Abstract
Background and aims
Despite the inclusion of Compulsive Sexual Behavior Disorder (CSBD) in the ICD-11, there are many open questions on its neuronal pathogenesis, especially regarding the role of the amygdala. In this study, we aimed to further unravel this issue via a parcellation method based on Recurrence Quantification Analysis (RQA).
Methods
The RQA pipeline was applied to resting-state functional magnetic resonance imaging data from 45 heterosexual males with CSBD and 26 Healthy Controls. Each amygdala was divided into two subdivisions in each group. In the CSBD group, the scores of psychological questionnaires were used as covariates in a second-level seed-to-voxel connectivity analysis with the amygdala as a region of interest.
Results
Obtained parcellations revealed bilateral differences in the sizes of dorsomedial (DM) and ventrolateral (VL) amygdala between groups. Mean values of Shannon's Entropy in the left DM and right VL amygdala correlated negatively with depression level, anxiety, and impulsivity, which might represent a vulnerability to CSBD, but only the right VL was implicated in the severity of CSBD symptoms. Multiple correlations between resting-state functional connectivity of the amygdala subdivisions and CSBD severity were observed, especially between the left VL amygdala and several default mode network nodes.
Discussion and Conclusions
This is the first attempt to explore the role of the amygdala in CSBD by a parcellation method. Our results suggest the importance of the right VL amygdala in understanding the pathogenesis of the severity of CSBD symptoms, which highlights the rising need to explore the amygdala as a complex structure with diverse functions.
Introduction
The amygdala has been known to play a role in the pathogenesis of several psychiatric disorders (Phillips, Drevets, Rauch, & Lane, 2003). One of them is Compulsive Sexual Behavior Disorder (CSBD), a disorder included in the 11th edition of the International Classification of Diseases (ICD-11) (WHO, 2019). CSBD is characterized by an inability to resist excessive engagement in various types of sexual behaviors, which can cause severe distress and greatly interfere with patients' daily lives.
Despite rising interest in research regarding neural correlates of CSBD, the role of the amygdala is still not completely understood and data regarding this matter is limited (Engel et al., 2023; Klucken, Wehrum-Osinsky, Schweckendiek, Kruse, & Stark, 2016; Schmidt et al., 2017; Voon et al., 2014). The amygdala is known to be engaged in different types of addictions (Wassum & Izquierdo, 2015) (alcohol addiction (Roberto, Kirson, & Khom, 2021), gambling disorder (Quester et al., 2015), and internet addiction disorder (Cheng & Liu, 2020)), and is also highly involved in anxiety disorders (Shin & Lizerbon, 2010). As studies show elevated anxiety among CSBD patients (Gola et al., 2017; Lew-Starowicz, Lewczuk, Nowakowska, Kraus, & Gola, 2020), it seems necessary to understand its role as a neural correlate of CSBD. Given its complex internal architecture, which consists of closely located several small nuclei (Swanson & Petrovich, 1998) comprising subdivisions (Amunts et al., 2005) it is necessary to consider their diverse functionality to understand their role in psychiatric disorders (Cao et al., 2022; Rausch et al., 2018), including CSBD.
Accordingly, amygdala parcellations have been performed in studies on Autism Spectrum Disorder (ASD) (Rausch et al., 2018) and Obsessive-Compulsive Disorder (OCD) (Cao et al., 2022). In individuals with ASD, a connectivity-based parcellation divided the amygdala into three functional parcels, from which one of them was significantly enlarged and two of them showed a trend towards a volume decrease compared to Healthy Controls (HC). The volume of the enlarged parcel, identified as the ventrolateral (VL), positively correlated with social skills impairment (Rausch et al., 2018). In OCD patients a connectivity-based parcellation into subdivisions corresponding to the basolateral (BLA) and centromedial (CMA) amygdala showed that the volume of the left BLA and right CMA were decreased compared to HC. Multiple alterations of resting-state functional connectivity (rs-FC) of the amygdala subregions were also revealed for both amygdalae in the OCD group (Cao et al., 2022). Therefore, we are convinced that in CSBD it is also important to investigate different amygdala subdivisions, as their involvement may have at least a few clinical implications. The complexity of the amygdala's functions may translate into the diversity of clinical profiles among CSBD patients, similarly as it has been recently presented in the case of eating disorders (Frank et al., 2023). Studying the diversity of the amygdala's subdivisions and their multiple roles not only in fear-related processes but also appetitive conditioning (Nguyen & Berridge, 2024; Warlow & Berridge, 2021; Warlow, Naffziger, & Berridge, 2020) may help to understand the development of CSBD symptoms and their persistence. Additionally, this could influence the treatment aims and provide information for a more personalized therapy focus on matters such as anxiety level or cue reactivity.
This is the first exploratory study addressing the connection between the amygdala's subdivisions' rs-FC and the severity of CSBD symptoms.
Methods
Recruitment procedure and participants
All recruited CSBD subjects were men, who were actively seeking treatment. This study was performed as a part of a longitudinal treatment study (Lew-Starowicz et al., 2022). The data, which has been previously also analyzed in a study addressing rs-FC in CSBD, was acquired as a baseline before the start of the pharmacotherapy (Draps, Adamus, Wierzba, & Gola, 2022).
A secure online survey was used to perform the screening for CSBD symptoms and other inclusion/exclusion criteria. The CSBD diagnoses were taken by psychologists and psychiatrists (CSBD diagnostic guidelines from ICD-11 (WHO, 2019) and at least 4 out of 5 of the Hypersexual Disorder criteria (HD; Kafka, 2010)). The HC group was recruited through online announcements among individuals without any psychiatric disorders.
Patients completed Sexual Addiction Screening Test-Revised (SAST-R; Gola et al., 2017) for the assessment of the presence of addictive sexual behavior, the Brief Pornography Screening Test for the assessment of the presence of problematic pornography use (BPS; Kraus et al., 2020), and the Hypersexual Behavior Inventory (HBI) for assessment of the extent of hypersexual urges (Reid, Garos, & Carpenter, 2011). These scales were used to determine the severity of CSBD symptoms. Obsessive-Compulsive Inventory (OCI-R; Foa et al., 2002), Hospital Anxiety and Depression Scale (HADS; Zigmond & Snaith, 1983), State-Trait Anxiety Inventory (STAI; Spielberger, 1989; Wrześniewski, Sosnowski, & Matusik, 2002), Barratt Impulsiveness Scale (BIS-11; Grzesiak, Beszłej, & Szechiński, 2008; Stanford et al., 2009) were used to psychologically characterize the participants.
Exclusion criteria applied to both groups consisted of contraindications for magnetic resonance imaging (MRI), history of alcohol abuse (Alcohol Use Disorder Identification Test – AUDIT (Babor, de la Fuente, Saunders, & Grant, 1992) scores over 14), problematic gambling (South Oaks Gambling Screen – SOGS (Lesieur & Blume, 1987) scores over 5), no current mood, or substance use disorders, and no history of neurological, and developmental disorders. We have also screened for paraphilias, and individuals diagnosed with paraphilic disorders were excluded from further procedures.
Participants and data acquisition and preprocessing
We analyzed data from 71 heterosexual men, which were divided into two groups: 45 CSBD patients (age 35.156 SD 8.434) and 26 Healthy Controls (HC) (age 34.115 SD 8.081).
Details about data acquisition and preprocessing are in the S1 and S2 sections of Supplementary Materials.
Amygdala parcellation pipeline
A wide range of amygdala parcellation methods has been developed over the years, however, no consensus has been reached regarding the ultimate strategy. By their application, the amygdala can be divided into two (Bach, Behrens, Garrido, Weiskopf, & Dolan, 2011; Bielski, Adamus, Kolada, Rączaszek-Leonardi, & Szatkowska, 2021), three (Amunts et al., 2005; Sylvester et al., 2020; Zhang et al., 2018), or even more (Klein-Flügge et al., 2022) smaller subparts.
The available methods can in general be divided into structural and functional approaches. The structural are based on structural connectivity analysis (Bach et al., 2011; Solano-Castiella et al., 2010), postmortem brain tissue cytoarchitectonic analysis (Amunts et al., 2005), in vivo anatomical segmentation (Solano-Castiella et al., 2011) or delineation of amygdala's subdivisions with topography-based methods (Entis, Doerga, Barrett, & Dickerson, 2012; Saygin et al., 2017; Tyszka & Pauli, 2016). The functional address the amygdala's subdivisions' functional specialization (Bzdok, Laird, Zilles, Fox, & Eickhoff, 2013; Kolada et al., 2023; Yang et al., 2016) or their resting-state functional connectivity with other brain structures (Mishra, Rogers, Chen, & Gore, 2014; Sylvester et al., 2020).
A more detailed methodological explanation regarding the introduced methods can be found in the work by Bielski et al. (2021).
A recently proposed algorithm has been used in the analysis of functional MRI data to perform amygdala parcellation via a method known as Recurrence Quantification Analysis (RQA) (Bielski et al., 2021). RQA focuses on the recognition of patterns of recurrences in a signal, which in this case is represented by the blood-oxygen-level-dependent (BOLD) signal from the voxels within the amygdala. The signal's trajectory can be reconstructed in a multidimensional phase space and the recurrences, which occur when the signal goes back to a previously visited state, can be illustrated on a plot known as a Recurrence Plot (Marwan, Romano, Thiel, & Kurths, 2007; Marwan & Webber, 2015). Analysis of the patterns of recurrences allows gathering information regarding the dynamics of the BOLD signal within the amygdala voxels, which enables its temporal characterization and division of the amygdala into functionally distinct subdivisions (Bielski et al., 2021).
Considering that this method does not require a priori assumptions regarding the characteristics of the BOLD signal dynamics, which are unavailable for data from CSBD patients, we decided to use it in this study. Additionally, this method was already well validated on data from healthy individuals.
We applied this algorithm to the preprocessed and denoised resting-state fMRI (rs-fMRI) data to create in-house amygdala masks. The parcellation was performed separately and simultaneously for the CSBD and the HC group (Fig. 1).
An illustration of the steps of amygdala parcellation based on the pipeline by Bielski et al. (2021). After preprocessing of rs-fMRI data time series consisting of BOLD signal values within the amygdala voxels were extracted. Next, RQA parameters were estimated and the analysis was performed. Clustering matrices were created and voxels were classified using hierarchical clustering. Finally, internal validation measures were calculated for the clustering results. All computations performed during the implementation of RQA were achieved using CRP Toolbox (Marwan et al., 2007) and MATLAB version R2020_b. Hierarchical clustering was performed using the following Python 3.8 packages: scikit-learn (version 1.3.2) (Perdegrosa et al., 2011), NumPy (version 1.24.4) (Olliphant, 2006), nibabel (version 5.1.0) (Brett et al., 2023), scipy (version 1.10.1) (Virtanen et al., 2020) and matplotlib (version 3.7.3) (Hunter, 2007)
Citation: Journal of Behavioral Addictions 2025; 10.1556/2006.2025.00014
Firstly time-series within the amygdala were extracted by application of the amygdala mask created by Bach et al. (2011). After obtaining values of the BOLD signal contained within voxels of both amygdalae, parameters of RQA were estimated (details in Supplementary Materials) (Marwan & Webber, 2015) and the analysis was performed for each subject separately.
Matrices containing the values of Shannon's Entropy were obtained after the analysis. This RQA measure is computed as the probability distribution of the lengths of the structures known as diagonal lines, present in a Recurrence Plot. Higher values of Shannon's Entropy are associated with more complex dynamics in a system, whereas lower are characteristic of uncorrelated noise (Marwan & Webber, 2015).
Upon analysis of the distribution of normalized Shannon's Entropy values in the amygdala voxels, positive and negative values mean that for analyzed voxels it is either higher or lower than its average value in the whole amygdala (Bielski et al., 2021).
Population-wise clustering was performed for both groups, on matrices consisting of rows filled with values of Shannon's entropy for each voxel within the left or right amygdala for each subject within the group. Each amygdala was divided into two clusters using hierarchical clustering with Ward metrics, repeated one thousand times. Obtained solutions were validated based on the values of internal validation measures such as Beta CV, Normalized Cut, and Silhouette Coefficient (details in Supplementary Materials).
Functional connectivity analysis
The parcellation obtained for the CSBD group and the mask by Bach et al. (2011) were used as amygdala masks for the CSBD group while performing seed-to-voxel functional connectivity analysis. Seed-based connectivity (SBC) measures were computed to assess connectivity patterns.
The scores of the psychological questionnaires played the role of second-level covariates. This analysis was done only among individuals with CSBD to show the effects connected to the severity of symptoms (measured by SAST-R, BPS, and HBI) on amygdala subdivisions' rs-FC. Following advanced Family-Wise control settings were applied to perform the analysis: voxel threshold p < 0.001 (p-uncorrected, two-sided) and cluster threshold p < 0.05 (cluster-size p-FDR corrected, parametric stats).
Ethics
All participants gave their informed consent upon entering the study. A double-blind procedure was employed to ensure anonymity (researchers responsible for acquiring data had no access to recruitment records and could not identify the patients). The study was approved by the local ethics committee of Institute of Psychology, Polish Academy of Sciences and was carried out according to the Declaration of Helsinki.
Results
Group characteristics
In the CSBD group, the scores on scales measuring the severity of CSBD were significantly higher than in the HC group. Individuals with CSBD also scored higher than HC in BIS-11, HADS, and STAI scales (Table 1). The sociodemographic characteristics of the CSBD and HC group is available in Table 2.
Characteristics of participants (comparison of the questionnaire statistics obtained for CSBD and HC group)
| CSBD (N = 45) [mean (SD)] | HC (N = 26) [mean (SD)] | p-value | |
| Brief Pornography Screening Test (BPS) | 7.86 (2.33) | 1.33 (1.66) | p < 0.001 |
| Sexual Addiction Screening Test – Revised (SAST-R) | 11.02 (3.68) | 2.79 (1.74) | p < 0.001 |
| Obsessive-Compulsive Inventory – Revised (OCI-R) | 16.98 (9.41) | 12.55 (7.87) | p = 0.04 |
| Hypersexual Behavior Inventory (HBI) (total score) | 63.51 (16.41) *subscale Coping: 21.30 (7.38); Consequences: 12.61 (4.12); Control: 29.61 (7.60) | 1.00 (1.50) | p < 0.001 |
| Hospital Anxiety and Depression Scale (HADS): Anxiety Scale | 10.18 (4.47) | 4.73 (2.43) | p < 0.001 |
| Hospital Anxiety and Depression Scale (HADS): Depression Scale | 7.40 (4.33) | 3.19 (2.77) | p < 0.001 |
| State-Trait Anxiety Inventory (STAI): state scale | 48.16 (12.48) | 33.65 (9.23) | p < 0.001 |
| State-Trait Anxiety Inventory (STAI): trait scale | 49.40 (2.68) | 47.50 (3.08) | p = 0.01 |
| Barratt Impulsiveness Scale (BIS-11) | 68.02 (12.46) | 61.23 (9.31) | p = 0.03 |
CSBD: Compulsive Sexual Behavior Disorder, SD: standard deviation. The significance level was calculated with either an independent sample t-test or U Mann-Whitney test, depending on the qualities of data distribution.
Group characteristics: income, work, education, and relationship status
| Monthly income (in PLN) | CSBD (N = 45) [mean (SD)] | HC (N = 26) [mean (SD)] | p-value | |
| 5181.11 (3643.91) | 4817.31 (3333.92) | p = 0.55 | ||
| CSBD (N = 45) [frequencies] | HC (N = 26) [frequencies] | chi2 | p-value | |
| work |
|
| chi2 = 0.51 | p = 0.92 |
| education |
|
| chi2 = 9.40 | p = 0.052 |
| relationship status |
|
| chi2 = 3.86 | p = 0.28 |
CSBD: Compulsive Sexual Behavior Disorder, HC: Healthy Controls, SD: standard deviation. The significance level was calculated with the U Mann-Whitney test or Chi-squared test, based on the qualities of data distribution and data type.
The participants among the CSBD group presented compulsive sexual behaviors connected to pornography watching (95.45%), masturbation (86.36%), paid sexual services (15.91%), casual sex (9.09%), sexual maladaptive daydreaming (29.55%) and online sexual services (e.g., chats, live video broadcasts, etc.) (11.36%).
Estimation of RQA parameters and RQA results
RQA was conducted for each subject in each dataset with the same values of Radius (1.15) and Embedding Dimension (4). Time Delay was not estimated as a single value, and distributions of its values are presented in Supplementary Materials.
The distributions of Shannon's Entropy values in the voxels of the left and right amygdala for the CSBD and HC group are presented in Fig. 2.
Right-skewed histograms of the values of Shannon's Entropy in voxels of the left and right amygdala for the Compulsive Sexual Behavior Disorder (CSBD) and Healthy Controls (HC) group
Citation: Journal of Behavioral Addictions 2025; 10.1556/2006.2025.00014
Differences in values of Shannon's Entropy between subdivisions
The mean values of Shannon's Entropy for each amygdala subdivision are presented in Fig. 3. The dorsomedial (DM) subdivision is characterized by negative values of mean normalized Shannon's Entropy for both the CSBD and HC group, whereas the ventrolateral (VL) part by positive mean values of this measure in both datasets.
Mean values of Shannon's Entropy in amygdala subdivisions (dorsomedial (DM) and ventrolateral (VL)) for the Compulsive Sexual Behavior Disorder (CSBD) and Healthy Controls (HC) group. The * sign indicates p-value < 0.05 and the ** sign p-value < 0.005
Citation: Journal of Behavioral Addictions 2025; 10.1556/2006.2025.00014
Values of Shannon's Entropy in voxels of both subdivisions were also compared between groups. Significant differences were present for the right DM and the left VL amygdala. Shannon's Entropy values were lower for the right DM subdivision for the HC group than the CSBD group (p < 0.01, t = 3.02, medium effect size), whereas for the left VL part, they were higher in the CSBD group compared to the HC group (p < 0.05, t = 2.45, medium effect size).
There were significant differences in Shannon's Entropy values for all comparisons within hemispheres for both the CSBD (left DM versus left VL (t = −9.07, p < 0.001, large effect size); right DM versus right VL (t = −8.59, p < 0.001, large effect size)) and the HC group (left DM versus left VL (t = −3.37, p < 0.001, medium effect size); right DM versus right VL (t = −8.85, p < 0.001, large effect size)). The values of Shannon's Entropy were lower for the DM subdivision in all cases.
Additionally, for comparisons between hemispheres within groups there was a significant difference between the left and right DM subdivision within the HC group (t = 3.53, p < 0.001, medium effect size) – the Shannon's Entropy values were lower for the right DM compared to the left one.
For details regarding how the effect size was calculated see Supplementary Materials.
Clustering results & values of internal validation measures
Application of the RQA-based pipeline (Bielski et al., 2021) resulted in amygdala parcellations with two subdivisions in the left and right amygdala (see Fig. 4), characterized by volumes presented in Fig. 5 by numbers of voxels. For the CSBD group, the DM part contained more voxels than the VL part for both amygdalae. In the HC group, however, the right DM subdivision was slightly smaller than the VL one, whereas for the left amygdala, the DM part was much bigger than the VL subdivision.
Visualization of amygdala parcellations obtained for the Compulsive Sexual Behavior Disorder (CSBD) and Healthy Controls (HC) group (red color – dorsomedial subdivision (DM), green color – ventrolateral subdivision (VL)). MNI coordinates: x = 104, y = 206, z = 95 (views: sagittal, coronal, axial). The visualization was made using MRIcron (version v1.0.20190902, www.nitrc.org/projects/mricron, Rorden, Karnath, & Bonilha, 2007)
Citation: Journal of Behavioral Addictions 2025; 10.1556/2006.2025.00014
Numbers of voxels in amygdala subdivisions (dorsomedial (DM) and ventrolateral (VL)) for the left and right amygdala for the parcellations obtained for the Compulsive Sexual Behavior Disorder (CSBD) and Healthy Controls (HC) group
Citation: Journal of Behavioral Addictions 2025; 10.1556/2006.2025.00014
Results of internal validation measures for the left and right parcellated amygdala for both groups are presented in Table 3 (for details regarding their interpretation see Supplementary materials).
Values of internal validation measures computed for the parcellations obtained for the Compulsive Sexual Behavior Disorder (CSBD) and Healthy Controls (HC) group
| CSBD | HC | |||
| Left amygdala | Right amygdala | Left amygdala | Right amygdala | |
| BetaCV | 0.701114 | 0.746515 | 0.872935 | 0.769215 |
| Normalized Cut | 0.730067 | 0.711009 | 0.651305 | 0.724856 |
| Silhouette Coefficient | 0.283635 | 0.245088 | 0.111966 | 0.201811 |
CSBD: Compulsive Sexual Behavior Disorder, HC: Healthy Controls.
Correlation of Shannon's Entropy values with scores of psychological questionnaires in the CSBD group
For each subject, the mean value of Shannon's Entropy from all voxels for each bilateral amygdala subdivision was calculated. These values were used in correlation analysis (Pearson's or Spearman's correlation coefficient) between mean values of Shannon's Entropy and scores of psychological questionnaires (Table 4). This analysis was performed only on data from the CSBD group.
Values of correlation coefficients (Pearson's or Spearman's) and p-values for correlations calculated between Shannon's Entropy values and scores of psychological questionnaires for the CSBD group for the left amygdala and right amygdala
| Left amygdala | Right amygdala | |||||||
| Dorsomedial | Ventrolateral | Dorsomedial | Ventrolateral | |||||
| Correlation coefficient value | p-value | Correlation coefficient value | p-value | Correlation coefficient value | p-value | Correlation coefficient value | p-value | |
| Brief Pornography Screening Test (BPS) | −0.1987 | 0.2015 | −0.2058 | 0.1854 | 0.1953 | 0.2095 | −0.1904 | 0.2213 |
| Sexual Addiction Screening Test-Revised (SAST-R) | 0.0046 | 0.9764 | 0.0251 | 0.8732 | 0.2606 | 0.0915 | −0.1866 | 0.2310 |
| Obsessive-Compulsive Inventory (OCI-R) | [0.2030] | 0.1930 | [0.0390] | 0.8030 | [−0.0830] | 0.5970 | −0.3878 | 0.0102 |
| Hypersexual Behavior Inventory (HBI): Coping | [−0.0290] | 0.8520 | [−0.0110] | 0.9420 | [0.0980] | 0.5330 | −0.3315 | 0.0299 |
| Hypersexual Behavior Inventory (HBI): Control | −0.0266 | 0.8653 | −0.1100 | 0.4825 | 0.2040 | 0.1896 | −0.3171 | 0.0383 |
| Hypersexual Behavior Inventory (HBI): Consequences | [0.0220] | 0.8880 | [0.0540] | 0.7290 | [0.1460] | 0.3520 | −0.2155 | 0.1651 |
| Hospital Anxiety and Depression Scale (HADS): Anxiety Scale | [−0.3010] | 0.0500 | [−0.0170] | 0.9130 | [−0.1190] | 0.4470 | −0.4922 | 0.0008 |
| Hospital Anxiety and Depression Scale (HADS): Depression Scale | [−0.3870] | 0.0100 | [0.0790] | 0.6160 | [−0.0460] | 0.7690 | −0.3096 | 0.0433 |
| State-Trait Anxiety Inventory (STAI): state scale | [−0.3700] | 0.0140 | [−0.2740] | 0.0750 | [−0.2120] | 0.1720 | −0.4166 | 0.0055 |
| State-Trait Anxiety Inventory (STAI): trait scale | [−0.009] | 0.9570 | [−0.0680] | 0.6650 | [−0.0390] | 0.8060 | −0.0920 | 0.5574 |
| Barratt Impulsiveness Scale (BIS-11) | −0.3180 | 0.0377 | −0.0379 | 0.8091 | −0.0479 | 0.7602 | −0.3558 | 0.0192 |
CSBD: Compulsive Sexual Behavior Disorder. In bold – statistically significant results, in brackets – Pearson's correlation coefficient, no brackets – Spearman's correlation coefficient.
For the left DM subdivision, negative correlations with scores of anxiety (ρ = −0.3010, medium effect size) and depression (ρ = −0.3870, medium effect size), state anxiety (ρ = −0.3700, medium effect size), and impulsivity (ρ = −0.3180, medium effect size), and for the right VL subdivision negative correlations with scores of obsessive-compulsive symptoms (ρ = −0.3878, medium effect size), level of deficits in controlling sexual behavior (ρ = −0.3171, medium effect size) and level of coping with psychological distress by engaging in sexual behavior (ρ = −0.3315, medium effect size), anxiety (ρ = −0.4922, medium effect size) and depression (ρ = −0.3096, medium effect size), state anxiety (ρ = −0.4166, medium effect size) and impulsivity (ρ = −0.3558, medium effect size) (Table 4).
Functional connectivity
Results of seed-to-voxel analysis regarding rs-FC are presented in Table 5. Several connections between connectivity patterns and the severity of CSBD symptoms evaluated by psychological questionnaires were detected. For the SAST-R used as a second-level covariate functional peaks for the right DM subdivision (covering the left middle temporal gyrus, left inferior temporal gyrus, and right angular gyrus) and the left VL subdivision (covering bilateral precentral and postcentral gyri, right superior temporal gyrus, bilateral middle temporal gyri, left supramarginal gyrus, and left angular gyrus) were observed. Functional peaks partially covering the left supramarginal gyrus and left parietal operculum cortex were also obtained for the left DM subdivision when HBI Control was used as a covariate. Analysis of HBI Consequences as a covariate and the left VL subdivision as a seed resulted in two functional peaks covering the posterior cingulate gyrus, precuneous cortex, left thalamus, left hippocampus, and left cerebellum. No significant results were obtained upon using BPS and HBI Coping as covariates.
Results of the seed-to-voxel analysis for the bilateral amygdala (amygdala mask by Dominik Bach et al., 2011) and amygdala parcellation regarding scores of psychological questionnaires as second-level covariates and the CSBD group. Advanced Family-Wise Error control settings: voxel threshold p < 0.001 (p-uncorrected, two-sided), cluster threshold p < 0.05 (cluster-size p-FDR corrected, parametric stats). Brain structures in the column “brain areas covered” are based on the Harvard-Oxford atlas of cortical and subcortical areas and AAL atlas of cerebellar areas
| Questionnaire | Seed & hemisphere based on peak | Cluster (x, y, z) | Size | Size p-value* | Brain areas covered | t-statistic | p-FDR | |
| Sexual Addiction Screening Test (SAST-R) | Amygdala | Left | −10 −24 + 72 | 115 | 0.001572 | mainly covered Precentral Gyrus Left & Postcentral Gyrus Left | −5.16 | 0.000008 |
| +18 −30 + 64 | 78 | 0.008075 | mainly covered Precentral Gyrus Right & partially Postcentral Gyrus Right (k = 27) | −5.05 | 0.000008 | |||
| Amygdala, dorsomedial subdivision | Right | −64 −44 −20 | 92 | 0.005873 | mainly covered Middle Temporal Gyrus, posterior division Left & partially Inferior Temporal Gyrus, posterior division Left (k = 29) & Inferior Temporal Gyrus, temporooccipital part Left (k = 2) | −5.58 | 0.000003 | |
| +50 −54 + 28 | 74 | 0.009945 | Angular Gyrus Right | −4.68 | 0.000029 | |||
| Amygdala, ventrolateral subdivision | Left | −14 −34 + 72 | 74 | 0.010895 | mainly covered Postcentral Gyrus Left & partially Precentral Gyrus Left (k = 5) | −4.75 | 0.000030 | |
| +66 −32 + 00 | 44 | 0.039404 | mainly covered Middle Temporal Gyrus, posterior division Right & partially Superior Temporal Gyrus, posterior division Right (k = 13) | −5.14 | 0.000013 | |||
| −60 −48 + 12 | 43 | 0.039404 | mainly covered Middle Temporal Gyrus, temporooccipital part Left & Supramarginal Gyrus, posterior division Left & partially Angular Gyrus Left (k = 3) | −4.62 | 0.000035 | |||
| +12 −30 + 54 | 37 | 0.049770 | partially covered Precentral Gyrus Right (k = 11) & Postcentral Gyrus Right (k = 11) | −6.30 | 0.000001 | |||
| Hypersexual Behavior Inventory (HBI): Control | Amygdala, dorsomedial subdivision | Left | −52 −42 + 30 | 64 | 0.037032 | mostly not labeled in atlas, partially covered Supramarginal Gyrus, posterior (k = 19) & anterior division Left (k = 6) & Parietal Operculum Cortex Left (k = 4) | 5.89 | 0.000001 |
| Hypersexual Behavior Inventory (HBI): Consequences | Amygdala, ventrolateral subdivision | Left | −10 −44 + 02 | 94 | 0.002324 | mainly covered Cingulate Gyrus, posterior division & partially Precuneous Cortex (k = 14) & Thalamus Left (k = 8) & Hippocampus Left (k = 5) & Cerebellum Left (k = 5) | 5.35 | 0.000007 |
| +10 −32 + 38 | 50 | 0.027803 | Cingulate Gyrus, posterior division | 5.13 | 0.000007 | |||
*p-value based on FDR correction, CSBD: Compulsive Sexual Behavior Disorder.
Discussion and conclusions
This study aimed to reveal the functional characteristics of the amygdala subdivisions in CSBD patients. This goal was achieved by using a new parcellation pipeline based on the analysis of the temporal dynamics of the BOLD signal (Bielski et al., 2021). We managed to obtain amygdala parcellations consisting of four clusters (left and right dorsomedial – DM, left and right ventrolateral – VL) and to correlate the severity of CSBD symptoms assessed by psychological questionnaires with functioning of the amygdala's subdivisions. This is the first attempt to explore the amygdala as a parcellated structure in terms of functional connectivity in patients with CSBD.
Behavioral results showed significant between-group differences in the scores of scales measuring the severity of CSBD (HBI, SAST-R, BPS), obsessive-compulsive symptoms (OCI-R), impulsivity (BIS-11), as well as depression and anxiety (HADS, STAI). Individuals with CSBD scored higher than HC on these scales. Group differences with respect to sizes and function of amygdala subdivisions were also observed. The left DM and right VL amygdala subdivisions were shown to be smaller in the CSBD group than in the control group, as measured with numbers of voxels in amygdala subdivisions, while the left VL and right DM parts were larger in the CSBD group.
Moreover, in the CSBD group, mean values of Shannon's Entropy in the left DM and right VL subdivisions correlated negatively with scores of anxiety and depression, state anxiety, and impulsivity, while only in the right VL subdivision the mean values of Shannon's Entropy correlated negatively with scores of obsessive-compulsive symptoms, level of deficits in controlling sexual behavior and level of coping with psychological distress by engaging in sexual behavior. The results suggest that both the left DM and right VL contribute to symptoms or traits co-occurring with CSBD symptoms (such as higher levels of depression, anxiety and impulsivity) which might represent vulnerability to CSBD, but only the right VL subdivision is implicated in compulsive sexual behavior. The result indicating that the lower the entropy values, the greater severity of CSBD, may suggest that in subjects with more severe CSBD the right VL subdivision is influenced by fewer brain systems than in subjects with less severe CSBD.
Recent evidence supports the notion that CSBD shares similarities, in terms of pathophysiological and neural mechanisms, with substance and behavioral addictions (Brand et al., 2019; Draps, Kowalczyk-Grębska, Marchewka, Shi, & Gola, 2021; Gola et al., 2017). The addictive behaviors are related to cue-reactivity, e.g., the abnormal attribution of motivational significance to reward-associated cues (statements from Incentive Salience Theory – Berridge & Robinson, 2016). Several studies point to the role of the right amygdala in cue-reactivity (e.g., García-Castro, Cancela, & Cárdaba, 2022). Given that cue-reactivity contributes to the development of habitual behaviors associated with addiction, and that the right amygdala shows greater habituation than the left one (Wright et al., 2001), it is likely that severity of CSBD can be related to the right amygdala function. Our findings additionally indicate that within the right amygdala, the right VL subdivision may be specifically involved in regulating behaviors related to compulsive sexual behavior and perhaps other addictive behaviors. This suggestion finds support in the results of a previous study based on the same parcellation method (Bielski et al., 2021) which showed that the VL subdivision corresponds to the anatomically defined basolateral region of the amygdala (BLA). The BLA is considered an integrative hub for processing emotional and rewarding stimuli (Janak & Tye, 2015) that has been implicated in the pathophysiology of addictive behaviors (DiLeo et al., 2024). Increased activity in the BLA is important for reward salience, motivation, and processing of drug associated cues (Wassum & Izquierdo, 2015). Unfortunately, little is known as to the functional asymmetry between the left and right BLA. Nevertheless, two lines of evidence linking either the right amygdala or bilateral BLA with addictive behaviors may indicate a specific relationship between the function of the right BLA and addiction.
On the other hand, seed-to voxel rs-FC analyses with the amygdala subdivisions as ROIs show that the severity of CSBD is mainly related to rs-FC between the left amygdala subdivisions (particularly VL) and cortical areas. In particular, rs-FC between the left VL and middle temporal gyrus extending to supramarginal gyrus correlated negatively with the severity of CSBD, whereas rs-FC between the left VL and posterior division of the cingulate cortex, precuneous cortex and hippocampus positively correlated with the severity of CSBD. Thus, we observed changed functional connectivity between the left VL and several nodes of the default mode network (DMN; Greicius, Supekar, Menon, & Dougherty, 2009), which processes self-relevant information during the resting-state. Given that the amygdala belongs to the salience network (SN) which mediates switching between the DMN and executive control network (ECN) (Menon, 2019), it may be that our finding reflects an abnormal interaction between the SN and DMN. Increasing number of studies have reported the deficient modulation of ECN versus DMN by SN in substance use disorders such as cocaine addiction (Zhai, Gu, Salmeron, Stein, & Yang, 2023) and behavioral addictions such as internet gaming disorder (Siste et al., 2022; Zhang et al., 2017). This indicates that impaired attention switching and incentive salience regulated by the SN might be a neural mechanism of addictive behavior. Interestingly, a previous study (Engel et al., 2023) found increased functional connectivity between the left amygdala and area of the right precuneous cortex in the CSBD group and this finding correlated positively with the average pornography consumption. A part of the precuneous cortex has also been shown to be implicated in internet gaming disorder, as the disorder severity modulated the precuneous cortex involvement (Dong et al., 2021).
FC analyses have also shown that connectivity between the left VL and cerebellum correlated positively, while connectivity between the left VL and sensorimotor cortex (precentral and postcentral gyri) correlated negatively with CSBD severity. The cerebellum plays a role in neural loops which sustain compulsive and impulsive behaviors (Bostan & Strick, 2018; Ding et al., 2013; Herrera-Meza et al., 2014) and cerebellar alterations in CSBD seem to be associated with traits related to compulsive behavior (Seok & Sohn, 2018). The amygdala-precentral/postcentral FC may reflect an integration of somatosensory information with emotional input and may link the perception of emotional stimuli to actions (Grèzes, Valabrègue, Gholipour, & Chevallier, 2014; Rizzo et al., 2018). The weakened VL-precentral/postcentral connectivity might thus represent the alterations of inhibitory mechanisms in sensory-motor integration processes, which underlie compulsive behavior (Cao et al., 2022; Russo et al., 2014). Overall, these results suggest that changes in connectivity between the left VL, cerebellum and sensorimotor cortex underlie severity of compulsive symptoms.
Although the left VL connectivity seems to play a crucial role in modulating the severity of CSBD, connectivities of other amygdala subdivisions are also important. Namely, we found the involvement of positive connectivity between the left DM and supramarginal gyrus, as well as negative connectivity between the right DM and several temporal cortical areas. The former confirms abnormal interaction between the SN and DMN in CSBD. The latter indicates a role of the temporal lobe structures. Similarly, previous studies have found that the temporal lobe is a crucial structure in the regulation of human sexual arousal (Baird, Wilson, Bladin, Saling, & Reutens, 2007), and symptoms of hypersexuality after temporal lobectomy confirm the role of the temporal lobe in the regulation of such type of behavior (e.g., Baird, Wilson, Bladin, Saling, & Reutens, 2002; Baird et al., 2007; Bladin, 1992; Blumer, 1970).
Taking all results into consideration, treating the amygdala as a complex structure enabled revealing multiple correlations between its rs-FC and the severity of CSBD symptoms assessed by psychological questionnaires, which are non-existent when the amygdala is treated as a non-parcellated structure. In the present study, our analysis showed several correlations between the rs-FC of the amygdala subdivisions and the severity of CSBD symptoms, contrary to the non-parcellated amygdala for which few significant results were acquired. Therefore, it might be hypothesized that some of the clinical effects regarding the amygdala can only be observed upon its analysis as a parcellated structure. In a recent study performed on the same dataset, a literature review revealed few or no results for the non-parcellated amygdala regarding rs-FC or connectivity during task in CSBD (Engel et al., 2023; Klucken et al., 2016; Schmidt et al., 2017; Voon et al., 2014). The performed ROI-to-ROI rs-FC analysis provided no significant results for this structure (Draps et al., 2022).
What is more, our results point to different contributions of the right and left amygdala (particularly the right and left VL) to CSBD. There are several hypotheses about the nature of differences between the right and left amygdalae during emotional information processing. According to one of them (Baas, Aleman, & Kahn, 2004; Gläscher & Adolphs, 2003), emotional information automatically activates the right amygdala, mediating a relatively global emotional reaction, while the left amygdala is involved in a more detailed, cognitive perceptual emotional information processing. Our findings fit well to this hypothesis and additionally show that such a functional asymmetry refers primarily to the VL amygdala. The result showing that the severity of CSBD mainly depends on the right VL function might reflect the role of the right VL in global emotional reaction. On the other hand, the role of connectivity between the left amygdala and cortical areas could be interpreted as the involvement of the left amygdala in more cognitive emotional information processing which relies on communication between “affective” and “cognitive” brain areas. Thus, our results might suggest that CSBD severity is associated with a deficiency in automatic, fast and global emotional reactions mediated by the right VL, as well as with a dysfunction in a more detailed and cognitive processing mediated by the left amygdala, particularly the left VL.
To conclude, our study provides the first amygdala parcellation in CSBD patients which revealed alterations in subdivisions' size compared to healthy individuals and multiple correlations of their functioning with the severity of CSBD symptoms. The results of our study have provided evidence that exploration of the amygdala on the level of its subdivisions is important to understand how the changes in its functional processing are related to the neural mechanisms behind CSBD. Given the above data, we can attempt to formulate the first insights for clinicians working with CSBD patients. Most recent studies on rodents, allowing for precise recording and optogenetic manipulation of the central nucleus of the amygdala (Nguyen & Berridge, 2024; Warlow & Berridge, 2021; Warlow et al., 2020) provide strong evidence for its involvement in the development of sensitization and strong attraction not only to appetitive but also aversive (e.g., providing electric shock) stimuli. If similar phenomena happened in CSBD, it would be worth to assess in more detail the patient's attitude towards their sexual behavior or watched pornographic content, as some individuals may acquire incentive silence to initially aversive or ambiguous stimuli. Such individuals usually experience guilt or disgust towards their sexual behaviors and psychoeducation around conditioning for such stimuli may provide relief. It also seems reasonable to test therapeutic strategies related to cue reactivity and incentive salience activation, i.e., through habituation and/or extinction processes. Cognitive-behavioral therapy (CBT) techniques (e.g., behavioral experiments or exposure therapy working based on habituation of the response to the cue) supported by pharmacotherapy (e.g., opioid antagonist-based treatment) could be potentially helpful tools (Lew-Starowicz et al., 2022). Relations of specific subdivisions of the amygdala with compulsive and depressive symptoms are also of interest, pointing to the potential use of CBT protocols aiming at the treatment of anxiety and mood disorders.
Our study highlights the need to treat the amygdala as a complex structure to explain its role in CSBD. We believe that further investigation of specific sections of the amygdala and their involvement in CSBD will contribute to a better understanding of this disorder and inform future treatment.
Limitations
Firstly, the patient population consisted solely of heterosexual men from an average-income country therefore the results might not necessarily apply to female and sexually diverse individuals with CSBD symptoms (Kowalewska, Bőthe, & Kraus, 2024; Weinstein, Katz, Eberhardt, Cohen, & Lejoyeux, 2015). Additionally, the amygdala parcellation from the study by Bielski et al. (2021) was acquired on a mixed-sex dataset.
Secondly, it is not possible to specify how the age and number of subjects might have influenced the parcellation results. It can only be hypothesized whether a larger, more homogenous in-age patient group would allow for a better amygdala parcellation.
Another limitation of this study is that the comparison of amygdala subdivisions' sizes can be done only subjectively because the application of the pipeline resulted in a single parcellation for each group. Therefore, there are no mean values to which one could refer to perform statistical tests. Additionally, no other amygdala parcellations for CSBD patients are available.
As a result, it is also not possible to calculate the effect size of such a comparison.
Lastly, in terms of quality, a better parcellation was obtained for the CSBD group than the HC group. This could be caused by the fact that in this study there were almost two times more CSBD patients than HC.
Funding sources
Data collection was conducted as a part of a study supported by the Polish National Science Centre OPUS grant (2014/15/B/HS6/03792) to MG. Data analysis was conducted as a part of the Polish National Science Centre PRELUDIUM grant (2016/23/N/HS6/02906) and MD is supported by the Foundation for Polish Science scholarship number START 014.2023. The study results were published with financial support from the Polish National Science Centre SHENG 2 grant (2021/40/Q/HS6/00219) to MG.
The Polish National Science Centre and Foundation for Polish Science have no role in the study design, collection, analysis, or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.
Authors' contribution
SA contributed to conceptualization, methods design, data analysis and interpretation, manuscript writing, and visualization. KB and IS contributed to conceptualization, data interpretation and manuscript writing. MG contributed to conceptualization, providing data, data interpretation, manuscript writing and obtaining funding. MD contributed to study supervision, conceptualization, providing data, data interpretation, manuscript writing and obtaining funding. All authors have accepted the final version of the manuscript.
Conflict of interest
SA, KB, IS, MG, and MD have nothing to declare.
Data and code availability
Matrices containing the results of Recurrence Quantification Analysis are available on osf.io website (
Supplementary material
Supplementary data to this article can be found online at https://doi.org/10.1556/2006.2025.00014.
Abbreviations
| AUDIT | Alcohol Use Disorder Identification Test |
| BIS-11 | Barratt Impulsiveness Scale |
| BLA | basolateral amygdala |
| BOLD | blood-oxygen-level-dependent |
| BPS | Brief Pornography Screening Test |
| CBT | Cognitive Behavioral Therapy |
| CSBD | Compulsive Sexual Behavior Disorder |
| CONN | CONN Functional Connectivity Toolbox |
| DM | dorsomedial |
| DMN | default mode network |
| ECN | executive control network |
| HADS | Hospital Anxiety and Depression Scale |
| HBI | Hypersexual Behavior Inventory |
| HC | Healthy Controls |
| HD | Hypersexual Disorder |
| ICD-11 | 11th edition of the International Classification of Diseases |
| OCI-R | Obsessive-Compulsive Inventory |
| MRI | magnetic resonance imaging |
| RQA | Recurrence Quantification Analysis |
| RR | Recurrence Rate |
| rs-FC | resting state functional connectivity |
| rs-fMRI | resting-state fMRI |
| SAST-R | Sexual Addiction Screening Test-Revised |
| SBC | seed-based connectivity |
| SN | salience network |
| STAI | State-Trait Anxiety Inventory |
| SOGS | South Oaks Gambling Screen |
| VL | ventrolateral |
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