A window into the intoxicated mind? Speech as an index of psychoactive drug effects

Social Homeostasis and Psychoactive Drugs: What Can We Learn From Opioid and Amphetamine Drug Challenge Studies in Humans?

Social disequilibrium, or disrupted social homeostasis, underlies many behavioral disorders, including problematic drug use. One way to study the relationship between drug use and social homeostasis is to determine whether single doses of psychoactive drugs relieve some of the discomfort of social isolation and promote social connection. In this narrative review, we discuss challenges and opportunities in studying the relationship between psychoactive drugs and social homeostasis. Using the examples of opioids and amphetamines, we discuss the evidence that drugs alleviate dysphoria related to lack of social connection or produce prosocial effects that improve connection. With regard to opioid drugs, we report that mu opioid agonists and kappa opioid antagonists reduce distress from social isolation, and mu opioid agonists enhance social reward. Amphetamine-like stimulant drugs, including MDMA, do not seem to act by reducing the distress of social isolation, but they have notable prosocial effects that increase both motivation for social contact and the pleasure derived from interacting socially. Many questions remain in understanding interactions between drugs and social equilibrium, including whether these effects contribute to problematic drug use. We identify gaps in knowledge, including the effects of drug withdrawal or dependence on social function or the responses of individuals with psychiatric symptomatology. Understanding these actions on social processes will help to develop novel pharmacological treatments for clinical problems related to social disequilibrium.

Quantitative natural language processing markers of psychoactive drug effects: A pre-registered systematic review

Psychoactive substances used for recreational purposes have mind-altering effects, but systematic evaluation of these effects is largely limited to self-reports. Automated analysis of expressed language (speech and written text) using natural language processing (NLP) tools can provide objective readouts of mental states. In this pre-registered systematic review, we investigate findings from applying the emerging field of computational linguistics to substance use with specific focus on identifying short-term effects of psychoactive drugs. From the literature identified to date, we note that all the studied drugs - stimulants, 3,4-methylenedioxymethamphetamine (MDMA), cannabis, ketamine and psychedelics - affect language production. Based on two or more studies per substance, we note some emerging patterns: stimulants increase verbosity; lysergic acid diethylamide reduces the lexicon; MDMA increases semantic proximity to emotional words; psilocybin increases positive sentiment and cannabis affects speech stream acoustics. Ketamine and other drugs are understudied regarding NLP features (one or no studies). One study provided externally validated support for NLP and machine learning-based identification of MDMA intoxication. We could not undertake a meta-analysis due to the high degree of heterogeneity among outcome measures and the lack of sufficient number of studies. We identify a need for harmonised speech tasks to improve replicability and comparability, standardisation of methods for curating and analysing speech and text data, theory-driven inquiries and the need for developing a shared 'substance use language corpus' for data mining. The growing field of computational linguistics can be utilized to advance human behavioral pharmacology of psychoactive substances. Achieving this will require concerted efforts towards consistency in research methods.

Design and methodology of the first open-label trial of MDMA-assisted therapy for veterans with post-traumatic stress disorder and alcohol use disorder: Considerations for a randomized controlled trial

Background

Posttraumatic stress disorder (PTSD) and alcohol use disorder (AUD) commonly co-occur and are associated with more severe symptomatology than either disorder alone, increased risk of suicide, and poorer response to existing treatments. A promising therapeutic intervention is the integration of 3,4-methylenedioxymethamphetamine (MDMA) and psychotherapy. The Food and Drug Administration (FDA) designated MDMA- assisted therapy (MDMA-AT) as a Breakthrough Therapy for PTSD based on results from six Phase 2 clinical trials. Case data from the first study evaluating MDMA-AT study for AUD found the treatment was well tolerated and alcohol use was significantly reduced post treatment.

Methods

This manuscript reports the premise, design, and methodology of the first open-label trial of MDMA-AT for military veterans (N = 12) with PTSD and AUD. Neuroimaging and biomarker data are included to evaluate brain changes, and neuroinflammation, pre-post treatment.

Conclusions

The clinical component (comorbidity) and the regulatory processes (Schedule I drug) for setting up this clinical trial are long and complex. The research community will benefit from this work to establish common clinical trial outcomes, standardized protocols, and risk assessments for FDA approval.

Clinicaltrialsgov

NCT05943665.

Automated Speech Analysis in Bipolar Disorder: The CALIBER Study Protocol and Preliminary Results

Background: Bipolar disorder (BD) involves significant mood and energy shifts reflected in speech patterns. Detecting these patterns is crucial for diagnosis and monitoring, currently assessed subjectively. Advances in natural language processing offer opportunities to objectively analyze them. Aims: To (i) correlate speech features with manic-depressive symptom severity in BD, (ii) develop predictive models for diagnostic and treatment outcomes, and (iii) determine the most relevant speech features and tasks for these analyses. Methods: This naturalistic, observational study involved longitudinal audio recordings of BD patients at euthymia, during acute manic/depressive phases, and after-response. Patients participated in clinical evaluations, cognitive tasks, standard text readings, and storytelling. After automatic diarization and transcription, speech features, including acoustics, content, formal aspects, and emotionality, will be extracted. Statistical analyses will (i) correlate speech features with clinical scales, (ii) use lasso logistic regression to develop predictive models, and (iii) identify relevant speech features. Results: Audio recordings from 76 patients (24 manic, 21 depressed, 31 euthymic) were collected. The mean age was 46.0 ± 14.4 years, with 63.2% female. The mean YMRS score for manic patients was 22.9 ± 7.1, reducing to 5.3 ± 5.3 post-response. Depressed patients had a mean HDRS-17 score of 17.1 ± 4.4, decreasing to 3.3 ± 2.8 post-response. Euthymic patients had mean YMRS and HDRS-17 scores of 0.97 ± 1.4 and 3.9 ± 2.9, respectively. Following data pre-processing, including noise reduction and feature extraction, comprehensive statistical analyses will be conducted to explore correlations and develop predictive models. Conclusions: Automated speech analysis in BD could provide objective markers for psychopathological alterations, improving diagnosis, monitoring, and response prediction. This technology could identify subtle alterations, signaling early signs of relapse. Establishing standardized protocols is crucial for creating a global speech cohort, fostering collaboration, and advancing BD understanding.

Interdisciplinary approach to identify language markers for post-traumatic stress disorder using machine learning and deep learning

Post-traumatic stress disorder (PTSD) lacks clear biomarkers in clinical practice. Language as a potential diagnostic biomarker for PTSD is investigated in this study. We analyze an original cohort of 148 individuals exposed to the November 13, 2015, terrorist attacks in Paris. The interviews, conducted 5-11 months after the event, include individuals from similar socioeconomic backgrounds exposed to the same incident, responding to identical questions and using uniform PTSD measures. Using this dataset to collect nuanced insights that might be clinically relevant, we propose a three-step interdisciplinary methodology that integrates expertise from psychiatry, linguistics, and the Natural Language Processing (NLP) community to examine the relationship between language and PTSD. The first step assesses a clinical psychiatrist's ability to diagnose PTSD using interview transcription alone. The second step uses statistical analysis and machine learning models to create language features based on psycholinguistic hypotheses and evaluate their predictive strength. The third step is the application of a hypothesis-free deep learning approach to the classification of PTSD in our cohort. Results show that the clinical psychiatrist achieved a diagnosis of PTSD with an AUC of 0.72. This is comparable to a gold standard questionnaire (Area Under Curve (AUC) ≈ 0.80). The machine learning model achieved a diagnostic AUC of 0.69. The deep learning approach achieved an AUC of 0.64. An examination of model error informs our discussion. Importantly, the study controls for confounding factors, establishes associations between language and DSM-5 subsymptoms, and integrates automated methods with qualitative analysis. This study provides a direct and methodologically robust description of the relationship between PTSD and language. Our work lays the groundwork for advancing early and accurate diagnosis and using linguistic markers to assess the effectiveness of pharmacological treatments and psychotherapies.

Toolkit to Examine Lifelike Language (TELL): An app to capture speech and language markers of neurodegeneration

Automated speech and language analysis (ASLA) is a promising approach for capturing early markers of neurodegenerative diseases. However, its potential remains underexploited in research and translational settings, partly due to the lack of a unified tool for data collection, encryption, processing, download, and visualization. Here we introduce the Toolkit to Examine Lifelike Language (TELL) v.1.0.0, a web-based app designed to bridge such a gap. First, we outline general aspects of its development. Second, we list the steps to access and use the app. Third, we specify its data collection protocol, including a linguistic profile survey and 11 audio recording tasks. Fourth, we describe the outputs the app generates for researchers (downloadable files) and for clinicians (real-time metrics). Fifth, we survey published findings obtained through its tasks and metrics. Sixth, we refer to TELL's current limitations and prospects for expansion. Overall, with its current and planned features, TELL aims to facilitate ASLA for research and clinical aims in the neurodegeneration arena. A demo version can be accessed here:  https://demo.sci.tellapp.org/ .

MDMA enhances positive affective responses to social feedback

BACKGROUND

The prosocial compound ± 3,4-methylenedioxymethamphetamine (MDMA) is an amphetamine derivative that has shown promise as an adjunct to psychotherapy in the treatment of post-traumatic stress disorder. MDMA increases positive responses to social images, and it has been suggested that the ability of MDMA to positively bias social perception may underlie its therapeutic efficacy as a psychotherapy adjunct. However, the effect of the compound on affective responses to positive or negative social feedback has not been tested.

AIMS

In this study, we aimed to test the effects of MDMA compared to placebo and the prototypical stimulant, methamphetamine (MA), on responses to positive and negative social feedback.

METHODS

This was a double-blind, placebo-controlled, crossover trial (NCT03790618), comparing the effects of two doses of MDMA (0.75 mg/kg, 1.5 mg/kg) to both placebo and MA (20 mg) on responses to a personalized social feedback task, similar to a dating app, in healthy adult volunteers ages 18-40 (N = 36, 18 women, 18 men).

RESULTS/OUTCOMES

The high dose of MDMA increased positive affective responses to social feedback.

CONCLUSIONS/INTERPRETATIONS

These findings suggest one process by which MDMA may facilitate social connection. Further work is needed to understand how MDMA affects responses to more generalized types of social feedback and to understand these effects in clinical populations.

MDMA-assisted psychotherapy for PTSD: Growing evidence for memory effects mediating treatment efficacy

The application of MDMA in conjunction with psychotherapy has in recent years seen a resurgence of clinical, scientific, and public interest in the treatment of posttraumatic stress disorder (PTSD). Clinical trials have shown promising safety and efficacy, but the mechanisms underlying this treatment form remain largely unestablished. This article explores recent preclinical and clinical evidence suggesting that the treatment's efficacy may be influenced by the mnemonic effects of MDMA. We review data on the effects of MDMA on fear extinction and fear reconsolidation and the utility of these processes for PTSD treatment. We corroborate our findings by incorporating research from cognitive psychology and psychopharmacology and offer recommendations for future research.

Pioneering Changes in Psychiatry: Biomarkers, Psychedelics, and AI

This viewpoint discusses integrating biomarkers, psychedelics, and AI into psychiatry for enhanced diagnostics, prognosis, and treatment. It highlights the potential of psychedelics in therapy, AI's role in predicting treatment response, and the challenges that must be addressed. The aim is to encourage research for more precise, personalized psychiatric care.

Social Psychopharmacology: Novel Approaches to Treat Deficits in Social Motivation in Schizophrenia

BACKGROUND AND HYPOTHESIS

Diminished social motivation is a negative symptom of schizophrenia and leads to severe functional consequences for many patients suffering from the illness. However, there are no effective medications available to treat this symptom. Despite the lack of approved treatments for patients, there is a growing body of literature on the effects of several classes of drugs on social motivation in healthy volunteers that may be relevant to patients. The aim of this review is to synthesize these results in an effort to identify novel directions for the development of medications to treat reduced social motivation in schizophrenia.

STUDY DESIGN

In this article, we review pharmacologic challenge studies addressing the acute effects of psychoactive drugs on social motivation in healthy volunteers and consider how these findings may be applied to deficits in social motivation in schizophrenia. We include studies testing amphetamines and 3,4-methylenedioxymethamphetamine (MDMA), opioids, cannabis, serotonergic psychedelics, antidepressants, benzodiazepines, and neuropeptides.

STUDY RESULTS

We report that amphetamines, MDMA, and some opioid medications enhance social motivation in healthy adults and may represent promising avenues of investigation in schizophrenia.

CONCLUSIONS

Given the acute effects of these drugs on behavioral and performance-based measures of social motivation in healthy volunteers, they may be particularly beneficial as an adjunct to psychosocial training programs in patient populations. It remains to be determined how these medications affect patients with deficits in social motivation, and in which contexts they may be most effectively administered.

Social anxiety and MDMA-assisted therapy investigation: a novel clinical trial protocol

Background

Social anxiety disorder (SAD) is a serious and prevalent psychiatric condition that heavily impacts social functioning and quality of life. Though efficacious treatments exist for SAD, remission rates remain elevated and a significant portion of those affected do not access effective treatment, suggesting the need for additional evidence-based treatment options. This paper presents a protocol for an open-label pilot study of MDMA-assisted therapy (MDMA-AT) for social anxiety disorder. The study aims to assess preliminary treatment outcomes, feasibility and safety, and psychological and physiological processes of change in the treatment of SAD with MDMA-AT. A secondary aim includes the development of a treatment manual for MDMA-AT for SAD.

Method

The outlined protocol is a randomized, open-label delayed treatment study. We will recruit 20 participants who meet criteria with moderate-to-severe social anxiety disorder (SAD) of the generalized subtype. Participants will be randomly assigned to an immediate treatment (n = 10) or delayed treatment condition (n = 10). Those in the immediate treatment condition will proceed immediately to active MDMA-AT consisting of three preparation sessions, two medicine sessions in which they receive oral doses of MDMA, and six integration sessions over approximately a 16-week period. The delayed treatment condition will receive the same intervention after a 16-week delay. Our primary outcome is SAD symptom reduction as measured by the Liebowitz Social Anxiety Scale administered by blinded raters at post-treatment and 6 month follow up. Secondary outcomes include changes in functional impairment, feasibility and safety measures, and novel therapeutic processes of change including shame and shame-related coping, belongingness, self-concealment, and self-compassion at post-treatment. Exploratory outcomes are also discussed.

Discussion

The results of this pilot trial advance the field's understanding of the acceptability and potential effectiveness of MDMA-AT for social anxiety disorder and provide an overview of relevant therapeutic mechanisms unique to SAD. We hope findings from this protocol will inform the design of subsequent larger-scale randomized controlled trials (RCT) examining the efficacy of MDMA-AT for SAD.

Clinical trial registration

https://clinicaltrials.gov/, NCT05138068.

Overcoming epistemic injustices in the biomedical study of ayahuasca: Towards ethical and sustainable regulation

After decades of biomedical research on ayahuasca's molecular compounds and their physiological effects, recent clinical trials show evidence of therapeutic potential for depression. However, indigenous peoples have been using ayahuasca therapeutically for a very long time, and thus we question the epistemic authority attributed to scientific studies, proposing that epistemic injustices were committed with practical, cultural, social, and legal consequences. We question epistemic authority based on the double-blind design, the molecularization discourse, and contextual issues about safety. We propose a new approach to foster epistemically fair research, outlining how to enforce indigenous rights, considering the Brazilian, Peruvian, and Colombian cases. Indigenous peoples have the right to maintain, control, protect, and develop their biocultural heritage, traditional knowledge, and cultural expressions, including traditional medicine practices. New regulations about ayahuasca must respect the free, prior, and informed consent of indigenous peoples according to the International Labor Organization Indigenous and Tribal Peoples Convention no. 169. The declaration of the ayahuasca complex as a national cultural heritage may prevent patenting from third parties, fostering the development of traditional medicine. When involving isolated compounds derived from traditional knowledge, benefit-sharing agreements are mandatory according to the United Nations' Convention on Biological Diversity. Considering the extremely high demand to treat millions of depressed patients, the medicalization of ayahuasca without adequate regulation respectful of indigenous rights can be detrimental to indigenous peoples and their management of local environments, potentially harming the sustainability of the plants and of the Amazon itself, which is approaching its dieback tipping point.

Natural language signatures of psilocybin microdosing

RATIONALE

Serotonergic psychedelics are being studied as novel treatments for mental health disorders and as facilitators of improved well-being, mental function, and creativity. Recent studies have found mixed results concerning the effects of low doses of psychedelics ("microdosing") on these domains. However, microdosing is generally investigated using instruments designed to assess larger doses of psychedelics, which might lack sensitivity and specificity for this purpose.

OBJECTIVES

Determine whether unconstrained speech contains signatures capable of identifying the acute effects of psilocybin microdoses.

METHODS

Natural speech under psilocybin microdoses (0.5 g of psilocybin mushrooms) was acquired from thirty-four healthy adult volunteers (11 females: 32.09 ± 3.53 years; 23 males: 30.87 ± 4.64 years) following a double-blind and placebo-controlled experimental design with two measurement weeks per participant. On Wednesdays and Fridays of each week, participants consumed either the active dose (psilocybin) or the placebo (edible mushrooms). Features of interest were defined based on variables known to be affected by higher doses: verbosity, semantic variability, and sentiment scores. Machine learning models were used to discriminate between conditions. Classifiers were trained and tested using stratified cross-validation to compute the AUC and p-values.

RESULTS

Except for semantic variability, these metrics presented significant differences between a typical active microdose and the inactive placebo condition. Machine learning classifiers were capable of distinguishing between conditions with high accuracy (AUC [Formula: see text] 0.8).

CONCLUSIONS

These results constitute first evidence that low doses of serotonergic psychedelics can be identified from unconstrained natural speech, with potential for widely applicable, affordable, and ecologically valid monitoring of microdosing schedules.

The altered state of consciousness induced by Δ9-THC

Altered states of consciousness (ASC) provide an opportunity for researchers to study the neurophysiological basis of changes in phenomenal experience. Δ9-tetrahydrocannabinol (THC) is the primary psychoactive constituent of cannabis, however whether the effects of THC include ASC features that are shared with other ASC induction mechanisms, such as classical psychedelics, has not been systematically addressed. We used survey (11D-ASC; State Mindfulness), self-report, and natural language processing (NLP) to assess 7.5 and 15 mg oral THC, relative to placebo, in 25 healthy, infrequent cannabis users. THC dose-dependently increased measures of ASC including Insightfulness, and increased ratings of mindfulness and mind-wandering. THC also increased language entropy as previously reported for LSD. Future studies may seek to determine whether reports of increased mindfulness or insight after THC are primarily representative of a psychotomimetic state (i.e., delusional thinking) or conversely, reflect an enhancement of conscious awareness that may be validated empirically.

Analysis of recreational psychedelic substance use experiences classified by substance

RATIONALE AND OBJECTIVES

Differences among psychedelic substances regarding their subjective experiences are clinically and scientifically interesting. Quantitative linguistic analysis is a powerful tool to examine such differences. This study compared five psychedelic substance report groups and a non-psychedelic report group on quantitative linguistic markers of psychological states and processes derived from recreational use-based online experience reports.

METHODS

Using 2947 publicly available online reports, we compared Ayahuasca and N,N-dimethyltryptamine (DMT, analyzed together), ketamine, lysergic acid diethylamide (LSD), 3,4-methylenedioxymethamphetamine (MDMA), psilocybin (mushroom), and antidepressant drug use experiences. We examined word frequencies related to various psychological states and processes and semantic proximity to psychedelic and mystical experience scales.

RESULTS

Linguistic markers of psychological function indicated distinct effect profiles. For example, MDMA experience reports featured an emotionally intensifying profile accompanied by many cognitive process words and dynamic-personal language. In contrast, Ayahuasca and DMT experience reports involved relatively little emotional language, few cognitive process words, increased analytical thinking-associated language, and the most semantic similarity with psychedelic and mystical experience descriptions. LSD, psilocybin mushroom, and ketamine reports showed only small differences on the emotion-, analytical thinking-, psychedelic, and mystical experience-related language outcomes. Antidepressant reports featured more negative emotional and cognitive process-related words, fewer positive emotional and analytical thinking-related words, and were generally not similar to mystical and psychedelic language.

CONCLUSION

This article addresses an existing research gap regarding the comparison of different psychedelic drugs on linguistic profiles of psychological states, processes, and experiences. The large sample of experience reports involving multiple psychedelic drugs provides valuable information that would otherwise be difficult to obtain. The results could inform experimental research into psychedelic drug effects in healthy populations and clinical trials for psychedelic treatments of psychiatric problems.

Couple Therapy With MDMA-Proposed Pathways of Action

MDMA's first identified potential as a therapeutic catalyst was for couple therapy. Early work in the 1970s and 1980s explored its potential amongst seasoned psychotherapists and their clients. With the completion of the first pilot trial of MDMA-assisted psychotherapy with couples for PTSD, and as the possibility of conducting MDMA-assisted psychotherapy trials expands due to new regulatory frameworks, we have an opportunity to explore and investigate how and why MDMA-assisted couples therapy works. This theoretical paper will explore the neurobiological and neurochemical effects of MDMA in a relational context, the emotional, behavioral, cognitive and somatic effects within a dyadic frame, and how empathy, communication, perception of social connection/support, non-avoidance, openness, attachment/safety, bonding/social intimacy and relationship satisfaction, are all impacted by MDMA, and can be harnessed to facilitate systems-level and interpersonal healing and growth. A model to support MDMA-assisted couple therapy is introduced, and future directions, including implications for intervention development and delivery, will be elucidated.

Predicting changes in substance use following psychedelic experiences: natural language processing of psychedelic session narratives

Background: Experiences with psychedelic drugs, such as psilocybin or lysergic acid diethylamide (LSD), are sometimes followed by changes in patterns of tobacco, opioid, and alcohol consumption. But, the specific characteristics of psychedelic experiences that lead to changes in drug consumption are unknown.Objective: Determine whether quantitative descriptions of psychedelic experiences derived using Natural Language Processing (NLP) would allow us to predict who would quit or reduce using drugs following a psychedelic experience.Methods: We recruited 1141 individuals (247 female, 894 male) from online social media platforms who reported quitting or reducing using alcohol, cannabis, opioids, or stimulants following a psychedelic experience to provide a verbal narrative of the psychedelic experience they attributed as leading to their reduction in drug use. We used NLP to derive topic models that quantitatively described each participant's psychedelic experience narrative. We then used the vector descriptions of each participant's psychedelic experience narrative as input into three different supervised machine learning algorithms to predict long-term drug reduction outcomes.Results: We found that the topic models derived through NLP led to quantitative descriptions of participant narratives that differed across participants when grouped by the drug class quit as well as the long-term quit/reduction outcomes. Additionally, all three machine learning algorithms led to similar prediction accuracy (~65%, CI = ±0.21%) for long-term quit/reduction outcomes.Conclusions: Using machine learning to analyze written reports of psychedelic experiences may allow for accurate prediction of quit outcomes and what drug is quit or reduced within psychedelic therapy.

Quantitative language features identify placebo responders in chronic back pain

ABSTRACT

Although placebo effect sizes in clinical trials of chronic pain treatments have been increasing, it remains unknown if characteristics of individuals' thoughts or previous experiences can reliably infer placebo pill responses. Research using language to investigate emotional and cognitive processes has recently gained momentum. Here, we quantified placebo responses in chronic back pain using more than 300 semantic and psycholinguistic features derived from patients' language. This speech content was collected in an exit interview as part of a clinical trial investigating placebo analgesia (62 patients, 42 treated; 20 not treated). Using a nested leave-one-out cross-validated approach, we distinguished placebo responders from nonresponders with 79% accuracy using language features alone; a subset of these features-semantic distances to identity and stigma and the number of achievement-related words-also explained 46% of the variance in placebo analgesia. Importantly, these language features were not due to generic treatment effects and were associated with patients' specific baseline psychological traits previously shown to be predictive of placebo including awareness and personality characteristics, explaining an additional 31% of the variance in placebo analgesia beyond that of personality. Initial interpretation of the features suggests that placebo responders differed in how they talked about negative emotions and the extent that they expressed awareness to various aspects of their experiences; differences were also seen in time spent talking about leisure activities. These results indicate that patients' language is sufficient to identify a placebo response and implie that specific speech features may be predictive of responders' previous treatment.

The Efficacy of MDMA (3,4-Methylenedioxymethamphetamine) for Post-traumatic Stress Disorder in Humans: A Systematic Review and Meta-Analysis

Background: 3,4-methylenedioxymethamphetamine (MDMA), known recreationally as "Molly" or "Ecstasy", is a triple monoamine reuptake inhibitor. MDMA specifically acts as a weak 5-HT1 and 5-HT2 receptor agonist, targeting 5-HT2A, 5-HT2B, and 5-HT2C receptors. Its potential use for therapeutic purposes with these pharmacological profiles remains a controversial subject. Studies have shown the potential benefits in clinical trials for post-traumatic stress disorder (PTSD). A larger amount of data has been provided for the push in support of MDMA-assisted psychotherapy in these patients.  Objective: The aim of this article is to compute a meta-analysis and conduct a systematic review of the effects of MDMA on PTSD, discussing the potential benefits and adverse events relative to dosing and stability of treatment. Methods: Articles were collected and analyzed for systematic review: 16 articles were included in the systematic review that met the criteria for the use of MDMA in the treatment of PTSD as well as assessing the safety and efficacy of the drug in human participants. Ten studies were used for the meta-analysis, with a cumulative sample size of 168 patients. The significance of the findings on dosing and efficacy of MDMA in healthy human participants was quantified based on the Clinician-Administered PTSD Scale for DSM-5 (CAPS-5) and PTSD symptom scores. Results: The disorders for which MDMA demonstrated a net positive or net negative effect on symptoms are presented separately. Adverse events in patients across all disease classes are presented. The therapeutic index for patients who demonstrated a benefit is also presented. An odds ratio for beneficial and adverse events is used to determine treatment-resistant patients who may benefit from clinical trials of MDMA. Discussion: Findings show promising evidence for the potential therapeutic use of MDMA alongside psychotherapy in the treatment of PTSD. The pharmacological profile of MDMA may provide direction for future drug developments to treat patients with treatment-resistant psychiatric disorders.

A Systematic Review of the MDMA Model to Address Social Impairment in Autism

BACKGROUND

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterised by repetitive behaviours, cognitive rigidity/inflexibility, and social-affective impairment. Unfortunately, no gold-standard treatments exist to alleviate the core socio-behavioural impairments of ASD. Meanwhile, the prosocial empathogen/entactogen 3,4-methylene-dioxy-methamphetamine (MDMA) is known to enhance sociability and empathy in both humans and animal models of psychological disorders.

OBJECTIVE

We review the evidence obtained from behavioural tests across the current literature, showing how MDMA can induce prosocial effects in animals and humans, where controlled experiments were able to be performed.

METHODS

Six electronic databases were consulted. The search strategy was tailored to each database. Only English-language papers were reviewed. Behaviours not screened in this review may have affected the core ASD behaviours studied. Molecular analogues of MDMA have not been investigated.

RESULTS

We find that the social impairments may potentially be alleviated by postnatal administration of MDMA producing prosocial behaviours in mostly the animal model.

CONCLUSION

MDMA and/or MDMA-like molecules appear to be an effective pharmacological treatment for the social impairments of autism, at least in animal models. Notably, clinical trials based on MDMA use are now in progress. Nevertheless, larger and more extended clinical studies are warranted to prove the assumption that MDMA and MDMA-like molecules have a role in the management of the social impairments of autism.

The entropic tongue: Disorganization of natural language under LSD

Serotonergic psychedelics have been suggested to mirror certain aspects of psychosis, and, more generally, elicit a state of consciousness underpinned by increased entropy of on-going neural activity. We investigated the hypothesis that language produced under the effects of lysergic acid diethylamide (LSD) should exhibit increased entropy and reduced semantic coherence. Computational analysis of interviews conducted at two different time points after 75 μg of intravenous LSD verified this prediction. Non-semantic analysis of speech organization revealed increased verbosity and a reduced lexicon, changes that are more similar to those observed during manic psychoses than in schizophrenia, which was confirmed by direct comparison with reference samples. Importantly, features related to language organization allowed machine learning classifiers to identify speech under LSD with accuracy comparable to that obtained by examining semantic content. These results constitute a quantitative and objective characterization of disorganized natural speech as a landmark feature of the psychedelic state.

Dreaming during the Covid-19 pandemic: Computational assessment of dream reports reveals mental suffering related to fear of contagion

The current global threat brought on by the Covid-19 pandemic has led to widespread social isolation, posing new challenges in dealing with metal suffering related to social distancing, and in quickly learning new social habits intended to prevent contagion. Neuroscience and psychology agree that dreaming helps people to cope with negative emotions and to learn from experience, but can dreaming effectively reveal mental suffering and changes in social behavior? To address this question, we applied natural language processing tools to study 239 dream reports by 67 individuals, made either before the Covid-19 outbreak or during the months of March and April, 2020, when lockdown was imposed in Brazil following the WHO’s declaration of the pandemic. Pandemic dreams showed a higher proportion of anger and sadness words, and higher average semantic similarities to the terms “contamination” and “cleanness”. These features seem to be associated with mental suffering linked to social isolation, as they explained 40% of the variance in the PANSS negative subscale related to socialization (p = 0.0088). These results corroborate the hypothesis that pandemic dreams reflect mental suffering, fear of contagion, and important changes in daily habits that directly impact socialization.

From discourse to pathology: Automatic identification of Parkinson's disease patients via morphological measures across three languages

Embodied cognition research on Parkinson's disease (PD) points to disruptions of frontostriatal language functions as sensitive targets for clinical assessment. However, no existing approach has been tested for crosslinguistic validity, let alone by combining naturalistic tasks with machine-learning tools. To address these issues, we conducted the first classifier-based examination of morphological processing (a core frontostriatal function) in spontaneous monologues from PD patients across three typologically different languages. The study comprised 330 participants, encompassing speakers of Spanish (61 patients, 57 matched controls), German (88 patients, 88 matched controls), and Czech (20 patients, 16 matched controls). All subjects described the activities they perform during a regular day, and their monologues were automatically coded via morphological tagging, a computerized method that labels each word with a part-of-speech tag (e.g., noun, verb) and specific morphological tags (e.g., person, gender, number, tense). The ensuing data were subjected to machine-learning analyses to assess whether differential morphological patterns could classify between patients and controls and reflect the former's degree of motor impairment. Results showed robust classification rates, with over 80% of patients being discriminated from controls in each language separately. Moreover, the most discriminative morphological features were associated with the patients' motor compromise (as indicated by Pearson r correlations between predicted and collected motor impairment scores that ranged from moderate to moderate-to-strong across languages). Taken together, our results suggest that morphological patterning, an embodied frontostriatal domain, may be distinctively affected in PD across languages and even under ecological testing conditions.

Long-term follow-up outcomes of MDMA-assisted psychotherapy for treatment of PTSD: a longitudinal pooled analysis of six phase 2 trials

RATIONALE

Posttraumatic stress disorder (PTSD) is a chronic condition that has wide-ranging negative effects on an individual's health and interpersonal relationships. Treatments with long-term benefits are needed to promote the safety and well-being of those suffering from PTSD.

OBJECTIVES

To examine long-term change in PTSD symptoms and additional benefits/harms after 3,4-methylenedioxymethamphetamine (MDMA)-assisted psychotherapy for treatment of PTSD.

METHODS

Participants received two to three active doses of MDMA (75-125 mg) during blinded or open-label psychotherapy sessions with additional non-drug therapy sessions. PTSD symptoms were assessed using the Clinician-Administered PTSD Scale for DSM IV (CAPS-IV) at baseline, 1 to 2 months after the last active MDMA session (treatment exit), and at least 12 months post final MDMA session (LTFU). A mixed-effect repeated-measures (MMRM) analysis assessed changes in CAPS-IV total severity scores. The number of participants who met PTSD diagnostic criteria was summarized at each time point. Participants completed a long-term follow-up questionnaire.

RESULTS

There was a significant reduction in CAPS-IV total severity scores from baseline to treatment exit (LS mean (SE) = - 44.8 (2.82), p < .0001), with a Cohen's d effect size of 1.58 (95% CI = 1.24, 1.91). CAPS-IV scores continued to decrease from treatment exit to LTFU (LS mean (SE) = - 5.2 (2.29), p < .05), with a Cohen's d effect size of 0.23 (95% CI = 0.04, 0.43). The number of participants who no longer met PTSD criteria increased from treatment exit (56.0%) to LTFU (67.0%). The majority of participants reported benefits, including improved relationships and well-being, and a minority reported harms from study participation.

CONCLUSIONS

PTSD symptoms were reduced 1 to 2 months after MDMA-assisted psychotherapy, and symptom improvement continued at least 12 months post-treatment. Phase 3 trials are investigating this novel treatment approach in a larger sample of participants with chronic PTSD.

TRIAL REGISTRATION

clinicaltrials.gov Identifier: NCT00090064, NCT00353938, NCT01958593, NCT01211405, NCT01689740, NCT01793610.

A Big Data Platform for Real Time Analysis of Signs of Depression in Social Media

In this paper we propose a scalable platform for real-time processing of Social Media data. The platform ingests huge amounts of contents, such as Social Media posts or comments, and can support Public Health surveillance tasks. The processing and analytical needs of multiple screening tasks can easily be handled by incorporating user-defined execution graphs. The design is modular and supports different processing elements, such as crawlers to extract relevant contents or classifiers to categorise Social Media. We describe here an implementation of a use case built on the platform that monitors Social Media users and detects early signs of depression.

Using Language Processing and Speech Analysis for the Identification of Psychosis and Other Disorders

Increasingly, data-driven methods have been implemented to understand psychopathology. Language is the main source of information in psychiatry and represents "big data" at the level of the individual. Language and behavior are amenable to computational natural language processing (NLP) analytics, which may help operationalize the mental status examination. In this review, we highlight the application of NLP to schizophrenia and its risk states as an exemplar of its use, operationalizing tangential and concrete speech as reductions in semantic coherence and syntactic complexity, respectively. Other clinical applications are reviewed, including forecasting suicide risk and detecting intoxication. Challenges and future directions are discussed, including biomarker development, harmonization, and application of NLP more broadly to behavior, including intonation/prosody, facial expression and gesture, and the integration of these in dyads and during discourse. Similar NLP analytics can also be applied beyond humans to behavioral motifs across species, important for modeling psychopathology in animal models. Finally, clinical neuroscience can inform the development of artificial intelligence.

Anomalies in language as a biomarker for schizophrenia

PURPOSE OF REVIEW

After more than a century of neuroscience research, reproducible, clinically relevant biomarkers for schizophrenia have not yet been established. This article reviews current advances in evaluating the use of language as a diagnostic or prognostic tool in schizophrenia.

RECENT FINDINGS

The development of computational linguistic tools to quantify language disturbances is rapidly gaining ground in the field of schizophrenia research. Current applications are the use of semantic space models and acoustic analyses focused on phonetic markers. These features are used in machine learning models to distinguish patients with schizophrenia from healthy controls or to predict conversion to psychosis in high-risk groups, reaching accuracy scores (generally ranging from 80 to 90%) that exceed clinical raters. Other potential applications for a language biomarker in schizophrenia are monitoring of side effects, differential diagnostics and relapse prevention.

SUMMARY

Language disturbances are a key feature of schizophrenia. Although in its early stages, the emerging field of research focused on computational linguistics suggests an important role for language analyses in the diagnosis and prognosis of schizophrenia. Spoken language as a biomarker for schizophrenia has important advantages because it can be objectively and reproducibly quantified. Furthermore, language analyses are low-cost, time efficient and noninvasive in nature.

Detection of acute 3,4-methylenedioxymethamphetamine (MDMA) effects across protocols using automated natural language processing

The detection of changes in mental states such as those caused by psychoactive drugs relies on clinical assessments that are inherently subjective. Automated speech analysis may represent a novel method to detect objective markers, which could help improve the characterization of these mental states. In this study, we employed computer-extracted speech features from multiple domains (acoustic, semantic, and psycholinguistic) to assess mental states after controlled administration of 3,4-methylenedioxymethamphetamine (MDMA) and intranasal oxytocin. The training/validation set comprised within-participants data from 31 healthy adults who, over four sessions, were administered MDMA (0.75, 1.5 mg/kg), oxytocin (20 IU), and placebo in randomized, double-blind fashion. Participants completed two 5-min speech tasks during peak drug effects. Analyses included group-level comparisons of drug conditions and estimation of classification at the individual level within this dataset and on two independent datasets. Promising classification results were obtained to detect drug conditions, achieving cross-validated accuracies of up to 87% in training/validation and 92% in the independent datasets, suggesting that the detected patterns of speech variability are associated with drug consumption. Specifically, we found that oxytocin seems to be mostly driven by changes in emotion and prosody, which are mainly captured by acoustic features. In contrast, mental states driven by MDMA consumption appear to manifest in multiple domains of speech. Furthermore, we find that the experimental task has an effect on the speech response within these mental states, which can be attributed to presence or absence of an interaction with another individual. These results represent a proof-of-concept application of the potential of speech to provide an objective measurement of mental states elicited during intoxication.

Speech-based markers for posttraumatic stress disorder in US veterans

BACKGROUND

The diagnosis of posttraumatic stress disorder (PTSD) is usually based on clinical interviews or self-report measures. Both approaches are subject to under- and over-reporting of symptoms. An objective test is lacking. We have developed a classifier of PTSD based on objective speech-marker features that discriminate PTSD cases from controls.

METHODS

Speech samples were obtained from warzone-exposed veterans, 52 cases with PTSD and 77 controls, assessed with the Clinician-Administered PTSD Scale. Individuals with major depressive disorder (MDD) were excluded. Audio recordings of clinical interviews were used to obtain 40,526 speech features which were input to a random forest (RF) algorithm.

RESULTS

The selected RF used 18 speech features and the receiver operating characteristic curve had an area under the curve (AUC) of 0.954. At a probability of PTSD cut point of 0.423, Youden's index was 0.787, and overall correct classification rate was 89.1%. The probability of PTSD was higher for markers that indicated slower, more monotonous speech, less change in tonality, and less activation. Depression symptoms, alcohol use disorder, and TBI did not meet statistical tests to be considered confounders.

CONCLUSIONS

This study demonstrates that a speech-based algorithm can objectively differentiate PTSD cases from controls. The RF classifier had a high AUC. Further validation in an independent sample and appraisal of the classifier to identify those with MDD only compared with those with PTSD comorbid with MDD is required.

REBUS and the Anarchic Brain: Toward a Unified Model of the Brain Action of Psychedelics

This paper formulates the action of psychedelics by integrating the free-energy principle and entropic brain hypothesis. We call this formulation relaxed beliefs under psychedelics (REBUS) and the anarchic brain, founded on the principle that-via their entropic effect on spontaneous cortical activity-psychedelics work to relax the precision of high-level priors or beliefs, thereby liberating bottom-up information flow, particularly via intrinsic sources such as the limbic system. We assemble evidence for this model and show how it can explain a broad range of phenomena associated with the psychedelic experience. With regard to their potential therapeutic use, we propose that psychedelics work to relax the precision weighting of pathologically overweighted priors underpinning various expressions of mental illness. We propose that this process entails an increased sensitization of high-level priors to bottom-up signaling (stemming from intrinsic sources), and that this heightened sensitivity enables the potential revision and deweighting of overweighted priors. We end by discussing further implications of the model, such as that psychedelics can bring about the revision of other heavily weighted high-level priors, not directly related to mental health, such as those underlying partisan and/or overly-confident political, religious, and/or philosophical perspectives. SIGNIFICANCE STATEMENT: Psychedelics are capturing interest, with efforts underway to bring psilocybin therapy to marketing authorisation and legal access within a decade, spearheaded by the findings of a series of phase 2 trials. In this climate, a compelling unified model of how psychedelics alter brain function to alter consciousness would have appeal. Towards this end, we have sought to integrate a leading model of global brain function, hierarchical predictive coding, with an often-cited model of the acute action of psychedelics, the entropic brain hypothesis. The resulting synthesis states that psychedelics work to relax high-level priors, sensitising them to liberated bottom-up information flow, which, with the right intention, care provision and context, can help guide and cultivate the revision of entrenched pathological priors.

Machine learning in mental health: a scoping review of methods and applications

BACKGROUND

This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.

METHODS

We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.

RESULTS

Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.

CONCLUSIONS

Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

Differential 28-Days Cyclic Modulation of Affective Intensity in Female and Male Participants via Social Media

The menstrual cycle affects many aspects of female physiology, from the immune system to behavioral and emotional regulation. It is unclear however if these physiological changes are reflected in everyday, naturalistic language production, and moreover whether these putative effects can be consistently quantified. Using a novel approach based on social networks, we characterized linguistic expression differences in female and male volunteers over the course of several months, while having no physiological or reported information of the female participants' menstrual cycles. We used a simple algorithm to quantify the linguistic affect intensity of 418 (184 females and 234 males) subjects using their social networks production and found a 7-day modulatory cycle of affect intensity that corresponds to labor-week fluctuations, with no significant difference by biological sex, and a 28-day cycle over which females are significantly different than males. Our results are consistent with the hypothesis that the menstrual cycle modulates affective features of naturalistic linguistic production.

Neurochemical models of near-death experiences: A large-scale study based on the semantic similarity of written reports

The real or perceived proximity to death often results in a non-ordinary state of consciousness characterized by phenomenological features such as the perception of leaving the body boundaries, feelings of peace, bliss and timelessness, life review, the sensation of traveling through a tunnel and an irreversible threshold. Near-death experiences (NDEs) are comparable among individuals of different cultures, suggesting an underlying neurobiological mechanism. Anecdotal accounts of the similarity between NDEs and certain drug-induced altered states of consciousness prompted us to perform a large-scale comparative analysis of these experiences. After assessing the semantic similarity between ≈15,000 reports linked to the use of 165 psychoactive substances and 625 NDE narratives, we determined that the N-methyl-D-aspartate (NMDA) receptor antagonist ketamine consistently resulted in reports most similar to those associated with NDEs. Ketamine was followed by Salvia divinorum (a plant containing a potent and selective κ receptor agonist) and a series of serotonergic psychedelics, including the endogenous serotonin 2A receptor agonist N,N-Dimethyltryptamine (DMT). This similarity was driven by semantic concepts related to consciousness of the self and the environment, but also by those associated with the therapeutic, ceremonial and religious aspects of drug use. Our analysis sheds light on the long-standing link between certain drugs and the experience of "dying", suggests that ketamine could be used as a safe and reversible experimental model for NDE phenomenology, and supports the speculation that endogenous NMDA antagonists with neuroprotective properties may be released in the proximity of death.

3,4-Methylenedioxymethamphetamine-assisted psychotherapy for treatment of chronic posttraumatic stress disorder: A randomized phase 2 controlled trial

BACKGROUND

Posttraumatic stress disorder often does not resolve after conventional psychotherapies or pharmacotherapies. Pilot studies have reported that 3,4-methylenedioxymethamphetamine (MDMA) combined with psychotherapy reduces posttraumatic stress disorder symptoms.

AIMS

This pilot dose response trial assessed efficacy and safety of MDMA-assisted psychotherapy across multiple therapy teams.

METHODS

Twenty-eight people with chronic posttraumatic stress disorder were randomized in a double-blind dose response comparison of two active doses (100 and 125 mg) with a low dose (40 mg) of MDMA administered during eight-hour psychotherapy sessions. Change in the Clinician-Administered PTSD Scale total scores one month after two sessions of MDMA served as the primary outcome. Active dose groups had one additional open-label session; the low dose group crossed over for three open-label active dose sessions. A 12-month follow-up assessment occurred after the final MDMA session.

RESULTS

In the intent-to-treat set, the active groups had the largest reduction in Clinician-Administered PTSD Scale total scores at the primary endpoint, with mean (standard deviation) changes of -26.3 (29.5) for 125 mg, -24.4 (24.2) for 100 mg, and -11.5 (21.2) for 40 mg, though statistical significance was reached only in the per protocol set ( p=0.03). Posttraumatic stress disorder symptoms remained lower than baseline at 12-month follow-up ( p<0.001) with 76% ( n=25) not meeting posttraumatic stress disorder criteria. There were no drug-related serious adverse events, and the treatment was well-tolerated.

CONCLUSIONS

Our findings support previous investigations of MDMA-assisted psychotherapy as an innovative, efficacious treatment for posttraumatic stress disorder.

Considering the context: social factors in responses to drugs in humans

BACKGROUND

Drugs are typically used in social settings. Here, we consider two factors that may contribute to this observation: (i) the presence of other people may enhance the positive mood effects of a drug, and conversely, (ii) drugs may enhance the value of social stimuli.

METHODS

We review evidence from controlled laboratory studies with human volunteers, which investigated either of these interactions between social factors and responses to drugs. We examine the bidirectional effects of social stimuli and single doses of alcohol, stimulants, opioids, and cannabis.

RESULTS

All four classes of drugs interact with social contexts, but the nature of these interactions varies across drugs, and depends on whether the context is positive or negative.

CONCLUSIONS

Alcohol and stimulant drugs enhance the attractiveness of social stimuli and the desire to socialize, and social contexts, in turn, enhance these drugs' effects. In contrast, opioids and cannabis have subtler effects on social interactions and their effects are less influenced by the presence of others. Overall, there is stronger evidence that drugs enhance positive social contexts than that they dampen the negativity of unpleasant social settings. Controlled research is needed to understand the interactions between drugs of abuse and social contexts, to model and understand the determinants of drug use outside the laboratory.

Prediction of psychosis across protocols and risk cohorts using automated language analysis

Language and speech are the primary source of data for psychiatrists to diagnose and treat mental disorders. In psychosis, the very structure of language can be disturbed, including semantic coherence (e.g., derailment and tangentiality) and syntactic complexity (e.g., concreteness). Subtle disturbances in language are evident in schizophrenia even prior to first psychosis onset, during prodromal stages. Using computer-based natural language processing analyses, we previously showed that, among English-speaking clinical (e.g., ultra) high-risk youths, baseline reduction in semantic coherence (the flow of meaning in speech) and in syntactic complexity could predict subsequent psychosis onset with high accuracy. Herein, we aimed to cross-validate these automated linguistic analytic methods in a second larger risk cohort, also English-speaking, and to discriminate speech in psychosis from normal speech. We identified an automated machine-learning speech classifier - comprising decreased semantic coherence, greater variance in that coherence, and reduced usage of possessive pronouns - that had an 83% accuracy in predicting psychosis onset (intra-protocol), a cross-validated accuracy of 79% of psychosis onset prediction in the original risk cohort (cross-protocol), and a 72% accuracy in discriminating the speech of recent-onset psychosis patients from that of healthy individuals. The classifier was highly correlated with previously identified manual linguistic predictors. Our findings support the utility and validity of automated natural language processing methods to characterize disturbances in semantics and syntax across stages of psychotic disorder. The next steps will be to apply these methods in larger risk cohorts to further test reproducibility, also in languages other than English, and identify sources of variability. This technology has the potential to improve prediction of psychosis outcome among at-risk youths and identify linguistic targets for remediation and preventive intervention. More broadly, automated linguistic analysis can be a powerful tool for diagnosis and treatment across neuropsychiatry.

The Experience Elicited by Hallucinogens Presents the Highest Similarity to Dreaming within a Large Database of Psychoactive Substance Reports

Ever since the modern rediscovery of psychedelic substances by Western society, several authors have independently proposed that their effects bear a high resemblance to the dreams and dreamlike experiences occurring naturally during the sleep-wake cycle. Recent studies in humans have provided neurophysiological evidence supporting this hypothesis. However, a rigorous comparative analysis of the phenomenology (“what it feels like” to experience these states) is currently lacking. We investigated the semantic similarity between a large number of subjective reports of psychoactive substances and reports of high/low lucidity dreams, and found that the highest-ranking substance in terms of the similarity to high lucidity dreams was the serotonergic psychedelic lysergic acid diethylamide (LSD), whereas the highest-ranking in terms of the similarity to dreams of low lucidity were plants of the Datura genus, rich in deliriant tropane alkaloids. Conversely, sedatives, stimulants, antipsychotics, and antidepressants comprised most of the lowest-ranking substances. An analysis of the most frequent words in the subjective reports of dreams and hallucinogens revealed that terms associated with perception (“see,” “visual,” “face,” “reality,” “color”), emotion (“fear”), setting (“outside,” “inside,” “street,” “front,” “behind”) and relatives (“mom,” “dad,” “brother,” “parent,” “family”) were the most prevalent across both experiences. In summary, we applied novel quantitative analyses to a large volume of empirical data to confirm the hypothesis that, among all psychoactive substances, hallucinogen drugs elicit experiences with the highest semantic similarity to those of dreams. Our results and the associated methodological developments open the way to study the comparative phenomenology of different altered states of consciousness and its relationship with non-invasive measurements of brain physiology.

Altered Insula Connectivity under MDMA

Recent work with noninvasive human brain imaging has started to investigate the effects of 3,4-methylenedioxymethamphetamine (MDMA) on large-scale patterns of brain activity. MDMA, a potent monoamine-releaser with particularly pronounced serotonin- releasing properties, has unique subjective effects that include: marked positive mood, pleasant/unusual bodily sensations and pro-social, empathic feelings. However, the neurobiological basis for these effects is not properly understood, and the present analysis sought to address this knowledge gap. To do this, we administered MDMA-HCl (100 mg p.o.) and, separately, placebo (ascorbic acid) in a randomized, double-blind, repeated-measures design with twenty-five healthy volunteers undergoing fMRI scanning. We then employed a measure of global resting-state functional brain connectivity and follow-up seed-to-voxel analysis to the fMRI data we acquired. Results revealed decreased right insula/salience network functional connectivity under MDMA. Furthermore, these decreases in right insula/salience network connectivity correlated with baseline trait anxiety and acute experiences of altered bodily sensations under MDMA. The present findings highlight insular disintegration (ie, compromised salience network membership) as a neurobiological signature of the MDMA experience, and relate this brain effect to trait anxiety and acutely altered bodily sensations-both of which are known to be associated with insular functioning.

Serotonin and brain function: a tale of two receptors

Previous attempts to identify a unified theory of brain serotonin function have largely failed to achieve consensus. In this present synthesis, we integrate previous perspectives with new and older data to create a novel bipartite model centred on the view that serotonin neurotransmission enhances two distinct adaptive responses to adversity, mediated in large part by its two most prevalent and researched brain receptors: the 5-HT1A and 5-HT2A receptors. We propose that passive coping (i.e. tolerating a source of stress) is mediated by postsynaptic 5-HT1AR signalling and characterised by stress moderation. Conversely, we argue that active coping (i.e. actively addressing a source of stress) is mediated by 5-HT2AR signalling and characterised by enhanced plasticity (defined as capacity for change). We propose that 5-HT1AR-mediated stress moderation may be the brain's default response to adversity but that an improved ability to change one's situation and/or relationship to it via 5-HT2AR-mediated plasticity may also be important - and increasingly so as the level of adversity reaches a critical point. We propose that the 5-HT1AR pathway is enhanced by conventional 5-HT reuptake blocking antidepressants such as the selective serotonin reuptake inhibitors (SSRIs), whereas the 5-HT2AR pathway is enhanced by 5-HT2AR-agonist psychedelics. This bipartite model purports to explain how different drugs (SSRIs and psychedelics) that modulate the serotonergic system in different ways, can achieve complementary adaptive and potentially therapeutic outcomes.

The effects of MDMA on socio-emotional processing: Does MDMA differ from other stimulants?

±3,4-Methylenedioxymethamphetamine (MDMA) is a popular recreational drug that enhances sociability and feelings of closeness with others. These "prosocial" effects appear to motivate the recreational use of MDMA and may also form the basis of its potential as an adjunct to psychotherapy. However, the extent to which MDMA differs from prototypic stimulant drugs, such as dextroamphetamine, methamphetamine, and methylphenidate, in either its behavioral effects or mechanisms of action, is not fully known. The purpose of this review is to evaluate human laboratory findings of the social effects of MDMA compared to other stimulants, ranging from simple subjective ratings of sociability to more complex elements of social processing and behavior. We also review the neurochemical mechanisms by which these drugs may impact sociability. Together, the findings reviewed here lay the groundwork for better understanding the socially enhancing effects of MDMA that distinguish it from other stimulant drugs, especially as these effects relate to the reinforcing and potentially therapeutic effects of the drug.

How language flows when movements don't: An automated analysis of spontaneous discourse in Parkinson's disease

To assess the impact of Parkinson's disease (PD) on spontaneous discourse, we conducted computerized analyses of brief monologues produced by 51 patients and 50 controls. We explored differences in semantic fields (via latent semantic analysis), grammatical choices (using part-of-speech tagging), and word-level repetitions (with graph embedding tools). Although overall output was quantitatively similar between groups, patients relied less heavily on action-related concepts and used more subordinate structures. Also, a classification tool operating on grammatical patterns identified monologues as pertaining to patients or controls with 75% accuracy. Finally, while the incidence of dysfluent word repetitions was similar between groups, it allowed inferring the patients' level of motor impairment with 77% accuracy. Our results highlight the relevance of studying naturalistic discourse features to tap the integrity of neural (and, particularly, motor) networks, beyond the possibilities of standard token-level instruments.

An examination of the language construct in NIMH's research domain criteria: Time for reconceptualization!

The National Institute of Mental Health's Research Domain Criteria (RDoC) Initiative "calls for the development of new ways of classifying psychopathology based on dimensions of observable behavior." As a result of this ambitious initiative, language has been identified as an independent construct in the RDoC matrix. In this article, we frame language within an evolutionary and neuropsychological context and discuss some of the limitations to the current measurements of language. Findings from genomics and the neuroimaging of performance during language tasks are discussed in relation to serious mental illness and within the context of caveats regarding measuring language. Indeed, the data collection and analysis methods employed to assay language have been both aided and constrained by the available technologies, methodologies, and conceptual definitions. Consequently, different fields of language research show inconsistent definitions of language that have become increasingly broad over time. Individually, they have also shown significant improvements in conceptual resolution, as well as in experimental and analytic techniques. More recently, language research has embraced collaborations across disciplines, notably neuroscience, cognitive science, and computational linguistics and has ultimately re-defined classical ideas of language. As we move forward, the new models of language with their remarkably multifaceted constructs force a re-examination of the NIMH RDoC conceptualization of language and thus the neuroscience and genetics underlying this concept. © 2016 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.