| Article Access Statistics|
| Viewed||768 |
| Printed||22 |
| Emailed||0 |
| PDF Downloaded||42 |
| Comments ||[Add] |
Click on image for details.
|Year : 2021
: 63 | Issue : 5 | Page
|An instrument for visual cue associated craving of HEroin (IV-CACHE): A preliminary functional neuroimaging-based study of validity and reliability
Shantanu Shukla1, Abhishek Ghosh2, Chirag Kamal Ahuja3, Debasish Basu2, Bharath Holla4
1 Department of Psychiatry, Postgraduate Institute of Medical Education and Research, Chandigarh, India
2 Department of Psychiatry, Drug De-addiction and Treatment Centre, Postgraduate Institute of Medical Education and Research, Chandigarh, India
3 Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, India
4 Department of Integrative Medicine, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India
Click here for correspondence address and
|Date of Submission||16-Dec-2020|
|Date of Decision||19-Apr-2021|
|Date of Acceptance||01-Jul-2021|
|Date of Web Publication||12-Oct-2021|
| Abstract|| |
Background: Craving is the subjective experience of desire for specific drugs. Lack of reliability and untested construct validity are limiting factors for the existing questionnaires to assess craving.
Aim: The aim of the study was to design and test the validity and reliability of an instrument to assess visual cue-induced craving for heroin dependence.
Materials and Methods: In the first stage of the study, a set of forty images (twenty each of heroin and neutral cues-) were captured and validated by expert consensus. Thirty male participants with heroin dependence rated their cue-induced craving on a six-point Likert scale while viewing this image-set. In the next stage, putative construct validity was examined using a pilot cue-reactivity functional magnetic resonance imaging paradigm with ten additional heroin-dependent patients.
Results: Cronbach's alpha for the instrument for visual cue-associated craving of HEroin (IV-CACHE) was 0.9, suggestive of high internal consistency. There were modest and significant correlations of IV-CACHE with the drug desire questionnaire (r = 0.43), and obsessive-compulsive drug use scale (r = 0.37), supporting concurrent validity. Patients with heroin dependence exhibited cue reactivity in the left fusiform area, right lingual gyrus, right precuneus region, right inferior frontal, inferior temporal gyri, and middle occipital gyri. The activated brain areas were largely aligned to the underlying neurobiological substrates of craving but might also have depicted nondrug-specific factors (aberrant face processing and attentional bias).
Conclusion: The present cue-task is a promising tool for the examination of cue-related craving for heroin in the Indian setting.
Keywords: Craving, cue-reactivity, heroin, neuroimaging
|How to cite this article:|
Shukla S, Ghosh A, Ahuja CK, Basu D, Holla B. An instrument for visual cue associated craving of HEroin (IV-CACHE): A preliminary functional neuroimaging-based study of validity and reliability. Indian J Psychiatry 2021;63:448-55
|How to cite this URL:|
Shukla S, Ghosh A, Ahuja CK, Basu D, Holla B. An instrument for visual cue associated craving of HEroin (IV-CACHE): A preliminary functional neuroimaging-based study of validity and reliability. Indian J Psychiatry [serial online] 2021 [cited 2022 Oct 3];63:448-55. Available from: https://www.indianjpsychiatry.org/text.asp?2021/63/5/448/328099
| Introduction|| |
Craving is the conscious, subjective experience of wanting to use a specific drug. Several researches have linked craving with drug-taking and relapse., A panel convened by the National Institute of Drug Abuse has recommended that craving should be included as a standard outcome measure across treatment studies. Recognizing the clinical relevance, the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), and the beta draft of the International Classification of Diseases 11th edition have included it as one of the diagnostic criteria for substance use disorders.
Although craving can be induced by several ways, exposure to drugs and drug-related cues is one of the common stimuli to elicit craving. In fact, craving (“wanting”) after exposure to conditioned cues surpasses the drug “liking” in people with substance use disorders. Despite its clinical importance, measurement of cue-induced craving is largely limited to single-item visual analog scales (VAS). One-item assessment understandably is the least reliable measure. Multi-item instruments measure either periodic craving or episodic craving, which are independent of the exposure to cues., Moreover, existing multi-item questionnaires often include a wide range of contents such as intentions and expectancies from drug use, however, these additional concepts are empirically distinguishable from drug desire., Therefore, the need to have a multi-item tool to elicit cue-induced craving is apparent. We aimed to design and test the psychometric properties of an instrument to assess cue-reactive craving for heroin. The World Drug Report 2019 informed 53 million opioid users globally and opioids were the second most commonly abused illicit drugs. Majority of those with opioid use disorders were primary heroin users. Most of the deaths and disabilities due to drug use disorders were attributed to opioid use disorders.
So far studies on people with heroin dependence have used images to examine cue-reactivity as functional imaging paradigms rather than an instrument to elicit craving.,, In these cue-reactivity paradigms, several brain regions such as precuneus, cingulate, inferior frontal, striatum, insula, temporal, and occipital gyri showed activated blood oxygen level-dependent (BOLD) signals.,,,,,,,,,,, Activation pattern signifies possible brain substrates of craving.
Putting all evidence together, we saw a potential for developing a multi-item (image-based) instrument for assessing cue-induced craving for heroin dependence-instrument for visual cue associated craving of HEroin (IV-CACHE). The objectives of our research were: (a) to design a tool for the assessment of visual cue-induced craving for heroin, (b) to test the psychometric properties of the new tool, i.e., to examine the reliability, face and concurrent validity, (c) finally, to examine the putative construct validity by using a pilot cue-reactivity functional magnetic resonance imaging (fMRI) paradigm. We hypothesized that among heroin-dependent subjects, visual cues related to heroin and neutral cues would be associated with different fMRI BOLD signals in brain regions linked to craving. In addition, fMRI BOLD signals with heroin visual cues would differ between heroin-dependent and controls.
| Materials and Methods|| |
It was a cross-sectional study. We recruited participants by purposive sampling from outpatient and inpatient settings of an addiction psychiatry training and research center in India. It was a 3-staged study. In the first stage, a set of forty images (twenty each of heroin and neutral cues-) were captured and validated by expert consensus. These images were sent to five internal and five external experts, working in the field of addiction psychiatry for at least 3 years. Face validity of the instrument was based on experts' opinions. In the second stage reliability and concurrent validity of the instrument were examined. Thirty participants selected for this stage would be able to detect modest correlation (r = 0.5) of craving scores between IV-CACHE and available instruments with adequate power (1 – β=80%) and <5% chance (α =0.05) of drawing a false-positive conclusion. In the final stage, putative construct validity was examined using a pilot cue-reactivity fMRI paradigm. Ten heroin-dependent and ten control participants were recruited in the pilot study.
In the second stage of study 19–45-year-old right-handed men with heroin dependence were selected from the outpatient clinics. Opioid dependence was diagnosed by a psychiatrist and confirmed by the Mini-International Neuropsychiatric Interview. Handedness was ascertained by Annett's handedness questionnaire. We excluded participants under intoxication, with psychotic illness, and those on neuroleptics, anti-anxiety, and antidepressant medications, on methadone, tramadol, buprenorphine, and naltrexone. For the final stage, ten additional heroin-dependent participants were recruited. The selection criteria for this stage were similar to the 2nd stage; in addition, participants having contraindications for MRI were excluded. Age- and gender-matched 10, non-drug using control participants were also recruited in the final stage.
The data collection period was from July 2018 to September 2019.
Written informed consent was obtained from all participants. The study was approved by the Institute Ethics Committee.
Designing Visual Cue Associated Craving of HEroin (IV-CACHE)
A set of forty images (twenty each of heroin and neutral cues-) were captured, either manually or from the internet. Images had two types of heroin-related cues- discrete and contextual. Discrete cues consisted of images of heroin or heroin-associated stimuli, for example, cotton swab, injecting person, and insulin syringes. Contextual cues were the images of settings or situations of heroin use. All the images were scaled down or up to fit into predetermined size. The color, brightness, and contrast were adjusted manually. We have named this new instrument with sets of cue-related and neutral images as-IV-CACHE.
Images were installed in a random order list, from a random number generating android application, with an even and odd-numbered slot for cue related and neutral images, respectively. The images were arranged into power-point slides which were shown to the patients as a slide show using a laptop. We kept the order of image delivery fixed across participants.
For each visual cue, the subjects were asked to rate their craving on 6-point (0–5) scale. There were descriptors for each level of the score, i.e., 0-I don't have any urge to take heroin/any other opioids; (1) If someone offers me any opioids (natural/pharmaceutical), I will take it; (2) If someone offers me heroin, I will take it; (3) If I go out of the hospital now, I will chase/inject heroin; (4) I want to leave now and chase/inject heroin; (5) Even if you tried to stop me, I would like to leave now and chase/inject heroin.
Standard tools for craving assessment
Craving assessment was made by obsessive-compulsive drug use scale (OCDUS) and desires for drug questionnaire (DDQ). OCDUS measures craving for 1-week, whereas DDQ assesses instantaneous craving., These instruments were shown to have modest-strong correlation with each other and with VAS of craving.,, First, participants were scored on the OCDUS. On the same sitting, craving rating was also done by IV-CACHE. Finally, scoring was done on DDQ.
Functional magnetic resonance imaging
BOLD fMRI scans were obtained with a Philips 3T Ingenia CX MRI using a 32 channel head coil with a gradient echo Echo-planar imaging (EPI) sequence. Five dummy scans were obtained in the first 10 s to allow for signal equilibration. These dummy scans were not taken into analysis. The scan parameters were as follows: TR = 2000 ms; TE = 30 ms; flip angle = 78; slice thickness = 3 mm; slice order: Descending; number of slice = 37; gap = 25%; matrix = 64 × 64 mm2, FOV = 192 × 192, recon voxel size = 3.0 × 3.0 × 3.75 mm3).
Functional magnetic resonance imaging paradigm
The fMRI experiment-related instructions and visual stimuli were presented on a plastic mirror mounted on the head coil such that the participants can view the MR-compatible monitor. Stimulus presentation protocol was generated via PsychoPy 3 (https://www.psychopy.org/), an open-source software package written in the Python programming language.
The fMRI schematic is presented in [Figure 1]. The Task consisted of 8 active blocks interspersed with 8 rest-blocks with a fixation cross (12s). The active block consisted of 5 visual-cue related and 5 neutral images. Each image would be on screen for 6 s. The total task duration was 336 s (8×12s [rest]+8×30[active]) resulting in 168 dynamic volumes. Each subject participated in two runs of the task with a total of 336 volumes.
This paradigm was installed on a laptop which was connected with the projector screen with HDMI cable and synced with the fMRI console.
The participants were educated about MRI procedures. For screening purposes, a part of the instrument (with 10 cue-related and 10 neutral images) was shown before the f-MRI session and participants were asked to score the intensity of craving on a six-point Likert scale; those with mean craving score of at least 3 underwent fMRI.
The scans were analyzed using Analysis of Functional Neuro-Images (AFNI) software, version AFNI_19.1.00 'Caligula'.57 In AFNI, @SSwarper program and afni_proc.py were used for preprocessing of T1w and fMRI data, respectively. In brief, each subject's T1w anatomical dataset was nonlinear warped to the Montreal Neurological Institute and Hospital (MNI) standard space through the @SSwarper command, which would also provide skull-stripped anatomical volumes. The warp estimates created by this script were passed as options of the “tlrc” block in afni_proc.py. In the afni_proc.py command, between-scan movements were corrected by re-aligning the EPI volumes to the volume with the fewest number of outliers in the initial EPI time series identified through heuristics, slice time corrected, co-registered to the T1w image using LPC+ZZ cost function. This was followed by spatial normalization using nonlinear warps. Finally, the data were smoothed using a 6 mm (FWHM) isotropic Gaussian Kernel.
AFNI's 3dREMLfit was used to perform generalized least squares modeling with a restricted maximum likelihood (REML) approach to account for temporal correlation, with two variables of interest (opioid and neutral cues) as well as regression of nuisance confounders. Any volumes with >20% outlier voxels or more than 0.3-mm point-to-point movement were censored from further analyses. The resulting effect estimates from 3dREMLfit for opioid cue–neutral cue contrast condition and the t-statistics from the subject level were then carried at the group level. A group-wise activation map comparing visual cue-related and neutral images was generated by the AFNI program 3dttest++ (whole-brain FDR corrected at P < 0.05).
Volume alignment and motion parameters
Cortical areas were clearly delineated and a clear distinction could be seen between the gray matter and white matter. BOLD signals were seen exclusively in the gray matter regions. The motion censoring task was run twice. Those volumes which exceeded the motion threshold of 0.3 mm were identified as outliers.
The study was conducted in four phases. In the first phase, the new tool (IV-CACHE) was developed to assess cue-induced craving for heroin. The next step was to establish the face validity of the images, i.e., subjective judgment whether the images truly represent cue associated with heroin use (or not associated, in cases of neutral images) and can induce craving in an individual with heroin dependence. All the forty images were sent to (through e-mail, in.pdf format) five internal and five external experts. They were requested to rate the level of accuracy or appropriateness of these images (in terms of the potential to induce or not induce craving) in a six-point Likert scale (0–5; 0 = not accurate; 5 = highly accurate). Experts were also requested to take the color, brightness, and contrast into account, while rating. Any score of 4 or 5 was taken as acceptable. However, if some expert scored below that level, he or she was requested to point out the images which have been perceived to be less accurate. Suggestions were sought regarding more accurate images and those were incorporated into the existing list of forty images. If more than one expert suggested changing one particular image that were replaced by the newly suggested image. The final set of images were re-circulated for confirmation of face validity. This cycle was continued till a score of 4 or 5 was received from all the experts.
The third step was to establish the reliability and concurrent validity of IV-CACHE. Thirty patients who fulfilled the selection criteria and provided informed consent were tested. First, they were scored on the DDQ and OCDUS. Subsequently, they were scored on IV-CACHE. This part of the study was conducted in the out-patient.
The final stage of the study was to establish the construct validity of IV-CACHE. This step was conducted in a different group of inpatients with the same selection criteria. This stage also had a control group. We used the cue-reactivity fMRI paradigm in this stage.
The descriptive data were represented by frequencies and percentages for categorical variables and mean and standard deviation for continuous variables. The comparisons of the continuous variables were done by the independent sample t-test, whereas the comparison of categorical variables was made by Pearson's Chi-square test.
The internal consistency of the instrument was tested by Cronbach's alpha. The concurrent validity of the instrument was assessed by testing correlations of the craving scores on IV-CACHE, DDQ, and OCDUS. The distributions of the scores in each of these instruments were tested for normality- by examining the histogram and Shapiro–Wilk test. Should the normality be present, Pearson's correlation coefficient was used. Should the normality not be fulfilled, the Spearman correlation test was used. The analysis of fMRI data had been mentioned before.
| Results|| |
Establishing face validity of the Instrument for Visual Cue Associated Craving of HEroin (IV-CACHE)
All forty images were sent to a total of 10 experts. We ended up replacing 4 images (3 neutral and one cue related) from the first set. Three of these were replaced because of image-quality related factors (brightness, color) and one was replaced for its questionable ability of discrimination between cue-related and neutral character. The final set of images was re-circulated for scoring. After two such circulations of the sets of images, a score of 4 or 5 was received from all the experts.
Establishing the reliability and concurrent validity Instrument for Visual Cue Associated Craving of HEroin (IV-CACHE)
The study population consisted of 30 men with a mean age of 26.1 years (±4.8). Majority of participants were Hindus (53.3%) with an education up to inter/diploma (60%) and belonged to nuclear families (43.3%). There was an equal distribution of patients from rural and urban backgrounds. The mean age of onset of opioid dependence was 22.9 years (±4.5) and the mean duration of dependence was 3.3 (±3.7) years.
The OCDUS scale had a nonnormal distribution (Shapiro–Wilk significance = 0.001), whereas IV-CACHE (Shapiro–Wilk significance = 0.23) and DDQ (Shapiro–Wilk significance = 0.46) had normal distributions. Therefore, Pearson's correlation test was done for the correlation of scores obtained on IV-CACHE and the DDQ. Spearman correlation test was used for the correlation of scores between the OCDUS and IV-CACHE. The correlation coefficient of IV-CACHE with DDQ and OCDUS were 0.428 (P = 0.018) and 0.373 (P = 0.042), respectively.
IV-CACHE had a Cronbach's alpha value was 0.948 representing a strong inter-correlation between the items tested. The split-half reliability was also tested where the images were divided into two equal parts of 20 images each. Cronbach's Alpha for Part 1 was 0.88 and for Part 2 was 0.92 suggesting very good internal consistency.
Establishing the construct validity of Instrument for Visual Cue Associated Craving of HEroin (IV-CACHE)
The patient population consisted of 10 men with a mean age of 27 years (±2.5), the mean age of first use of opioids at 22.1 years (±3), with mean duration of opioid use for 4.4. years (±1.5). The mean age of dependence on opioids was 23.4 years (±3.0) with a mean duration of 3.6 years of dependence. The mean craving score on IV-CACHE before the intake was 3.98 (±0.41). The control participants consisted of 10 men with a mean age of 28.6 years (±2.4).
The activated brain regions relative to the “heroin cue-neutral contrast” in the group with heroin dependence are- left fusiform, right lingual gyrus, right precuneus, right inferior frontal and inferior temporal gyri, and middle occipital gyri (of both sides) [Figure 2]. In all these areas the change of fMRI-BOLD signal had levels of significance at P < 0.05, after corrections for multiple comparisons. The cluster table [Table 1] showed the details of the volume of clusters, maximum (peak t value) and mean intensity of the signal, and peak values across the three MNI coordinates (right to left, anterior to posterior, and inferior to superior). The minus sign with the maximum intensity depicted the deactivation of that particular brain region.
|Figure 2: The false discovery rate corrected activated brain regions relative to the “heroin cue-neutral contrast” among subjects with heroin dependence. The areas showing significant activation are left fusiform, right lingual gyrus, right precuneus, right inferior frontal and inferior temporal gyri, and middle occipital gyri (of both sides)|
Click here to view
|Table 1: Activated (or deactivated) brain areas (at P<0.05 level of significance) for the heroin-dependent group in response to heroin-related versus neutral 1: Activated (or deactivated) brain areas (at P<0.05 level of significance) for the heroin-dependent group in response to heroin-related versus neutral|
Click here to view
No supra (or sub)-threshold activation in relation to the heroin-related cues was observed in the control group.
Between group differences
The activated brain regions (at P < 0.05 level of significance, uncorrected) for the heroin-dependent group in contrast to the control group in response to heroin related vs. neutral cues are inferior frontal (both sides), inferior temporal (both sides), right precuneus, superior and inferior occipital gyri, right insula, left anterior cingulate, superior parietal lobule (both sides), and cerebellar vermis. Significant deactivation was seen in the right supplementary motor cortex. However, following corrections for multiple comparisons the statistical significance was lost [Supplementary Table 1].
| Discussion|| |
We designed IV-CACHE. IV-CACHE has a total of 40 items. Multi-item instruments are likely to have higher reliability and sensitivity., Each item of the IV-CACHE was based on a six-point Likert scale depicting a continuum of desire to take heroin. Moreover, the Likert scale was based on the verbal expression of the intensity of desire for drug use. Therefore, craving items had uniform meaning for all participants and among researchers. Verbal descriptions would also help the participants to understand their internal state. Both of these characteristics of IV-CACHE fulfilled desirable criteria for an Instrument to assess craving. In addition, IV-CACHE's verbal descriptors took care of the “ceiling effect” and the “floor effect” VAS. IV-CACHE was based on a continuum of desire rather than “extreme urge” to use drugs. Hence, craving as measured by IV-CACHE has a broader base (4). IV-CACHE had high internal consistency (>0.9) and split-half reliability.
The concurrent validity of IV-CACHE was established by comparing the scores with DDQ and OCDUS. IV-CACHE was shown to have a significant correlation with both DDQ and OCDUS. Nevertheless, the strength of correlation was more for DDQ than for OCDUS. IV-CACHE defines craving as an instantaneous urge to have heroin. This definition is closely aligned to the definition of DDQ- might explain the higher strength of correlation.
The construct validity of IV-CACHE was based on the neurobiological understanding of craving. With-in group comparison of brain activation pattern between the cue related and neutral images among the individuals with opioid dependence showed significantly increased activity in the left fusiform, right lingual gyrus, right precuneus region, right inferior frontal, and inferior temporal gyri, and middle occipital gyri (of both sides). Besides, with-in group comparison in the control group did not show the difference in the activation pattern. IV-CACHE was successful in generating a differential brain activation pattern between heroin-dependents and controls. The fusiform and lingual gyri are responsible for sensory attention. These structures are activated by salient stimuli of the environment and may lead to early and accelerated processing of drug-related cues, which would influence the decision-making and subsequent drug-seeking behavior., However, activation of fusiform gyri might also have resulted from aberrant face processing. Precuneus is said to be involved in three processes related to substance use- attention salience, motor preparation and imagery, and self-referential processing., Greater activation of the precuneus in the present study would indicate higher salience for the heroin cues, anticipation, and impaired appraisal of the risk-benefit of heroin. The inferior frontal region is associated with response inhibition and emotion regulation., Inferior temporal gyrus has been linked with object recognition and classification, and motor planning. Patients with substance use disorders acquire an ability to recognize and classify the objects related to the cues of their substances of choice. This expertise resulted in efficient and differential processing of cues. Motor planning deals with the spatial representations for reaching to the drug-related stimuli, guiding an individual to drug-seeking behavior. The activation of the middle occipital area has been consistently reported in the literature with studies using visual cues. Middle occipital gyri linked with sensory perception and processing shows greater activation following exposure to highly salient stimuli., In our study, the salient stimuli were the cues related to heroin and these had resulted in significantly greater activation of the middle occipital area. Therefore, activation patterns associated with heroin-related visual cues had involved brain regions, which could be linked with craving. Between groups (heroin-dependent versus control) comparison of the activation pattern after exposure to cue-related images did not show any statistically significant result after corrections for multiple comparisons. The small sample size of our study could have resulted in this false-negative finding. Existing studies, which had looked into the brain activation pattern in patients with heroin dependence in response to visual cue paradigm, had some overlapping (temporal, occipital, anterior cingulate, inferior frontal, cerebellum, insula, and precuneus) and some nonoverlapping areas (striatum, dorsolateral prefrontal cortex, posterior cingulate cortex, amygdala) with the present study.,,, The overlapping brain areas showed the findings of our study were in line with the literature on cue-reactivity tasks on patients with heroin dependence and differences could be because of the difference in the clinical characteristics of the study population.,
In sum, f-MRI results of within-group comparisons showed individuals with heroin dependence perceived the heroin cues differently from the neutral cues. The differential activation of the brain areas corresponded to the areas implicated in craving. These findings supported the putative construct validity of IV-CACHE.
First, the application of the IV-CACHE is only limited to patients with heroin use (or use disorder) but not for the patients with other forms of opioid use- natural and pharmaceutical. Second, the sample size testing construct validity was small. We recognize that this was a pilot study showing encouraging results, which should be replicated later in a larger sample. Finally, software-based image-normalization was not done for IV-CACHE. Nevertheless, manual adjustments for size, brightness, color, and contrast were done. Experts' opinion too was considered for image quality. All these must have minimized limitations due to a lack of image normalization.
| Conclusion|| |
IV-CACHE has acceptable psychometric properties: high internal consistency, face and concurrent validity, and putative construct validity. It is a promising tool for the examination of cue-related cravings for heroin.
Financial support and sponsorship
Indian Council of Medical Research (ICMR), New Delhi.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Tiffany ST, Wray JM. The clinical significance of drug craving. Ann N Y Acad Sci 2012;1248:1-17.
Anton RF. What is craving? Models and implications for treatment. Alcohol Res Health 1999;23:165-73.
Robinson TE, Berridge KC. The neural basis of drug craving: An incentive-sensitization theory of addiction. Brain Res Brain Res Rev 1993;18:247-91.
Tiffany ST, Friedman L, Greenfield SF, Hasin DS, Jackson R. Beyond drug use: A systematic consideration of other outcomes in evaluations of treatments for substance use disorders. Addiction 2012;107:709-18.
Hasin DS, Fenton MC, Beseler C, Park JY, Wall MM. Analyses related to the development of DSM-5 criteria for substance use related disorders: 2. Proposed DSM-5 criteria for alcohol, cannabis, cocaine and heroin disorders in 663 substance abuse patients. Drug Alcohol Depend 2012;122:28-37.
Robinson TE, Berridge KC. Review. The incentive sensitization theory of addiction: Some current issues. Philos Trans R Soc Lond B Biol Sci 2008;363:3137-46.
Anton RF, Moak DH, Latham P. The Obsessive Compulsive Drinking Scale: A self-rated instrument for the quantification of thoughts about alcohol and drinking behavior. Alcohol Clin Exp Res 1995;19:92-9.
Hassani-Abharian P, Mokri A, Ganjgahi H, Oghabian MA, Ekhtiari H. Validation for persian versions of “desire for drug questionnaire” and “obsessive compulsive drug use scale” in heroin dependents. Arch Iran Med 2016;19:659-65.
Brandon TH, Juliano LM, Copeland AL. Expectancies for tobacco smoking. In: Kirsch I, editor. How Expectancies Shape Experience. Washington, DC: American Psychological Association; 1999. p. 263-99.
Kozlowski LT, Pillitteri JL, Sweeney CT, Whitfield KE, Graham JW. Asking questions about urges or cravings for cigarettes. Psychol Addict Behav 1996;10:248.
United Nations Office of Drug and Crime. World drug Report 2019. New York: United Nations; 2019. Available from: https://wdr.unodc.org/wdr2019/
. [Last accessed on 2019 Dec 29].
Whiteford HA, Degenhardt L, Rehm J, Baxter AJ, Ferrari AJ, Erskine HE, et al.
Global burden of disease attributable to mental and substance use disorders: Findings from the Global Burden of Disease Study 2010. Lancet 2013;382:1575-86.
Langleben DD, Moriarty JC. Using brain imaging for lie detection: Where science, law and research policy collide. Psychol Public Policy Law 2013;19:222-34.
Li Z, Zhu Y, Childress AR, Detre JA, Wang Z. Relations between BOLD fMRI-derived resting brain activity and cerebral blood flow. PLoS One 2012;7:e44556.
Lou M, Wang E, Shen Y, Wang J. Cue-elicited craving in heroin addicts at different abstinent time: An fMRI pilot study. Subst Use Misuse 2012;47:631-9.
Brody AL, Mandelkern MA, Olmstead RE, Jou J, Tiongson E, Allen V, et al.
Neural substrates of resisting craving during cigarette cue exposure. Biol Psychiatry 2007;62:642-51.
Chester DS, Lynam DR, Milich R, DeWall CN. Craving versus control: Negative urgency and neural correlates of alcohol cue reactivity. Drug Alcohol Depend 2016;163 Suppl 1:S25-8.
Childress AR, Mozley PD, McElgin W, Fitzgerald J, Reivich M, O'Brien CP. Limbic activation during cue-induced cocaine craving. Am J Psychiatry 1999;156:11-8.
Filbey FM, Claus E, Audette AR, Niculescu M, Banich MT, Tanabe J, et al.
Exposure to the taste of alcohol elicits activation of the mesocorticolimbic neurocircuitry. Neuropsychopharmacology 2008;33:1391-401.
Hartwell KJ, Johnson KA, Li X, Myrick H, LeMatty T, George MS, et al.
Neural correlates of craving and resisting craving for tobacco in nicotine dependent smokers. Addict Biol 2011;16:654-66.
Huang Y, Mohan A, De Ridder D, Sunaert S, Vanneste S. The neural correlates of the unified percept of alcohol-related craving: A fMRI and EEG study. Sci Rep 2018;8:923.
Kilts CD, Gross RE, Ely TD, Drexler KP. The neural correlates of cue-induced craving in cocaine-dependent women. Am J Psychiatry 2004;161:233-41.
Liu J, Claus ED, Calhoun VD, Hutchison KE. Brain regions affected by impaired control modulate responses to alcohol and smoking cues. J Stud Alcohol Drugs 2014;75:808-16.
McClernon FJ, Hutchison KE, Rose JE, Kozink RV. DRD4 VNTR polymorphism is associated with transient fMRI-BOLD responses to smoking cues. Psychopharmacology (Berl) 2007;194:433-41.
Myrick H, Anton RF, Li X, Henderson S, Drobes D, Voronin K, et al.
Differential brain activity in alcoholics and social drinkers to alcohol cues: Relationship to craving. Neuropsychopharmacology 2004;29:393-402.
Tabatabaei-Jafari H, Ekhtiari H, Ganjgahi H, Hassani-Abharian P, Oghabian MA, Moradi A, et al.
Patterns of brain activation during craving in heroin dependents successfully treated by methadone maintenance and abstinence-based treatments. J Addict Med 2014;8:123-9.
Xiao Z, Lee T, Zhang JX, Wu Q, Wu R, Weng X, et al.
Thirsty heroin addicts show different fMRI activations when exposed to water-related and drug-related cues. Drug Alcohol Depend 2006;83:157-62.
Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, et al
. The Mini-International Neuropsychiatric Interview (MINI): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychol 1998;59:22-57.
Dragovic M, Hammond G. A classification of handedness using the Annett Hand Preference Questionnaire. Br J Psychol 2007;98:375-87.
Mottola CA. Measurement strategies: The visual analogue scale. Decubitus 1993;6:56-8.
McMillan DE, Gilmore-Thomas K. Stability of opioid craving over time as measured by visual analog scales. Drug Alcohol Depend 1996;40:235-9.
Franken IH, Hendriksa VM, van den Brink W. Initial validation of two opiate craving questionnaires the obsessive compulsive drug use scale and the desires for drug questionnaire. Addict Behav 2002;27:675-85.
Sayette MA, Shiffman S, Tiffany ST, Niaura RS, Martin CS, Shadel WG. The measurement of drug craving. Addiction 2000;95 Suppl 2:S189-210.
Sayette MA. The role of craving in substance use disorders: Theoretical and methodological issues. Annu Rev Clin Psychol 2016;12:407-33.
Wiggins JS. Personality and Prediction: Principles of Personality Assessment. Massachusetts: Addison-Wesley; 1973.
Shiffman S. The concept of craving: Let us not throw out the baby with the bathwater. Br J Addict 1987;82:37-8.
Engelmann JM, Versace F, Robinson JD, Minnix JA, Lam CY, Cui Y, et al.
Neural substrates of smoking cue reactivity: A meta-analysis of fMRI studies. Neuroimage 2012;60:252-62.
Luijten M, Veltman DJ, van den Brink W, Hester R, Field M, Smits M, et al.
Neurobiological substrate of smoking-related attentional bias. Neuroimage 2011;54:2374-81.
Mel'nikov ME, Shtark MB, Korostyshevskaya AM, Sevelov AA, Petrovskii ED, Pokrovskii MA, et al.
Dynamic mapping of the brain in substance-dependent individuals: Functional magnetic resonance imaging. Bull Exp Biol Med 2014;158:260-3.
Goldstein RZ, Tomasi D, Rajaram S, Cottone LA, Zhang L, Maloney T, et al.
Role of the anterior cingulate and medial orbitofrontal cortex in processing drug cues in cocaine addiction. Neuroscience 2007;144:1153-9.
Prisciandaro JJ, Joseph JE, Myrick H, McRae-Clark AL, Henderson S, Pfeifer J, et al.
The relationship between years of cocaine use and brain activation to cocaine and response inhibition cues. Addiction 2014;109:2062-70.
Park MS, Sohn JH, Suk JA, Kim SH, Sohn S, Sparacio R. Brain substrates of craving to alcohol cues in subjects with alcohol use disorder. Alcohol Alcohol 2007;42:417-22.
Yalachkov Y, Kaiser J, Naumer MJ. Sensory and motor aspects of addiction. Behav Brain Res 2010;207:215-22.
Charboneau EJ, Dietrich MS, Park S, Cao A, Watkins TJ, Blackford JU, et al
. Cannabis cue-induced brain activation correlates with drug craving in limbic and visual salience regions: Preliminary results. Psychiatry Res Neuroimaging 2013;214:122-31.
Chase HW, Eickhoff SB, Laird AR, Hogarth L. The neural basis of drug stimulus processing and craving: An activation likelihood estimation meta-analysis. Biol Psychiatry 2011;70:785-93.
Department of Psychiatry, Drug De-addiction and Treatment Centre, Postgraduate Institute of Medical Education and Research, Chandigarh
Source of Support: None, Conflict of Interest: None
[Figure 1], [Figure 2]