Three decades of research have investigated brain processing of visual sexual stimuli with neuroimaging methods. These researchers have found that sexual arousal stimuli elicit activity in a broad neural network of cortical and subcortical brain areas that are known to be associated with cognitive, emotional, motivational and physiological components. However, it is not completely understood how these neural systems integrate and modulated incoming information. Therefore, we identify cerebral areas whose activations were correlated with sexual arousal using event-related functional magnetic resonance imaging and used the dynamic causal modeling method for searching the effective connectivity about the sexual arousal processing network. Thirteen heterosexual males were scanned while they passively viewed alternating short trials of erotic and neutral pictures on a monitor. We created a subset of seven models based on our results and previous studies and selected a dominant connectivity model. Consequently, we suggest a dynamic causal model of the brain processes mediating the cognitive, emotional, motivational and physiological factors of human male sexual arousal. These findings are significant implications for the neuropsychology of male sexuality.
There have been lots of studies using neuroimaging techniques to the evaluation of sexual arousal in healthy males. These researchers suggested that the experience of sexual arousal is a complex interaction between cognitive, emotional, motivational and physiological factors.1, 2, 3, 4 The cognitive factor of sexual arousal comprises labeling process (categorization as a sexual incentive), appraisal (evaluation of the intensity of sexual incentive) and attention to stimuli evaluated as sexual incentive. Evidence obtained from these studies indicates that inferior temporal cortex, the right lateral orbitofrontal cortex and superior/inferior parietal cortex are relevant to cognitive component. The emotional factor includes evaluation of hedonic quality of sexual arousal stimulus (the pleasure associated with rising arousal). Amygdala, primary somatosensory cortex and posterior insula participate in emotional process. The rostral anterior cingulate cortex and ventral striatum are correlated to the motivational factor that direct behavior to a sexual goal by integrating the perceived impulse to explicit sexual arousal and the perceived need to suppress any overt sexual behavior in the current circumstances. The physiological factor is related to various responses (that is, genital, cardiovascular, respiratory change and so on) making subject to a state of readiness for sexual behavior.3, 4 Anterior insula, putamen and hypothalamus participate in physiological component. However, previous studies did not completely explain the interaction, inter-relationship and integrated performance of these different areas.
Dynamic causal modeling (DCM) is a useful method for interpreting functional neuroimaging data and developed for making and estimating intrinsic coupling (task-dependent interactions) within a set of regions and how that coupling is influenced by experimental factor (that is, time or context).5
It was the aim of this study to investigate the connectivity pattern subserving communication between different regions related to four components during sexual arousal. Therefore, we used event-related functional magnetic resonance imaging (fMRI) combined with the innovative technique of DCM method to investigate the neuronal interactions of different brain regions associated with sexual arousal and how these interactions are modulated by an experimental factor.
Materials and methods
Thirteen heterosexual and right-handed males with normal sexual function (mean age=27.2 years; range=21–36) participated in the current fMRI experiment. The recommended sample size is >12 subjects to obtain 80% statistical power in fMRI study.6 None of the subjects had a history of head injury or other neurological condition. The participants arrived in the laboratory 20 min before the experiment, received information about the experimental procedure and signed the consent form. Then our experiments were performed under an approved IRB protocol of Chungnam National University (201309-SB-003-01).
This study was performed using an event-related paradigm in which visual stimuli comprising erotic and neutral pictures were presented pseudorandomly. To induce sexual arousal, we used 20 erotic pictures. Twenty neutral pictures were used for comparison purposes. Erotic stimuli were extracted from the International Affective Picture System (IAPS)7 and Google image. Each picture was shown for 5 s and was followed by a fixation cross (inter-stimulus interval). Walter et al.8 indicates that 5 s is the optimal length for the presentation duration to reliably invoke the subjective experience of sexual arousal. A jittered inter-stimulus interval was used, varying randomly from 6 to 10 s (Figure 1).
Self-report of subjective sexual arousal
In order to investigate how psychological factor modulates DCM, the subjective intensity of sexual arousal was obtained after MR scanning (from 1 for calm to 5 for highly sexually aroused).
fMRI image acquisition
Imaging was conducted using a 3.0 T Philips MR scanner (Philips Achieva, Philips, Best, The Netherlands) equipped with whole-body gradients and a quadrature head coil. Single-shot echo-planar fMRI scans were continuously acquired in 35 slices, parallel to the anterior commissure–posterior commissure line. The parameters for fMRI included the following: the repetition time/echo time (TR/TE) were 2000/28 ms, respectively, flip angle was 80°, field of view was 240 mm, matrix was 64 × 64, slice thickness was 5 mm, and the in-plane resolution was 3.75 mm. Three dummy scans from the beginning of the run were excluded to decrease the effect of non-steady state longitudinal magnetization. T1-weighted anatomic images were obtained using a 3-D FLAIR sequence (TR/TE=280/14 ms, flip angle=60°, FOV=240 mm, matrix=256 × 256, slice thickness=4 mm).
Analysis of fMRI processing
The first four image volumes were discarded to avoid the instability of the initial MRI signal. After that, preprocessing steps, carried out with the SPM 8 toolbox (www.fil.ion.ucl.ac.uk/spm), were slice-timing correction for interleaved acquisition, motion correction and spatial normalization into a standard template provided by the Montreal Neurological Institute. Then spatial smoothing with 8-mm Gaussian kernel was processed. For each subject, a design matrix modeling of erotic and neutral pictures was defined. Intensity of sexual arousal and time modulation were also entered into a design matrix as regressors. By using the general linear model, effects at every voxel were estimated. Voxel values for each contrast yielded a statistical parametric map of the t-statistic, subsequently transformed to the unit normal distribution. A random-effects model was utilized to produce the sexual arousal (erotic stimuli) minus control trial (neutral stimuli) contrasts. The threshold was set at P<0.05 with false discovery rate correction.
Analysis of DCM processing
We used the DCM option in SPM 8 toolbox to evaluate the effective connectivity between ROIs (regions of interest). Definition of ROIs for DCM relied on activation clusters obtained from result of group analysis (sexual arousal trial>control trial). We considered five main ROI for our DCM analysis: the right fusiform gyrus, the right amygdala, the anterior cingulate cortex, the right orbitofrontal gyrus, and putamen (Table 1). Friston et al.5 invented the principle of the DCM approach, which is used to infer the effective connectivity using fMRI data and to implement a predefined causal model of how the observed data are caused.9 DCM approximates the dynamics of interacting neuronal populations based on bilinear differential equations. These neuronal state equations are mixed from a forward model and the balloon model,5, 10 which links neuronal activity to the predicted regional blood oxygen level-dependent (BOLD) signal by considering how changes in neuronal activity elicit a vasodilatory signal, changes in blood flow and in blood volume and deoxy-hemoglobin content. The combined neuronal and hemodynamic parameters of the DCM are estimated from the measured BOLD signal by means of iterative Bayesian estimation to select the optimized model in terms of its evidence. We created a subset of seven models based on our results and previous studies (Figure 2).3, 4 A DCM was specified for all subjects with bidirectional and intrinsic connectivity between five regions, and the main effect of erotic stimuli as the external input entering the visual processing center (fusiform gyrus). Then the six variant models assuming the information conductance pattern (parallel, serial or full connectivity) and connected direction (unidirection or bidirection) were produced. We used a different combination of effective connectivity to compare across several competing models and to select a dominant connectivity model. The connectivity parameters were averaged using Bayesian model averaging across subjects and seven models were compared via Bayesian model selection combined with the random-effect Bayesian method on the group level.
Conventional fMRI analysis
To identify brain regions specifically contributing to the processing of sexual arousal, BOLD responses during the two trials (sexual arousal, control) were contrasted. By using subtraction method, activation within the bilateral middle occipital gyrus (BA 18, 19), fusiform gyrus (BA 37), precuneus (BA 19), inferior parietal cortex (BA 40), orbitofrontal cortex (BA 47), rostral anterior cingulate cortex (BA 24), thalamus, putamen, insula (BA 13), globus pallidus and amygdala could be ascribed to the identification of sexual arousal (Table 2, Figure 3).
Dynamic causal modeling
The graph of Figure 4 compares the seven dynamic causal models resulting from the Bayesian model selection. Under the Bayesian approach, it is commonly perceived that the best predictive model is the model with highest posterior probability among a set of competing models. In our result, model 7 shows the highest posterior probability (16.83%). To evaluate the coupling parameters among the regions of model 7, the Bayesian model averaging method was used and the effective connectivity strengths were estimated during sexual arousal trials (Figure 5). Table 3 shows the neuronal coupling among the regions of interest resulted from the Bayesian model averaging across all subjects and models (P<0.05, uncorrected for multiple comparisons). These results revealed the importance of the intrinsic and unidirected connections among the fusiform–amygdala, amygdala–anterior cingulate cortex and anterior cingulate cortex–putamen in the network. Furthermore, the coupling parameters define the endogenous connection strengths between regions, describing the strength of a coupling in terms of the rate (in Hz). Positive coupling rates represent that the origin area exerts a enhancing influence on the activity of the target region. Negative coupling rates represent that the source area exerts a decreasing or inhibitory effect on the activity of a target area. As a result, the effective connection between amygdale and anterior cingulate cortex was stronger than any other connectivities (i.e., between the fusiform gyrus and the amygdala and between the anterior cingulate cortex and putamen).
Our results integrate and expand evidences from previous studies1, 2, 3, 4, 8, 11, 12, 13 and clarify the neural circuit subserving the processing of erotic stimuli. The results of conventional analysis are consistent with a recently published study on sexual arousal1, 2, 3, 4, 8, 11, 12, 13 and show that fusiform gyrus, amygdala, orbitofrontal cortex, anterior cingulate gyrus and putamen were found to be involved in the processing of sexual arousal. These areas are known to be associated with processing of emotional visual information, emotional processing, cognitive processing, motivational processing and physiological processing of sexual arousal, respectively.4
Neuroimaging studies suggested that the fusiform gyrus may be related to the recognition and determination of a stimulus as a sexual stimulus.4, 14, 15 The prevailing interpretation of amygdala activation in response to sexual stimulation has reported that the amygdala had a key role in the assessment of the emotional aspects (that is, the degree of pleasure) of complicated sensory information related to sexual stimuli.3, 16 The orbitofrontal cortex is known to be involved in cognitive or motivational processing. FMRI studies17, 18 found the orbitofrontal cortex was related to perception of facial attractiveness. In addition to this role in cognitive process, Burns and Swerdlow19 pointed out that the orbitofrontal cortex may be associated with inhibition of the sexual urge and sexual orientation. The rostral anterior cingulate cortex are correlated to the motivational component that direct behavior to a sexual goal by integrating the perceived urge to enact sexual arousal and the perceived need to withhold any overt sexual behavior in the current circumstances. The physiological component is relevant to various responses (that is, genital, cardiovascular, respiratory change and so on) leading subject to a state of readiness of sexual behavior,3, 4 and putamen participates in physiological component. However, previous studies did not completely explain the organization, inter-relationship and integrated performance of these different regions. So we investigated dynamic causality to sexual arousal processing network.
To identify intrinsic connections within these areas, DCM method was used. With respect to the intrinsic connectivity structure, various competing models for processing of sexual arousal were compared. Across all subjects, our modeling result on the intrinsic connectivity of regions involved in processing of the sexual arousal suggest that various areas conduct sequential processing steps from fusiform gyrus to anterior cingulate gyrus and then parallel connectivity pattern from anterior cingulate gyrus to putamen and orbitofrontal cortex were specified. Interestingly, the functional connectivity suggested by this study were similar to the anatomical connectivity.3 Anatomically, the anterior cingulate cortex receives massive projections from the amygdala. The amygdala receives from the activated temporo-occipital cortex, including the fusiform gyrus dense projections. In addition, the anterior cingulate cortex is reciprocally connected with the orbitofrontal cortex. Finally, the anterior cingulate cortex projects to the putamen.3
The present study has several limitations that need to be considered. First, this study was limited to male of South Korean nationality. So caution should be needed in generalizing results from the present study to people of other gender and ethnicities. Second, our findings do not eliminate the possibility that some ROIs may be activated not only by sexual arousal but also by other emotions (that is, pleasure or disgust). Although we cannot exclude the confounding effect from other factors, not measured here, our findings are consistent with previous results from sexual arousal studies indicating that the ROIs, including fusiform gyrus, amygdala, orbitofrontal cortex, anterior cingulate gyrus and putamen, may be associated with processing of sexual arousal.
Despite these limitations, we identified a dynamic causal model of the brain processes that mediates the cognitive, emotional, motivational and physiological components of human male sexual arousal. Our results are significant implications for pathophysiological models of individuals with sexual dysfunction, which goes a step beyond the descriptions of neural activation in various brain areas presented in previous studies.
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This research was supported by Individual Basic Science & Engineering Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(NRF-2015R1D1A1A01059095).
The authors declare no conflict of interest.
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Seok, JW., Park, MS. & Sohn, JH. Neural pathways in processing of sexual arousal: a dynamic causal modeling study. Int J Impot Res 28, 184–188 (2016). https://doi.org/10.1038/ijir.2016.27