Authors:
Charlotte MarkertDepartment of Psychotherapy and Systems Neuroscience, University of Giessen, Germany
Bender Institute of Neuroimaging (BION), University of Giessen, Germany
Center of Mind, Brain and Behavior, Universities of Marburg and Giessen, Germany

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Sanja KleinDepartment of Psychotherapy and Systems Neuroscience, University of Giessen, Germany
Bender Institute of Neuroimaging (BION), University of Giessen, Germany
Center of Mind, Brain and Behavior, Universities of Marburg and Giessen, Germany

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Jana StrahlerDepartment of Psychotherapy and Systems Neuroscience, University of Giessen, Germany
Bender Institute of Neuroimaging (BION), University of Giessen, Germany

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Onno KruseDepartment of Psychotherapy and Systems Neuroscience, University of Giessen, Germany
Bender Institute of Neuroimaging (BION), University of Giessen, Germany
Department of Clinical Psychology, University of Siegen, Germany

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Rudolf StarkDepartment of Psychotherapy and Systems Neuroscience, University of Giessen, Germany
Bender Institute of Neuroimaging (BION), University of Giessen, Germany
Center of Mind, Brain and Behavior, Universities of Marburg and Giessen, Germany

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Abstract

Background and aims

The use of pornography, while unproblematic for the majority, can grow into addiction-like behavior which in its extreme form is labeled as compulsive sexual behavioral disorder in the ICD-11 (WHO, 2018). The aim of this study was to investigate the addiction-specific reactivity to cues in order to better understand underlying mechanisms in the development of this disorder.

Methods

We have used an optimized Sexual Incentive Delay Task to study brain activity in reward associated brain areas during an anticipation phase (with cues predicting pornographic videos, control videos or no videos) and a corresponding delivery phase in healthy men. Correlations to indicators of problematic pornography use, the time spent on pornography use, and trait sexual motivation were analyzed.

Results

The results of 74 men showed that reward-related brain areas (amygdala, dorsal cingulate cortex, orbitofrontal cortex, nucleus accumbens, thalamus, putamen, caudate nucleus, and insula) were significantly more activated by both the pornographic videos and the pornographic cues than by control videos and control cues, respectively. However, we found no relationship between these activations and indicators of problematic pornography use, time spent on pornography use, or with trait sexual motivation.

Discussion and conclusions

The activity in reward-related brain areas to both visual sexual stimuli as well as cues indicates that optimization of the Sexual Incentive Delay Task was successful. Presumably, associations between reward-related brain activity and indicators for problematic or pathological pornography use might only occur in samples with increased levels and not in a rather healthy sample used in the present study.

Abstract

Background and aims

The use of pornography, while unproblematic for the majority, can grow into addiction-like behavior which in its extreme form is labeled as compulsive sexual behavioral disorder in the ICD-11 (WHO, 2018). The aim of this study was to investigate the addiction-specific reactivity to cues in order to better understand underlying mechanisms in the development of this disorder.

Methods

We have used an optimized Sexual Incentive Delay Task to study brain activity in reward associated brain areas during an anticipation phase (with cues predicting pornographic videos, control videos or no videos) and a corresponding delivery phase in healthy men. Correlations to indicators of problematic pornography use, the time spent on pornography use, and trait sexual motivation were analyzed.

Results

The results of 74 men showed that reward-related brain areas (amygdala, dorsal cingulate cortex, orbitofrontal cortex, nucleus accumbens, thalamus, putamen, caudate nucleus, and insula) were significantly more activated by both the pornographic videos and the pornographic cues than by control videos and control cues, respectively. However, we found no relationship between these activations and indicators of problematic pornography use, time spent on pornography use, or with trait sexual motivation.

Discussion and conclusions

The activity in reward-related brain areas to both visual sexual stimuli as well as cues indicates that optimization of the Sexual Incentive Delay Task was successful. Presumably, associations between reward-related brain activity and indicators for problematic or pathological pornography use might only occur in samples with increased levels and not in a rather healthy sample used in the present study.

Intoduction

Internet pornography use is a very widespread behavior in the general population (Blais-Lecours, Vaillancourt-Morel, Sabourin, & Godbout, 2016; Bőthe, Tóth-Király, Potenza, Orosz, & Demetrovics, 2020; Martyniuk, Okolski, & Dekker, 2019). While the vast majority shows unproblematic pornography use, in a few individuals it is accompanied by distress, a perceived lack of control, and the inability to reduce the behavior in spite of negative consequences (around 8%, depending on the criteria used; Cooper, Scherer, Boies, & Gordon, 1999; Gola, Lewczuk, & Skorko, 2016; Grubbs, Volk, Exline, & Pargament, 2015). Pornography use accompanied by masturbation is the most common problematic behavior among individuals with compulsive sexual behaviors (Kraus, Voon, & Potenza, 2016; Reid et al., 2012; Wordecha et al., 2018). For the first time, the World Health Organization (WHO) has defined specific diagnostic criteria for these symptoms in the 11th edition of the International Classification of Disorders (ICD-11) under the term Compulsive Sexual Behavior Disorder (CSBD, World Health Organization, 2018). For a better understanding of both recreational and problematic porn use, its neurobiological underpinnings must be elucidated.

Although the correct classification of problematic pornography use is a controversial topic, neuroscientific findings suggest its proximity to addiction disorders (Love, Laier, Brand, Hatch, & Hajela, 2015; Stark, Klucken, Potenza, Brand, & Strahler, 2018). Robinson and Berridge described in their Incentive Sensitization Theory for the development of addictions how repeated drug exposure leads to neuroadaptive changes within the reward circuits (Robinson & Berridge, 1993, 2008). During addiction development, the response to cues (“wanting”) increases while the desired effect of drug intake (“liking”) might even decrease. Therefore, cue reactivity which encompasses the emotional, behavioral, physiological and cognitive response to addiction-related stimuli (Berridge & Robinson, 2016; Tiffany & Wray, 2012) is an important concept to explain the transition from occasional use of a drug to addictive use (Brand et al., 2019; Koob & Volkow, 2010; Volkow, Koob, & McLellan, 2016).

Studies on patients with diverse substance-related disorders have found increased reactivity in the ventral striatum, the dorsal striatum, the anterior cingulate cortex (ACC), the orbitofrontal cortex (OFC), the insula and the amygdala to substance-related cues (Jasinska, Stein, Kaiser, Naumer, & Yalachkov, 2014; Kühn & Gallinat, 2011a; Stippekohl et al., 2010; Zilverstand, Huang, Alia-Klein, & Goldstein, 2018). With regard to behavioral addictions, there are several reviews that demonstrate increased activity in reward-associated regions to addiction-related cues (Antons, Brand, & Potenza, 2020; Fauth-Bühler, Mann, & Potenza, 2017; Starcke, Antons, Trotzke, & Brand, 2018; Van Holst, van den Brink, Veltman, & Goudriaan, 2010). Whether the processes involved in CSBD resemble those of substance use disorders and behavioral addictions is still a matter of scientific debate.

Several reviews show increased activity of the ventral and the dorsal striatum, OFC, ACC, insula, caudate nucleus, putamen, amygdala, thalamus, and hypothalamus in healthy participants when looking at visual sexual stimuli (VSS) compared to neutral stimuli (Georgiadis & Kringelbach, 2012; Poeppl, Langguth, Laird, & Eickhoff, 2014; Stoléru, Fonteille, Cornélis, Joyal, & Moulier, 2012). In addition, there are studies on neural responses to cues which predict VSS but do not contain any sexual content (e.g., Banca et al., 2016: colored patterns; Klucken, Wehrum-Osinsky, Schweckendiek, Kruse, & Stark, 2016: colored squares; Stark et al., 2019: category-describing terms). The brain responses to these cues preceding VSS (Banca et al., 2016; Klucken et al., 2016; Stark et al., 2019) were similar to responses to VSS (ventral striatum, OFC, occipital cortex, insula, putamen, thalamus). Moreover, persons with problematic pornography use (PPU) compared to control participants showed an increased amygdala reactivity to geometric figures associated with VSS (Klucken et al., 2016). Using VSS as cues, Voon et al. (2014) found higher responses in the dorsal anterior cingulate, ventral striatum and amygdala of persons with PPU. These findings of an increased reactivity towards cues predicting VSS in persons with PPU are in line with the expectations drawn from Incentive Sensitization Theory.

To study the development of addiction, the Monetary Incentive Delay Task (MIDT) is an established instrument to investigate altered neural responses to cues and stimuli (Balodis & Potenza, 2015). The MIDT starts with an anticipatory phase in which cues signal whether a monetary win or loss is possible during the succeeding delivery phase. Originally, this task was used to assess the general reward sensitivity in addiction with, however, inconsistent results regarding the anticipatory and the delivery phase (Balodis & Potenza, 2015; Beck et al., 2009; Bustamante et al., 2014; Jia et al., 2011; Nestor, Hester, & Garavan, 2010). To examine cue reactivity in PPU, a modified version of the established MIDT (Knutson, Fong, Adams, Varner, & Hommer, 2001; Knutson, Westdorp, Kaiser, & Hommer, 2000) was proposed: The Sexual Incentive Delay Task (SIDT) using sexual cues and rewards. Three studies have employed incentive delay tasks with sexual cues and rewards so far (Gola et al., 2017; Sescousse, Li, & Dreher, 2015; Sescousse, Redouté, & Dreher, 2010). Sescousse and colleagues investigated differential activity patterns regarding erotic and monetary rewards in healthy adults and identified the posterior part of the OFC and the amygdala as regions specifically activated by erotic rewards (Sescousse et al., 2010). Gola and colleagues (2017) compared men with PPU and control men with regard to their brain activity to a mixed MIDT/SIDT. Whereas PPU participants showed increased activity in the ventral striatum for cues predicting sexual rewards, they did not differ from controls regarding the brain activity to sexual rewards. Consistent with the Incentive Sensitization Theory, the authors argued for an increased “wanting” of sexual rewards in PPU participants while the “liking” of sexual stimuli remains unaffected.

Although prior studies using SIDT are highly promising regarding the examination of cue reactivity towards sexual cues and rewards in healthy persons and persons with PPU, there are some methodological aspects which have to be discussed. Regarding the external validity, previous studies used static images instead of videos, although the latter are the most widely used form of pornography (Solano, Eaton, & O'Leary, 2020). Concerning the control condition, former studies used scrambled versions of VSS as control conditions (Gola et al., 2017; Sescousse et al., 2010, 2015). Consequently, the experimental and control conditions differed with regard to several characteristics (naturalistic setting vs. abstract patterns, image resolution, human portrayal vs. non-human portrayal). It is questionable if these stimuli represent optimal control stimuli. Moreover, the researchers used pictograms of naked women as cues. In this way the cues could not only have a predictive value, but also represent sexual content. Further, it would be helpful to investigate the influence of risk factors for the development of a CSBD, where the following appear to be the most relevant: self-reported problems concerning pornography use (Brand, Snagowski, Laier, & Maderwald, 2016; Laier, Pawlikowski, Pekal, Schulte, & Brand, 2013), time spent watching pornography (Kühn & Gallinat, 2014) and trait sexual motivation (Baranowski, Vogl, & Stark, 2019; Kagerer et al., 2014; Klucken et al., 2016; Stark et al., 2018; Strahler, Kruse, Wehrum-Osinsky, Klucken, & Stark, 2018).

Therefore, the aims of the present study were the following: (1) We wanted to establish an optimized SIDT using film clips instead of static images. We expected the activity patterns during the anticipation phase and the delivery phase to be similar to results in former studies showing the involvement of ACC, OFC, thalamus, insula, amygdala, nucleus accumbens (NAcc), caudate, and putamen. (2) We wanted to investigate the extent to which risk factors for CSBD (self-reported PPU, time spent on pornography use, and trait sexual motivation) are connected to neural activity during the anticipation phase and the delivery phase in a non-clinical sample. According to the Incentive Sensitization Theory of Robinson and Berridge (1993), we expected the neural activity of the above-mentioned brain regions during the anticipation phase of the SIDT to be positively correlated with these risk factors. In accordance with the study of Gola et al. (2017), we expected the neural activity of the above-mentioned regions during the delivery phase not to be correlated with these risk factors.

Methods

Participants

Seventy-eight heterosexual healthy men between 18 and 45 years were recruited via mailing lists, postings and media press releases. Two participants had to be excluded due to technical difficulties, two because of image artifacts and one due to atypical neuroanatomy. The final sample consisted of 73 men with a mean age of 25.47 (SD = 4.44) years. Most of the participants (n = 65; 89.04%) were students. Thirty-three (45.21%) participants were singles, 36 (49.32%) lived in a romantic relationship and four (5.48%) participants were married. Twenty-four (32.88%) participants described themselves as religious (“Do you profess a religion or denomination?” “yes”/“no”). The following inclusion criteria were applied: absence of current somatic/mental diseases, no current psychotherapeutic/pharmacological treatment, no harmful use of alcohol/nicotine, no contra-indication for fMRI, and fluency in the German language.

Procedure

At study entry, the participants signed an informed consent document. The present sample comes from a larger study investigating the effects of acute stress on VSS processing by comparing a stress condition to a control condition. One other study using data from this project has been published so far. Klein et al. (2020) examined the influence of individual preference on neural reactivity to VSS. The analyses showed that several reward-associated brain areas correlate positively with the individual rating of the VSS and that this correlation correlates positively with the level of PPU. No data reported herein were previously published. Participants from the present analysis were randomly allocated to the control condition and underwent the non-stressful placebo version of the Trier Social Stress Test (placebo TSST, 15 min, Het, Rohleder, Schoofs, Kirschbaum, & Wolf, 2009) prior to MRI scanning. This test consists of two easy mental tasks (a free speech and simple mental arithmetic) that do neither induce significant mental strain nor marked physiological changes in the participants, an influence on the following SIDT is therefore not expected. Subsequent to the placebo TSST, the participants took part in the SIDT. After leaving the scanner, participants rated the film clips alone in a separate room to ensure privacy and validity of the rating. Part of the socio-demographic and non-sexual questionnaire data was already collected before the TSST started (duration about 45 min) using the Internet-based SoSci Survey platform. After the MRI scanning, the participants were given time to rate the film clips and fill in further questionnaires (about 60 min).

Measures

Sexual Incentive Delay Task

We used a SIDT derived from the established MIDT (Knutson et al., 2001). Monetary rewards were replaced in this study by six-second-long film clips that were presented without sound and either showed VSS (VSS clip), non-sexual massage videos (control clip) or a black screen (none). The use of massage videos assured comparability of visual aspects (social interaction, partial nudity, rhythmic movements, etc.) to the film clips showing VSS. In a preliminary study, all film clips were rated with respect to pleasantness (from “1” = “very unpleasant” to “9” = “very pleasant”) and sexual arousal (from “1” = “not sexually arousing at all” to “9” = “very sexually arousing”) by an independent sample of 58 non-homosexual men. Values above 5 were interpreted as high. The 21 VSS clips used in the actual study achieved average scores of high valence (M = 6.20, SD = 1.12) and high sexual arousal (M = 6.29, SD = 1.34 in the pre-study, whereas medium to high scores for valence (M = 5.44, SD = 0.97) and low scores for sexual arousal (M = 1.86, SD = 0.81) were reported for the 21 control clips. Each film clip was only presented once during the task. The experiment was realized with the Presentation software package (Version 17.0, Neurobehavioral Systems, Inc, USA) and lasted for about 20 min. The SIDT included 63 trials consisting of an anticipation phase and a delivery phase with three conditions (21 × VSS, 21 × control, 21 × none).

During the anticipation phase, three different geometric figures, were presented as cues announcing either the VSS clip (CueVSS), the control clip (CueControl) or a black screen (CueNone, see also Fig. 1). The assignment of the geometric figures to the potential outcomes (VSS clip, control clip, none) was randomized across participants. We used geometric figures as cues to ensure that there were no previous associations between these cues and VSS. The participants were informed about the associations between cues and videos before the fMRI experiment. These associations were trained in 21 exercise trials outside the scanner. After one of the cues was visible for 4 s, a fixation cross followed for a variable interstimulus interval of 1–3 s. Then the target stimulus (white square, 200 × 200 pixel) was shown between 16 ms (minimum) and 750 ms (maximum). Regardless of the previously presented cue, the instruction was to respond to the target as quickly as possible by pressing a button. If CueVSS or CueControl appeared and the participants pressed the button while the target stimulus was visible, the participants “won” a film clip. The target was followed by the presentation of another fixation cross for a variable interstimulus interval of 0–2 s. Subsequently, the participants were shown a VSS clip, a control clip or a black screen for a duration of 6 s. The exercise trials before scanning also served to calculate the individual average reaction times (meanRT) and standard deviations (SDRT) to determine the presentation times of the target stimulus (win: meanRT+2 × SDRT; no win: MeanRT–2 × SDRT). Wins were planned for approximately 71% of VSS and control trials (15 out of 21 trials), while nothing trials were never combined with a win. The first three trials presented CueControl, CueVSS, and CueNone in random order. These CueControl and CueVSS trials were always planned as winning trials. After the first three trials, subblocks of 6 trials each were formed (2 × CueControl, 2 × CueVSS and 2 × CueNone). Between winning trials (VSS winning trials or control winning trials) no more than 5 other trials (other winning trials or none trials) were allowed. The same condition could be presented a maximum of 2 times in a row. The presentation of the target stimulus was adjusted online by subtraction or addition of 20 ms each if the participants won in unplanned trials or did not win in planned trials to ensure the reinforcement rate in future trials. VSS trials and control trials, which did not result in outcomes as planned, were repeated in scheduled trials with the new duration of target presentation.

Fig. 1.
Fig. 1.

Sexual Incentive Delay Task. During the anticipation phase, the participants saw a cue (geometric figure). Following a variable time interval, a target was presented for a short time, to which the participants were asked to react as quickly as possible by pressing a button. If the cue in the anticipation phase was a CueVSS or a CueControl, a corresponding video could be obtained by reacting quickly to the target (see also Klein et al., 2020)

Citation: Journal of Behavioral Addictions 10, 1; 10.1556/2006.2021.00018

Assessment of psychometric data

After the SIDT, participants rated their current level of sexual arousal on a 9-point Likert-scale while still inside the scanner. The film clips were rated using Self-Assessment-Manikin scales (Bradley & Lang, 1994) for valence (from 1 = very unpleasant to 9 = very pleasant) and sexual arousal (from 1 = not sexually arousing to 9 = very sexually arousing) after leaving the scanner in a separate room.

The time spent on watching VSS in everyday life was assessed with the item “How much time did you spend on consuming pornography, basing your answer on the last month?”. The participants were able to choose hours and min “per month”, “per week” or “per day” to specify their answer. Prior to analyses, different answer formats were converted into “hours per month”.

PPU was measured by German versions of the short Internet Addiction Test (s-IAT) (Pawlikowski, Altstötter-Gleich, & Brand, 2013) modified for cybersex (s-IATsex; Laier et al., 2013) and by the Hypersexual Behavior Inventory (HBI; Reid, Garos, & Carpenter, 2011). Internal reliability of the collected questionnaire data was calculated for the current sample. Each of the twelve items of the s-IATsex is rated on a 5-point Likert scale ranging from 1 (never) to 5 (very often). The total score (s-IATsex sum, 12 Items, Cronbach's ɑ = 0.90) ranges from 12 to 60. Two subscales can be additionally calculated: loss of control (6 items, Cronbach's ɑ = 0.89) and craving (6 items, Cronbach's ɑ = 0.73). The HBI consists of 19 items rated from 1 (never) to 5 (very often) with a total score (HBIsum, 19 items, Cronbach's ɑ = 0.89) ranging from 19 to 95. Three subscales can be calculated: control (8 items, Cronbach's ɑ = 0.89), coping (7 items, Cronbach's ɑ = 0.84) and consequences (4 items, Cronbach's ɑ = 0.76). Internal consistencies were in acceptable to good ranges in the present study (see data above).

Trait sexual motivation was measured by the Trait Sexual Motivation Questionnaire (TSMQ; Stark et al., 2015). The TSMQ consists of 35 items loading on 4 subscales: solitary sexuality (10 items, Cronbach's ɑ = 0.77), importance of sex (15 items, Cronbach's ɑ = 0.89), seeking sexual encounters (4 items, Cronbach's ɑ = 0.92), and comparison with others (6 items, Cronbach's ɑ = 0.86). Further, a general index for trait sexual motivation (TSMQmean) can be calculated as the mean of all 35 items (Cronbach's ɑ = 0.91). Each item is rated on a 6-point Likert scale ranging from 0 (not at all) to 5 (very much). The participants are instructed to relate their statements to the last five years. The term “sexual motivation” used in this scale includes sexual activities with a partner as well as solitary sexual activities. Higher values indicate higher trait sexual motivation.

Behavioral data

Reaction time was defined as the time between target onset and response onset. Reaction time data was screened for outliers by excluding data below 100 ms or above mean + 1.5 × SD per condition based on sample statistical values. According to this, there were three outliers within the whole sample (one per condition). Descriptive statistics were computed excluding outliers and missing values in the data. Missing values consisted of too late reactions or no reactions to the fixation cross. Differences in the medians of the reaction times on successful trials were analyzed using Kruskal-Wallis test and Dunn-Bonferroni tests. Finally, Pearson's correlations between the reaction times of the three conditions and the risk factors for CSBD were calculated.

fMRI data acquisition and statistical analysis

Functional and anatomical images were acquired using a 3 Tesla whole-body MR tomograph (Siemens Prisma) with a 64-channel head coil. The structural image acquisition encompassed 176 T1-weighted sagittal slices (slice thickness 0.9 mm; FoV = 240 mm; TR = 1.58 s; TE = 2.3 s). For functional imaging, a total of 632 images were recorded using a T2-weighted gradient echo-planar imaging (EPI) sequence with 36 slices covering the whole brain (voxel size = 3 × 3 × 3.5 mm; gap = 0.5 mm; descending slice acquisition; TR = 2 s; TE = 30 ms; flip angle = 75; FoV = 192 × 192 mm2; matrix size = 64 × 64; GRAPPA = 2). The field of view was positioned automatically relative to the AC-PC line with an orientation of -30°. Statistical Parametrical Mapping (SPM12, Wellcome Department of Cognitive Neurology, London, UK; 2014) implemented in Matlab Mathworks Inc., Sherbourn, MA; 2012) was used for preprocessing the raw data, as well as first and second level analysis.

Preprocessing of the EPI images comprised coregistration to a Montreal Neurological Institute (MNI) template, segmentation, realignment and unwarping, slice time correction, normalization to MNI standard space as well as smoothing with a Gaussian kernel at 6 mm FWHM. Functional data were analyzed for outlying volumes using a distribution free approach for skewed data (Schweckendiek et al., 2013). Each resulting outlying volume was later modeled within the general linear model (GLM) as a regressor of no interest. Each of the experimental conditions (CueVSS, CueControl, CueNone, DeliveryVSS, NoDeliveryVSS, DeliveryControl, NoDeliveryControl, NoDeliveryNone and target) was modelled as a regressor of interest. All regressors were convolved with the canonical hemodynamic response function. Six movement parameters were entered as covariates in addition to the regressors for the identified outlying volumes. The time series was filtered with a high pass filter (time constant = 128 s).

On the group level, two contrasts were examined: CueVSS-CueControl and DeliveryVSS-DeliveryControl. One-sample t-tests as well as linear regressions with the following variables as predictors were performed with the contrasts: s-IATsex, HBI, time spent on pornography use (hours per month), and TSMQ. For the TSMQ and for the HBI, multiple regressions containing all subscales at once were done. We used linear regressions for the amount of time spent on pornography use and for the s-IATsex.

ROI analyses on the voxel level were conducted using small volume correction (SVC) with P < 0.05 (family-wise-error corrected: FWE-corrected). Caudate, NAcc, putamen, dorsal anterior cingulate cortex (dACC), amygdala, insula, OFC, and thalamus were chosen as ROIs because they have been previously reported in studies on cue reactivity and VSS processing (Ruesink & Georgiadis, 2017; Stoléru et al., 2012). Bilateral anatomical ROI masks for OFC and dACC were created in MARINA (Walter et al., 2003); all other masks were taken from the Harvard Oxford Cortical Atlas (HOC). The left and right variants of a ROI were merged to one mask. For these eight ROIs, analyses on the voxel level were conducted with P < 0.05 FWE-corrected.

We computed linear regressions of the questionnaire scores and pornography use on the CueVSS–CueControl contrast and the DeliveryVSS–DeliveryControl contrast. Only significant (SVC, FWE-corrected) voxels from the one-sample t-tests within the ROIs where used for SVC. Therefore, smaller ROIs were used for the regression analyses. Exploratory whole brain analyses (FWE-corrected) supplemented the ROI analyses.

Ethics

The study was approved by the local ethics committee and was conducted in accordance with the 1964 declaration of Helsinki and its later amendments. All participants provided informed consent prior to any assessment. A neurological doctor was available to clarify suspect neuroanatomical abnormalities.

Results

Sample characteristics

Table 1 summarizes the descriptive statistics. Bivariate correlations between the questionnaire constructs yielded medium-strong correlations that show both content overlaps and incremental shares of the different constructs (see Fig. 2).

Table 1.

Psychometric measurements and ratings of the sexual and control videos used in the sexual incentive delay task (N = 73)

Mean (SD)Range
s-IATsexLoss of control10.56 (4.66)6.00–30.00
Craving9.60 (3.44)6.00–26.00
s-IATsex total score20.16 (7.74)12.00–56.00
HBIControl14.86 (6.28)8.00–39.00
Coping17.92 (5.48)7.00–32.00
Consequences6.71 (2.81)4.00–20.00
HBIsum39.49 (11.48)20.00–90.00
TimePU [h/month]6.49 (7.21)0.00–42.00
TSMQSolitary sexuality3.74 (0.68)1.80–5,00
Importance of sex3.82 (0.74)1.27–5.00
Seeking sexual encounters1.50 (1.40)0.00–4.75
Comparison with others1.73 (1.10)0.00–4.33
TSMQmean2.70 (0.69)1.05–4.35
Ratings of the sexual stimuliValence6.35 (1.17)2.14–8.67
Sexual arousal6.63 (1.16)2.14–8.62
Ratings of the control stimuliValence5.51 (1.27)2.95–8.86
Sexual arousal2.01 (0.97)1.00–5.00

Note: s-IATsex = short version of the Internet Addiction Test modified for cybersex (Laier et al., 2013), HBI = Hypersexual Behavior Inventory (Reid et al., 2011), TimePU = Time spend on pornography use; TSMQ = Trait Sexual Motivation Questionnaire (Stark et al., 2015).

Fig. 2.
Fig. 2.

Intercorrelation of the addiction-associated characteristics (N = 73): s-IATsex and HBI = sum scores for problematic pornography use, TimePU = time spent on pornography in h/month; TSMQ = mean value for trait sexual motivation

Citation: Journal of Behavioral Addictions 10, 1; 10.1556/2006.2021.00018

A Kruskal–Wallis test showed significant differences between the median reaction times in response to the target in the three conditions (CueNone, CueControl, CueVSS; Χ2(2) = 12.05, P < 0.01). Table 2 summarizes the descriptive statistics of the reaction times during the SIDT. Subsequent post hoc tests (Dunn–Bonferroni tests) revealed that the reaction time to the target in the condition CueVSS was significantly faster than the reaction time in the condition CueControl (z = 2.68, P < 0.05, Cohen's d = -0.65) and in the condition CueNone (z = 3.35, P < 0.01, Cohen's d = -0.82). In contrast, the reaction times to the target stimulus in the conditions CueControl and to CueNone did not differ significantly from each other (z = 0.59, P = 0.56). No significant correlations were found between the reaction times of the three conditions and risk factors for CSBD (all r < 0.1, P > 0.10). CueNone was followed by 75 (4.89%) missing responses, CueControl was followed by 51 (3.33%) missing responses, and CueVSS was followed by 17 (1.11%) missing responses across all participants.

Table 2.

Descriptive statistics of reaction times in the sexual incentive delay task (N = 73)

Median (SD)
CueVSS235.11 (60.94)
CueControl296.63 (135.01)
CueNone314.42 (158.64)

Note: CueVss = cue announcing a pornographic video, CueControl = cue announcing a massage video, CueNone = cue announcing no video.

Hemodynamic responses

Cues signaling VSS as compared to cues signaling control clips elicited a higher blood-oxygenation-level dependent (BOLD) response in NAcc, caudate, putamen, and insula (all bilateral), as well as in the right dACC and thalamus. A higher BOLD response was also found in the left NAcc and OFC, in the bilateral caudate, putamen, dACC, insula, amygdala, and thalamus during delivery of VSS clips compared to control clips (all results see Table 3 and Fig. 3).

Table 3.

ROI results for the contrasts CueVSS–CueControl and DeliveryVSS–DeliveryControl (One Sample t-tests) with cluster size (k) and statistics (FWE-corrected; N = 73)

ContrastStructureSidexyzkTmaxPcorr
CueVSS–CueControlNAccL−68−4778.71<0.001
R810−4657.50<0.001
caudateL−81024499.66<0.001
R101444768.18<0.001
putamenL−168−27746.72<0.001
R24247667.42<0.001
dACCR1216361,69710.77<0.001
insulaL−341465929.43<0.001
R381446048.65<0.001
thalamusR8−202,1648.91<0.001
DeliveryVSS–DeliveryControlNAccL−814−8699.49<0.001
caudateL−12−618564.24<0.01
R16−1622715.32<0.001
putamenL−1812−103146.58<0.001
R32−12−10637.28<0.001
dACCL−220289535.43<0.001
R44329539.19<0.001
amygdalaL−22−4−1623210.71<0.001
R20−4−1428012.20<0.001
insulaL−36−4145179.52<0.001
R382−164769.19<0.001
OFCL−644−182,82517.45<0.001
thalamusL−20−30−21,74725.67<0.001
R20−2801,74724.08<0.001
Fig. 3.
Fig. 3.

ROI activity for the contrasts CueVSS–CueControl (A) and DeliveryVSS–DeliveryControl (B). Lines on the sagittal slice on the right side indicate the coronal slices depicted on the left. Cues signaling VSS (CueVSS) as compared to cues signaling massage clips (CueControl) elicited a higher BOLD response in putamen, NAcc, caudate, and insula. VSS clips (DeliveryVSS) compared to massage clips (DeliveryControl) elicited a higher BOLD response in thalamus, insula, amygdala, putamen, and OFC. Displayed t-values are thresholded at t < 5

Citation: Journal of Behavioral Addictions 10, 1; 10.1556/2006.2021.00018

Whole brain analyses revealed higher hemodynamic responses in a continuous cluster including large parts of the brain for the contrast CueVSS compared to CueControl (Cluster extent k = 174,054 voxel) and again for the contrast DeliveryVSS compared to DeliveryControl (k = 134,654)

Risk factors for CSBD and hemodynamic responses

None of the regression analyses on the links between risk factors for CSBD (self-reported PPU, time spent on pornography use, and trait sexual motivation) and discriminative neural activity in any ROI during the anticipation phase (CueVSS–CueControl) or the delivery phase (DeliveryVSS–DeliveryControl) yielded any significant effects. Figure 4 presents the associations between these risk factors and left nucleus accumbens' peak voxel activity.

Fig. 4.
Fig. 4.

Correlation between the left nucleus accumbens' peak voxel activity and s-IATsex, HBI, time spent on pornography use in h/month (TimePU) and total scores of the TSMQ during the anticipation phase (upper row, NAcc [-6 8 -4]) and the delivery phase (bottom row, NAcc [-8 14 -8]) of the Sexual Incentive Delay Task (N = 73)

Citation: Journal of Behavioral Addictions 10, 1; 10.1556/2006.2021.00018

Discussion

The first aim of this report was to investigate the reward-related brain activity during the anticipation and the delivery phase of VSS in a large non-clinical sample using an SIDT. We found that the presentation of pornographic videos as well as the presentation of cues preceding pornographic videos was associated with higher brain activity in predefined reward-related brain areas (NAcc, amygdala, OFC, putamen, caudate nucleus, insula, thalamus, and dACC) compared to the presentation of massage videos or cues preceding massage videos, respectively. Our results are in line with the findings of Sescousse et al. (2015, 2010), who compared the neural response to VSS and non-VSS stimuli (here monetary) stimuli in a sample of healthy men during an incentive delay task. Regarding the brain responses to VSS cues, they found higher activity in the ventral striatum with increasing expected reward intensity. During delivery, they found also reward-specific brain activity to VSS in part of the OFC as well as in the bilateral amygdala. Additionally, they identified regions that were involved in the processing of both kinds of rewards (ventral striatum, midbrain, ACC, anterior insula).

The behavioral data showed that the reaction times were significantly faster to target stimuli in the condition presenting pornographic cues than in the conditions with control cues or cues that announced no video at all. This indicates that the expectation of VSS activates the motor system, which underlines the high motivational value of VSS.

The second aim was to explore the relationship between the neural responses to VSS as well as cues and risk factors for CSBD. The measured risk factors showed correlative relationships of medium strength among each other, indicating similarities as well as incremental parts of the constructs. Neither questionnaires measuring PPU (HBI and s-IATsex), nor the amount of time spend on porn, nor trait sexual motivation (TSMQ) were significantly correlated with brain activities of the reward-related brain areas during delivery and anticipation of sexual stimuli.

To appropriately discuss the missing correlation between risk factors for CSBD and neural responses to VSS, it is helpful to consult the existing literature of studies which either compare the neural responses of CSBD with control participants (group comparison approach) or analyze the correlation of risk factors for CSBD with the NAcc responses to VSS (correlational approach). Following the group comparison approach, some studies found greater neural responses towards VSS in the ventral striatum as well as in other reward-associated brain areas in participants with PPU compared to control participants (Gola et al., 2017; Seok & Sohn, 2015; Voon et al., 2014). An important result of the study by Gola et al. (2017) was that cues that predicted VSS were associated with higher striatal activity in CSBD participants than in healthy subjects. While Gola et al. (2017) investigated a mixed sexual and monetary incentive delay paradigm with pictograms of naked women as cues, Klucken et al. (2016) examined an appetitive conditioning paradigm with geometric cues. As a result, they found increased amygdala activity during conditioning for the CS+ (cue predicting VSS) versus the CS− (cue predicting nothing) in participants with CSBD compared to control participants, but no differences in the ventral striatum. In contrast, in the appetitive conditioning paradigm of Banca et al. (2016) there were no group effects between CSBD participants and control participants regarding the neural responses to different cues (colored patterns predicting VSS, monetary rewards or nothing).

Studies following the correlational approach revealed inconsistent results regarding the correlation between risk factors for CSBD and the neural responses to VSS: While Kühn and Gallinat (2014) found a negative correlation between the time spent on pornography and activity in the left putamen, Brand et al. (2016) reported no statistically significant correlation of ventral striatum responses and usual time spent on pornography. However, they found that the ventral striatum activity was positively correlated with the level of self-assessed PPU (measured by the s-IATsex). In addition, in one of our previous studies we could not find any significant influence of time spend on pornography or trait sexual motivation on the neural response to VSS (Stark et al., 2019). Accordingly, current research concerning the processing of VSS in subjects with varying degrees of risk factors for CSBD appears inconsistent. Rather uniform findings of studies employing the group comparison approach but inconsistent results from correlational studies might suggest that the neural processing of VSS in CSBD substantially differs from that in subclinical samples. This suggestion, however, is of interest in light of the Incentive Sensitization Theory of Robinson and Berridge (1993) which suggests increasing neural responses to cues during addiction development. So far, it remains unclear whether the theory applies to CSBD and if so, whether the increasing neural responses to VSS change dimensionally or whether a critical level of addictive behavior must be exceeded.

Interestingly, also in substance-related addictions the results concerning the Incentive Sensitization Theory are inconsistent. Several meta-analyses showed an increased cue reactivity in the reward system (Chase, Eickhoff, Laird, & Hogarth, 2011; Kühn & Gallinat, 2011b; Schacht, Anton, & Myrick, 2012), but some studies could not confirm these findings (Engelmann et al., 2012; Lin et al., 2020; Zilberman, Lavidor, Yadid, & Rassovsky, 2019). Also for behavioral addictions a higher cue reactivity in the reward network of addictive subjects in comparison to healthy subjects was only found in a minority of the studies as summarized in a most recent review by Antons et al. (2020). From this summary, the conclusion can be drawn that cue reactivity in addiction is modulated by several factors like individual factors and study-specific factors (Jasinska et al., 2014). Our zero findings regarding the correlations between striatal activity and risk factors of CSBD may also be due to the fact that even with our large sample we could only consider a small selection of possible influencing factors. Further large-scale studies are needed to do justice to multicausality. In terms of design, for example, the sensory modality of cues or the individualization of cues could be important (Jasinska et al., 2014).

According to our large sample size (in contrast to other studies) it is unlikely that a lack of statistical power caused the null findings with regard to the correlation of risk factors for CSBD and neural responses to VSS and cues of VSS. More probably, the evolutionary-driven, generally highly motivational value of VSS activates reward-associated brain areas highly uniformly leaving only small space for individual differences (ceiling effect). This hypothesis is supported by studies showing that there are hardly any sex differences with regard to the processing of VSS in the reward network (Poeppl et al., 2016; Stark et al., 2019; Wehrum et al., 2013). Nevertheless, the reasons for inconsistencies between the studies need to be uncovered by further studies.

Limitations and recommendations for further research

Several limitations have to be considered. In our study we only examined western-culture, heterosexual men. A replication of the study with a more diverse sample in terms of gender, sexual orientation, and socio-cultural factors seems necessary to ensure ecological validity. In addition, data was derived from a non-clinical sample, future studies will have to also consider samples with clinically relevant CSBD symptoms. The cues used in this study were described as neutral cues without any individually different previous experience. However, the price of this procedure with high internal validity might be a lack of external validity since pornography cues in everyday life are highly individualized.

Another limitation is the flexible response format (per day/per week/per month) regarding the assessment of pornography use. According to Schwarz and Oyserman (2001) responses to the same question are of limited comparability when the response format refers to different time periods. The main reason for choosing this response format was that the extent of pornography use in samples can vary greatly (from a few hours a year to several hours a day). In addition, it seemed relevant that a fixed response format would potentially impose a norm as to what level of pornography use is appropriate. Therefore, we decided to use the flexible response format for this intimate question, despite its known weakness.

Moreover, the laboratory represents an artificial setting, since pornography use in daily life is usually accompanied by masturbation. Therefore, it remains uncertain whether the reward comes from masturbation/orgasm and/or from the pornographic material itself. Gola et al. (2016) convincingly argued that sexual stimuli can be both cues and rewards. If the pornographic films are also interpreted as cues, future studies might allow masturbation to realize a true delivery phase. However, ethical and technical difficulties need to be considered to conduct such a study. To better understand the development of CSBD, studies covering the entire spectrum of CSBD symptoms (healthy, subclinical, clinical) are necessary.

Conclusions

Our study examined the processing of cues and VSS stimuli using a SIDT in a large non-clinical sample. Further, our modified SIDT optimizes previous SIDT by using film clips instead of static pictures, by using massage videos as control condition instead of scrambled pictures, and by using cues not containing sexual information. We were able to replicate the results showing the involvement of the reward system during both the processing of cues and of VSS. Contrary to our hypotheses, we could not identify effects of personal characteristics thought as risk factors for the development of CSBD on the neural responses in any ROI connected to the reward system. Future research should examine the entire spectrum of CSBD symptoms to better understand how pornography use develops into pathological behavior and which factors can predict this development.

Funding sources

This study was funded by the German research association (DFG STA 475/16-1).

Author's contribution

JS, OK, and RS conceived the study, RS obtained funding, SK and CM analyzed the data, CM drafted the article, RS, SK, JS, and OK revised the article for important intellectual content.

All authors had full access to all data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Conflict of interest

The authors declare no conflicts of interest.

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The author instruction is available in PDF.
Please, download the file from HERE

Dr. Zsolt Demetrovics
Institute of Psychology, ELTE Eötvös Loránd University
Address: Izabella u. 46. H-1064 Budapest, Hungary
Phone: +36-1-461-2681
E-mail: jba@ppk.elte.hu

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  • CABI

2021  
Web of Science  
Total Cites
WoS
5223
Journal Impact Factor 7,772
Rank by Impact Factor Psychiatry SCIE 26/155
Psychiatry SSCI 19/142
Impact Factor
without
Journal Self Cites
7,130
5 Year
Impact Factor
9,026
Journal Citation Indicator 1,39
Rank by Journal Citation Indicator

Psychiatry 34/257

Scimago  
Scimago
H-index
56
Scimago
Journal Rank
1,951
Scimago Quartile Score Clinical Psychology (Q1)
Medicine (miscellaneous) (Q1)
Psychiatry and Mental Health (Q1)
Scopus  
Scopus
Cite Score
11,5
Scopus
CIte Score Rank
Clinical Psychology 5/292 (D1)
Psychiatry and Mental Health 20/529 (D1)
Medicine (miscellaneous) 17/276 (D1)
Scopus
SNIP
2,184

2020  
Total Cites 4024
WoS
Journal
Impact Factor
6,756
Rank by Psychiatry (SSCI) 12/143 (Q1)
Impact Factor Psychiatry 19/156 (Q1)
Impact Factor 6,052
without
Journal Self Cites
5 Year 8,735
Impact Factor
Journal  1,48
Citation Indicator  
Rank by Journal  Psychiatry 24/250 (Q1)
Citation Indicator   
Citable 86
Items
Total 74
Articles
Total 12
Reviews
Scimago 47
H-index
Scimago 2,265
Journal Rank
Scimago Clinical Psychology Q1
Quartile Score Psychiatry and Mental Health Q1
  Medicine (miscellaneous) Q1
Scopus 3593/367=9,8
Scite Score  
Scopus Clinical Psychology 7/283 (Q1)
Scite Score Rank Psychiatry and Mental Health 22/502 (Q1)
Scopus 2,026
SNIP  
Days from  38
submission  
to 1st decision  
Days from  37
acceptance  
to publication  
Acceptance 31%
Rate  

2019  
Total Cites
WoS
2 184
Impact Factor 5,143
Impact Factor
without
Journal Self Cites
4,346
5 Year
Impact Factor
5,758
Immediacy
Index
0,587
Citable
Items
75
Total
Articles
67
Total
Reviews
8
Cited
Half-Life
3,3
Citing
Half-Life
6,8
Eigenfactor
Score
0,00597
Article Influence
Score
1,447
% Articles
in
Citable Items
89,33
Normalized
Eigenfactor
0,7294
Average
IF
Percentile
87,923
Scimago
H-index
37
Scimago
Journal Rank
1,767
Scopus
Scite Score
2540/376=6,8
Scopus
Scite Score Rank
Cllinical Psychology 16/275 (Q1)
Medicine (miscellenous) 31/219 (Q1)
Psychiatry and Mental Health 47/506 (Q1)
Scopus
SNIP
1,441
Acceptance
Rate
32%

 

Journal of Behavioral Addictions
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 850 EUR/article
Printed Color Illustrations 40 EUR (or 10 000 HUF) + VAT / piece
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription Information Gold Open Access

Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
Volumes
per Year
1
Issues
per Year
4
Founder Eötvös Loránd Tudományegyetem
Founder's
Address
H-1053 Budapest, Hungary Egyetem tér 1-3.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2062-5871 (Print)
ISSN 2063-5303 (Online)

Senior editors

Editor(s)-in-Chief: Zsolt DEMETROVICS

Assistant Editor(s): Csilla ÁGOSTON

Associate Editors

  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Beáta BŐTHE (University of Montreal, Canada)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Luke CLARK (University of British Columbia, Canada)
  • Daniel KING (Flinders University, Australia)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • H. N. Alexander LOGEMANN (ELTE Eötvös Loránd University, Hungary)
  • Astrid MÜLLER (Hannover Medical School, Germany)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)
  • Attila SZABÓ (ELTE Eötvös Loránd University, Hungary)
  • Róbert URBÁN (ELTE Eötvös Loránd University, Hungary)
  • Aviv M. WEINSTEIN (Ariel University, Israel)

Editorial Board

  • Max W. ABBOTT (Auckland University of Technology, New Zealand)
  • Elias N. ABOUJAOUDE (Stanford University School of Medicine, USA)
  • Hojjat ADELI (Ohio State University, USA)
  • Alex BALDACCHINO (University of Dundee, United Kingdom)
  • Alex BLASZCZYNSKI (University of Sidney, Australia)
  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Kenneth BLUM (University of Florida, USA)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • Wim VAN DEN BRINK (University of Amsterdam, The Netherlands)
  • Gerhard BÜHRINGER (Technische Universität Dresden, Germany)
  • Sam-Wook CHOI (Eulji University, Republic of Korea)
  • Damiaan DENYS (University of Amsterdam, The Netherlands)
  • Jeffrey L. DEREVENSKY (McGill University, Canada)
  • Naomi FINEBERG (University of Hertfordshire, United Kingdom)
  • Marie GRALL-BRONNEC (University Hospital of Nantes, France)
  • Jon E. GRANT (University of Minnesota, USA)
  • Mark GRIFFITHS (Nottingham Trent University, United Kingdom)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • Heather HAUSENBLAS (Jacksonville University, USA)
  • Tobias HAYER (University of Bremen, Germany)
  • Susumu HIGUCHI (National Hospital Organization Kurihama Medical and Addiction Center, Japan)
  • David HODGINS (University of Calgary, Canada)
  • Eric HOLLANDER (Albert Einstein College of Medicine, USA)
  • Jaeseung JEONG (Korea Advanced Institute of Science and Technology, Republic of Korea)
  • Yasser KHAZAAL (Geneva University Hospital, Switzerland)
  • Orsolya KIRÁLY (Eötvös Loránd University, Hungary)
  • Emmanuel KUNTSCHE (La Trobe University, Australia)
  • Hae Kook LEE (The Catholic University of Korea, Republic of Korea)
  • Michel LEJOXEUX (Paris University, France)
  • Anikó MARÁZ (Humboldt-Universität zu Berlin, Germany)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Frederick GERARD MOELLER (University of Texas, USA)
  • Daniel Thor OLASON (University of Iceland, Iceland)
  • Nancy PETRY (University of Connecticut, USA)
  • Bettina PIKÓ (University of Szeged, Hungary)
  • Afarin RAHIMI-MOVAGHAR (Teheran University of Medical Sciences, Iran)
  • József RÁCZ (Hungarian Academy of Sciences, Hungary)
  • Rory C. REID (University of California Los Angeles, USA)
  • Marcantanio M. SPADA (London South Bank University, United Kingdom)
  • Daniel SPRITZER (Study Group on Technological Addictions, Brazil)
  • Dan J. STEIN (University of Cape Town, South Africa)
  • Sherry H. STEWART (Dalhousie University, Canada)
  • Attila SZABÓ (Eötvös Loránd University, Hungary)
  • Ferenc TÚRY (Semmelweis University, Hungary)
  • Alfred UHL (Austrian Federal Health Institute, Austria)
  • Johan VANDERLINDEN (University Psychiatric Center K.U.Leuven, Belgium)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Kimberly YOUNG (Center for Internet Addiction, USA)

 

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