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  • Journal article
    Simonsson O, Carlbring P, Carhart-Harris R, Davis AK, Nutt DJ, Griffiths RR, Erritzoe D, Goldberg SBet al., 2023,

    Assessing the risk of symptom worsening in psilocybin-assisted therapy for depression: A systematic review and individual participant data meta-analysis

    , PSYCHIATRY RESEARCH, Vol: 327, ISSN: 0165-1781
  • Journal article
    Thurgur H, Schlag AK, Iveson E, Hosseini A, Lynskey M, Nutt DJet al., 2023,

    Cannabis-based medicinal products (CBMPs) for the treatment of Long COVID symptoms: current and potential applications

    , Exploration of Medicine, Pages: 487-503

    <jats:p>Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection can result in a range of persistent symptoms impacting everyday functioning for a considerable proportion of patients, a condition termed Long coronavirus disease (COVID) or post COVID-19 syndrome. The severity and set of symptoms vary between patients, and include fatigue, cognitive dysfunction, sleep disturbances, palpitations, tachycardia, pain, depression, and anxiety. The high prevalence of Long COVID combined with the lack of treatment approaches has resulted in considerable unmet clinical needs. There is a growing body of evidence that cannabis-based medicinal products (CBMPs) can be used to treat symptoms including pain, anxiety, depression, fatigue, sleep, headaches, and cognitive dysfunction, which are commonly reported in Long COVID. This article provides an overview of the pathophysiology of Long COVID and discusses preliminary pre-clinical, clinical trials, and real-world evidence (RWE) for CBMPs in the context of Long COVID. This review summarises current clinical trials and studies exploring CBMPs in Long COVID. The current evidence provides a rationale to further explore CBMPs as a treatment for Long COVID symptoms. In addition to further randomised controlled trials (RCTs), the increasing availability of CBMPs globally, coupled with the continued prevalence of Long COVID in the population, also highlights the value of real-world data in the research of CBMPs in Long COVID. Critically, there is an evident need for multidisciplinary approaches of CBMPs and Long COVID in real-world clinical practice settings.</jats:p>

  • Journal article
    Zeifman RJ, Kettner H, Pagni BA, Mallard A, Roberts DE, Erritzoe D, Ross S, Carhart-Harris RLet al., 2023,

    Co-use of MDMA with psilocybin/LSD may buffer against challenging experiences and enhance positive experiences

    , Scientific Reports, Vol: 13, Pages: 1-11, ISSN: 2045-2322

    Psilocybin and lysergic acid diethylamide (LSD) experiences can range from very positive to highly challenging (e.g., fear, grief, and paranoia). These challenging experiences contribute to hesitancy toward psychedelic-assisted psychotherapy among health care providers and patients. Co-use of 3,4-Methylenedioxy methamphetamine (MDMA) with psilocybin/LSD anecdotally reduces challenging experiences and enhances positive experiences associated with psilocybin/LSD. However, limited research has investigated the acute effects of co-use of MDMA and psilocybin/LSD. In a prospective convenience sample (N = 698) of individuals with plans to use psilocybin/LSD, we examined whether co-use of MDMA with psilocybin/LSD (n = 27) is associated with differences in challenging or positive experiences. Challenging experiences were measured using the Challenging Experiences Questionnaire and positive experiences were measured using the Mystical Experience Questionnaire and single-item measures of self-compassion, compassion, love, and gratitude. Potentially confounding variables were identified and included as covariates. Relative to psilocybin/LSD alone, co-use of psilocybin/LSD with a self-reported low (but not medium–high) dose of MDMA was associated with significantly less intense total challenging experiences, grief, and fear, as well as increased self-compassion, love and gratitude. Co-use of psilocybin/LSD and MDMA was not associated with differences in mystical-type experiences or compassion. Findings suggest co-use of MDMA with psilocybin/LSD may buffer against some aspects of challenging experiences and enhance certain positive experiences. Limitations include use of a convenience sample, small sample size, and non-experimental design. Additional studies (including controlled dose–response studies) that examine the effects and safety of co-administering MDMA with psilocybin/LSD (in healthy controls and clinical samples) are warranted an

  • Journal article
    Dougherty RF, Clarke P, Atli M, Kuc J, Schlosser D, Dunlop BW, Hellerstein DJ, Aaronson ST, Zisook S, Young AH, Carhart-Harris R, Goodwin GM, Ryslik GAet al., 2023,

    Psilocybin therapy for treatment resistant depression: prediction of clinical outcome by natural language processing.

    , Psychopharmacology (Berl)

    RATIONALE: Therapeutic administration of psychedelics has shown significant potential in historical accounts and recent clinical trials in the treatment of depression and other mood disorders. A recent randomized double-blind phase-IIb study demonstrated the safety and efficacy of COMP360, COMPASS Pathways' proprietary synthetic formulation of psilocybin, in participants with treatment-resistant depression. OBJECTIVE: While the phase-IIb results are promising, the treatment works for a portion of the population and early prediction of outcome is a key objective as it would allow early identification of those likely to require alternative treatment. METHODS: Transcripts were made from audio recordings of the psychological support session between participant and therapist 1 day post COMP360 administration. A zero-shot machine learning classifier based on the BART large language model was used to compute two-dimensional sentiment (valence and arousal) for the participant and therapist from the transcript. These scores, combined with the Emotional Breakthrough Index (EBI) and treatment arm were used to predict treatment outcome as measured by MADRS scores. (Code and data are available at https://github.com/compasspathways/Sentiment2D .) RESULTS: Two multinomial logistic regression models were fit to predict responder status at week 3 and through week 12. Cross-validation of these models resulted in 85% and 88% accuracy and AUC values of 88% and 85%. CONCLUSIONS: A machine learning algorithm using NLP and EBI accurately predicts long-term patient response, allowing rapid prognostication of personalized response to psilocybin treatment and insight into therapeutic model optimization. Further research is required to understand if language data from earlier stages in the therapeutic process hold similar predictive power.

  • Journal article
    Zeifman RJ, Wagner AC, Monson CM, Carhart-Harris RLet al., 2023,

    How does psilocybin therapy work? An exploration of experiential avoidance as a putative mechanism of change.

    , J Affect Disord, Vol: 334, Pages: 100-112

    BACKGROUND: Psilocybin therapy is receiving attention as a mental health intervention with transdiagnostic potential. In line with psychotherapeutic research, qualitative research has highlighted the role of reductions in experiential avoidance (and increases in connectedness) within psilocybin therapy. However, no quantitative research has examined experiential avoidance as a mechanism underlying psilocybin therapy's therapeutic effects. METHOD: Data was used from a double-blind randomized controlled trial that compared psilocybin therapy (two 25 mg psilocybin session plus daily placebo for six weeks) with escitalopram (two 1 mg psilocybin sessions plus 10-20 mg daily escitalopram for six weeks) among individuals with major depressive disorder (N = 59). All participants received psychological support. Experiential avoidance, connectedness, and treatment outcomes were measured at pre-treatment and at a 6 week primary endpoint. Acute psilocybin experiences and psychological insight were also measured. RESULTS: With psilocybin therapy, but not escitalopram, improvements in mental health outcomes (i.e., well-being, depression severity, suicidal ideation, and trait anxiety) occurred via reductions in experiential avoidance. Exploratory analyses suggested that improvements in mental health (except for suicidal ideation) via reduction in experiential avoidance were serially mediated through increases in connectedness. Additionally, experiences of ego dissolution and psychological insight predicted reductions in experiential avoidance following psilocybin therapy. LIMITATIONS: Difficulties inferring temporal causality, maintaining blindness to condition, and reliance upon self-report. CONCLUSIONS: These results provide support for the role of reduced experiential avoidance as a putative mechanism underlying psilocybin therapy's positive therapeutic outcomes. The present findings may help to tailor, refine, and optimize psilocybin therapy and i

  • Journal article
    Szigeti B, Nutt D, Carhart-Harris R, Erritzoe Det al., 2023,

    The difference between 'placebo group' and 'placebo control': a case study in psychedelic microdosing

    , Scientific Reports, Vol: 13, Pages: 1-10, ISSN: 2045-2322

    In medical trials, ‘blinding’ ensures the equal distribution of expectancy effects between treatment arms in theory; however, blinding often fails in practice. We use computational modelling to show how weak blinding, combined with positive treatment expectancy, can lead to an uneven distribution of expectancy effects. We call this ‘activated expectancy bias’ (AEB) and show that AEB can inflate estimates of treatment effects and create false positive findings. To counteract AEB, we introduce the Correct Guess Rate Curve (CGRC), a statistical tool that can estimate the outcome of a perfectly blinded trial based on data from an imperfectly blinded trial. To demonstrate the impact of AEB and the utility of the CGRC on empirical data, we re-analyzed the ‘self-blinding psychedelic microdose trial’ dataset. Results suggest that observed placebo-microdose differences are susceptible to AEB and are at risk of being false positive findings, hence, we argue that microdosing can be understood as active placebo. These results highlight the important difference between ‘trials with a placebo-control group’, i.e., when a placebo control group is formally present, and ‘placebo-controlled trials’, where patients are genuinely blind. We also present a new blinding integrity assessment tool that is compatible with CGRC and recommend its adoption.

  • Journal article
    Szigeti B, Phillips LDD, Nutt D, 2023,

    Bayesian analysis of real-world data as evidence for drug approval: Remembering Sir Michael Rawlins

    , BRITISH JOURNAL OF CLINICAL PHARMACOLOGY, ISSN: 0306-5251
  • Journal article
    Luppi AI, Cabral J, Cofre R, Mediano PAM, Rosas FE, Qureshi AY, Kuceyeski A, Tagliazucchi E, Raimondo F, Deco G, Shine JM, Kringelbach ML, Orio P, Ching S, Perl YS, Diringer MN, Stevens RD, Sitt JDet al., 2023,

    Computational modelling in disorders of consciousness: closing the gap towards personalised models for restoring consciousness

    , NeuroImage, Vol: 275, ISSN: 1053-8119

    Disorders of consciousness are complex conditions characterised by persistent loss of responsiveness due to brain injury. They present diagnostic challenges and limited options for treatment, and highlight the urgent need for a more thorough understanding of how human consciousness arises from coordinated neural activity. The increasing availability of multimodal neuroimaging data has given rise to a wide range of clinically- and scientifically-motivated modelling efforts, seeking to improve data-driven stratification of patients, to identify causal mechanisms for patient pathophysiology and loss of consciousness more broadly, and to develop simulations as a means of testing in silico potential treatment avenues to restore consciousness. As a dedicated Working Group of clinicians and neuroscientists of the international Curing Coma Campaign, here we provide our framework and vision to understand the diverse statistical and generative computational modelling approaches that are being employed in this fast-growing field. We identify the gaps that exist between the current state-of-the-art in statistical and biophysical computational modelling in human neuroscience, and the aspirational goal of a mature field of modelling disorders of consciousness; which might drive improved treatments and outcomes in the clinic. Finally, we make several recommendations for how the field as a whole can work together to address these challenges.

  • Journal article
    Wall MB, Lam C, Ertl N, Kaelen M, Roseman L, Nutt DJ, Carhart-Harris RLet al., 2023,

    Increased low-frequency brain responses to music after psilocybin therapy for depression

    , JOURNAL OF AFFECTIVE DISORDERS, Vol: 333, Pages: 321-330, ISSN: 0165-0327
  • Journal article
    Morales PA, Korbel J, Rosas FE, 2023,

    Thermodynamics of exponential Kolmogorov-Nagumo averages

    , New Journal of Physics, Vol: 25, ISSN: 1367-2630

    This paper investigates generalized thermodynamic relationships in physical systems where relevant macroscopic variables are determined by the exponential Kolmogorov–Nagumo average. We show that while the thermodynamic entropy of such systems is naturally described by Rényi's entropy with parameter γ, an ordinary Boltzmann distribution still describes their statistics under equilibrium thermodynamics. Our results show that systems described by exponential Kolmogorov–Nagumo averages can be interpreted as systems originally in thermal equilibrium with a heat reservoir with inverse temperature β that are suddenly quenched to another heat reservoir with inverse temperature $\beta^{^{\prime}} = (1-\gamma)\beta$. Furthermore, we show the connection with multifractal thermodynamics. For the non-equilibrium case, we show that the dynamics of systems described by exponential Kolmogorov–Nagumo averages still observe a second law of thermodynamics and the H-theorem. We further discuss the applications of stochastic thermodynamics in those systems—namely, the validity of fluctuation theorems—and the connection with thermodynamic length.

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