific templates that weregenerated from all images in this study through non-linear warpingusing the DARTEL toolbox (Diffeomorphic Anatomical RegistrationThrough Exponentiated Lie Algebra) (Ashburner, 2007). DARTEL is anovel image-registration method that uses large deformation in aninverse-consistent framework for spatial normalization in the SPMtoolbox. Recent studies indicated that the DARTEL algorithm couldimprove inter-subject registration and was useful for population-based research (Bergouignan et al., 2009; Yassa and Stark, 2009).
The deformation parameters obtained in the spatial normalizationstepwere applied to individual tissue segments in rigidly aligned space.The Jacobian determinants derived from non-linear deformation for thecorrection of volume changes were also applied during the non-linearspatial transformation in order to preserve the overall amount of eachtissue segment after normalization. Since DARTEL worked with imageswith averaged brain size of total participants in this study, additionalaffine transformation between average group space andMNI (MontrealNeurological Institute) standard space was needed. Because the MNIstandard space was constructed by affine registration of a number ofsubjects to a common standard coordinate system, it was reasonable touse only affine transformation to achieve a suitable alignment betweenthese two spaces. The optimal-normalized tissue segments of eachindividual had an identical voxel size of 1×1×1 mm.
All normalized, segmented, and modulated MNI standard spaceimages were smoothed with an 8-mm Gaussian kernel prior to tissuevolume calculation and voxel-wise group comparisons. Overall tissuevolumes (i.e. GM, WM and CSF) were estimated in mm3 by countingthe voxels representing GM, WM and CSF in standard space. The totalintra-cranial volume (TIV)was determined as the sum of GM,WMandCSF volumes.
2.5. Statistical analysis
2.5.1. Analysis between groupsClinical data, including gender, history of smoking, and alcohol
consumption, for the two groups were analyzed by Chi-square orFisher's exact tests, where appropriate. Mean age, education, total intra-cranial volume, and gray matter and white matter volumes werecompared using Student's t test. Statistical differences in NP testsbetween the two groups were estimated by one-way analysis ofcovariance (ANCOVA) with participant's age, gender, and educationlevel as covariates. The threshold for statistical significance was pb0.05.
Smoothed modulated gray matter segments were analyzed withSPM5within the framework of a General LinearModel (GLM). Analysisof covariance ANCOVAwas performedwith the covariation of age, sex,education level, TIV, and histories of smoking and alcohol consump-tion to investigate regional gray matter volume differences betweenthe two groups. To avoid possible partial volume effects around themargin between GM and WM, all voxels with GM probability valueb0.2 (range from 0 to 1) were eliminated. Non-stationary correction(part of VBM5 toolbox) for correcting non-isotropic smoothness of thedata was used to investigate group differences (Hayasaka et al., 2004).
Due to the exploratory study design, stricter criteria were used toobtain the findings. The lower height of voxel-level thresholds(uncorrected pb0.05) might sensitize the cluster inference forspatially extended and lower spatial resolutions. In contrast, higher(uncorrected pb0.001) thresholds might generate a higher spatialcluster resolution but result in the cost of spatial extent. In this study,the voxel-level threshold was set to uncorrected pb0.001 and a non-stationary cluster extent threshold of pb0.05 corrected for multiplecomparisons with family-wise error (FWE) (Worsley et al., 1999)correction in order to obtain precise findings with higher spatialcluster resolution.
To minimize coordinate transformation discrepancies betweenMNI and Talairach space, GingerALE provided by BrainMap (TheBrainMap Development Team; available online at http://brainmap.org/ale/index.html) was used to transform MNI coordinates intoTalairach coordinates. Anatomic structures of the coordinates repre-senting significant clusters were identified based on the Talairach andTournoux atlas (Talairach and Tournoux, 1988).
2.5.2. Correlation analysisTo clarify the neuro-anatomic correlation of individual differences
in the severity of depression, anxiety, neuro-psychiatric decline,duration of heroin use and MMT, and dose of heroin and methadone
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administration, partial correlation analyses with age, sex, educationlevel, TIV, and histories of smoking and alcohol consumption asconfounding covariates were performed between clinical variableswith regional brain volumes of all participants. The regional graymatter volumes were extracted from the peak coordinates showingsignificant differences. The threshold for statistical significance wasset at pb0.05 with correction for multiple comparisons by Bonferronicorrection. All statistical analyses were performed using the SPSSsoftware, version 10.0 (SPSS Inc, Chicago, IL).
3. Results
3.1. Clinical characteristics and cognitive profiles between groups
The mean duration of heroin use was 13.9±6.4 years (range, 2–24 years) and the age of first heroin use ranged from 12 to 42 with amean of 22.9±6.8 years. Daily heroin consumption was estimated foreach subject and varied between 0.5 g and 1.2 g/day (range, 0.1–4.0 g)before they entered the MMT program. Participants received MMT foran average of 20.7±10.0 months (range, 6–43 months) before beingscanned. The last dose of methadone before the scanning dayaveraged 36.0±28.1 mg (range, 5–110 mg). Except for a lower levelof education, there were no significant differences involving age,
Table 1Clinical characteristics and cognitive variables between the MMP group and normal contro
Group MMP (N=27)
Age (years) 36.78±6.64Gender 1 F/26 MEducation (years) 10.33±2.00Alcohol (yes)a 13Nicotine(yes)b 15TIV (cm3) 1495.6±119.3GM (cm3) 667.9±48.4WM (cm3) 471.5±43.6Memory
Verbal episodic memorySix-Object Memory Test, correct 28.89±0.42Six-Object Memory Test, delay 127.11±52.95WSLT, correct 44.96±9.35WSLT, position 35.81±14.78WSLT, learning 6.74±7.22
Nonverbal episodic memoryBVRT, correct 12.48±2.28BVRT, delay 2.59±0.64
Verbal semantic memoryRemote Life Events Test 14.52±0.28Recent Life Events Test 14.00±1.44Semantic Association of Verbal Fluencyc 11.07±2.18
Executive functionDigital Span Forward (WASI-R) 7.85±0.95Digital Span Backward (WASI-R) 4.89±1.58Arithmetic (WASI-R) 8.48±2.6Digit symbol coding (WASI-R) 9.26±2.47Similarity (WASI-R) 6.52±1.72Proverbs 7.19±2.42
LanguageObject Naming 15.85±0.36Token Test 11.44±0.89
Visual constructional praxis3D block construction model, correct 28.89±0.423D block construction model, correct time 127.44±52.95Block design 7.96±2.01Object assembly 5.81±1.98
Beck Depression Inventory 18.51±2.61Beck Anxiety Inventory 8.97±1.49
MMP, methadone maintenance patient, WASI, Wechsler Abbreviated Scale of Intelligence, Wintracranial volume; GM: gray matter; WM: white matter. Statistical threshold was set at P
a Subjects occasionally drank alcohol during their social activities.b Subjects smoked more than 10 cigarettes a day.c Used to measure associative verbal fluency for animals, fruit and vegetables, and replac
gender, or cigarette and alcohol consumption between the two groups(Table 1).
The BDI scores (F(1, 45)=13.39; p=0.001) and BAI scores (F(1,45)=11.34; p=0.002) were higher in MMT patients than in normalcontrols. As regards executive function, MMT patients scored lowerthan controls on Proverbs measures (F(1, 45)=5.207; p=0.027) andexhibited deficits of visual construction function in Block design (F(1,45)=7.371; p=0.009) and Object assembly (F(1, 45)=5.299;p=0.026). Regarding memory performance, patients did not differsignificantly from controls in non-verbal episodic memory measuredwith the Benton Visual Retention Test (F(1, 45)=3.748, p=0.059) orverbal semantic memory measured with Semantic Verbal Fluency (F(1,45)=2.885, p=0.096). There were no significant differences inverbal episodic memory or language and visual constructional praxisamong groups.
3.2. Regional gray matter volume (GMV) differences between groups
The location and extent of regions with significant differences inGMV are presented in Table 2 and Fig. 1. Based on a non-stationarycluster extent threshold of pb0.05 corrected for multiple comparisonswith FWE, there was significantly smaller GMV in the left inferiorfrontal gyrus and the left cerebellar vermis in the MMT group. Byusing a p value of 0.001 (uncorrected), the MMT patients showed
ls.
Control (N=23) F or χ2 p-value
34.04±7.28 0.033 0.1751 F/22 M 0.013 0.71315.36±1.14 10.18 0.000*6 2.566 0.14811 0.297 0.7771501.7±111.6 0.021 0.851667.9±53.8 0.007 0.934478.3±45.3 0.010 0.921
29.00±0.00 0.295 0.59090.52±25.78 0.579 0.45152.78±7.50 0.541 0.46645.60±13.76 0.132 0.71811.91±6.52 0.003 0.957
14.43±0.79 3.748 0.0593.00±0.00 2.300 0.136
14.39±0.31 0.076 0.78414.87±0.63 1.729 0.19513.91±3.11 2.885 0.096
8.39±0.84 0.381 0.5406.04±1.36 0.004 0.94911.14±3.16 0.055 0.81611.86±2.19 0.065 0.8008.87±1.79 2.636 0.1119.00±1.24 5.207 0.027*
15.91±0.29 0.142 0.70811.96±0.21 2.142 0.150
29.00±0.00 0.295 0.59090.52±25.78 0.579 0.45111.77±2.58 7.371 0.009*9.32±2.81 5.299 0.026*2.49±2.89 13.39 0.001*0.65 ±1.65 11.34 0.002*
SLT, word sequence learning test correct, BVRT, Benton Visual Retention Test, TIV: totalb0.05.
e the original subtest of Controlled Oral Word Association of the MAE.
Table 2Gray matter anatomical regions demonstrating reduced gray matter volume in the MMT group compared with the normal control group.
Anatomic region MNI atlas coordinates Regional GMV mean (SD)
x y z Voxels size Brodmann area Control (N=23) MMP (N=27) z-score Cohen d
Atrophy volume in MMP vs. control (un-corrected pb0.001)L cerebellum, tuber of vermis −2 −78 −27 3955 a 3.083(0.298) 2.778(0.241) 4.27 1.16L medial frontal gyrus −24 43 18 582 9 0.334(0.064) 0.283(0.049) 4.05 0.92R insula 49 −18 15 1341 13 0.895(0.136) 0.763(0.112) 4.01 1.09L postcentral gyrus −26 −26 68 477 3 0.195(0.027) 0.160(0.027) 3.92 1.32L inferior frontal gyrus −17 20 −28 885 47 0.452(0.045) 0.406(0.041) 3.92 1.09R cerebellum, declive 26 −78 −22 1524 a 1.391(0.144) 1.222(0.134) 3.83 1.24L subcallosal cingulate gyrus −12 4 −23 167 32 0.067(0.007) 0.059(0.006) 3.71 1.26L insula −30 19 −17 315 13 0.209(0.019) 0.183(0.017) 3.62 1.48L anterior cingulate −4 12 −16 126 25 0.083(0.009) 0.075(0.009) 3.43 0.91R inferior frontal gyrus 15 38 −27 100 11 0.040(0.003) 0.037(0.005) 3.36 0.73
Atrophy volume in MMP vs. control (non-stationary correction)L cerebellum, tuber of vermis −17 −78 −27 3955 a 3.083(0.298) 2.778(0.241) 4.27 1.16L inferior frontal gyrus −17 20 −28 885 47 0.452(0.047) 0.406(0.042) 3.92 1.09
MNI, Montreal Neurological Institute, GMV, graymatter volume, MMP,methadonemaintenance patient. The unit of regional graymatter volume is cm3. Statistical threshold of upperpart of the table: uncorrected pb0.001. Statistical threshold of lower part of the table: uncorrected pb0.001 with cluster extent correction family-wise error (FWE) corrected pb0.05.
a Indicated that there is no Brodmann area region around the center of 5 mm radius search range.
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extensively smaller GMV in the frontal lobe, including the left medialfrontal cortex (BA 9), left inferior frontal gyrus (BA 47), and rightinferior frontal gyrus (BA 11). The MMT patients also showed sig-nificantly smaller GMV in the left sub-callosal cingulate gyrus (BA 32),left anterior cingulate gyrus (BA 25), postcentral gyrus (BA 3), andbilateral insula (BA 13). Aside from cerebral deficits, there wassignificantly smaller GMV in the left cerebellar vermis and the rightcerebellar declive.
3.3. Relationships between depression and anxiety symptom ratings,cognition, duration and dose of heroin use and MMT, and gray mattervolume
3.3.1. Depression and anxiety symptom rating vs. gray matter volumeTable 3 showed significant negative correlations between BDI
scores and the GMV of the left medial frontal region, left post-centralgyrus (Fig. 2a), left inferior frontal gyrus, right cerebellar declive, leftsub-callosal cingulate gyrus, left insula (Fig. 2b), and right inferiorfrontal gyrus (Fig. 2c). There were also significant negative correla-tions between BAI and the GMV of the left medial frontal gyrus, leftpost-central gyrus, left sub-callosal cingulate gyrus, and right inferiorfrontal gyrus (Fig. 2d).
Fig. 1. Hot colormap: uncorrected pb0.001, Cold colormap: uncorrected pb0.001 with cluspartial uncorrected pb0.001 clusters. a: Left medial frontal gyrus, BA9; b: Right insula, BA13gyrus, BA34; f: Left insula, BA13; g: Left anterior cingulate gyrus, BA25; H: Right inferior fro
3.3.2. Cognitive function vs. gray matter volumePoorer Six-Object Memory Test and Semantic Verbal Fluency
positively correlated with smaller GMV in the left insula. An impairedProverbs test score was positively associated with smaller GMV in theright cerebellar declive (Table 3).
3.3.3. Duration and dose of heroin use and MMT vs. gray matter volumePartial correlation analysis between GMV and the duration and
dose of heroin use and MMT did not reveal any significant effects.
4. Discussion
VBM investigations of structural deficits in heroin users are limited(Lyoo et al., 2006; Liu et al., 2009; Yuan et al., 2009). The current studyidentifies significant local anatomical alterations in multiple ventralmedial frontal gyri, left sub-callosal cingulate gyrus, left anteriorcingulate gyrus, post-central gyrus, bilateral insulae, left cerebellarvermis and right cerebellar declive in heroin-dependent individualson MMT. This is consistent with results of previous studies with theexception of alterations in the straight gyrus and fusiform gyrus (Yuanet al., 2008; Liu et al., 2009). Enrolled patients are strictly heroinabusers without a history of dependence on other commonly abused
ter extent correction family-wise error (FWE) corrected pb0.05, clusters overlap with; c: Left postcentral gyrus, BA3; d: Right cerebellar declive; e: Left subcallosal cingulatental gyrus, BA11; i: Left cerebellar vermis; j: Left inferior frontal gyrus, BA47.
Table 3Correlation (adjusted for age, education, total intracranial volume and history of smoking and alcohol use) between gray matter abnormalities, BDI, BAI and cognitive variables.
Clinical variable Anatomical regions Brodmann area Correlation (r) P-value
Beck Depression Inventory L medial frontal gyrus 9 −0.320 0.034L postcentral gyrus 3 −0.421 0.004*L inferior frontal gyrus 47 −0.362 0.016R cerebellum, declive a −0.312 0.039L subcallosal cingulate gyrus 32 −0.384 0.010L insula 13 −0.438 0.003*R inferior frontal gyrus 11 −0.444 0.003*
Beck Anxiety Inventory L medial frontal gyrus 9 −0.337 0.025L postcentral gyrus 3 −0.387 0.009L subcallosal cingulate gyrus 32 −0.314 0.038R inferior frontal gyrus 11 −0.448 0.002*
Cognitive testsSix-Object Memory Test, delayed L insula 13 0.355 0.018Proverbs R cerebellum, declive a 0.340 0.024Semantic Association of Verbal Fluency L insula 13 0.325 0.032
a The threshold for statistical significance was set at pb0.05 with correction for multiple comparisons.
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drugs. The results provide evidence for structural changes in thebrains of heroin-dependent individuals treated with methadone.
The present study also reveals that differential regional volumedeficits areproportional to scoresondepression, anxiety, andworseningcognitive performance. To date, this is the first study explicitlycorrelating GMV deficits with multiple co-morbid conditions in heroinusers on MMT without the concurrent use of other psychoactive drugs.
When stricter criteria are used for more precise findings withhigher spatial cluster resolution, there is significantly smaller GMVonly in the left inferior frontal gyrus (BA 47) and left cerebellar vermisin theMMT group. The left inferior frontal cortex in particular acts as alink between left lateral pre-frontal circuits involved in re-appraisingthe emotional significance of affective stimuli and amygdala circuitscrucial for generating emotional responses. In the current study,
Fig. 2. Correlations between depression and anxiety symptom ratings and gray matter voluthe left post-central gyrus significantly negatively correlated with BDI; b, regional volume of tinferior frontal gyrus significantly negatively correlated with BDI; d, regional volume of the
deficits in the left inferior frontal cortex (BA 47), left pre-frontal cortex(BA 9) and right inferior frontal cortex (BA 11), and their associationwith poor BDI and BAI scores are consistent with an inappropriate orinefficient engagement of the pre-frontal regulatory circuitry in MMT.
In addition to abnormalities of the frontal gray matter, there arealso cerebellar alterations in MMT, especially in the left vermis. Thesefindings are congruent with prior VBM reports of heroin (Yuan et al.,2009) and cocaine users (Sim et al., 2007). In the current study, suchcerebellar abnormalities are associated with worse emotional regu-lation and poorer cognitive functions. The vermis receives dopami-nergic projections from the ventral tegmental area and may beaffected by drugs of abuse and contribute to adaptations leading todependence. In this process, dopaminergic system alterations affectcerebellar neuroplasticity in the consolidation of mood-related
me. BDI, Beck Depression Inventory; BAI, Beck Anxiety Inventory; a, regional volume ofhe left insula significantly negatively correlated with BDI; c, regional volume of the rightright inferior frontal gyrus significantly negatively correlated with BAI.
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memory and cognitive procedural learning (Miquel et al., 2009) andresult in depression. Previous reports that the cerebellum is associatedwith depression in many psychiatric disorders are further corrobo-rated by the findings here (Baldacara et al., 2008). However, thepotentially confounding effect of nicotine-induced cerebellar impair-ment cannot be eliminated (Gallinat et al., 2006).
Another important finding in the present study is the deficit in thesub-callosal cingulate gyrus (BA 32) and anterior cingulate (BA 25),which are perhaps the most implicated regions in the etiology ofdepression. A series of reports have shown that the cortical volume ofthe sub-callosal cingulate gyrus is smaller in patients with familialdepression (Drevets et al., 1997), women with early-onset depression(Botteron et al., 2002), and patients with psychotic depression (Coryellet al., 2005). As in the amygdala, imaging studies in depression disordershave often shown increased sub-callosal cingulate gyrus activity anddecreased activity after treatment with a variety of interventions(Hamani et al., 2011). The coupling of sub-callosal cingulate gyrus, pre-frontal cortex and amygdala plays a role in the lack of interest anddisruption of reward mechanisms that underlie anhedonia.
The insula is an integral component of the cortico-mesolimbicsystem (Shinonaga et al., 1994) and is responsible for limbic sensoryrepresentation of subjective “feelings”. In heroin users, the associationbetween morphologic change and severity of depression confirms therole of the insula in the processing of self-induced and/or internallygenerated emotional recall (Lee et al., 2005). The smaller GMV in theinsula results in insufficient emotional regulation for both drugs andpleasurable events.
Smaller insular volumes correlated with lower scores in verbalepisodic memory and verbal fluency tests, indicating that the insula isalso implicated in a wide range of high cognitive functions (Balleineand Dickinson, 2000). Findings of insular atrophy further support itshypersensitivity to cues for drug craving, as shown in another study(Daglish et al., 2003), although the potentially confounding effect ofnicotine-induced insular impairment cannot be totally eliminated(Naqvi et al., 2007).
Compared to controls, MMT patients also show smaller GMV in theleft BA 3. This correlates with the BDI and BAI scores. The smallervolume in the left BA 3 of the primary sensori-motor cortex is not afinding frequently reported with heroin dependence, but the VBMresults are consistentwith findings of a previousMRI study that showshyperactivity in response to heroin-related cues (Mei et al., 2010). Theassociation between the left BA 3 and BDI in the current studyduplicates findings of a previous study in depressive disorders (Yuanet al., 2008; Li et al., 2010). The clinical impact is still unknown.
In the present study, the longer duration and higher dose of heroinare not associated with smaller GMV in any region. This contradictsthe recent reports of Yuan et al. (Yuan et al., 2009). Several studies ofother abused substances such as cocaine (Sim et al., 2007) andmarijuana (Matochik et al., 2005) show that progressive brain tissueloss correlates with duration of drug abuse. Yuan et al. enrolled youngheroin-dependent individuals within a narrow age range andrelatively short duration of heroin use (4.29 years). The older andwider age range (23–53) and longer history of heroin use (13.9 years)in this study suggest that a totally different patient population maylead to diverse conclusions.
Although it is possible that the deficits seen already existed prior todrug use, one possible interpretation of the findings here is thatobserving a ceiling effect in the drug-users in this study due to thelong heroin abuse histories may have masked any differential effect ofchronic opiate use on brain structure. Since a previous behavioralstudy shows worse performance of inhibitory control according to theduration of drug use (Monterosso et al., 2005), a cumulative effect ofsubstances on the brain is possible and reasonable. Whether or notthese structural changes are a result of neuronal degeneration fromrepeated heroin use or another co-morbidity like depression(Rajkowska et al., 2005) requires further study.
There is no correlation of either the duration or intensity ofmethadone administration with the degree of GMV deficit on any ofthe anatomic measures. Prosser et al. (2006) have reported abnormalcognition and absence of an effect of length or amount of methadonetherapy on neuro-psychologicalmeasures. Peles et al. (2007) have alsoreported that the risk for depression is independent of the duration ofmethadone treatment. There is depression even in abstinent heroin-dependent subjects (Gerra et al., 2000). We believe the effect ofmethadone on depression and its relationship with anatomic deficitscan be clarified by comparing heroin dependent abstainers with andwithout methadone replacement treatment in the future.
The interpretation of the findings needs to be tempered by somelimitations. First, gender differences may influence GMV deficits indepression (Taki et al., 2005). The female gender is reportedly a riskfactor for developing depressive symptoms in MMT. Because of malepredominance in the enrolled groups, the effect of gender on GMVneeds further analysis. Second, although routine negative urine drug-screening test was a condition for heroin-dependent subjects to be inthe MMT, this test was not performed immediately before the neuro-psychiatric examination and MRI scan. Short-term use of othersubstances may also potentially confound the results. Our resultsalso need further study to determine whether the observed effects aredue to pre-existing conditions, methamphetamine alone, or thecombination of methamphetamine with methadone treatment. Inspite of structural modifications, research into the role of thecerebellum in dependence is still at the beginning stages. An adequatedescription and explanation for cerebellar involvement in clinicalbehavior requires further animal studies for clarification. Lastly, across-sectional study was performed on two sample groups ratherthan a longitudinal study of MMT patients following detoxification.
In conclusion, there are significant structural differences in theemotion circuit and cerebellum between heroin-dependent patientson MMT and healthy controls. Many of the structural abnormalitiescorrelate with the severity of depression, anxiety, and cognitivedysfunction. Therefore, we tentatively speculate that MMT patientshave GMV changes of certain brain regions hypothesized to influencecognition and emotion, and that these regions might thus be involvedin the psychopathology and pathophysiology of those co-morbiditiesin MMT. A natural history-based approach with recruitment ofdifferent ethnic groups may improve the understanding of biologicalcharacteristics of heroin dependence in the future.
Conflict of interest
The authors declare no conflict of interest.
Acknowledgments
This work was supported by grants from Chang Gung MemorialHospital (Chang GungMedical Research Project CMRPG 880561 to C-CChen and CMRPG870482 to W-C Lin). The authors acknowledge theMR support from the MRI Core Facility of CGMH. The authors alsothank Tsui-Min Chiu, Yu-Hsin Hsieh, and all subjects who participatedin this study.
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