Correction: The impact of internet pornography addiction on brain function: a functional near-infrared spectroscopy study

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IntroductionWith the proliferation of technology and changes in the social environment, pornographic content, especially pornographic films, has become widely disseminated through the internet. There has been a rapid increase in the frequency and duration of internet pornographic use, particularly during the 2019 coronavirus (COVID-19) pandemic (Van Loo et al., 1997; Yunengsih and Setiawan, 2021). A survey in the United States revealed that 66% of teenagers had encountered internet pornography unintentionally, while 34% had deliberately sought it out (Wolak et al., 2007). A survey conducted in China showed that in recent years, there has been a rapid increase in the frequency and amount of internet pornography accessed by adolescents (Dong et al., 2020). Due to the prolonged overactivation of the rewards system, ceasing the supernormal stimulus of watching pornography for an extended period can lead to withdrawal reactions, resulting in negative emotions such as anxiety, irritability, depression, and anger; all of these can seriously affect cognitive function (Koob and Volkow, 2010). Previous studies have found that internet pornography addiction and drug addiction exhibit similar phenomena (Love et al., 2015), but the relationship between the two has not yet been clarified, and the underlying mechanisms have not yet been fully elucidated.The sustained and intense release of dopamine while watching pornography can lead to a strong craving for and dependence on it. A functional magnetic resonance imaging (fMRI) study on internet pornography (Kühn and Gallinat, 2014) found a significant negative correlation between the volume of the right caudate gray matter and functional activity in the left putamen cue-response paradigm in individuals who had viewed pornography for an extended period of time. The functional connection between the right caudate nucleus and the left dorsolateral prefrontal cortex correlated negatively with the duration of watching pornography. The problematic Internet pornography use scale (PIPUS) is a tool for evaluating the problematic consumption of internet pornography (Chen and Jiang, 2020). It is used to measure and identify signs of dependence and related issues in an individual’s use of internet pornography. The Stroop Color Task is a psychological test used to measure cognitive interference and the ability to manage conflicting information (Williams et al., 1996). Thanks to the rapid development of non-invasive measurement technology, functional near-infrared spectroscopy (fNIRS) is an optical, non-invasive neuroimaging technique that can measure changes in the concentrations of oxyhemoglobin and deoxyhaemoglobin in specific brain regions (Scholkmann et al., 2014). It can continuously monitor the hemodynamic changes in specific brain regions while an individual performs a specific task and simultaneously calculates the average changes in hemoglobin during this period. Compared to fMRI, fNIRS offers a more compact form and is easier to operate. Its convenient operation, including a quiet measurement environment, no limitations on task performance, and low interference with other equipment, makes it particularly well-suited for investigating alterations in brain functional connectivity during the viewing of internet pornography. Subsequently, fNIRS was used to evaluate the performance of functional connectivity in participants while they viewed pornography. Previous studies have also applied fNIRS to investigate drug abuse (Qi et al., 2022). Furthermore, life signs and facial expressions were measured continuously during the viewing. The questionnaires, including PIPUS, the self-rating depression scale (SDS) (Zung, 1965), and self-rating anxiety scale (SAS) (Dunstan and Scott, 2020), were also completed accordingly. We used the above methods to investigate the mechanisms of internet pornography addiction and identify potential treatment approaches.Materials and methodsParticipantsSixteen healthy college students who occasionally watched pornographic films and five college students with severe internet pornography addiction were invited to participate in the experiment. The inclusion criteria included no illness or disease and right-handedness. The exclusion criteria included red-green color blindness, non-heterosexuality, smoking, substance abuse issues, and being unhealthy. Masturbation was prohibited during the experimental period. Each participant provided written informed consent prior to starting the experiment.Experimental materials and proceduresThe SCWT was conducted using the E-Prime 3.0 software (Psychology Software Tools, Inc. United States). A 10 min internet pornographic video—selected on how often it had been viewed— was used in this test. Each participant completed the SCWT, watched the 10 min pornographic video (Figure 1), and then completed the SCWT again while changes in hemoglobin were measured with fNIRS. Their life signs and facial expressions were also recorded. The SCWT was administered 18 times per group; each trial lasted 2 s and was skipped if no response was given within the timeframe (Figure 2). Subsequently, the participants were asked to complete three survey questionnaires: PIPUS, SDS, and SAS.Measurement of facial expressions and vital signsFacial expressions were measured using the Noldus Face Reader 7.0 (Noldus Information Technology, NL). The Mindray (VS- 600) was used to measure the following life signs: heart rate, percutaneous arterial oxygen saturation (SpO2), the standard deviation of normal-to-normal interbeat intervals (SDNN), systolic blood pressure (SBP), and diastolic blood pressure (DBP) (Zhang et al., 2024).fNIRS data collectionThe fNIRS signals were acquired using a multi-channel system (NirSmartII-3000A, Danyang Huichuan Medical Equipment Co., Ltd., China) with a sampling rate of 11 Hz and dual wavelengths (730 and 850 nm). Each participant wore a stretchable hood that covered the frontal, bilateral temporal, parietal, and occipital lobes. The cap comprised 21 sources and 16 detectors with spacings of 3 cm, resulting in 48 measurement channels. The spatial coordinates of the sources, detectors, and anchor points (at Nz, Cz, Al, Ar, and Iz, following the international 10–20 electrode placement system) were determined using an electromagnetic three-dimensional (3D) digitizer (Patriot, Polhemus, Colchester, Vermont, United States) on the head mold(Figure 3). Supplementary Table 1 presents the anatomical regions corresponding to each channel and their respective coverage percentages. The obtained coordinates were converted to the Montreal Neurological Institute (MNI) space and mapped onto the MNI standard brain template using the spatial registration method provided by NirSpace (China Limited, Danyang).Data analysisThe fNIRS signals were pre-processed using NirSpark V1.8.1 (Danyang Huichuang Medical Equipment Co., Ltd, Danyang). First, head motion was corrected. Second, digital band-pass filtering was applied for the range of 0.01–0.2 Hz. Third, the relative oxyhemoglobin, deoxyhemoglobin, and total hemoglobin concentration curves were obtained using the modified Beer–Lambert law (Wang et al., 2017). The path length factor for each wavelength was set to 6 to determine therelative deoxyhemoglobin, oxyhemoglobin, and total hemoglobin concentrations. Finally, the post-preprocessing channel integrity was verified, and missing data were excluded. By applying a band-pass filter (0.01–0.2 Hz) to each participant’s data during the video viewing, the 1OD signals were converted into 1[HbO2] and 1[Hb]. The Pearson correlation coefficients between the time series of different channels were calculated to construct a functional connectivity correlation matrix (R-value) for each participant during the video viewing period. These R-values were subsequently transformed into Fisher’s z-scores to approximate a normal distribution for further statistical analysis to assess intergroup differences in fu

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