Resting state functional connectivity in adolescent synthetic cannabinoid users with and without attention‐deficit/ hyperactivity disorder Zeki Yüncü 1 | Zehra Cakmak Celik2 | Ciğdem Colak3 | Tribikram Thapa4 | Alex Fornito4 | Emre Bora5 | Omer Kitis6 | Nabi Zorlu7 1 Department of Child Psychiatry, Ege University School of Medicine, Izmir, Turkey 2 Department of Child Psychiatry, Cizre State Hospital, Sirnak, Turkey 3 Department of Psychiatry, Cigli Regional Training Hospital, Izmir, Turkey 4 Brain & Mental Health Laboratory, Monash Institute of Cognitive and Clinical Neurosciences and School of Psychological Sciences, Monash University, Victoria, Australia 5 Department of Psychiatry, Dokuz Eylül University Medical School, Izmir, Turkey 6 Department of Radiodiagnostics, Ege University School of Medicine, Izmir, Turkey 7 Department of Psychiatry, Katip Celebi University, Ataturk Training and Research Hospital, Izmir, Turkey Correspondence Nabi Güçlü, Katip Celebi University Ataturk Training and Research Hospital, Department of Psychiatry, Izmir, ̇ Turkey. Email: [email protected] Funding information Ege University Science and Technology Application and Research Center Abstract Objective: Synthetic cannabinoids (SCs) have become increasingly popular in recent years, especially among adolescents. The first aim of the current study was to examine resting‐state functional connectivity (rsFC) in SC users compared to controls. Our second aim was to examine the influence of comorbid attention‐deficit/ hyperactivity disorder (ADHD) symptomatology on rsFC changes in SC users compared to controls. Methods: Resting‐state functional magnetic resonance imaging (fMRI) analysis included 25 SC users (14 without ADHD and 11 with ADHD combined type) and 12 control subjects. Results: We found (i) higher rsFC between the default mode network (DMN) and salience network, dorsal attention network and cingulo‐opercular network, and (ii) lower rsFC within the DMN and between the DMN and visual network in SC users compared to controls. There were no significant differences between SC users with ADHD and controls, nor were there any significant differences between SC users with and without ADHD. Conclusions: We found the first evidence of abnormalities within and between resting state networks in adolescent SC users without ADHD. In contrast, SC users with ADHD showed no differences compared to controls. These results suggest that comorbidity of ADHD and substance dependence may show different rsFC alterations than substance use alone. KEYWORDS ADHD, connectomics, functional connectivity, synthetic cannabinoids 1 | INTRODUCTION Synthetic cannabinoids (SCs) have become increasingly popular in the last few years, especially among adolescents and young adults (Forrester et al., 2012; Spaderna et al., 2013). SCs are marketed under various names such as “Spice,” “Bonzai,” “K2,” “Aroma,” or “Kronic.” Unlike Δ‐9‐ tetrahydrocannabinol (THC), the main psychoactive component found in cannabis, which is only a partial agonist of endocannabinoid receptors (Paronis et al., 2012), SCs are full agonists of cannabinoid receptor‐1 and cannabinoid receptor‐2 with greater affinity than THC (Atwood et al., 2010). Motives for using SCs instead of natural cannabis are curiosity, legality, availability, potent Hum Psychopharmacol Clin Exp. 2021;1–8. wileyonlinelibrary.com/journal/hup © 2021 John Wiley & Sons Ltd. – 1 psychoactivity, price, shorter duration of action, nondetection in drug testing, and the reduction of cannabis use (Barratt et al., 2013; Spaderna et al., 2013). Veri on human toxicity of SCs are limited but there is growing evidence that SCs are associated with serious negative psychiatric effects. For example, SCs are reported to be associated with acute psychosis and suicidal ideation (Weinstein et al., 2017). Furthermore, SCs also seem to have dependence liability (Vandrey et al., 2012). However, less is known about the effects of SCs on the human brain. In one previous voxel‐based morphometry study, young adult SC users showed significantly reduced bilateral thalamic and left cerebellar gray matter volumes compared to healthy controls (Nurmedov et al., 2015). A recent whole‐brain voxel‐based morphometry study reported lower gray matter volume in the right frontal lobe of SC users compared to controls (Livny et al., 2018). In addition, young adult SC users showed lower fractional anisotropy, a marker of white matter microstructure, in the inferior fronto‐occipital fasciculus, inferior longitudinal fasciculus, fornix, cingulum–hippocampus and corticospinal tracts than controls in a recent diffusion tensor imaging study (Zorlu et al., 2016). Another recent study found that adolescent SC users had significantly weaker white matter connectivity compared to controls, most notably in connections linking putamen and hippocampus to cortical regions (Çelik et al., 2020). Together, these findings suggest that SC use might be associated with both morphological and structural connectivity abnormalities in the human brain. Regarding brain function, one task‐based functional‐imaging study reported that SC users show reduced activation mainly in parietal and temporal regions including the lingual gyrus, the cuneus and precuneus during working memory (Livny et al., 2018). To date, no study has investigated resting‐state functional connectivity (rsFC) in chronic SC users. In users of natural cannabis, seed‐based analyses have identified reduced functional connectivity of default mode network (DMN) and subcortical structures (Blanco‐Hinojo et al., 2017; Manza et al., 2018; Pujol et al., 2014; Wetherill et al., 2015). One additional study using independent component analysis (ICA) found greater rsFC in the salience network (SN) and posterior cingulate gyrus in controls compared to cannabis users (Filbey et al., 2018). To date, no study has adopted a connectome‐ wide approach to mapping functional connectivity changes associated with cannabis use across all possible inter‐regional connections. A further consideration in this context is the role of comorbid psychiatric conditions. In particular, attention‐deficit/hyperactivity disorder (ADHD) is overrepresented in patients with substance use across both adolescents and adults compared to the general population (Notzon et al., 2016). Furthermore, ADHD also appears to interact with cannabis use in its effect on functional brain networks (Kelly et al., 2017). However, most previous studies examining the impact of cannabis use on rsFC did not control for ADHD, which might be a potential confounder. The first aim of the current study was to examine rsFC in SC users compared to controls. Our second aim was to examine the influence of comorbid ADHD symptomatology on rsFC changes in SC users compared to controls. To achieve these aims, we conducted a connectome‐wide analysis of rsFC in three demographically matched groups: adolescent SC users with and without ADHD and nonusing controls. 2 | MATERIALS AND METHODS 2.1 | Participants Twenty‐eight SC users (15 without ADHD and 13 with ADHD combined type) with a history of using SC more than three times per week for at least 6 months prior to study enrollment were enrolled in the study along with 13 controls. Patients were abstinent for at least 2 days before scanning. Users and controls were right‐handed. The groups were matched for age and duration of education. All the participants including controls were male and active cigarette smokers. Exclusion criteria for SC users were: illicit drugs consumed on more than 15 occasions in the past year or more than 12 alcoholic beverages consumed per week; history of any diagnosed psychiatric illness; use of psychoactive medications in the last 2 months; loss of consciousness for more than 10 min; hepatic, endocrine or renal disease; history or presence of a neurological disorder; and contraindications for magnetic resonance imaging (MRI). Controls met the same criteria as patients, except for a history of SC use and ADHD. Kiddie‐Schedule for Affective Disorders and Schizophrenia Present and Lifetime Version (K‐ SADS‐PL; Kaufman et al., 1997) was used to assess the presence of ADHD with combined type and other psychiatric diagnoses. The severity of ADHD symptoms was assessed with Turgay DSM‐IV‐Based Child and Adolescent Behavioral Disorders Screening and Rating Scale (Turgay, 1994). It is based on the DSM‐IV diagnostic criteria and assesses hyperactivity/ impulsivity (nine items), inattention (nine items), opposition/defiance (eight items) and conduct disorder (15 items). Symptoms are scored by assigning a severity estimate for each symptom on a 4‐point Likert‐ type scale (0 = not at all, 1 = just a little, 2 = quite a bit, and 3 = very much). Greater scores reflect increase in severity. All subjects/parents gave written informed consent to participate in the study. The study was approved by local ethics committees. 2.2 | MRI veri acquisition and preprocessing All MRI scans were performed using 3T MR system (Siemens, Verio) with a standard quadrature head coil. üç boyutlu‐T1‐weighted MP‐RAGE images scanned to get structural images. In these sequences TR/TE/ TI = 1900/2,21/900 ms, voxel size = 1 � 1mm, slice thickness = 1 mm, FoV = 224 � 256 mm, slice number = 176, FA = 9°, matrix = 224 � 256, scanning time = 6 min. Whole‐brain resting‐state functional magnetic resonance imaging (rs‐fMRI) veri were obtained using echo‐planar echoplanar imaging (EPI) sequence images with the following parameters: 36 axial slices, in plane voxel = 3 � 3 mm, slice thickness = 3.75 mm, slice gap = 0.75 mm, repetition time 2 – YÜ NCÜ ET AL. (TR) = 3000 ms, echo time (TE) = 30 ms, flip angle = 90° and matrix size = 64�64. For each subject, 240 echo‐planar volumes were collected. The subjects were instructed to keep the eyes open, and not to think anything during rs‐fMRI recording. Imaging veri were preprocessed using FMRIPREP version 1.3.0 (Esteban et al., 2019) a Nipype based tool (Gorgolewski et al., 2011). Each T1‐weighted volume was corrected for intensity nonuniformity (INU) using N4BiasFieldCorrection v2.1.0 (Tustison et al., 2010) and skull‐stripped using antsBrainExtraction.sh v2.1.0 (using the OASIS template). Spatial normalization to the ICBM 152 Nonlinear Asymmetrical template version 2009c (Fonov et al., 2009) was performed through nonlinear registration with the antsRegistration tool of ANTs v2.1.0 (Avants et al., 2008), using brain‐extracted versions of both T1w volume and template. Brain tissue segmentation of cerebrospinal fluid (CSF), white‐matter, and gray‐matter (GM) was performed on the brain‐extracted T1w using fast (FSL v5.09; Zhang et al., 2001). Functional veri were slice‐time corrected using üç boyutluTshift from AFNI v16.2.07 (Cox, 1996) and motion‐corrected using mcflirt (FSL v5.0.9) (Jenkinson et al., 2002). This was followed by co‐registration to the corresponding T1w using boundary‐based registration (Greve & Fischl, 2009) with nine degrees of freedom, using flirt (FSL v5.09). The motion correcting transformations, BOLD‐to‐T1w transformation and T1w‐to‐template (MNI) warps were concatenated and applied in a single step using antsApplyTransforms (ANTs v2.1.0) using Lanczos interpolation. Frame‐wise displacement (Power et al., 2014) was calculated for each functional run using the implementation of Nipype. ICA‐ based Automatic Removal Of Motion Artifacts (AROMA) was used to generate nuisance signals related to motion and other noise sources (Pruim et al., 2015). Many internal operations of FMRIPREP use Nilearn (Abraham et al., 2014), principally within the BOLD‐ processing workflow. For more details of the pipeline see https:// fmriprep.readthedocs.io/en/latest/workflows.html. Identified time courses from white matter, CSF regions, küresel whole brain signals, and noise components identified by ICA‐AROMA were removed from the BOLD time series using FSL’s regfilt function with an aggressive denoising strategy, as per Parkes et al. (2018). Recent comparisons of the efficacy of different processing pipelines in mitigating motion‐related artifact have identified the combination of ICA‐AROMA and küresel signal regression (GSR) as showing consistently strong performance across multiple different quality control benchmarks (Ciric et al., 2017; Parkes et al., 2018). Finally, bandpass filtering between 0.008 and 0.08 Hz using the fast Fourier transform were performed. Following Parkes et al. (2018), we excluded participants if any of the following criteria were true: (i) mean framewise displacement (FD) (Jenkinson et al., 2002) > 0.25 mm; (ii) more than 20% of the FDs were above 0.2 mm; and (iii) if any FDs were greater than 5 mm. Four individuals (1 from SC users, 2 from SC users + ADHD, 1 from controls) were excluded due to excessive head movement. The final rs‐fMRI analysis included 25 SC users (14 without ADHD and 11 with ADHD combined type) and 12 control subjects. Mean FD did not differ between groups (F [2,34] = 0.620, p = 0.544; Table 1). 2.3 | Network construction After preprocessing, we generated functional connectivity networks using a parcellation containing 264 nonoverlapping cortical and subcortical regions (Power et al., 2011). The networks were generated in MATLAB (The MathWorks, Inc.). We estimated Pearson correlation coefficients between each pair of regional averaged time series. These correlations can be represented as a network, where edges connecting pairs of nodes (brain regions) represent correlation coefficients between rs‐fMRI time series. Eleven ROIs from the Power parcellation were discarded from analyses due to low signal or poor anatomical coverage in the fMRI images. Each of the 253 remaining nodes were assigned to one of 14 functional networks based on the designation reported by (Power et al. 2011). These networks included the DMN, frontoparietal, cingulo‐opercular network (CON), salience (SN), subcortical, auditory, visual network (VN), ventral attention, sensorimotor hand and mouth networks, dorsal attention network (DAN), memory retrieval network, cerebellum, and uncertain networks. 2.4 | Network‐based statistics We evaluated differences between (i) SC users and controls (ii) SC users + ADHD and controls, and (iii) SC users and SC users + ADHD using the network‐based statistic toolbox (NBS, http://www.nitrc. org/projects/nbs) (Zalesky et al., 2010). The NBS localizes differences in connection strengths to specific sub‐networks while controlling the family‐wise error (FWE). An F‐test was performed at each pairwise connection linking 253 regions to test for group differences. The resulting F‐statistic matrices were then threshold adjusted using a primary, component‐forming threshold of p < 0.05 uncorrected to identify connected components of edges showing common effects. The statistical significance of the size of these components was determined using permutation testing (10,000 permutations), and significant components showing group differences were identified using a threshold of p < 0.05, FWE‐corrected. We repeated the analyses using primary thresholds of 0.01, 0.005 and 0.001 to evaluate the robustness of our findings to this particular choice. 3 | RESULTS 3.1 | Demographic, clinical and SC use variables Table 1 shows the demographics, clinical variables and measures of SC use. 3.2 | SC users without ADHD versus controls Analysis with the NBS identified a single significant component showing different rsFC between groups, comprising 253 nodes and YÜ NCÜ ET AL. - 3 1988 edges (component‐wide, fwe‐corrected p = 0.019). This component largely comprised a set of edges in which, relative to controls, SC users showed higher rsFC between the DMN and SN, DAN and CON, coupled with a set of edges in which SC users showed lower rsFC within the DMN and between the DMN and VN (Figure 1). We repeated NBS analyses using different primary threshold levels to test the robustness of our results. At a primary threshold of p < 0.01, we found single network comprising 223 nodes connected by 452 edges (fwe‐corrected p = 0.026). At a primary threshold of p < 0.005, we found single network comprising 178 nodes connected by 233 edges (fwe‐corrected p = 0.018). Finally, at a primary threshold of p < 0.001, we found single network comprising 27 nodes connected by 27 edges (fwe‐corrected p = 0.008). The patterns of differences were largely consistent across all thresholds, with the most salient and robust being: (i) higher rsFC between the DMN and SN, DAN and CON, and (ii) lower rsFC within the DMN and between the DMN and VN in SC users compared to controls (Figure 1). To test for correlations between functional connectivity and clinical variables, we identify the set of edges comprising the difference networks shown in Figure 1 and computed the first principal component as a summary measure. This component accounted for 22% of the variance in functional connectivity. There were no significant correlations between this rsFC component and the duration of SC use (spearman's rho = 0.009, p = 0.976), amount of daily use (spearman's rho = −0.194, p = 0.505), or the age at which SC use started (spearman's rho = 0.036, p = 0.903) in the SC users without ADHD at any primary threshold level. There were no significant differences between SC users with ADHD and controls, nor where there any significant differences between SC users with and without ADHD. 4 | DISCUSSION In this study, we examined functional network organization in SC users with and without ADHD, as well as in controls. To our knowledge, this is the first study to examine rsFC in adolescent SC users. Network‐based analysis revealed that SC users without ADHD had significantly weaker functional connectivity compared to controls within DMN network. Furthermore, SC users without ADHD also showed greater connectivity mostly between DMN and both task‐ positive networks and SN compared to controls. In contrast, SC users with ADHD showed no differences in functional connectivity within or between resting state networks (RSNs) compared to controls. We found that SC users without ADHD showed widespread reductions in functional connectivity within nodes of DMN. Consistent with our findings, previous studies in cannabis users (Wetherill et al., 2015), and also in users of other substances including as alcohol (Müller‐Oehring et al., 2014), heroin (Ma et al., 2015; Wang et al., 2016) and psilocybin (Carhart‐Harris et al., 2012) have found lower rsFC within DMN nodes compared to controls. The DMN is thought to be involved in multiple cognitive processes such as emotional regulation, planning the future, social cognition and autobiographical memory (Andrews‐Hanna et al., 2014; Anticevic et al., 2012). In line with this, a recent task‐fMRI study reported lower activation of the DMN in chronic SC users while performing TABLE 1 Demographic and clinical characteristics SC users without ADHD (n = 14) SC users with ADHD (n = 11) Controls (n = 12) Statistic p value Age (years) 15.9 ± 0.9 16.0 ± 1.3 16.6 ± 1.2 F(2,34) = 1.864 0.171 Age range (years) 14–17 14–18 14–18 Education (years) 8.9 ± 0.7 9.0 ± 0.8 9.2 ± 0.8 F(2,34) = 0.859 0.433 Age at first use (years) 13.0 ± 1.8 13.6 ± 1.6 t = −1.123 0.273 Duration of regular use (months) 14.2 ± 4.7 14.3 ± 5.4 t = −0.029 0.977 Daily use (gr/day) 4.9 ± 1.1 5.1 ± 1.0 t = −0.380 0.707 Duration of abstinence (days) 8.4 ± 5.5 8.9 ± 5.9 t = −0.210 0.836 Mean FD 0.14 ± 0.07 0.12 ± 0.06 0.12 ± 0.03 F(2,34) = 0.620 0.544 T‐DSM‐IV‐S scores Inattention 5.7 ± 2.5 17.3 ± 3.3 5.9 ± 4.8 F(2,34) = 38.742 controls contrast (left) and the SC < controls contrast (right). The number of edges that fall within and between brain networks identified by the Power parcellation are presented in matrix form in the middle of each subplot. Matrix plots are split into lower (SC > Controls) and upper (SC < controls) triangles. Anatomical renderings were generated using NeuroMarvl http://immersive.erc.monash./edu.au/neuromarvl. Subnetworks in the matrix plot are denoted as follows: A, auditory; C, cerebellum; CO, cingulo‐opercular; DM, default mode; DA, dorsal attention; FP, fronto‐parietal; MR, memory retrieval; SAL, salience; SC, subcortical; SMH, sensorimotor hand; SSM, sensorimotor mouth; U, uncertain; VIS, visual; VA, ventral attention YÜ NCÜ ET AL. - 5 the N‐back task (Livny et al. 2018). It is therefore possible that reduced rsFC within DMN may be related to the cognitive deficits that have been shown in SC users (Cengel et al., 2018). We found that SC users without ADHD showed increased positive correlation (which also means reduced anticorrelation or functional segregation) between DMN and task‐positive networks, particularly the CON and DAN. The anticorrelation between DMN and task‐positive networks connectivity has been proposed as a key mechanism for the optimal balance between internal focus and external task‐related cognition requiring attention (Andrews‐Hanna et al., 2014; Anticevic et al., 2012; Raichle, 2015), although this effect may be context‐dependent (Fornito et al., 2012). Previous studies in normally developing adolescents have shown increased segregation between RSNs with age, including between DMN and task‐positive networks (Power et al., 2010; Satterthwaite et al., 2013). Furthermore, the results of a recent study suggest that decreased segregation between DMN and task‐positive networks might be a common neurobiological mechanism underlying vulnerability to a wide range of psychiatric symptoms (Xia et al., 2018). Therefore, decreased segregation between these two RSNs might be associated with increased risk for substance use. However, due to our cross‐sectional design, we are unable to establish a clear temporal relationship between SC use and rsFC alterations. A longitudinal study would be required to elucidate these relationships. The SN is thought to be a modulate interactions between central executive network (CEN) and the DMN (Goulden et al., 2014; Sridharan et al., 2008). Higher positive correlation between DMN and SN is consistent with the triplenetwork model of addiction (Sutherland et al., 2012). According to this model, increased connectivity between SN and DMN resulted in increased attention towards to internal states, leading to internal ruminations such as craving, especially in the early abstinence phase of addiction. We are not aware of any previous research that has investigated interactions between SN and DMN in cannabis or SC users at RSNs level. However, our results are in line with previous studies of patients with heroin (Li et al., 2018), alcohol (Kohno et al., 2017), and nicotine (Fedota et al., 2018; Lerman et al., 2014) dependence that have found higher positive correlation between DMN and SN compared to controls. Higher connectivity between DMN and SN also has been shown in individuals with internet gaming disorder (Zhang et al., 2017). Moreover, the decreased anticorrelation between DMN and SN connectivity in nicotine users (Lerman et al., 2014) and individuals with internet gaming disorder (Zhang et al., 2017) have been shown to be related with craving. Speculatively, given the SC users included to our study were abstinent at least for 2 days, the greater rsFC between DMN and SN may underline abstinence related craving. In contrast to SC users without ADHD, we did not find any differences between SC users with ADHD and controls. One possible explanation might be that opposite alterations in rsFC due to ADHD may mask the expected alterations in SC users with ADHD group that we found to show in SC users without ADHD. For instance, two previous studies reported greater rsFC within DMN in adults with ADHD than controls (McCarthy et al., 2013; Sidlauskaite et al., 2016). Thus, speculatively, adolescent ADHD patients might also use SC to improve their rsFC abnormalities and symptoms. Another possible explanation for the failure to find a significant difference may be inadequate power due to a small sample size. However, in this present study we specially examined the influence of comorbid ADHD symptomatology on the alteration of functional network topology in SC users rather than interactions between substance dependence and ADHD. Therefore, we did not include a separate group of patients with ADHD. In summary, our results suggest that comorbidity of ADHD and substance dependence may show different rsFC alterations than substance use alone. We also did not find any significant differences in rsFC metrics between SC users with and without ADHD. One possible explanation for the failure to find a significant difference may be inadequate power due to a small sample size. Second, nonsignificant alterations in rsFC caused by SC use may have masked the effects of ADHD between groups. The current study has several potential limitations. The most obvious is its cross‐sectional nature, and thus potential changes in rsFC over the course of illness in SC users remain speculative. Second, our sample was relatively small, due to strict exclusion and inclusion criteria, which may have reduced power to detect group differences in NBS. Third, we were unable to determine the chemical contents of the SC products to which individuals were exposed. A previous study in Turkey found that ADB‐FUBINACA was the most frequent type of SCs (Göl & Çok, 2017). Therefore, it could be argued that our findings were mainly due to the use of the ADB‐FUBINACA. Another limitation is that the measures of alcohol, past substance and SC use were self‐reported, and under‐reporting is a reasonable concern. In addition, we cannot extend our findings to females with SC use as our sample included only males. Finally, we did not have patients with ADHD, which limits our ability to test the effect of ADHD on rsFC in a sample free from SC use. In addition, the lack of an ADHD only group also did not allow us to elucidate whether the study was under‐powered or not to detect differences between the SC users with ADHD and controls or with SC users without ADHD. Given the limitations of this study, our results should be considered preliminary. 5 | CONCLUSION In this study, we found the first evidence of abnormalities within and between RSNs in adolescent SC users without ADHD. In contrast, SC users with ADHD showed no differences compared to controls. These results suggest that comorbidity of ADHD and substance dependence may show different rsFC alterations than substance use alone. Therefore, future rsFC studies in the substance use population should account for the presence of ADHD in their samples, which may be associated with disparate functional connectivity profiles. 6 - YÜ NCÜ ET AL. ACKNOWLEDGEMENTS This research was funded by Ege University Science and Technology Application and Research Center (grant number 2015 EGEBAM 001) which had no role in the design of the study, collection and analysis of veri and decision to publish. N. Kuvvetli received financial support from TUBITAK‐BIDEB 2219‐International Postdoctoral Research Fellowship Programme (Grant no. 53325897‐115.02‐39691) for this study. CONFLICT OF INTEREST The authors declare that they have no conflict of interest. VERİ AVAILABILITY STATEMENT The dataset analyzed during the current study is available from the corresponding author on reasonable request. ORCID Nabi Şiddetli https://orcid.org/0000-0002-2340-6156 REFERENCES Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., & Varoquaux, G. (2014). Machine learning for neuroimaging with scikit‐learn. Frontiers in Neuroinformatics, 8, 14. Andrews‐Hanna, J. R., Smallwood, J., & Spreng, R. N. (2014). 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