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Federal government websites often end in. The site is secure. Preview improvements coming to the PMC website in October Learn More or Try it out now. Treatment outcomes for individuals with substance use disorders SUDs are variable and more individualized approaches may be needed. Cross-validated, machine-learning methods are well-suited for probing neural mechanisms of treatment outcomes. Our prior work applied one such approach, connectome-based predictive modeling CPM , to identify dissociable and substance-specific neural networks of cocaine and opioid abstinence. In Study 1, we aimed to replicate and extend prior work by testing the predictive ability of the cocaine network in an independent sample of 43 participants from a trial of cognitive behavioral therapy for SUD, and evaluating its ability to predict cannabis abstinence. In Study 2, CPM was applied to identify an independent cannabis abstinence network. Additional participants were identified for a combined sample of 33 with cannabis use disorder. Participants underwent fMRI scanning before and after treatment. Additional samples of 53 individuals with co-occurring cocaine and opioid use disorders and 38 comparison subjects were used to assess substance specificity and network strength relative to participants without SUDs. Results demonstrated a second external replication of the cocaine network predicting future cocaine abstinence, however it did not generalize to cannabis abstinence. An independent CPM identified a novel cannabis abstinence network, which was i anatomically distinct from the cocaine network, ii specific for predicting cannabis abstinence, and for which iii network strength was significantly stronger in treatment responders relative to control particpants. Results provide further evidence for substance-specificity of neural predictors of abstinence and provide insight into neural mechanisms of successful cannabis treatment, thereby identifying novel treatment targets. Despite the availability of effective evidence-based treatment approaches, outcomes for individuals seeking treatment for substance use disorders SUDs remain variable across individuals and success rates are suboptimal 1 , 2. There is a growing consensus that alterations in neural functioning contribute importantly to the pathophysiologies of SUDs, yet incorporation of neuroimaging into clinical addiction treatment remains rare 3 and findings have been inconsistent across studies, in part due to reliance on methods such as correlation and regression that may overfit models to small datasets 4. Nonetheless, when combined with robust, data-driven predictive modeling, neuroimaging may provide a powerful tool for elucidating neural bases of recovery and has the potential to uncover novel treatment targets to facilitate the development of more individualized treatment approaches 4 , 5. Accordingly, our prior work has identified dissociable neural networks of cocaine 6 and opioid 7 treatment outcomes using a whole-brain predictive modeling approach called connectome-based predictive modeling CPM 8 , 9. CPM is a data-driven method for identifying brain-behavior relationships that incorporates cross-validation to protect against overfitting and improve the generalizability of identified models. Although some neural substrates of SUDs may be shared across substances, using CPM we found cocaine and opioid abstinence networks to be anatomically distinct from one another and specific for predicting cocaine versus opioid abstinence, even within the same individuals 7. These results, which are consistent with preclinical work 10 , suggest that there may be key differences in the neural factors linked to abstinence from different substances, which could have important implications for improving existing treatment approaches. These findings align with prior work emphasizing the heterogeneity of SUDs and the need to develop more individually-tailored treatment approaches These data highlight the utility of parsing some of the heterogeneity among individuals with SUDs to develop more individually-targeted interventions and improve treatment outcomes. Consistent with this, our prior CPM results provide preliminary evidence that the development of treatment approaches that target neural mechanisms specific to different substances could have promise for improving SUD treatment outcomes. Nonetheless, further replication and extension is needed to confirm the substance specificity of previously identified abstinence networks. Cannabis is the most widely used illicit drug worldwide with global rates of use increasing steadily over the past decade Recent estimates suggest that Notably, the prevalence of CUD has increased as rates of use have risen, particularly among more vulnerable populations 18 , spurring increased demand for effective CUD treatments Nonetheless, there are currently no efficacious pharmacological treatments for CUD, although several medications appear to be effective for symptoms of cannabis withdrawal e. There are several psychosocial treatment options for CUD, with cognitive behavioral therapy, motivational enhancement therapy, relapse prevention, and contingency management showing an overall moderate effect size in the short-term, yet there is little evidence for longer term efficacy of any available interventions 17 , Therefore, elucidating neural features predicting successful CUD treatment has the potential to identify promising new treatment targets 3 , 4 , 5 , Here we seek to replicate and extend our prior work by analyzing neuroimaging data from a heterogeneous sample of individuals with cocaine and cannabis use disorders. Aims were threefold: In Study 1, we sought to i test the replicability of the cocaine network in another external sample and ii to determine whether the cocaine abstinence network would generalize to predict cannabis abstinence. Based on our earlier work indicating substance specificity of abstinence networks, we anticipated that the cocaine abstinence network would replicate in an external sample, but that it would not generalize to predict cannabis abstinence. Thus, in Study 2, we further aimed to iii conduct an independent CPM analysis to identify separate neural substrates as predictive of cannabis abstinence. Based on extant literature on neural correlates of cannabis use 23 , we hypothesized that cannabis abstinence networks would be characterized by connections within and between frontoparietal, frontostriatal, and cerebellar regions. Participants for the cocaine network replication and initial application to cannabis abstinence Study 1 were drawn from a randomized clinical trial RCT of cognitive-behavioral treatments for SUDs NCT ; The trial included a heterogeneous polysubstance-using sample of outpatient treatment-seeking individuals. Within this sample, 18 individuals met criteria for cocaine use disorder and 39 individuals met criteria for lifetime cannabis use disorder; 14 of these participants met criteria for both lifetime cocaine and cannabis use disorders. Following this, a separate CPM was conducted to test whether an independent network might predict cannabis abstinence Study 2. Although most participants met criteria for multiple lifetime substance use disorders, they also indicated their primary drug of choice at the time of treatment entry, and only participants who reported cannabis as their primary drug were included in the second analysis. All parent RCTs monitored abstinence via weekly urine toxicology screens during the study treatment period, as well as at 1-, 3-, and 6-month post-treatment follow-up visits. All participants provided written informed consent approved by the Yale School of Medicine IRB following description of study procedures, and all experiments were performed in accordance with relevant guidelines and regulations. Neuroimaging data were preprocessed using SPM8 and the Bioimage Suite 28 , as described previously 6 , 29 , and runs with motion exceeding. Consistent with our prior work 6 , 29 , data were parcellated into nodes defined using the Shen brain atlas 30 , and mean time courses for each node were used to compute pairwise Pearson correlations between every node pair. Reward task matrices were generated for participants with at least one usable run out of two, i. Network strength of the cocaine abstinence network, previously identified and validated using CPM 6 , was extracted from reward and cognitive control matrices. Predictive accuracy i. As only a positive association between network strength and abstinence indicates accuracy i. As described above, CPM is a cross-validated machine-learning approach that takes whole-brain connectomes and a behavioral variable of interest as inputs and identifies positive and negative features that are predictive of the given behavioral variable 8 , such as abstinence 6 , CPM analyses were conducted using custom scripts in Python, based on Shen et al. During training, Pearson correlation coefficients r were calculated across participants between edge weights in the input matrices and cannabis abstinence i. A summary statistic was then calculated for each individual by subtracting the sum of negative edge weights from the sum of positive edge weights, and a linear regression model was trained on this statistic to predict abstinence. The predictive features identified in the training data were then extracted from the task-based matrices from the testing data, and the trained models were applied to the summary statistic of the testing data to generate predictions, as in prior work 6 , 7 , 8 , 9 , 31 , 33 , 34 , Model performance was quantified as the Spearman correlation rho between the testing-data predictions and actual values across the whole sample. To further improve the reliability of our results and to prevent over-fitting to a random split of the data, models using 5-fold CV were repeated times to generate Spearman rho values, consistent with current recommendations Permutation testing was adopted to evaluate the significance of the observed Spearman rho values. For 1, iterations, abstinence values were randomly permuted and then fed into CPMs. The resulting 1, Spearman rho values formed a null distribution and a one-tailed p -value was calculated by contrasting the actual Spearman rho against the null distribution:. Again, one-tailed p -values were chosen over two-tailed p -value because rho actual was expected to be positive i. To determine the specificity of the identified cannabis abstinence network for predicting future abstinence during treatment i. To assess substance specificity of the cannabis abstinence network, we extracted the cannabis network from an independent sample of methadone-maintained individuals with co-occurring opioid and cocaine use disorders details on this sample have been described previously, see 6 , 7 , 36 ; also see Supplement for Subject Characteristics and recruitment information. Spearman correlations were computed to assess whether cannabis abstinence network strength would predict cocaine or opioid abstinence in this independent sample. Consistent with prior work 6 , 7 , we also sought to assess whether the strength and predictive ability of the cannabis abstinence network would be stable across pre- and post-treatment data. Finally, to assess how cannabis abstinence network strength varies between individuals with CUD and comparison participants, the cannabis network was extracted from a healthy comparison sample. Network strength was compared between control participants, treatment responders, and non-responders, and independent samples t-tests were used to compare network strength between groups. Findings from this analysis should nonetheless be considered exploratory. While not identical, network comparisons indicated significant anatomical overlap between networks see Supplement for details. Given the similarities in both predictive accuracy and anatomical features, we next combined the cognitive control and reward task matrices to test the predictive accuracy of a multi-task model. Therefore, the combined model was used as the primary cannabis model for all subsequent analyses described below. Panel A displays model performance for positive, negative, and combined cannabis abstinence network CPM models. Panel B illustrates network anatomy based on overlap with macroscale brain regions; edges of the positive network are depicted with red lines and edges of the negative network are depicted with blue lines. Panel C illustrates network anatomy based on overlap with canonical neural networks. Darker shading indicates that network connections account for a greater percentage of the total network. Similar to previously identified cocaine and opioid abstinence networks 6 , 7 , the cannabis abstinence network was complex and included connections within and between multiple brain regions and networks. Therefore, despite its complexity, the connections included were also quite specific. Figure 1b summarizes cannabis network anatomy based on overlap with macroscale brain regions, including connections between frontal, parietal, temporal, occipital, limbic, and subcortical regions. The highest degree nodes i. Figure 1c summarizes cannabis abstinence network anatomy based on overlap with canonical networks e. The positive network was predominantly characterized by connections between the motorsensory network and frontoparietal, medial frontal, and salience networks. The negative network was largely comprised of within-network connections of the motorsensory network. Cannabis and cocaine abstinence networks showed very distinct patterns of network anatomy Figure 2a. Whereas the positive cocaine abstinence network was characterized by connections between the motorsensory network and salience and cerebellar networks, as well as between the frontoparietal and medial frontal network, the positive cannabis abstinence network was dominated by connections between the motorsensory network and frontoparietal and medial frontal networks. Furthermore, there were no overlapping edges between the positive cocaine abstinence and positive cannabis abstinence networks. Similarly, whereas the negative cocaine abstinence network was comprised of connections between the salience, medial frontal, frontoparietal, default mode, visual, visual association, motorsensory, and cerebellar networks, the negative cannabis abstinence network was characterized by a much more focal pattern of within-network motorsensory connectivity. Furthermore, the negative cocaine and negative cannabis abstinence networks shared only 5 edges, connecting nodes within the subcortical network to the medial frontal and visual networks, as well as connecting nodes between the salience and default mode networks. Panel A illustrates the anatomical specificity of cocaine and cannabis abstinence networks. Cells shaded in green represent network connections that are more characteristic of the cannabis versus cocaine abstinence network and cells shaded in orange represent network connections that are more characteristic of the cocaine versus cannabis abstinence networks. Panel B illustrates the substance specificity of abstinence networks by depicting the effect size for each network for predicting cocaine and cannabis abstinence. Given that this sample was characterized by co-occurring cocaine and opioid use disorders, we also assessed whether cannabis abstinence network strength would relate to opioid abstinence. However, when comparing control participants to treatment responders and non-responders, we found that control participants displayed an intermediate level of network strength, consistent with our prior work in other, non-cannabis SUDs 7. Here we demonstrate that a previously identified cocaine abstinence network 6 successfully predicted cocaine abstinence during treatment in a third independent sample. Consistent with our earlier work identifying substance-specific networks predicting cocaine and opioid abstinence 7 , the cocaine abstinence network was also found to be specific for predicting cocaine, but not cannabis abstinence. Accordingly, we applied CPM to test for a separate cannabis abstinence network in a sample of individuals entering treatment for CUD. This independent CPM was successful and identified a network that was anatomically distinct from the cocaine abstinence network, and specific for predicting cannabis versus other substance use. The current finding that the cocaine abstinence network generalizes to a third independent sample supports the utility of CPM for identifying neural substrates of addiction recovery that generalize across individuals and settings and may therefore represent useful treatment targets. Indeed, we are currently investigating whether directly targeting CPM-derived networks via real-time connectome-based neurofeedback may improve outcomes for individuals engaged in treatment for opioid use disorder. Given that the cocaine abstinence network was found to be specific for predicting cocaine, but not cannabis abstinence, we also applied CPM to identify a novel cannabis abstinence network. Cannabis abstinence was primarily associated with increased connectivity between the motorsensory network and frontoparietal, medial frontal, and salience networks, as well as decreased within-network connectivity of the motor sensory network. The current finding that sensorimotor connections played a key role in predicting cannabis abstinence is consistent with prior data-driven work identifying neural features predictive of chronic cannabis use 40 , as well as opioid abstinence during treatment 7. This is hypothesized to relate to the automatization of drug use behaviors with extended substance use 7 , Accordingly, increased connectivity between frontoparietal, medial frontal, and motorsensory networks may facilitate greater top-down control over automatized drug use behaviors, rendering these individuals less vulnerable to relapse. Therefore, reduced insula connectivity may also facilitate success in treatment via reduced cannabis craving and drug-seeking. These results have implications for informing the development of improved treatments for CUD. For instance, it may be possible to directly target this neurocircuitry using neuromodulatory approaches, such as real-time connectome-based neurofeedback Alternatively, this pattern of results can also inform the development of behavioral treatment approaches. For example, existing treatments rarely consider or target acquired automaticity of drug use behaviors. Prior literature demonstrates that different brain states are optimal for revealing different brain-behavior relationships using functional connectivity data Congruently, our prior work revealed that identification of both the cocaine and opioid abstinence networks was brain-state specific, such that a cocaine abstinence network could be identified using reward but not cognitive control data and the opposite was found for the opioid abstinence network 7. The current analyses revealed that both reward and cognitive control brain states were relevant for predicting cannabis abstinence in treatment. Therefore, data from both brain states were combined to generate the cannabis abstinence network. Nonetheless, future work using data acquired during brain states more closely related to CUD treatment e. The current results also demonstrate that comparison subjects displayed intermediate network strength relative to treatment responders and non-responders, consistent with our prior work 7. Furthermore, we also observed that network strength remained consistent from pre- to post-treatment among the CUD group. Therefore, this pattern of results further supports the idea that targeting these connections directly may help improve treatment outcomes for individuals entering treatment for cannabis use. The current study has several limitations. Future studies with larger samples of females are essential The sample is also characterized by multiple lifetime substance use and other diagnoses. Therefore, co-occurring disorders may have impacted results. Additionally, although we used a rigorous cross-validation approach 5-fold cross-validation across iterations , we did not include an external validation sample. Nonetheless, the current finding of a third independent replication of the cocaine abstinence network supports the utility of applying CPM to identify networks in modest samples that do ultimately generalize across different samples. Future work is needed to assess whether the cannabis network also replicates to predict cannabis treatment outcomes in independent samples of treatment-seeking individuals. Furthermore, analyses applying CPM to data acquired during brain states specific to cannabis use and treatment e. Related, it is critical that future research assess whether directly targeting the cannabis abstinence network via neurofeedback, neuromodulation, or other novel therapeutic approaches may improve treatment outcomes for individuals seeking treatment for CUD. The current study sought to replicate our earlier work CPM to identify a neural network predictive of cocaine abstinence in treatment. We further aimed to extend these findings by assessing whether the cocaine abstinence network would extend to also predict cannabis abstinence in treatment. The present findings demonstrate that the cocaine abstinence network generalized to predict cocaine treatment outcome in a third independent sample, supporting the utility of CPM for identifying robust, reproducible, and clinically relevant neural networks. Consistent with prior work demonstrating the substance specificity of CPM-derived cocaine and opioid abstinence networks, we found that the cocaine abstinence network was specific for predicting cocaine, but not cannabis abstinence. Accordingly, we then applied CPM to identify a novel cannabis abstinence network, characterized by increased connectivity between the motorsensory network and frontoparietal, medial frontal, and salience networks, as well as decreased within-network connectivity of the motor sensory network. These results have implications for elucidating the neural mechanisms underlying successful cannabis-use treatment, as well as potentially uncovering novel treatment targets to improve treatment outcomes for this population. Lichenstein, Kohler and Ye report no competing financial interests in relation to the work described. Yip is a consultant for Sparian Biosciences. As a library, NLM provides access to scientific literature. Mol Psychiatry. Author manuscript; available in PMC Feb Sarah D. Yip , MSc, PhD 1, 3. Marc N. Sarah W. MNP and BK oversaw acquisition of the data. SDL and FY drafted the article. All authors revised the article critically for important intellectual content and provided final approval fo the version to be published. Lichenstein, PhD, 40 Temple St. PMC Copyright notice. The publisher's final edited version of this article is available at Mol Psychiatry. Abstract Treatment outcomes for individuals with substance use disorders SUDs are variable and more individualized approaches may be needed. Introduction Despite the availability of effective evidence-based treatment approaches, outcomes for individuals seeking treatment for substance use disorders SUDs remain variable across individuals and success rates are suboptimal 1 , 2. Subjects and Methods Participants Participants for the cocaine network replication and initial application to cannabis abstinence Study 1 were drawn from a randomized clinical trial RCT of cognitive-behavioral treatments for SUDs NCT ; Table 1. Demographic and clinical characteristics. Open in a separate window. Neuroimaging data acquisition and preprocessing fMRI data were acquired during the Monetary Incentive Delay task reward processing and the Stroop task cognitive control at baseline and following treatment. Cocaine abstinence network replication Network strength of the cocaine abstinence network, previously identified and validated using CPM 6 , was extracted from reward and cognitive control matrices. Cannabis abstinence network identification As described above, CPM is a cross-validated machine-learning approach that takes whole-brain connectomes and a behavioral variable of interest as inputs and identifies positive and negative features that are predictive of the given behavioral variable 8 , such as abstinence 6 , Post-hoc sensitivity testing Assessing relationship with pre-treatment cannabis use To determine the specificity of the identified cannabis abstinence network for predicting future abstinence during treatment i. Cannabis abstinence network strength across substance use outcomes To assess substance specificity of the cannabis abstinence network, we extracted the cannabis network from an independent sample of methadone-maintained individuals with co-occurring opioid and cocaine use disorders details on this sample have been described previously, see 6 , 7 , 36 ; also see Supplement for Subject Characteristics and recruitment information. Application to post-treatment data Consistent with prior work 6 , 7 , we also sought to assess whether the strength and predictive ability of the cannabis abstinence network would be stable across pre- and post-treatment data. Cannabis abstinence network strength relative to heathy comparison participants Finally, to assess how cannabis abstinence network strength varies between individuals with CUD and comparison participants, the cannabis network was extracted from a healthy comparison sample. Figure 1. Cannabis Abstinence Network. Network Anatomy Similar to previously identified cocaine and opioid abstinence networks 6 , 7 , the cannabis abstinence network was complex and included connections within and between multiple brain regions and networks. Comparison between cocaine and cannabis abstinence network anatomy Cannabis and cocaine abstinence networks showed very distinct patterns of network anatomy Figure 2a. Figure 2. Specificity of Cannabis and Cocaine Abstinence Networks. Discussion Here we demonstrate that a previously identified cocaine abstinence network 6 successfully predicted cocaine abstinence during treatment in a third independent sample. Supplementary Material Supplemental Material Click here to view. Conflict of Interest Drs. References 1. A meta-analytic review of psychosocial interventions for substance use disorders. Am J Psychiatry. The neurobiology of substance use and addiction: evidence from neuroimaging and relevance to treatment. Bjpsych Adv. Front Psychiatry. Biol Psychiatry Cogn Neurosci Neuroimaging. Toward biomarkers of the addicted human brain: Using neuroimaging to predict relapse and sustained abstinence in substance use disorder. Prog Neuropsychopharmacol Biol Psychiatry. Connectome-Based Prediction of Cocaine Abstinence. Dissociable neural substrates of opioid and cocaine use identified via connectome-based modelling. Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nat Protoc. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat Neurosci. Opiate versus psychostimulant addiction: the differences do matter. Nat Rev Neurosci. Carroll KM. The profound heterogeneity of substance use disorders: Implications for treatment development. Curr Dir Psychol Sci. Conrod PJ, Nikolaou K. Annual Research Review: On the developmental neuropsychology of substance use disorders. J Child Psychol Psychiatry. Conrod PJ. Curr Addict Rep. Edalati H, Conrod PJ. World Drug Report Contract No. The global burden of disease attributable to alcohol and drug use in countries and territories, — a systematic analysis for the Global Burden of Disease Study Lancet Psychiatry. Cannabis use and cannabis use disorder. Nat Rev Dis Primers. State medical marijuana laws, cannabis use and cannabis use disorder among adults with elevated psychological distress. Drug Alcohol Depend. New vistas on cannabis use disorder. Psychosocial interventions for cannabis use disorder. Cochrane Database Syst Rev. The Neuroscience of Drug Reward and Addiction. Physiol Rev. Systematic review of structural and functional neuroimaging studies of cannabis use in adolescence and emerging adulthood: evidence from 90 studies and participants. The neurobiology of successful abstinence. Curr Opin Neurobiol. Combining cognitive behavioral therapy and contingency management to enhance their effects in treating cannabis dependence: less can be more, more or less. Unified framework for development, deployment and robust testing of neuroimaging algorithms. Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. A neuromarker of sustained attention from whole-brain functional connectivity. Cereb Cortex. Ten simple rules for predictive modeling of individual differences in neuroimaging. Behavioral and brain signatures of substance use vulnerability in childhood. Dev Cogn Neurosci. Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets. J Clin Psychiatry. Bupropion and Naltrexone in Methamphetamine Use Disorder. N Engl J Med. Reproducible brain-wide association studies require thousands of individuals. How to establish robust brain-behavior relationships without thousands of individuals. An interpretable and predictive connectivity-based neural signature for chronic cannabis use. Sensory and motor aspects of addiction. Behav Brain Res. The insula: a critical neural substrate for craving and drug seeking under conflict and risk. Ann N Y Acad Sci. Connectome-based neurofeedback: A pilot study to improve sustained attention. Task-induced brain state manipulation improves prediction of individual traits. Nat Commun. Copy Download.

Distinct neural networks predict cocaine versus cannabis treatment outcomes

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Mary Butt, one of Crowley's recruits, pointed out, 'It was one thing to partake in the rite of preparing the Cakes of Light, in which Crowley, in a scarlet-and-black robe, sacrified a cockerel for its blood, but quite another to participate in bestiality. The goat represented the Devil. Crowley hoped that this was to be the beginning of his new religion. He regularly took trips to Palermo staying at Hotel des Palmes, where Richard Wagner some 40 years before completed Parsifal and Naples in search of drugs, supplies and prostitutes. The trips were probably also made to escape the jealous arguing of his two mistresses at Thelema, Leah and Ninette, referred to by Crowley as 'first concubine' and 'second concubine'. I will work for wickedness. I will kill my heart. I will be shameless before all men. I will freely prostitue my body to all creatures. Crowley described the scenario as 'perfectly happy Undertaking widespread correspondences, Crowley continued to paint, wrote a commentary on The Book of the Law, and revised the third part of Book 4. Crowley offered a libertine education for the children, allowing them to play all day and witness acts of sex magic. He occasionally travelled to Palermo to visit rent boys and buy supplies, including drugs; his heroin addiction came to dominate his life, and cocaine began to erode his nasal cavity. There was no cleaning rota, and wild dogs and cats wandered throughout the building, which soon became unsanitary. New followers continued to arrive at the Abbey to be taught by Crowley. Another was Cecil Frederick Russell, who often argued with Crowley, disliking the same-sex sexual magic that he was required to perform, and left after a year. More conducive was the Australian Thelemite Frank Bennett, who also spent several months at the Abbey. In February , Crowley returned to Paris for a retreat in an unsuccessful attempt to kick his heroin addiction. On publication, it received mixed reviews; he was lambasted by the Sunday Express, which called for its burning and used its influence to prevent further reprints. Subsequently, a young Thelemite named Raoul Loveday moved to the Abbey with his wife Betty May; while Loveday was devoted to Crowley, May detested him and life at the commune. She later claimed that Loveday was made to drink the blood of a sacrificed cat, and that they were required to cut themselves with razors every time they used the pronoun 'I'. Raoul drank from a local polluted stream, soon developing a liver infection resulting in his death in February Returning to London, May told her story to the press. John Bull proclaimed Crowley 'the wickedest man in the world' and 'a man we'd like to hang', making various slanderous accusations against him, but he was unable to afford the legal fees to sue them. As a result, John Bull continued its attack, with the stories also being picked up by newspapers in North America and throughout Europe. The Fascist government of Benito Mussolini learned of Crowley's activities and in April he was given a deportation notice forcing him to leave Italy; without him, the Abbey closed. Just opposite the entrance of the cemetary, there is a small road going upwards towards the football arena. Follow this a couple of hundred meters, and you will see the football stadium to your right. Crowley's house is right below the tribune with the metal construction over the seats not more c. The Abbey of Thelema is almost invisible from the road. The house with a hole in the ceiling, to the left of the stadium, is Aleister Crowley's Abbey of Thelema. Beware that the house is in ruins, and that it is unsafe to enter. Do you see any errors or have any suggestions, please write to the editor …. Abbey of Thelema.

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