Introduction: The development of cocaine use disorder in females is suggested to be more strongly related to neural mechanisms underlying stress-reactivity, whereas in males it is suggested to be more strongly related to neural mechanisms underlying drug cue-reactivity. Existing evidence, however, is based on neuroimaging studies that either lack a control group and/or have very small sample sizes that do not allow to investigate sex differences. Methods: The main objective of the current study was to investigate sex differences in the neural correlates of cocaine and negative emotional cue-reactivity within high-risk intranasal cocaine users (CUs: 31 males and 26 females) and non-cocaine-using controls (non-CUs: 28 males and 26 females). A region of interest (ROI) analysis was applied to test for the main and interaction effects of group, sex, and stimulus type (cocaine cues vs. neutral cocaine cues and negative emotional cues vs. neutral emotional cues) on activity in the dorsal striatum, ventral striatum (VS), amygdala, and dorsal anterior cingulate cortex (dACC). Results: There were no significant sex or group differences in cocaine cue-reactivity in any of the ROIs. Results did reveal significant emotional cue-reactivity in the amygdala and VS, but these effects were not moderated by group or sex. Exploratory analyses demonstrated that emotional cue-induced activation of the dACC and VS was negatively associated with years of regular cocaine use in female CUs, while this relationship was absent in male CUs. Conclusions: While speculative, the sex-specific associations between years of regular use and emotional cue-reactivity in the dACC and VS suggest that, with longer years of use, female CUs become less sensitive to aversive stimuli, including the negative consequences of cocaine use, which could account for the observed “telescoping effect” in female CUs.

Cocaine is one of the most commonly used illicit substances around the world and has severe economic, psychosocial, and psychiatric consequences [1]. Generally, cocaine use disorder (CUD) is more prevalent in men than in women, but this gap is slowly closing due to increased cocaine use in women [1]. Furthermore, there are several differences between women and men in the clinical profiles of CUD with significant implications for treatment [2]. For instance, in cocaine using (CU) women, there are higher rates of comorbid psychiatric mood disorders, whereas CU men more often have comorbid alcohol abuse, attention deficit hyperactivity disorder (ADHD), and/or antisocial personality disorder [3, 4]. Accordingly, researchers have begun to explore the various mechanisms underlying differences between men and women in the development of substance use disorders (SUDs), including CUD [2]. While these differences between men and women may be related to both social cultural gender-related factors and biological sex-related factors, research often only distinguishes between men and women based on sex-assigned at birth [2]. Therefore, we will refer to sex differences, using the terms male and females, throughout the manuscript.

One of the most frequently applied methods for understanding the underlying neurobiological mechanisms in SUDs are cue-reactivity paradigms in which subjects are exposed to substance-related and/or emotional cues (e.g., pictures or videos) during functional magnetic resonance imaging (fMRI) in order to identify brain regions that mediate the development, persistence, and treatment of SUD [5]. Yet, one recent systematic review identified only six neuroimaging studies to date that statistically evaluated sex differences in these neurobiological biomarkers in SUD [6]. Ultimately, this necessitates the need for hypothesis-driven neuroimaging research into sex differences, primarily due to its implications in understanding the neurobiology of SUD [2, 5‒7] and developing sex-tailored treatment designs.

Accumulating evidence demonstrates that in individuals with an SUD, substance-related cues induce hyperactivation of various regions within the salience network, including the amygdala, ventral (VS) and dorsal striatum (DS), and dorsal anterior cingulate cortex (dACC) [8, 9]. Importantly, differential reactivity of these regions to negative emotional cues has been reported in psychiatric disorders, including SUD, as well [10]. Importantly, there is increasing evidence of sex-specific involvement of the mesocorticolimbic circuit during the processing of cocaine or negative emotional cues [11‒14]. More specifically, hyperactivation of the striatum, medial PFC/dACC, and amygdala in response to drug cues has been reported in male CUs, while hypoactivation of these regions is generally found in female CUs [13‒16]. Similarly, hyperactivation of the mesocorticolimbic circuit in response to negative emotional cues has been demonstrated in female CUs, while hypoactivation has been demonstrated in male CUs [12, 13]. Similar findings have been demonstrated in other SUDs, like alcohol use disorder [17], although findings concerning sex differences in alcohol-cue-reactivity are conflicting [18‒20]. While not all research findings are consistent [11, 15], the overall finding is that male CUs demonstrate hyperactivation of the mesocorticolimbic circuit, including the (DS) and VS, dACC, and amygdala in response to cocaine-related cues (i.e., reward), while female CUs demonstrate hyperactivation of these regions in response to negative emotional or stress-related cues [6]. Therefore, it has been suggested that substance use, including cocaine use, and relapse in females are more strongly triggered by stressors, while in males it is more strongly triggered by substance-related cues, although for the latter the evidence is not conclusive [6]. Nevertheless, results from previous studies should be taken with caution due to a lack of control groups [12, 14‒16], small sample sizes (e.g., N = 8 to N = 27) [11, 12, 14, 16], and variations in studied populations ranging from non-treatment-seeking cocaine users [11, 12, 21] to individuals in treatment for a CUD [13, 15, 16]. Accordingly, the overall goal of the current study was to replicate existing findings in a large sample of non-treatment-seeking regular CUs and non-drug-using controls.

The main objective of the study was to investigate sex-dependent differences in the neural correlates of cocaine and (negative) emotional cue-reactivity within CUs (intranasal users) and non-CUs, within four regions of interest (ROIs), including the VS, DS, amygdala, and dACC. In line with previous research, it was hypothesized that male CUs, compared to female CUs and non-drug-using controls, would show stronger activation in all ROIs in response to cocaine-related cues, whereas female CUs compared to male CUs and non-drug-using controls, would show stronger activation of the ROIs to negative emotional cues [6]. A secondary objective was to explore whether there was a sex-specific relationship between cocaine use characteristics and cocaine and emotional cue-induced activation within the ROIs. Based on the assumption that cocaine use in females is more strongly driven by stress-related processes and cocaine use in males is more strongly related by reward-related processes, it was hypothesized that cocaine use characteristics (i.e., cocaine use per month, severity of use, age of onset, and duration of regular use) would be positively related to cocaine-cue-related activation of the ROIs in male CUs, whereas these characteristics would be positively related to emotional cue-related activation of the ROIs in female CUs. Finally, several exploratory analyses were performed to investigate the effect of menstrual phase and hormonal contraceptive use on emotional and cocaine cue-reactivity, as there is increasing evidence that fluctuating sex hormones in natural cycling females and synthetic hormones in females using hormonal contraceptives strongly influence processes of positive and negative reinforcement [22, 23]. The methods and results of his exploratory analysis are described in the online supplementary material (for all online suppl. material, see https://doi.org/10.1159/000538599).

Participants

The current study is part of a larger project aimed to investigate sex differences in the neurocognitive mechanisms underlying high-risk cocaine use [24, 25]. The total sample (n = 111), aged 18–43 years, consisted of regular CUs (n = 57, 45.6% females) and non-CUs (n = 54, 48.1% females). General exclusion criteria were potential post-traumatic stress disorder (≥2 on the Jellinek-post-traumatic stress disorder screening questionnaire) [26], an age outside the range of 18–45, and contraindications for MRI scanning. In order to include a non-CU group that did not have any SUD, including tobacco use disorder, individuals in the non-CU group were excluded if they smoked cigarettes, or had a score higher than 12 on the Alcohol Use Disorders Identification Test (AUDIT) [27], used cocaine more than five times in their life, or used illicit substances more than six times in the past 6 months. Individuals were included in the CU group when they used cocaine (intranasally) at least four times per month. Participants provided informed consent and received monetary compensation for participation. The current study was approved by the Ethics Review Board of the Faculty of Social and Behavioral Sciences, University of Amsterdam (ERB number: 2019-DP-9964).

Procedures

All participants were recruited through online and offline advertisements (e.g., social media and poster advertisements) in the area of Amsterdam, the Netherlands. Participants were asked to provide online informed consent for an online screening procedure to assess in- and exclusion criteria. On the research day, all participants provided written informed consent for participation in the study, completed all the questionnaires, and underwent an MRI scan. All participants received instructions to abstain from any drug use 24 h prior to the MRI scan (except for tobacco). No urine test was conducted to detect cocaine use because it can show traces in urine for up to 6 days after the last use in frequent users, longer than the actual effects of the substance [28]. Instead, we relied on the time-line follow-back (TLFB) method to evaluate cocaine (and other substance) use before the experiment. This method is widely acknowledged as reliable for gathering information about substance use, including cocaine, among treatment-seeking and non-treatment-seeking populations [29]. There were no other instructions regarding food or (nonalcoholic) beverage intake prior to the study procedures. The whole study procedure took approximately 90 min.

Assessment of Substance Use, Psychological Functioning, and Menstrual Phase

As non-CUs were excluded if they used substances more than 6 times in the past 6 months, substance use (except for alcohol use) was only assessed in CUs. For cocaine use severity and related problems in the past 12 months, the Drug Use Disorder Identification Test for cocaine (DUDIT) [30] was used. In addition, the TLFB procedure [29] was completed to determine cocaine use (grams and days per month), alcohol use, and cannabis use in the 28 days prior to study participation. The age of onset was measured by using an in-house questionnaire. Tobacco use was assessed using an in-house questionnaire (number of smoking days per week and cigarettes per day). Finally, a self-reported questionnaire, based on the DSM5 criteria for SUD [31], was used to apply to test for the severity of CUD, cannabis use severity, and alcohol use severity [32]. All participants completed several self-report questionnaires to assess psychological functioning. Specifically, severity of depressive symptoms was measured using the Beck Depression Inventory (BDI-II) [33], state and trait anxiety was measured by using the State and Trait Anxiety Inventory (STAI) [34], ADHD symptom severity was assessed through the ADHD Rating Scale (ADHD-RS) [35], childhood trauma through the Childhood Trauma Questionnaire (CTQ) [36] impulsivity was measured by using the Barratt Impulsiveness Scale (BIS-11) [37] and education level was assessed with an in-house questionnaire. Moreover, using an in-house developed questionnaire, hormonal contraceptive use or menstrual phase was determined. For naturally cycling females, the menstrual phase was determined using an in-house developed questionnaire that assessed the average duration of the menstrual cycle and days since last menstruation. Based on this information, females were grouped as hormonal contraceptive users, being in the luteal phase of the menstrual cycle or being in the follicular phase of the menstrual cycle for exploratory analyses. See online supplementary materials for more information.

Cocaine and Emotional Cue-Reactivity Task

During the fMRI scan, participants completed a cocaine and emotional cue-reactivity paradigm that consisted of 30 full-color cocaine images, 30 emotionally negative images, 30 neutral cocaine images, and 30 neutral emotional images, resulting in a total of 120 images (see Fig. 1). The paradigm consisted of 3 cocaine blocks, 3 emotionally negative blocks, 3 neutral cocaine blocks, and 3 neutral emotional blocks. Every block contained 10 pictures that were each presented for 2.5 s. The order of the blocks was randomized for each participant (see Fig. 1). Before the first block and after each block, two questions were shown to assess current craving (5 s) and affect (5 s) which was followed by a fixation cross that was shown another 2.5 s. The total task duration was approximately 7.5 min. All pictures were full-color images and were rescaled to a 448 × 336 pixel dimension. The negative valence pictures were selected from the 65 images of the open affective standardized image set (OASIS) [38] that were rated lowest on the valence scale in both sexes. The neutral images were also selected from the OASIS database, particularly those that were rated neutral on the valence scale in both sexes (i.e., between 3.8 and 4.2). Cocaine pictures were derived from an earlier dataset [39]. All neutral images were matched accordingly to the negative images or cocaine images with respect to the scene presented, composition, colors, and sex of the people shown.

Fig. 1.

The task started with a baseline measure of arousal (“How do you feel?”) and craving (“How much are you craving cocaine at this moment?”). Then, a total of 12 blocks were shown in random order, which consisted of 3 emotionally negative blocks, 3 cocaine blocks, 3 control blocks matched to the emotionally negative blocks and 3 control blocks matched to the cocaine block. Each block consisted of 10 images (total 120 images) and was shown for 2.5 s. Both craving and affect were assessed after every block.

Fig. 1.

The task started with a baseline measure of arousal (“How do you feel?”) and craving (“How much are you craving cocaine at this moment?”). Then, a total of 12 blocks were shown in random order, which consisted of 3 emotionally negative blocks, 3 cocaine blocks, 3 control blocks matched to the emotionally negative blocks and 3 control blocks matched to the cocaine block. Each block consisted of 10 images (total 120 images) and was shown for 2.5 s. Both craving and affect were assessed after every block.

Close modal

Participants were presented with the cue-reactivity paradigm on a screen behind the MRI scanner. They were able to view this screen through a mirror that was placed on the MRI head coil. Before the start of the fMRI task, the participants practiced answering questions using the response buttons. The questions were answered through response buttons in which participants had to select a number on a visual analog scale (VAS) that ranged from 1 (“Sad”/“Not all all”) to 9 (“Happy”/“Extremely”). Participants had 5 s to respond to each question. If no response was given within 5 s, the task continued. All fMRI scans were conducted between 3:00 PM and 6:00 PM to minimize the potential effects of the time of day on craving.

fMRI Data Acquisition and Processing

All images were acquired through a 3.0-T Philips Achieva DS scanner (Philips Medical Systems, Best, the Netherlands) with a 32-channel head coil. First, a T1-3D anatomical scan (TR/TE 8.2 s/3.8; matrix 240 × 240; 1 × 1 × 1 mm3 voxel; transverse slices) was taken. During the cue-reactivity task, echo planar images (EPIs) covering the whole brain were taken with a total of 36 ascending axial slices (3 × 3 × 3 mm3 voxel size; slice gap 3 mm; TR/TE 2 s/28 ms; matrix 80 × 80; field of view: 216 × 216 × 130, 686 dynamics).

The MRI data were preprocessed using the fMRI prep 1.3.2 pipeline [40]. First, the anatomical data were corrected for intensity nonuniformity, skull-stripped, spatially normalized, and segmented into cerebrospinal fluid, white matter, and gray matter. Second, the functional data were corrected for susceptibility distortions by using a deformation field followed by co-registration, motion correction, and smoothing. Third, an independent component analysis for automatic removal of motion artifacts [41] was performed that automatically removed (head) motion artifacts, and the data were resampled to standard space. All scans were individually inspected after preprocessing for potential artefacts. No scans were excluded following this quality control.

The current study utilized SPM12 to further analyze the fMRI data (http://www.fil.ion.ucl.ac.uk/spm). First-level models included five separate regressors for (1) the three cocaine blocks, (2) the three neutral cocaine blocks, (3) the three negative emotional cocaine blocks, (4) the three neutral emotional blocks, and (5) the implicit baseline that contain the fixation cross (2.5 s) and the rating of craving (5 s) and affect (5 s). These regressors were modelled with a box-car function with the duration of the blocks and convolved with a canonical hemodynamic response function. A high pass filter (1/128 Hz) was included in the first-level model to correct for low-frequency signal drift. The following four first-level contrasts were subsequently entered into a second-level whole-brain model: one contrast for the main effect of cocaine blocks, one contrast for the main effect of the neutral cocaine blocks, one contrast for main effect of the negative emotional blocks, one contrast for the main effect of neutral emotional blocks. Subsequently, the Marsbar toolbox (http://marsbar.sourceforge.net) was used to extract the mean activity (beta-weights) for the contrasts for each ROI (left and right DS, VS, amygdala, and dACC). The automated anatomical labeling (AAL) atlas [42] was used to define the amygdala. In line with previous research, the dACC was defined as the higher (z-coordinate> 7) part of the ACC defined by the AAL atlas, and the VS was defined as the nucleus accumbens from the Harvard-Oxford subcortical structure probability atlas (http://www.cma.mgh.harvard.edu/fsl_atlas.html) [18]. The DS was subsequently defined as the caudate and putamen from the AAL atlas [42] minus the VS. An average of the left and right values for each ROI was calculated and subsequently used as the dependent variable in the ROI analyses.

Statistical Analyses

Potential sex and group differences in demographic and clinical characteristics were analyzed using a one-way ANOVA, including as ADHD symptoms, depression, anxiety, childhood trauma, and impulsivity. In case of sex differences, group differences, or sex-specific group differences in these variables, these variables were included as confounders in follow-up exploratory analyses to determine whether sex and/or group differences in cocaine cue or emotional reactivity might (in part) be explained by group and/or sex differences in these demographic and clinical characteristics. For the CU group, sex differences in DSM5 symptom count for cocaine, alcohol, and cannabis use disorder were tested using χ2 tests. Sex differences in age of onset of regular cocaine use, years of regular cocaine use, cocaine use per month, cocaine use severity (DUDIT scores), and nicotine use severity (FTND-scores) were analyzed using a one-way ANOVA. To analyze group and sex differences in cue-induced craving and affect, difference scores in craving and affect for each block were calculated by subtracting the scores after the block from the scores before the block. The means of these difference scores for each block type were subsequently used as outcome variables in two mixed factorial ANOVAs, with cue type (cocaine, neutral image, negative emotional, neutral emotional) as within subject factor, group and sex as between subject factor, and difference scores for craving and negative affect as outcome variables. With regard to the assessment of affect during the cue-reactivity paradigm, the current study asked for the participant’s rating in affect (a negative value represented a negative affect and a positive value reflected a positive affect). Negative or lower scores represent an increase in negative affect, while increases or positive scores represent an increase in positive affect. A one-way ANOVA was performed to test for sex and/or group differences in percentages of questions answered within the allotted timeframe (5 s) during the fMRI task. Lastly, a one-way ANOVA was used to test for group and/or sex differences in framewise displacement (FD).

Sex-dependent differences in negative emotional and cocaine cue activation of the ROIs were tested using mixed factorial ANOVAs. More specifically, cue type (cocaine and neutral or negative emotional and neutral emotional) was included as within subject factor in two separate analyses with group and sex as independent variables and mean activation of the voxels within each ROI as dependent variable. Mean FD, as quantified by the algorithm in the fMRI prep pipeline [43] was entered as a covariate of non-interest in all analyses. In case of significant sex by group by cue type interaction effects, within-group and sex follow-up tests were performed.

To investigate the moderating role of cocaine use characteristics on emotional and cocaine cue-reactivity, several within CU group mixed factorial ANOVA analyses were performed, with cue type (cocaine and neutral cocaine or negative emotional and neutral emotional) as within subject factor in two separate analyses, mean activation in the ROIs as the dependent variable, sex as between-group factor, and FD as covariate of non-interest. The following variables were included as covariates of interest, including years of regular cocaine use (current age minus onset age of regular use), age of onset of regular cocaine use, cocaine use severity (DUDIT scores), and cocaine use per month in grams. In case of significant sex by covariate interaction effects, within-sex follow-up tests were performed.

An exploratory analysis was performed to investigate differences between cocaine cue- and emotional cue-reactivity and whether group and/or sex effects are present. To this extent, an additional RM ANOVA was performed with cocaine cue-reactivity (cocaine cues – neutral cocaine cues) and emotional cue-reactivity (negative emotional cues – neutral cues) as within subject factor, and group as between subject factor, mean activation in the ROIs as dependent variable and FD as covariate of non-interest.

To test for main and interaction effects of group, sex, and cue type outside the predefined ROIs, the first-level contrasts (one contrast for the cocaine blocks, one contrast for the neutral cocaine blocks, one contrast for the negative emotional blocks, one contrast for the neutral emotional blocks) were entered into a second-level full-factorial whole-brain model. Here, we tested for the main effect of block type (cocaine block – neutral cocaine block, and negative emotional block – negative neutral block), as well as group, sex, and group by sex differences in these effects. An exploratory analysis was performed to test for group and/or sex differences between cocaine cue (cocaine cue-neutral cocaine cues) and emotional cue (emotional cues – neutral emotional cues) reactivity. In these analyses, mean FD values for each subject were included as covariate of non-interest to account for potential motion differences. Whole-brain analyses were family-wise error (FWE) rate corrected on cluster level (p < 0.05), with an initial height threshold on voxel level of p < 0.001.

For all analyses, only the statistics for the significant results will be reported. Because we include four ROIs in our ROI analyses, we applied a Bonferroni correction to all ROI results. As such, only p values below 0.0125 are considered significant. Findings with p values between 0.0125 and 0.05, and their corresponding effect sizes will be reported as trend significant as this can provides important insight for follow-up research. The methods for analyzing the influence of menstrual phase and hormonal contraceptive use are described in the online supplementary material.

Demographics, Clinical Characteristics, and Task Performance

Four values of age of onset were missing, due to a technical failure of the data collection software. As excluding these participants from all analyses was deemed undesirable, linear regression imputation was used to replace these missing values, based on the regression between age and onset age of regular use in males and females, separately. The following formula was used for years of regular use: B (constant) + B (age) × age. This resulted in the following formulas: 4.11 + (0.69 × age) for males and 10.5 + (0.41 × age) for females.

The CU and non-CU groups did not differ in age. Yet, the CU group reported significantly higher scores on ADHD symptom severity, depressive symptoms, impulsivity, childhood trauma, and drinking severity. Moreover, there was a sex-by-group interaction effect in childhood trauma, depressive symptoms, and impulsivity, where female CUs reported significantly higher scores. Within the non-CU group, there were no sex differences in these variables. Accordingly, these clinical characteristics have been included as potential confounders in exploratory analyses of the cocaine and emotional cue-reactivity data. Of all participants, 1 non-CU female, 5 non-CU males, 6 CU females, and 6 CU males had a score above 40 on the state anxiety questionnaire, which is indicative of a clinical level of anxiety [44]. Moreover, 2 non-CU females, 1 non-CU males, 15 CU females, and 7 CU males had a score of 14 or higher on the BDI-II questionnaire, which is indicative of a clinical level of depression [33]. None of the non-CUs reported the use of GHB, cocaine, amphetamine, or methamphetamine, but some reported the use of cannabis, ketamine, N20 gas, LSD, and XTC/MDMA. An overview of all demographic and clinical data and statistics is presented in Table 1.

Table 1.

Demographic and clinical information in male (non-)cocaine users and female (non-)cocaine users

Cocaine usersControlsMain effect groupMain effect sexSex * group interaction
Males (n= 31)Females (n= 26)Males (n= 28)Females (n= 26)
Age 29.2 (6.8) 26.5 (6.9) 25.9 (5.7) 26.1 (5.1) p = 0.12, η2 = 0.02 p = 0.28, η2 = 0.01 p = 0.23, η2 = 0.01 
Educationa, n (%)     p = 0.09, x2 = 21.6 p = 0.34, x2 = 15.6  
 Elementary school 0 (0%) 1 (3.7%) 0 (0%) 0 (0%)    
 (Prevocational) secondary 0 (0%) 0 (0%) 2 (7.1%) 0 (0%)    
 Senior general/preuniversity 15 (48.4%) 13 (48.1%) 11 (39.3%) 10 (35.7%)    
 Higher professional/university 16 (51.6%) 13 (48.1%) 15 (53.6%) 18 (64.3%)    
State anxiety (STAI) 23.8 (8.4) 34.0 (7.5) 31.5 (8.0) 30.6 (6.4) p = 0.12, η2 0.02 p = 0.91, η2 < 0.01 p = 0.09, η2 = 0.03 
ADHD symptom severity (ADHD-RS) 48.0 (22.7) 50.2 (28.0) 35.8 (21.8) 27.7 (19.0) p < 0.01, η2 = 0.13 p = 0.50, η2 < 0.01 p = 0.24, η2 = 0.01 
Depression (BDI-II) 9.0 (8.6) 14.6 (8.2) 5.0 (4.7) 4.5 (5.2) p < 0.01, η2 = 0.22 p = 0.07, η2 = 0.03 p < 0.05, η2 = 0.06 
Impulsivity (BIS-11) 63.8 (10.6) 71.9 (12.6) 55.9 (8.3) 55.6 (8.9) p < 0.01, η2 = 0.27 p < 0.05, η2 = 0.04 p < 0.05, η2 = 0.04 
Childhood trauma (CTQ) 48.1 (8.3) 55.0 (15.7) 46.3 (5.7) 45.0 (4.3) p < 0.01, η2 = 0.09 p = 0.12, η2 = 0.02 p < 0.05, η2 = 0.05 
Menstrual phase/hormonal contraceptive useb, n (%) 
 Follicular phase  3 (11.5%)  6 (23.1%)    
 Luteal phase  5 (19.2%)  7 (26.9%)    
 Hormonal contraceptive user  16 (61.5%)  13 (61.5%)    
Alcohol use severity (AUDIT) 10.7 (5.8) 12.3 (3.9) 3.8 (3.2) 4.4 (3.1) p = <0.01, η2 = 0.45 p= 0.15, η2 = 0.02 p=0.49, η2< 0.01 
Substance use in past 6 months (yes) 
 GHB n = 8 n = 11 n = 0 n = 0    
 Amphetamine n = 12 n = 14 n = 0 n = 0    
 Methamphetamine n = 2 n = 4 n = 0 n = 0    
 Cannabis n = 23 n = 20 n = 4 n = 1    
 Ketamine n = 22 n = 17 n = 1 n = 0    
 N2O gas n = 19 n = 18 n = 2 n = 2    
 LSD n = 6 n = 6 n = 1 n = 0    
 XTC/MDMA n = 23 n = 23 n = 0 n = 4    
Cocaine usersControlsMain effect groupMain effect sexSex * group interaction
Males (n= 31)Females (n= 26)Males (n= 28)Females (n= 26)
Age 29.2 (6.8) 26.5 (6.9) 25.9 (5.7) 26.1 (5.1) p = 0.12, η2 = 0.02 p = 0.28, η2 = 0.01 p = 0.23, η2 = 0.01 
Educationa, n (%)     p = 0.09, x2 = 21.6 p = 0.34, x2 = 15.6  
 Elementary school 0 (0%) 1 (3.7%) 0 (0%) 0 (0%)    
 (Prevocational) secondary 0 (0%) 0 (0%) 2 (7.1%) 0 (0%)    
 Senior general/preuniversity 15 (48.4%) 13 (48.1%) 11 (39.3%) 10 (35.7%)    
 Higher professional/university 16 (51.6%) 13 (48.1%) 15 (53.6%) 18 (64.3%)    
State anxiety (STAI) 23.8 (8.4) 34.0 (7.5) 31.5 (8.0) 30.6 (6.4) p = 0.12, η2 0.02 p = 0.91, η2 < 0.01 p = 0.09, η2 = 0.03 
ADHD symptom severity (ADHD-RS) 48.0 (22.7) 50.2 (28.0) 35.8 (21.8) 27.7 (19.0) p < 0.01, η2 = 0.13 p = 0.50, η2 < 0.01 p = 0.24, η2 = 0.01 
Depression (BDI-II) 9.0 (8.6) 14.6 (8.2) 5.0 (4.7) 4.5 (5.2) p < 0.01, η2 = 0.22 p = 0.07, η2 = 0.03 p < 0.05, η2 = 0.06 
Impulsivity (BIS-11) 63.8 (10.6) 71.9 (12.6) 55.9 (8.3) 55.6 (8.9) p < 0.01, η2 = 0.27 p < 0.05, η2 = 0.04 p < 0.05, η2 = 0.04 
Childhood trauma (CTQ) 48.1 (8.3) 55.0 (15.7) 46.3 (5.7) 45.0 (4.3) p < 0.01, η2 = 0.09 p = 0.12, η2 = 0.02 p < 0.05, η2 = 0.05 
Menstrual phase/hormonal contraceptive useb, n (%) 
 Follicular phase  3 (11.5%)  6 (23.1%)    
 Luteal phase  5 (19.2%)  7 (26.9%)    
 Hormonal contraceptive user  16 (61.5%)  13 (61.5%)    
Alcohol use severity (AUDIT) 10.7 (5.8) 12.3 (3.9) 3.8 (3.2) 4.4 (3.1) p = <0.01, η2 = 0.45 p= 0.15, η2 = 0.02 p=0.49, η2< 0.01 
Substance use in past 6 months (yes) 
 GHB n = 8 n = 11 n = 0 n = 0    
 Amphetamine n = 12 n = 14 n = 0 n = 0    
 Methamphetamine n = 2 n = 4 n = 0 n = 0    
 Cannabis n = 23 n = 20 n = 4 n = 1    
 Ketamine n = 22 n = 17 n = 1 n = 0    
 N2O gas n = 19 n = 18 n = 2 n = 2    
 LSD n = 6 n = 6 n = 1 n = 0    
 XTC/MDMA n = 23 n = 23 n = 0 n = 4    

STAI, State-Trait Anxiety Inventory; ADHD-RS, Attention Deficit Hyperactivity Disorder-Rating Scale; BDI, Beck Depression Inventory II; BIS, Barratt Impulsiveness Scale; CTQ, Childhood Trauma Questionnaire; AUDIT, Alcohol Use Disorders Identification Test.

aHighest finished educational level.

bSee online supplementary material for methods.

Male and female CUs were well matched on cocaine use severity, cocaine use per month, and years of regular use. However, females reported regular use of cocaine at an earlier age than males. 77.4% of the male CUs and 88.5% of the female CUs met the DSM5 criteria for a mild, moderate, or severe CUD. Of all male CUs, 77.4% met the DSM5 criteria for a mild, moderate, or severe AUD, 29% met the DSM5 criteria for a cannabis use disorder. 51.1% of all male CUs used tobacco. Of all female CUs, 76.9% met the DSM5 criteria for an AUD, and 15.4% met the DSM5 criteria for a cannabis use disorder. 73.1% of all female CUs used tobacco. An overview of all substance use characteristics and statistics is presented in Table 2.

Table 2.

Substance use characteristics in male and female cocaine users

Cocaine usersMain effect sex
Male (n = 31)Female (n = 26)
CUD (DSM5)a   p = 0.49, φ = 0.04 
 No CUD 7 (22.6%) 3 (11.5%)  
 Mild CUD 7 (22.6%) 4 (15.4%)  
 Moderate CUD 6 (19.4%) 7 (26.9%)  
 Severe CUD 11 (35.5%) 13 (50.0%)  
Age of onset regular cocaine use 24.2 (5.3) 21.3 (4.4) p < 0.05, η2= 0.08 
Years of regular cocaine use 4.7 (4.4) 5.2 (5.3) p = 0.69, η2 < 0.01 
Cocaine use per month, g 5.9 (4.9) 3.9 (3.8) p = 0.10, η2 = 0.05 
Cocaine use severity (DUDIT) 16.9 (5.9) 16.4 (4.7) p = 0.74, η2 < 0.01 
 Range 8–29 10–28  
Alcohol use disorder (DSM5)a   p = 0.71, φ = 0.02 
 No alcohol use disorder 7 (22.6%) 6 (23.1%)  
 Mild alcohol use disorder 7 (22.6%) 3 (11.5%)  
 Moderate alcohol use disorder 7 (22.6%) 6 (23.1%)  
 Severe alcohol use disorder 10 (32.2%) 11 (42.3%)  
Cannabis use disorder (DSM5)a   p = 0.32, φ = 0.06 
 No cannabis use disorder 22 (71.0%) 22 (84.6%)  
 Mild cannabis use disorder 4 (12.9%) 1 (3.8%)  
 Moderate cannabis use disorder 4 (12.9%) 1 (3.8%)  
 Severe cannabis use disorder 1 (3.2%) 2 (7.7%)  
Tobacco user 21 (51.1%) 23 (73.1%) p = 0.34, φ = 0.01 
Nicotine Use Severity (FTND)b 2.30 (2.49) 2.81 (2.38) p = 0.50, η2 = 0.01 
 Low dependence 13 (61.9%) 11 (47.8%)  
 Mild to moderate dependence 3 (14.3%) 6 (26.1%)  
 Moderate dependence 7 (33.3%) 2 (8.7%)  
 High Dependence 0 (0%) 4 (17.4%)  
Cocaine usersMain effect sex
Male (n = 31)Female (n = 26)
CUD (DSM5)a   p = 0.49, φ = 0.04 
 No CUD 7 (22.6%) 3 (11.5%)  
 Mild CUD 7 (22.6%) 4 (15.4%)  
 Moderate CUD 6 (19.4%) 7 (26.9%)  
 Severe CUD 11 (35.5%) 13 (50.0%)  
Age of onset regular cocaine use 24.2 (5.3) 21.3 (4.4) p < 0.05, η2= 0.08 
Years of regular cocaine use 4.7 (4.4) 5.2 (5.3) p = 0.69, η2 < 0.01 
Cocaine use per month, g 5.9 (4.9) 3.9 (3.8) p = 0.10, η2 = 0.05 
Cocaine use severity (DUDIT) 16.9 (5.9) 16.4 (4.7) p = 0.74, η2 < 0.01 
 Range 8–29 10–28  
Alcohol use disorder (DSM5)a   p = 0.71, φ = 0.02 
 No alcohol use disorder 7 (22.6%) 6 (23.1%)  
 Mild alcohol use disorder 7 (22.6%) 3 (11.5%)  
 Moderate alcohol use disorder 7 (22.6%) 6 (23.1%)  
 Severe alcohol use disorder 10 (32.2%) 11 (42.3%)  
Cannabis use disorder (DSM5)a   p = 0.32, φ = 0.06 
 No cannabis use disorder 22 (71.0%) 22 (84.6%)  
 Mild cannabis use disorder 4 (12.9%) 1 (3.8%)  
 Moderate cannabis use disorder 4 (12.9%) 1 (3.8%)  
 Severe cannabis use disorder 1 (3.2%) 2 (7.7%)  
Tobacco user 21 (51.1%) 23 (73.1%) p = 0.34, φ = 0.01 
Nicotine Use Severity (FTND)b 2.30 (2.49) 2.81 (2.38) p = 0.50, η2 = 0.01 
 Low dependence 13 (61.9%) 11 (47.8%)  
 Mild to moderate dependence 3 (14.3%) 6 (26.1%)  
 Moderate dependence 7 (33.3%) 2 (8.7%)  
 High Dependence 0 (0%) 4 (17.4%)  

DSM5, Fifth edition of the Diagnostic and Statistical Manual of Mental Disorders Diagnostic; DUDIT, Drug Use Disorder Identification Test for cocaine; AUDIT, Alcohol Use Disorders Identification Test.

a2–3 symptoms are mild CUD, 4–5 symptoms are moderate CUD, and six or more symptoms are severe CUD.

bFTND = Fagerström test of nicotine dependence; mean scores are reported only for participants that indicated that they smoke. 1–2 = low dependence, 3–4 = low to moderate dependence, 5–7 = moderate dependence, 8+ = high dependence.

During the fMRI task, affect and craving were both assessed 12 times. The number of questions answered during the fMRI scan ranged from 20 (98.4%) to 24 (100%): non-CU females responded to 100% of the questions, non-CU males responded to 99.4% of all questions, CU females responded to 99.0% of all questions, and CU males responded to 98.5% of all questions. There were no group, sex, or group-by-sex interactions in task performance. FD during the fMRI task was significantly higher in CUs (0.18 ± 0.10 SD) than in non-CUs (0.12 ± 0.10 SD), but FD was not different between males or females.

Cocaine Cue-Reactivity

Subjective Effects

Cocaine cue-induced craving was significantly moderated by group (p < 0.001, η2 = 0.24) but not by sex. More specifically, cocaine cues significantly increased craving in CUs (p < 0.001, η2 = 0.46) but not in non-CUs (see Fig. 2a). There were no significant main or interaction effects between group, sex, and cue type on cocaine-cue-induced affect.

Fig. 2.

a The values represent the scores for craving and affect following the cocaine cues deducted from the scores for craving and affect following the neutral cocaine blocks. Thus, negative values reflect an increase in negative affect or a decrease in positive affect. Self-reported craving significantly increased following cocaine cues versus neutral cues in CUs (p < 0.001) but not in non-CUs (p = 0.23). There were no group or sex effects on cocaine cue-induced negative affect. There was a significant interaction effect between cocaine use severity (DUDIT) scores and cocaine cue-reactivity in the DS (p = 0.029) and amygdala (p = 0.024). b There were no main effects of cocaine cues versus neutral cues on activation in the ROIs. c Cocaine cue-induced activation of the DS was positive but nonsignificantly associated with cocaine use severity in female CUs (B = 0.33, p = 0.09), while this relationship was negative and nonsignificant in male CUs (B = −0.07, p = 0.73). d Cocaine cue-induced activation of the amygdala was positive but nonsignificantly associated with cocaine use severity in female CUs (B = 0.31, p = 0.12), while this relationship was negative and nonsignificantly in male CUs (B = −0.27, p = 0.15).

Fig. 2.

a The values represent the scores for craving and affect following the cocaine cues deducted from the scores for craving and affect following the neutral cocaine blocks. Thus, negative values reflect an increase in negative affect or a decrease in positive affect. Self-reported craving significantly increased following cocaine cues versus neutral cues in CUs (p < 0.001) but not in non-CUs (p = 0.23). There were no group or sex effects on cocaine cue-induced negative affect. There was a significant interaction effect between cocaine use severity (DUDIT) scores and cocaine cue-reactivity in the DS (p = 0.029) and amygdala (p = 0.024). b There were no main effects of cocaine cues versus neutral cues on activation in the ROIs. c Cocaine cue-induced activation of the DS was positive but nonsignificantly associated with cocaine use severity in female CUs (B = 0.33, p = 0.09), while this relationship was negative and nonsignificant in male CUs (B = −0.07, p = 0.73). d Cocaine cue-induced activation of the amygdala was positive but nonsignificantly associated with cocaine use severity in female CUs (B = 0.31, p = 0.12), while this relationship was negative and nonsignificantly in male CUs (B = −0.27, p = 0.15).

Close modal

Group and Sex Differences in Cocaine Cue-Reactivity

Cocaine cues did not induce any significant activation in any of the ROIs (i.e., DS, VS, amygdala, and dACC), nor was this moderated by group or sex (see Fig. 2b). These effects did not change upon correcting for potential confounders (i.e., ADHD, BDI, BIS, CTQ, and AUDIT for the group effects; BIS for the sex effects and BIS, BDI, and CTQ for the group-by-sex interaction effects).

Within CU Group Analyses on the Relationship Cocaine Use Characteristics and Cocaine Cue-Reactivity in the ROIs

Within the CU group, cocaine cue-reactivity was unrelated to years of regular use, onset age of regular use, cocaine use per month in grams, and cocaine use severity. However, the relationship between cocaine use severity (DUDIT score) and cocaine cue-reactivity in the DS (p = 0.029, η2 = 0.10) and amygdala (p = 0.024, η2 = 0.11) was trend-significantly moderated by sex. To interpret this interaction, follow-up regression analyses were applied within males and females separately. While the association between cocaine use severity and cocaine cue-reactivity in the DS and amygdala were not significant within sexes separately, they did indicate a positive relationship between cocaine use severity and cue-reactivity in the DS for female CUs, while this relationship was absent in male CUs (see Fig. 2c). Similarly, cocaine use severity was positively related to cocaine cue-reactivity in the amygdala in female CUs, while this relationship was negative in male CUs (see Fig. 2d).

Emotional Cue-Reactivity

Subjective Effects

On average participants reported significantly more negative affect (and thus less positive affect) (p < 0.001, η2 = 0.57) and lower craving scores (p < 0.001, η2 = 0.10) following the presentation of negative emotional cues compared to neutral cues (see Fig. 3a). Furthermore, emotional cue-induced craving was significantly moderated by group (p < 0.01, η2 = 0.10). More specifically, results demonstrated that emotional cues significantly reduced craving in CUs (p < 0.01, η2 = 0.19), but not in non-CUs. The other main and interaction effects were not significant.

Fig. 3.

a The values represent the scores for craving and affect reported following the negative emotional block deducted from the scores for craving or affect reported following the neutral emotional block. Thus, negative values reflect an increase in negative affect or a decrease in positive affect. Emotional cues significantly reduced craving in CUs (p < 0.01), but not in non-CUs. Emotional cues significantly reduced affect in both groups (p < 0.001). b There was a significant effect of emotional versus neutral cues in the DS (p = 0.01) and amygdala (p < 0.001). The relationship between years of regular cocaine use and emotional cue-reactivity in the dACC (p < 0.01) and VS (p = 0.013) was significantly moderated by sex. c Emotional cue-induced activation of the dACC was negatively associated with years of regular use in females (B = −0.47, p = 0.02), but this relationship was positive and non significant in males (B = 0.38, p = 0.08). d. Emotional cue-induced activation of the VS was negatively associated with years of regular use in female CUs (B = −0.54, p = 0.002) while this relationship was positive and nonsignificant in male CUs (B = 0.28, p = 0.20).

Fig. 3.

a The values represent the scores for craving and affect reported following the negative emotional block deducted from the scores for craving or affect reported following the neutral emotional block. Thus, negative values reflect an increase in negative affect or a decrease in positive affect. Emotional cues significantly reduced craving in CUs (p < 0.01), but not in non-CUs. Emotional cues significantly reduced affect in both groups (p < 0.001). b There was a significant effect of emotional versus neutral cues in the DS (p = 0.01) and amygdala (p < 0.001). The relationship between years of regular cocaine use and emotional cue-reactivity in the dACC (p < 0.01) and VS (p = 0.013) was significantly moderated by sex. c Emotional cue-induced activation of the dACC was negatively associated with years of regular use in females (B = −0.47, p = 0.02), but this relationship was positive and non significant in males (B = 0.38, p = 0.08). d. Emotional cue-induced activation of the VS was negatively associated with years of regular use in female CUs (B = −0.54, p = 0.002) while this relationship was positive and nonsignificant in male CUs (B = 0.28, p = 0.20).

Close modal

Group and Sex Differences in Emotional Cue-Reactivity

Mixed factorial ANOVAs investigating neural activation within the ROIs, with CU group and sex as independent factors and FD as covariate of non-interest, demonstrated that emotional cues significantly increased neural activation in the DS (p = 0.01, η2 = 0.06) and amygdala (p < 0.001, η2 = 0.22) (see Fig. 3b). Moreover, emotional cue-induced activation of the amygdala was trend-significantly stronger in females compared to males (p = 0.046, η2 = 0.04). Emotional cue-induced neural activation of the ROIs did not differ between CUs and non-CUs. These effects did not change upon correcting for potential confounders (i.e., ADHD, BDI, BIS, CTQ, and AUDIT for the group effects; BIS for the sex effects and BIS, BDI, and CTQ for the group-by-sex interaction effects).

Within CU Group Analyses on the Relationship between Cocaine Use Characteristics and Emotional Cue-Reactivity in the ROIs

The relationship between years of regular use and emotional cue-reactivity in the VS (p = 0.013, η2 = 0.13) and dACC (p < 0.01, η2 = 0.16) was significantly moderated by sex. Follow-up regression analyses demonstrated a significant negative association between years of regular cocaine use and emotional cue-induced activation of the dACC in female CUs (B = −0.47, p = 0.02), while there was a nonsignificant relationship in male CUs (see Fig. 3c). Similarly, emotional cue-reactivity in the VS was significantly and negatively related to years of regular use in female CUs (B = −0.54, p = 0.002), while this relationship was nonsignificant in male CUs (see Fig. 3d).

Exploratory Whole-Brain Analyses

Whole-brain analyses were family-wise error (FWE) rate corrected on cluster level (p < 0.05), with an initial height threshold at voxel level of p < 0.001.

Cocaine Cue-Reactivity

Cocaine cues induced significant activation in the reward network, including the bilateral medial PFC, dACC, right caudate, left amygdala, and putamen (Table 3). These effects were, however, not moderated by sex and or group, indicating that these patterns of activity were not specific for the CU group. For visualization purposes, the within-group task-effects are shown in online supplementary Figure 3.

Table 3.

Main and interaction effects of stimulus type (cocaine vs. neutral), sex, and group

Cluster pFWE correctedCluster sizePeak voxel Z-valueMNI coordinatesRegion
Cocaine cue reactivity 
Main effect stimulus type 
 Cocaine cues > neutral cues 
 <0.001 1,920 7.09 0, 64, 0 Frontal_Sup_Medial_L 
   3.93 12, 68, 6 Frontal_Sup_Medial_R 
   5.91 −2, 46, −2 Cingulum_Ant_L 
   3.72 4, 38, 10 Cingulum_Ant_R 
   5.85 −10, 66, –4 Frontal_Med_Orb_L 
   3.76 −20, 68, 14 Frontal_Sup_L 
   3.4 16, 52, 12 Frontal_Sup_R 
 <0.001 1,041 6.8 −12, −98, −2 Calcarine_L 
   5.39 18, −96, 4 Calcarine_R 
 <0.001 1,365 6.54 −2, −50, 30 Cingulum_Post_L 
   6.03 10, −52, 26 Precuneus_R 
   3.75 −6, −46, 10 Precuneus_L 
 0.007 252 5.99 64, −14, 28 Postcentral_R 
 <0.001 928 5.93 −62, −22, 36 SupraMarginal_L 
   5.12 −54, −28, 40 Parietal_Inf_L 
 0.001 344 4.5 6, 12, −4 Caudate_R 
   3.48 −14, −4, −18 ParaHippocampal_L 
   3.43 −18, −4, −16 Amygdala_L 
   3.37 −12, 10, −8 Putamen_L 
 <0.001 602 5.26 56, 26, 26 Frontal_Inf_Tri_R 
   3.26 48, 38, 34 Frontal_Mid_R 
 0.009 241 5.05 −14, −80, −32 Cerebelum_Crus2_L 
 Neutral cues > Cocaine cues: no significant clusters 
 Stimulus type * group interaction effect: no significant clusters 
 Stimulus type * sex interaction effect: no significant clusters 
 Stimulus type * sex * group interaction effect: no significant clusters 
Cluster pFWE correctedCluster sizePeak voxel Z-valueMNI coordinatesRegion
Cocaine cue reactivity 
Main effect stimulus type 
 Cocaine cues > neutral cues 
 <0.001 1,920 7.09 0, 64, 0 Frontal_Sup_Medial_L 
   3.93 12, 68, 6 Frontal_Sup_Medial_R 
   5.91 −2, 46, −2 Cingulum_Ant_L 
   3.72 4, 38, 10 Cingulum_Ant_R 
   5.85 −10, 66, –4 Frontal_Med_Orb_L 
   3.76 −20, 68, 14 Frontal_Sup_L 
   3.4 16, 52, 12 Frontal_Sup_R 
 <0.001 1,041 6.8 −12, −98, −2 Calcarine_L 
   5.39 18, −96, 4 Calcarine_R 
 <0.001 1,365 6.54 −2, −50, 30 Cingulum_Post_L 
   6.03 10, −52, 26 Precuneus_R 
   3.75 −6, −46, 10 Precuneus_L 
 0.007 252 5.99 64, −14, 28 Postcentral_R 
 <0.001 928 5.93 −62, −22, 36 SupraMarginal_L 
   5.12 −54, −28, 40 Parietal_Inf_L 
 0.001 344 4.5 6, 12, −4 Caudate_R 
   3.48 −14, −4, −18 ParaHippocampal_L 
   3.43 −18, −4, −16 Amygdala_L 
   3.37 −12, 10, −8 Putamen_L 
 <0.001 602 5.26 56, 26, 26 Frontal_Inf_Tri_R 
   3.26 48, 38, 34 Frontal_Mid_R 
 0.009 241 5.05 −14, −80, −32 Cerebelum_Crus2_L 
 Neutral cues > Cocaine cues: no significant clusters 
 Stimulus type * group interaction effect: no significant clusters 
 Stimulus type * sex interaction effect: no significant clusters 
 Stimulus type * sex * group interaction effect: no significant clusters 

MNI, Montreal Neurological Institute; FWE, family-wise error.

Emotional Cue-Reactivity

Emotional cues significantly increased activation of various regions in the mesocorticolimbic circuit, including the medial prefrontal cortex, temporal cortex, and visual cortex (Table 4; Fig. 4a). There was a significant group by stimulus type interaction effect in the right insula. More specifically, the right insula was more strongly activated by emotional cues in CUs compared to non-CUs (see Fig. 4fig4b). There was also a significant stimulus type by sex interaction effect, demonstrating that the right insula and amygdala were more strongly activated in females compared to males. However, there were no significant sex-dependent differences in the neural correlates of emotional cue-reactivity between CUs and non-CUs. For visualization purposes, the within-group task-effects are shown in online supplementary Figure 4.

Table 4.

Main and interaction effects of stimulus type (emotional vs. neutral), sex, and group

Cluster p value FWE-correctedCluster size (in voxels)Peak voxel z-valuePeak voxel MNI coordinates (x, y, z)Region
Emotional cue reactivity 
Main effect stimulus type 
 Emotional cues > neutral cues 
 <0.001 59,230 >9.99 50, −72, 12 Temporal_Mid_R 
   >9.99 44, −48, −16 Fusiform_R 
   >9.99 −38, −58, −16 Fusiform_L 
   >9.99 48, −70, −8 Temporal_Inf_R 
   >9.99 −46, 48, −20 Temporal_Inf_L 
   >9.99 42, −82, 10 Occipital_Mid_R 
   >9.99 20, −4, −14 Hippocampus_R 
   >9.99 −8, −78, −42 Cerebelum_7b_L 
 <0.001 5,589 7.09 −4, 60, 28 Frontal_Sup_Medial_L 
   6.06 8, 64, 26 Frontal_Sup_Medial_R 
   5.78 2, −6, 38 Cingulum_Mid_L 
   5.77 8, 8, 60 Supp_Motor_Area_R 
   5.28 4, 20, 26 Cingulum_Ant_R 
   4.9 0, 56, −8 Frontal_Med_Orb_L 
 <0.001 2382 6.72 −32, −6, 50 Precentral_L 
   6.64 −40, 8, 28 Frontal_Inf_Oper_L 
   5.02 −42, −14, 44 Postcentral_L 
   4.06 −20, 6, 56 Frontal_Mid_L 
 <0.001 454 4.79 6, −46, 52 Precuneus_R 
   4.65 −4, −48, 50 Precuneus_L 
 0.029 184 4.72 −54, −10, −12 Temporal_Mid_L 
Neutral cues > emotional cues 
 0.035 175 4.37 44, −50, 42 Parietal_Inf_R 
   3.65 46, −58, 40 Angular_R 
Stimulus type (emotional cues > neutral cues) * group interaction effect 
 CU > non-CU 0.017 210 4.15 38, 12, −14 Insula_R 
 Non-CU > CU No significant clusters 
Stimulus type (emotional cues > neutral cues) * sex interaction effects 
 Female > male 0.043 166 4.12 −36, 0, −12 Insula_L 
   3.81 −28, −6, −12 Amygdala_L 
Stimulus type * sex * group interaction effects: no significant clusters 
Cluster p value FWE-correctedCluster size (in voxels)Peak voxel z-valuePeak voxel MNI coordinates (x, y, z)Region
Emotional cue reactivity 
Main effect stimulus type 
 Emotional cues > neutral cues 
 <0.001 59,230 >9.99 50, −72, 12 Temporal_Mid_R 
   >9.99 44, −48, −16 Fusiform_R 
   >9.99 −38, −58, −16 Fusiform_L 
   >9.99 48, −70, −8 Temporal_Inf_R 
   >9.99 −46, 48, −20 Temporal_Inf_L 
   >9.99 42, −82, 10 Occipital_Mid_R 
   >9.99 20, −4, −14 Hippocampus_R 
   >9.99 −8, −78, −42 Cerebelum_7b_L 
 <0.001 5,589 7.09 −4, 60, 28 Frontal_Sup_Medial_L 
   6.06 8, 64, 26 Frontal_Sup_Medial_R 
   5.78 2, −6, 38 Cingulum_Mid_L 
   5.77 8, 8, 60 Supp_Motor_Area_R 
   5.28 4, 20, 26 Cingulum_Ant_R 
   4.9 0, 56, −8 Frontal_Med_Orb_L 
 <0.001 2382 6.72 −32, −6, 50 Precentral_L 
   6.64 −40, 8, 28 Frontal_Inf_Oper_L 
   5.02 −42, −14, 44 Postcentral_L 
   4.06 −20, 6, 56 Frontal_Mid_L 
 <0.001 454 4.79 6, −46, 52 Precuneus_R 
   4.65 −4, −48, 50 Precuneus_L 
 0.029 184 4.72 −54, −10, −12 Temporal_Mid_L 
Neutral cues > emotional cues 
 0.035 175 4.37 44, −50, 42 Parietal_Inf_R 
   3.65 46, −58, 40 Angular_R 
Stimulus type (emotional cues > neutral cues) * group interaction effect 
 CU > non-CU 0.017 210 4.15 38, 12, −14 Insula_R 
 Non-CU > CU No significant clusters 
Stimulus type (emotional cues > neutral cues) * sex interaction effects 
 Female > male 0.043 166 4.12 −36, 0, −12 Insula_L 
   3.81 −28, −6, −12 Amygdala_L 
Stimulus type * sex * group interaction effects: no significant clusters 

MNI, Montreal Neurological Institute; FWE, family-wise error.

Fig. 4.

a Emotional cues significantly increased activation of the salience network. The threshold of the figure is p < 0.001, uncorrected. b There was a significant group by stimulus type interaction effect in the left insula. The threshold of the figure is p < 0.001, uncorrected. NB: left is right; figure made in MRIcron.

Fig. 4.

a Emotional cues significantly increased activation of the salience network. The threshold of the figure is p < 0.001, uncorrected. b There was a significant group by stimulus type interaction effect in the left insula. The threshold of the figure is p < 0.001, uncorrected. NB: left is right; figure made in MRIcron.

Close modal

The Relationship between Cocaine Cue- and Emotional Cue-Reactivity

The ROI analyses demonstrated that the DS (F1, 106 = 2.29, p = 0.05, η2 = 0.04) and amygdala (F1, 106 = 8.15, p < 0.01, η2 = 0.07) responded significantly stronger to negative emotional cues (vs. emotional neutral cues) than to cocaine cues (vs. neutral cocaine cues), but the effects in the DS did not remain significant following a Bonferroni correction. This effect did not differ between groups and/or sexes. Similar effects were found for the whole-brain analyses, that demonstrated that negative emotional cues (vs. neutral emotional cues) elicited stronger activation compared to cocaine cues (vs. neutral cues) within the bilateral medial superior and inferior frontal gyrus as well as within the cerebellum and right middle temporal gyrus (Table 5). Negative emotional cues (vs. neutral emotional cues) elicited less activation compared to cocaine cues (vs. neutral cues) within the bilateral posterior cingulate cortex and precuneus (Table 5).

Table 5.

Association between emotional cue- and cocaine cue-reactivity

Cluster p value FWE-correctedCluster size (in voxels)Peak voxel z-valuePeak voxel MNI coordinates (x, y, z)Region
[Negative emotional cues – neutral emotional cues] > [cocaine cues – neutral cocaine cues] 
 <0.001 38,492 >0.99 54, −72, 0 Temporal_Mid_R 
   >0.99 42, −48, −18 Fusiform_R 
   >0.99 −44, −52, −18 Fusiform_L 
   >0.99 48, −82, 4 Occipital_Mid_R 
   >0.99 −8, −80, −42 Cerebelum_Crus2_L 
   >0.99 8, −78, −38 Cerebelum_Crus2_R 
   >0.99 50, 22, 26 Frontal_Inf _R 
 <0.001 1,391 6.48 −38, 8, 30 Precentral_L 
   3.45 −40, −12, 46 Postcentral_L 
   5.36 −54, 16, 28 Frontal_Inf_L 
 <0.001 1,091 4.93 6, 16, 52 Supp_Motor_Area_R 
   3.69 −6, 10, 56 Supp_Motor_Area_L 
   4.58 0, 40, 52 Frontal_Sup_Medial_L 
   3.77 6, 48, 38 Frontal_Sup_Medial_R 
[Cocaine cues – neutral cocaine cues] > [negative emotional cues – neutral emotional cues] 
 <0.001 1,391 5.32 12, −54, 30 Precuneus_R 
   4.43 −2, −64, 36 Precuneus_L 
   3.79 −4, −48, 32 Cingulum_Post_L 
   3.59 4, −44, 28 Cingulum_Post_R 
Cluster p value FWE-correctedCluster size (in voxels)Peak voxel z-valuePeak voxel MNI coordinates (x, y, z)Region
[Negative emotional cues – neutral emotional cues] > [cocaine cues – neutral cocaine cues] 
 <0.001 38,492 >0.99 54, −72, 0 Temporal_Mid_R 
   >0.99 42, −48, −18 Fusiform_R 
   >0.99 −44, −52, −18 Fusiform_L 
   >0.99 48, −82, 4 Occipital_Mid_R 
   >0.99 −8, −80, −42 Cerebelum_Crus2_L 
   >0.99 8, −78, −38 Cerebelum_Crus2_R 
   >0.99 50, 22, 26 Frontal_Inf _R 
 <0.001 1,391 6.48 −38, 8, 30 Precentral_L 
   3.45 −40, −12, 46 Postcentral_L 
   5.36 −54, 16, 28 Frontal_Inf_L 
 <0.001 1,091 4.93 6, 16, 52 Supp_Motor_Area_R 
   3.69 −6, 10, 56 Supp_Motor_Area_L 
   4.58 0, 40, 52 Frontal_Sup_Medial_L 
   3.77 6, 48, 38 Frontal_Sup_Medial_R 
[Cocaine cues – neutral cocaine cues] > [negative emotional cues – neutral emotional cues] 
 <0.001 1,391 5.32 12, −54, 30 Precuneus_R 
   4.43 −2, −64, 36 Precuneus_L 
   3.79 −4, −48, 32 Cingulum_Post_L 
   3.59 4, −44, 28 Cingulum_Post_R 

There were no significant group and or sex difference in the association between emotional cue-reactivity and cocaine cue-reactivity.

MNI, Montreal Neurological Institute; FWE, family-wise error.

Exploratory Analyses on the Influence of Menstrual Cycle and Hormonal Contraceptive Use on Cocaine and Emotional Cue-Reactivity

Exploratory analyses on the influence of menstrual cycle phase and hormonal contraceptive use did not reveal any significant effects on cocaine cue-reactivity in any of the ROIs (online suppl. Table 1; online suppl. Fig. 1). However, emotional cue-induced activation of the DS was significantly stronger in females in the luteal phase of their menstrual cycle, compared to males. On the other hand, emotional cue-induced activation of the amygdala was significantly stronger in females using hormonal contraceptives compared to males (online suppl. Table 2; online suppl. Fig. 2). It should be noted that these exploratory subgroup analyses were performed on very small sample sizes as there were 13 non-CU females and 16 CU females on hormonal contraceptives, 6 non-CU females and 3 CU females in the follicular phase of the menstrual cycle, and 7 non-CU females and 5 CU females in the luteal phase of the menstrual cycle.

In the past decade, increases in cocaine use in females have been observed in comparison to males [1]. There are indications for sex differences in clinical profiles of persons with CUD, such as psychiatric comorbidity and an accelerated progression to compulsive use in female CUs [2]. Yet, the exact neural mechanisms underlying sex differences underlying the development of CUD remain poorly understood with potential implications for the development of sex-tailored treatment strategies [7]. Accordingly, the main objective of the current study was to investigate sex differences in the neural correlates of cocaine and negative emotional cue-reactivity within regular intranasal cocaine users and non-cocaine-using controls in various ROIs, including the DS, VS, amygdala, and dACC.

In contrast to our main hypotheses, no sex-dependent differences in cocaine or emotional cue-reactivity were found when comparing CUs to non-CUs. Exploratory analyses demonstrated trend-significant sex differences in the association between cocaine use severity and cocaine cue-induced activation of the DS and amygdala. Similarly, significant sex differences were demonstrated in the relationship between years of regular use and emotional cue-induced activation of the dACC and VS.

Strikingly, no group differences in neutral cocaine cue-reactivity were found (neither in the ROIs nor other brain regions). This contradicts numerous studies that consistently demonstrated that substance-related cues induce strong activation within the mesocorticolimbic circuit, including the striatum, amygdala, and medial PFC [5]. Indeed, using the same cues in a similar population of male CUs, we did demonstrate strong activation in these brain regions [39]. An important difference between the current study and the previous study is that we applied a block design instead of an event-related design. As block designs are, however, generally associated with more robust brain responses [45], this is unlikely to explain the lack of significant cocaine cue-reactivity in CUs in the current study. Alternatively, 22.6% of the male CUs and 11.5% of the female CUs did not meet the DSM5 criteria for a CUD. As a consequence, this heterogeneity in cocaine use severity might explain the lack of group differences in cocaine cue-reactivity. A more likely explanation for not finding group differences in cocaine cue-reactivity, however, is that in the current fMRI paradigm blocks with cocaine-related cues were alternated with blocks of negative emotional cues. In support of this, various studies demonstrated that stress, aversive, or negative emotional cues attenuate striatal [46, 47], and amygdala activation during reward anticipation [48, 49]. It is suggested that this is specific to populations with psychiatric disorders [49]. Therefore, sex-dependent differences in the neural mechanisms of cocaine cue-reactivity would have been better studied using a separate task as opposed to combining negative emotional and cocaine cues in the same task. Consequently, the current findings on sex-dependent differences in the neural correlates of cocaine cue-reactivity should be taken with caution.

While the current study’s main hypotheses for cocaine cue-reactivity were not confirmed, exploratory analyses revealed interesting findings. A trend-significant sex difference in the association between cocaine use severity and cocaine cue-reactivity in the DS and amygdala was found. However, any clinical implications of the sex-dependent relationship between cocaine use severity and cocaine cue-reactivity should be taken with caution because of the (i) lack of group differences in cocaine cue-reactivity in the current study, ii) the fact that sex differences in the association between cocaine use severity and cocaine cue-reactivity findings did not remain significant following a Bonferroni correction.

Second, exploratory analyses revealed significant sex differences in the association between years of regular use and emotional cue-induced activation of the dACC and VS. Follow-up analyses demonstrated that this relationship in both brain regions was significant and negative in female CUs, but absent in male CUs. Previous research has consistently demonstrated decreased neural responses to emotional stimuli in CUD [50, 51], particularly among female CUs in the medial prefrontal cortex/ACC region [11]. This attenuated response to emotional cues is consistent with the impaired response inhibition and salience attribution model [52] in which individuals devalue non-drug-related rewards and negative stimuli that leads to risky behaviors [11] and poor treatment outcome [53]. Moreover, the VS is posited to play a pivotal role in mediating responses to aversive stimuli [53]. Presumably, this is due to the “rewarding effects” of successfully avoiding aversive or punishing events [54, 55], which have been shown to promote future behavior in animals [56]. Consistently, one human study demonstrated decreased activation of the NAc (part of the VS) during passive avoidance in response to aversive stimuli, whereas greater activation of the NAc was found during active avoidance [57]. Taken together, this indicates that female CUs with more years of cocaine use assign less salience toward negative emotional stimuli and are less able to actively avoid aversive stimuli, including the negative consequences of cocaine use, primarily due to hypoactivation in the dACC and VS, respectively. This could make female CUs more prone to risky behavior as the addiction develops with more years of use, which could account for the observed “telescoping effect” in female substance users [2].

Finally, whole-brain analyses revealed significant emotional cue-induced activation of the salience network, including the medial PFC. Moreover, emotional cue-induced activation of the right insula was significantly stronger in CUs compared to non-CUs, but no sex-dependent differences. This is inconsistent with earlier research which demonstrated greater insula activity in female CUs compared to male CUs when exposed to stress cues [12, 13]. Potentially, this could be explained by the particular representation of emotional cues, i.e., negative valence pictures in the current study versus personalized stress imagery in earlier studies [12, 13]. Indeed, personalized cues potentially maximize cue-reactivity, but they simultaneously lead to heterogeneity that limits generalizability and interpretation [58]. Lastly, whole-brain analyses demonstrated that the right insula and amygdala were more strongly activated in females compared to males in response to emotional cues, but there were no significant sex-dependent differences between CUs and non-CUs.

An important strength of the current study is its design to specifically investigate sex differences in both cocaine and emotional cue-reactivity in regular CUs and non-CUs. Specifically, the current study comprised a relatively large sample of non-treatment-seeking CUs, of which the majority met the DSM5 criteria for CUD according to a self-report questionnaire [32]. Moreover, all CUs had a score of 8 or higher on the DUDIT, which is indicative of a SUD [59]. While male and female CUs were matched on most cocaine use-related variables, female CUs were associated with earlier onset of regular cocaine use when compared to male CUs but did not differ significantly in total years of regular use or use per month (in grams). Another strength of the current study is that the negative emotional, and neutral cues were obtained from the OASIS database [38] and the cocaine cues were obtained from an earlier study [39] that facilitates replicability for future research. Lastly, exploratory analyses were performed on the influence of menstrual phase and the use of hormonal contraceptives as there is increasing evidence that fluctuating sex hormones strongly influence processes of positive and negative reinforcement [22, 23]. In line with this, our results suggest that emotional cue-induced activation of the amygdala and DS is moderated by hormonal contraceptive use and menstrual phase, thus highlighting the relevance of including measures of menstrual phase and hormonal contraceptive use in future studies.

One limitation of the current study is its cross-sectional design which precludes the ability to make any conclusions about cause and effect regarding the aforementioned relationships. Furthermore, the current study did not perform a urine screening while participants were instructed to abstain from cocaine use 24 h prior to study participation. This is important as cocaine metabolites can be detected in urine up to 6 days after last use [28], which is much longer than its psychopharmacological effects. Alternatively, the current study used the TLFB prior to the experiment which is a highly reliable method to assess cocaine use [29]. Although several studies show a high concordance between self-report and urine screening [60, 61], it remains impossible to conclude that participants were not (still) intoxicated or inebriated. Another limitation of the current study is the possible presence of withdrawal symptoms in the CUs. As the participants were not daily users, however, it is unlikely that they suffered from cocaine withdrawal. Moreover, as there were no explicit instructions to the participants to abstain from tobacco use and the whole study procedures took approximately 90 min, it is unlikely that the participants experienced withdrawal from nicotine. However, as we did not explicitly test for the presence of withdrawal symptoms, we cannot exclude this possibility. Moreover, analysis based on the clinical cutoff values for the STAI and BDI-II, however, do indicate that of the non-CU group, 6 reported clinical relevant levels of anxiety and 3 reported clinical relevant levels of depression. However, we did not perform a structured clinical interview to diagnose the presence of psychiatric disorders; we cannot exclude the possibility that some of the participants in either the non-drug-using control or CU group, had a psychiatric disorder. Finally, a potential limitation of the applied fMRI task was the relatively short resting period in between blocks. While the periods between blocks in the fMRI paradigm were approximately 12.5 s, they also included the rating of arousal and craving. Hence, these blocks cannot be considered actual resting blocks. This could have contributed to a carryover effect of the different blocks, potentially explaining the lack of significant effects of the cocaine versus neutral cocaine blocks in the CU group. In future, research either longer resting periods between blocks should be applied, or the drug and emotional cue-reactivity paradigms should be applied in different tasks, to circumvent possible carryover effects of reward and emotional cues [58].

To further establish the current findings, future research should take into account duration and severity of use when conducting cue-reactivity paradigms by performing longitudinal and prospective research (e.g., ecological momentary assessments) to gain a better understanding of sex-dependent trajectories in positive and negative reinforcement within the development of CUD. Moreover, understanding sex differences in the neural mechanisms underlying CUD is clinically significant because of potential differential treatment effects in both males and females [7]. For instance, guanfacine is able to elicit a greater reduction in stress- and cue-mediated cocaine craving in female CUs [62] due to improvements in cognitive control [63]. Moreover, oxytocin has shown promise in diminishing the stress response (i.e., less amygdala activation) to cocaine cues in male CUs with a history of childhood trauma, whereas it increased the stress response in female CUs with childhood trauma [64].

In conclusion, the current study found no significant sex differences in cocaine and emotional cue-reactivity in regular CUs. Exploratory analyses did demonstrate significant sex differences in the association between cocaine use severity and cocaine-cue-induced activation of the amygdala and DS. Yet, these findings should be taken with caution due to the lack of significant cue-induced activation of the ROIs in CUs compared to non-CUs. Importantly, years of regular cocaine use were shown to be associated with emotional cue-reactivity in female CUs, but not in male CUs, which may underlie the telescoping effect that has been reported in female substance users. It is important to keep improving our understanding of sex differences in the development of CUD due to its implications for treatment efficacy in both males and females.

The authors would like to thank Nutsa Nanuashvili, Daantje de Bruin, and Annel Koomen for their assistance in data collection. The authors would like to thank Eila Stipriaan, Nolan Barret, and Tetyana Andriyivna Pryshchepova for their assistance in data analyses.

The current study was approved by the Ethics Review Board of the Faculty of Social and Behavioral Sciences, University of Amsterdam (ERB number: 2019-DP-9964). For the screening procedure, participants provided online consent. On the research-testing day, all participants provided written informed consent for study participation.

The authors have no conflicts of interest to declare.

The project was funded by an Amsterdam Brain and Cognition Talent Grant. The funder had no role in the design, data collection, data analysis, and reporting of this study.

A.M.K. was responsible for the study concept, design, data acquisition, analysis. S.T. was responsible for preparing the first draft of the manuscript. A.M.K. and A.E.G. provided critical revision of the manuscript for important intellectual content. E.G.S. was responsible for the supplement. All authors critically reviewed content and approved final version for publication.

The data that support the findings of this study are not publicly available due to privacy reasons but are available from the corresponding author upon request. A preprint version of this article is available on Authorea [64].

1.
Mustaquim
D
,
Jones
CM
,
Compton
WM
.
Trends and correlates of cocaine use among adults in the United States, 2006–2019
.
Addict Behav
.
2021
;
120
:
106950
.
2.
Fonseca
F
,
Robles-Martínez
M
,
Tirado-Muñoz
J
,
Alías-Ferri
M
,
Mestre-Pintó
JI
,
Coratu
AM
, et al
.
A gender perspective of addictive disorders
.
Curr Addict Rep
.
2021
;
8
(
1
):
89
99
.
3.
Griffin
ML
,
Weiss
RD
,
Mirin
SM
,
Lange
U
.
A comparison of male and female cocaine abusers
.
Arch Gen Psychiatry
.
1989
;
46
(
2
):
122
6
.
4.
Rounsaville
BJ
,
Anton
SF
,
Carroll
K
,
Budde
D
,
Prusoff
BA
,
Gawin
F
.
Psychiatric diagnoses of treatment-seeking cocaine abusers
.
Arch Gen Psychiatry
.
1991
;
48
(
1
):
43
51
.
5.
Courtney
KE
,
Schacht
JP
,
Hutchison
K
,
Roche
DJO
,
Ray
LA
.
Neural substrates of cue reactivity: association with treatment outcomes and relapse
.
Addict Biol
.
2015
;
21
(
1
):
3
22
.
6.
Nicolas
C
,
Zlebnik
NE
,
Farokhnia
M
,
Leggio
L
,
Ikemoto
S
,
Shaham
Y
.
Sex differences in opioid and psychostimulant craving and relapse: a critical review
.
Pharmacol Rev
.
2022
;
74
(
1
):
119
40
.
7.
Orsini
CA
,
Brown
TE
,
Hodges
TE
,
Alonso-caraballo
Y
,
Winstanley
CA
,
Becker
JB
.
Neural mechanisms mediating sex differences in motivation for reward : cognitive bias, food, gambling, and drugs of abuse
.
J Neurosci
.
2022
;
42
(
45
):
8477
87
.
8.
Kühn
S
,
Gallinat
J
.
Common biology of craving across legal and illegal drugs – a quantitative meta-analysis of cue-reactivity brain response
.
Eur J Neurosci
.
2011
;
33
(
7
):
1318
26
.
9.
Zeng
J
,
Yu
S
,
Cao
H
,
Su
Y
,
Dong
Z
,
Yang
X
.
Neurobiological correlates of cue-reactivity in alcohol-use disorders: a voxel-wise meta-analysis of fMRI studies
.
Neurosci Biobehav Rev
.
2021
;
128
(
May
):
294
310
.
10.
McTeague
LM
,
Rosenberg
BM
,
Lopez
JW
,
Carreon
DM
,
Huemer
J
,
Jiang
Y
, et al
.
Identification of common neural circuit disruptions in emotional processing across psychiatric disorders
.
Physiol Behav
.
2020
;
177
(
5
):
411
21
.
11.
Canterberry
M
,
Peltier
MR
,
Brady
KT
,
Hanlon
CA
.
Attenuated neural response to emotional cues in cocaine-dependence: a preliminary analysis of gender differences
.
Am J Drug Alcohol Abuse
.
2016
;
42
(
5
):
577
86
.
12.
Li
C-SR
,
Kosten
TR
,
Sinha
R
.
Sex differences in brain activation during stress imagery in abstinent cocaine users: a functional magnetic resonance imaging study
.
Biol Psychiatry
.
2005
;
57
(
5
):
487
94
.
13.
Potenza
MN
,
Hong
KIA
,
Lacadie
CM
,
Fulbright
RK
,
Tuit
KL
,
Sinha
R
.
Neural correlates of stress-induced and cue-induced drug craving: influences of sex and cocaine dependence
.
Am J Psychiatry
.
2012
;
169
(
4
):
406
14
.
14.
Volkow
ND
,
Tomasi
D
,
Wang
G-J
,
Fowler
JS
,
Telang
F
,
Goldstein
RZ
, et al
.
Reduced metabolism in brain “control networks” following cocaine-cues exposure in female cocaine abusers
.
PLoS One
.
2011
;
6
(
2
):
e16573
.
15.
Smith
K
,
Lacadie
CM
,
Milivojevic
V
,
Fogelman
N
,
Sinha
R
.
Sex differences in neural responses to stress and drug cues predicts future drug use in individuals with substance use disorder
.
Drug Alcohol Depend
.
2023
;
244
(
January
):
109794
.
16.
Kilts
CD
,
Gross
RE
,
Ely
TD
,
Drexler
KPG
.
The neural correlates of cue-induced craving in cocaine-dependent women
.
Am J
.
2004
;
161
(
2
):
233
41
.
17.
Peltier
MR
,
Verplaetse
TL
,
Mineur
YS
,
Petrakis
IL
,
Cosgrove
KP
,
Picciotto
MR
, et al
.
Sex differences in stress-related alcohol use
.
Neurobiol Stress
.
2019
;
10
(
February
):
100149
.
18.
Kaag
AM
,
Wiers
RW
,
de Vries
TJ
,
Pattij
T
,
Goudriaan
AE
.
Striatal alcohol cue-reactivity is stronger in male than female problem drinkers
.
Eur J Neurosci
.
2018
;
50
(
3
):
2264
73
.
19.
Sawyer
KS
,
Maleki
N
,
Urban
T
,
Marinkovic
K
,
Karson
S
,
Ruiz
SM
, et al
.
Alcoholism gender differences in brain responsivity to emotional stimuli
.
Elife
.
2019
;
8
:
e41723
28
.
20.
Gerhardt
S
,
Hoffmann
S
,
Tan
H
,
Gerchen
MF
,
Kirsch
P
,
Vollstädt-Klein
S
, et al
.
Neural cue reactivity is not stronger in male than in female patients with alcohol use disorder
.
Front Behav Neurosci
.
2022
;
16
(
November
):
1
11
.
21.
Goldstein
RZ
,
Volkow
ND
.
Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications
.
Nat Rev Neurosci
.
2011
;
12
(
11
):
652
69
.
22.
Voorhees
EE
,
Mitchel
JT
,
McClernon
FJ
,
Beckham
JC
,
Kollins
SH
.
Sex, ADHD symptoms, and smoking outcomes: an integrative model
.
Psychiatr Serv
.
2012
;
65
(
12
):
1465
73
.
23.
Kokane
SS
,
Perrotti
LI
.
Sex differences and the role of estradiol in mesolimbic reward circuits and vulnerability to cocaine and opiate addiction
.
Front Behav Neurosci
.
2020
;
14
(
May
):
74
21
.
24.
Malek
GSA
,
Goudriaan
AE
,
Kaag
AM
.
The relationship between craving and insular morphometry in regular cocaine users : does sex matter
.
2022
. p.
1
10
.
25.
Cousijn
J
,
Ridderinkhof
KR
,
Kaag
AM
.
Sex-dependent prefrontal cortex activation in regular cocaine users: a working memory functional magnetic resonance imaging study
.
Addict Biol
.
2021
;
26
(
5
):
e13003
11
.
26.
van Dam
D
,
Ehring
T
,
Vedel
E
,
Emmelkamp
PMG
.
Screening for posttraumatic stress disorder in civilian substance use disorder patients: cross-validation of the Jellinek-PTSD screening questionnaire
.
J Subst Abuse Treat
.
2013
;
44
(
1
):
126
31
.
27.
Saunders
JB
,
Aasland
OG
,
Babor
TF
,
De La Fuente
JR
,
Grant
M
.
Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption: II
.
Addiction
.
1993
;
88
(
6
):
791
804
.
28.
Preston
KL
,
Epstein
DH
,
Cone
EJ
,
Wtsadik
AT
,
Huestis
MA
,
Moolchan
ET
.
Urinary elimination of cocaine metabolites in chronic cocaine users during cessation
.
J Anal Toxicol
.
2002
;
26
(
7
):
393
400
.
29.
Robinson
SM
,
Sobell
LC
,
Sobell
MB
,
Leo
GI
.
Reliability of the Timeline Followback for cocaine, cannabis, and cigarette use
.
Psychol Addict Behav
.
2014
;
28
(
1
):
154
62
.
30.
Berman
AH
,
Bergman
H
,
Palmstierna
T
,
Schlyter
F
.
Evaluation of the Drug Use Disorders Identification Test (DUDIT) in criminal justice and detoxification settings and in a Swedish population sample
.
Eur Addict Res
.
2005
;
11
(
1
):
22
31
.
31.
Sheehan
DV
,
Lecrubier
Y
,
Harnett Sheehan
K
,
Janavs
J
,
Weiller
E
,
Keskiner
A
, et al
.
The validity of the mini international neuropsychiatric interview (MINI) according to the SCID-P and its reliability
.
Eur Psychiatry
.
1997
;
12
(
5
):
232
41
.
32.
Hallgren
KA
,
Matson
TE
,
Oliver
M
,
Caldeiro
RM
,
Kivlahan
DR
,
Bradley
KA
.
Practical assessment of DSM-5 alcohol use disorder criteria in routine care: high test-retest reliability of an Alcohol Symptom Checklist
.
Alcohol Clin Exp Res
.
2022
;
46
(
3
):
458
67
.
33.
Beck
AT
,
Ward
CH
,
Mendelson
M
,
Mock
J
,
Erbaugh
J
.
An inventory for measuring depression
.
Arch Gen Psychiatry
.
1961
;
4
(
6
):
561
71
.
34.
Bados
A
,
Gómez-benito
J
.
The state-trait anxiety inventory, trait version: does it really measure anxiety? The state-trait anxiety inventory, trait version – does it really measure anxiety
.
J Pers
.
2010
;
92
(
6
):
37
41
.
35.
Sandra Kooij
JJ
,
Marije Boonstra
A
,
Swinkels
SHN
,
Bekker
EM
,
de Noord
I
,
Buitelaar
JK
.
Reliability, validity, and utility of instruments for self-report and informant report concerning symptoms of ADHD in adult patients
.
J Atten Disord
.
2008
;
11
(
4
):
445
58
.
36.
Bernstein
DP
,
Stein
JA
,
Newcomb
MD
,
Walker
E
,
Pogge
D
,
Ahluvalia
T
, et al
.
Development and validation of a brief screening version of the Childhood Trauma Questionnaire
.
Child Abuse Negl
.
2003
;
27
(
2
):
169
90
.
37.
Patton
JH
,
Stanford
MS
,
Barratt
ES
.
Factor structure of the Barratt impulsiveness scale
.
J Clin Psychol
.
1995
;
51
(
6
):
768
74
.
38.
Kurdi
B
,
Lozano
S
,
Banaji
MR
.
Introducing the open affective standardized image set (OASIS)
.
Behav Res Methods
.
2017
;
49
(
2
):
457
70
.
39.
Kaag
AM
,
Reneman
L
,
Homberg
JR
,
Van Den Brink
W
,
van Wingen
GA
.
Enhanced amygdala-striatal functional connectivity during the processing of cocaine cues in male cocaine users with a history of childhood trauma
.
Front Psychiatry
.
2018
;
9
(
70
):
70
13
.
40.
Esteban
O
,
Markiewicz
CJ
,
Blair
RW
,
Moodie
CA
,
Isik
AI
,
Erramuzpe
A
, et al
.
fMRIPrep: a robust preprocessing pipeline for functional MRI
.
Nat Methods
.
2019
;
16
(
1
):
111
6
.
41.
Pruim
RHR
,
Mennes
M
,
van Rooij
D
,
Llera
A
,
Buitelaar
JK
,
Beckmann
CF
.
ICA-AROMA: a robust ICA-based strategy for removing motion artifacts from fMRI data
.
Neuroimage
.
2015
;
112
:
267
77
.
42.
Tzourio-Mazoyer
N
,
Landeau
B
,
Papathanassiou
D
,
Crivello
F
,
Etard
O
,
Delcroix
N
, et al
.
Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain
.
Neuroimage
.
2002
;
15
(
1
):
273
89
.
43.
Power
JD
,
Barnes
KA
,
Snyder
AZ
,
Schlaggar
BL
,
Petersen
SE
.
Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion
.
Neuroimag
.
2012
;
59
(
3
):
2142
54
.
44.
Ercan
I
,
Hafizoglu
S
,
Ozkaya
G
,
Kirli
S
,
Yalcintas
E
,
Akaya
C
.
Examining cut-off values for the state-trait anxiety inventory
.
Rev Argentina Clin Psicol
.
2015
;
24
:
143
8
.
45.
Jasinska
AJ
,
Stein
EA
,
Kaiser
J
,
Naumer
MJ
,
Yalachkov
Y
.
Factors modulating neural reactivity to drug cues in addiction: a survey of human neuroimaging studies
.
Neurosci Biobehav Rev
.
2014
;
38
:
1
16
.
46.
Kaag
AM
,
Schluter
RS
,
Karel
P
,
Homberg
J
,
van den Brink
W
,
Reneman
L
, et al
.
Aversive counterconditioning attenuates reward signaling in the ventral striatum
.
Front Hum Neurosci
.
2016
;
10
(
August
):
418
0
.
47.
Kumar
P
,
Berghorst
LH
,
Nickerson
LD
,
Dutra
SJ
,
Goer
FK
,
Greve
DN
, et al
.
Differential effects of acute stress on anticipatory and consummatory phases of reward processing
.
Neuroscience
.
2014
;
266
:
1
12
.
48.
Berghorst
LH
,
Kumar
P
,
Greve
DN
,
Deckersbach
T
,
Ongur
D
,
Dutra
SJ
, et al
.
Stress and reward processing in bipolar disorder: a functional magnetic resonance imaging study
.
Bipolar Disord
.
2016
;
18
(
7
):
602
11
.
49.
van Leeuwen
JMC
,
Vink
M
,
Joëls
M
,
Kahn
RS
,
Hermans
EJ
,
Vinkers
CH
.
Increased responses of the reward circuitry to positive task feedback following acute stress in healthy controls but not in siblings of schizophrenia patients
.
Neuroimage
.
2019
;
184
:
547
54
.
50.
Asensio
S
,
Romero
MJ
,
Sanchez
A
,
Senabre
I
,
Morales
JL
,
Carcelen
R
, et al
.
Altered neural response of the appetitive emotional system in cocaine addiction : an fMRI Study
;
2010
; p.
504
16
.
51.
Goldstein
RZ
,
Alia-Klein
N
,
Tomasi
D
,
Carrillo
JH
,
Maloney
T
,
Woicik
PA
, et al
.
Anterior cingulate cortex hypoactivations to an emotionally salient task in cocaine addiction
.
Proc Natl Acad Sci U S A
.
2009
;
106
(
23
):
9453
8
.
52.
Ceceli
AO
,
Bradberry
CW
,
Goldstein
RZ
.
The neurobiology of drug addiction: cross-species insights into the dysfunction and recovery of the prefrontal cortex
.
Neuropsychopharmacology
.
2022
;
47
(
1
):
276
91
.
53.
Konova
AB
,
Moeller
SJ
,
Goldstn
RZ
.
Common and distinct neural targets of treatment: changing brain function in substance addiction
.
Bone
.
2008
;
23
(
1
):
1
7
.
54.
Kim
H
,
Shimojo
S
,
O’Doherty
JP
.
Is avoiding an aversive outcome rewarding? Neural substrates of avoidance learning in the human brain
.
PLoS Biol
.
2006
;
4
(
8
):
e233
.
55.
Oleson
EB
,
Gentry
RN
,
Chioma
VC
,
Cheer
JF
.
Subsecond dopamine release in the nucleus accumbens predicts conditioned punishment and its successful avoidance
.
J Neurosci
.
2012
;
32
(
42
):
14804
8
.
56.
Wenzel
JM
,
Rauscher
NA
,
Cheer
JF
,
Oleson
EB
.
A role for phasic dopamine release within the nucleus accumbens in encoding aversion: a review of the neurochemical literature
.
ACS Chem Neurosci
.
2015
;
6
(
1
):
16
26
.
57.
Levita
L
,
Hoskin
R
,
Champi
S
.
Avoidance of harm and anxiety: a role for the nucleus accumbens
.
Neuroimage
.
2012
;
62
(
1
):
189
98
.
58.
Ekhtiari
H
,
Zare-Bidoky
M
,
Sangchooli
A
,
Janes
AC
,
Kaufman
MJ
,
Oliver
JA
, et al
.
A methodological checklist for fMRI drug cue reactivity studies: development and expert consensus
.
Nat Protoc
.
2022
;
17
(
3
):
567
95
.
59.
Voluse
AC
,
Gioia
CJ
,
Sobell
LC
,
Dum
M
,
Sobell
MB
,
Simco
ER
.
Psychometric properties of the Drug Use Disorders Identification Test (DUDIT) with substance abusers in outpatient and residential treatment
.
Addict Behav
.
2012
;
37
(
1
):
36
41
.
60.
Darke
S
.
Self-report among injecting drug users: a review
.
Drug Alcohol Depend
.
1998
;
51
(
3
):
253
68
.
61.
Wilcox
CE
,
Bogenschutz
MP
,
Nakazawa
M
,
Woody
G
.
Concordance between self-report and urine drug screen data in adolescent opioid dependent clinical trial participants
.
Addict Behav
.
2013
;
38
(
10
):
2568
74
.
62.
Fox
HC
,
Morgan
PT
,
Sinha
R
.
Sex differences in guanfacine effects on drug craving and stress arousal in cocaine-dependent individuals
.
Neuropsychopharmacology
.
2014
;
39
(
6
):
1527
37
.
63.
Milivojevic
V
,
Fox
HC
,
Jayaram-Lindstrom
N
,
Hermes
G
,
Sinha
R
.
Sex differences in guanfacine effects on stress-induced stroop performance in cocaine dependence
.
Drug Alcohol Depend
.
2017
;
179
:
275
9
.
64.
Joseph
JE
,
McRae-Clark
A
,
Sherman
BJ
,
Baker
NL
,
Moran-Santa Maria
M
,
Brady
KT
.
Neural correlates of oxytocin and cue reactivity in cocaine-dependent men and women with and without childhood trauma
.
Psychopharmacology
.
2019
;
237
:
249
61
.
65.
Tap
S
,
Stipriaan
E
,
Goudriaan
A
,
Kaag
AM
.
Sex-dependent differences in the neural correlates of cocaine and emotional cue-reactivity in Regular cocaine users and non-drug using controls : understanding the role of duration and severity of use
.
Authorea
.
2023
:
1
20
.