Applied Minds (United States)

This chapter explores the relationship between autism and bipolar disorder, two disorders that often are comorbid. In this chapter, the authors discuss how to recognize common traits, explore etiology of the two's relationship, offer an in-depth look into treatment options, and offer templates for future search. Additional factors such as culture, gender, geographic location, hormones, and lifestyle are explained as well. Lastly, the chapter provides a detailed look into the assessment process and offers options to diagnosis for both autism and bipolar disorder.

This chapter examines the intersection of autism spectrum disorder (ASD) and gender diversity, highlighting the disproportionately high co-occurrence of these identities and exploring its developmental, cognitive, social, and clinical implications. Drawing upon theoretical, empirical, and qualitative sources, including interviews with neurodivergent gender-diverse individuals, the chapter situates this overlap within broader movements toward neurodiversity and gender affirmation. It reviews historical shifts in diagnostic frameworks and neurocognitive and sociocultural models of identity formation. Clinical sections address diagnostic bias, minority stress, and strategies for affirming mental health care. The author argues for a paradigm shift away from deficit-based frameworks toward affirming, participatory models that value autonomy, self-knowledge, and systemic inclusion. Ultimately, the convergence of neurodiversity and gender diversity is presented as an opportunity to understand human variation, authenticity, and the social construction of identity.

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BackgroundPrevious research has demonstrated that the COVID-19 pandemic led to a global increase in mental distress. However, few studies have examined the impact of the pandemic on mental health stigma.AimsTo investigate changes in measures of mental health stigma, including knowledge, attitudes and behavioural intentions, in 2021 and 2023 in Hong Kong; to examine the mediating role of attitudes on the relationship between knowledge and behavioural intentions; and to explore how disclosure of mental illness contributes to enhanced overall well-being.MethodData were collected as part of a larger research project focusing on mental well-being in Hong Kong. A total of 1010 and 1014 participants were surveyed in 2021 and 2023, respectively. The participants were Hong Kong residents aged 18 years and above.ResultsOur findings demonstrate that all measures of mental health stigma showed increases in severity between 2021 and 2023. In addition, our mediation analyses observed both full and partial mediation effects of attitudes on the relationship between knowledge and behavioural intentions. The results also showed that mental illness disclosure was associated with higher well-being; however, despite these benefits, there was a decrease in willingness to disclose in 2023 compared with 2021.ConclusionsThis study highlights the ongoing issue of mental health stigma in Hong Kong. Future mental health programmes and interventions should aim to address various facets of mental health knowledge, including symptom recognition, access to support resources and the deleterious consequences of mental health stigma.

IntroductionGeneralized anxiety disorder (GAD) is a chronic and disabling disorder with an estimated lifetime prevalence of up to 7.8% in the US. MM120 (lysergide D-tartrate), a tartrate salt of D-lysergic acid diethylamide (LSD), is currently under development as a potential treatment for GAD. The mechanism of action (MOA) by which MM120 may elicit profound lasting psychological changes is not fully understood. Studies suggest that it has significant effects on functional brain activity across several networks, likely a result of serotonergic agonism mediated by 5HT2A activity and subsequent changes in neural network connectivity.ObjectivesA series of Good Laboratory Practice (GLP) compliant studies were conducted to evaluate abuse potential of MM120.MethodsIn vitro assessments of MM120, and its main metabolite 2-oxo-3-hydroxy LSD, receptor binding activity were performed using Eurofins CEREP BioPrint. To examine single dosing effects, MM120 was administered by oral gavage to male Sprague Dawley rats (n=6/group) at doses of 0 (vehicle only), 0.5, 2.0, and 6.0 mg/kg. A Modified Irwin test evaluated shorter acting drug effects at 15, 60, 120, and 240 minutes post-dosing compared to pre-dose baseline, with long-term, single dose neurological effects studied in a functional observation battery (FOB) on the day of dosing and 24 hours post dose. To compare potential chronic MM120 effects, male and female Sprague Dawley rats were dosed daily for four weeks via oral gavage at 0 (vehicle only), 0.5, 2.0, and 6.0 mg/kg (n=10-15/sex/group) followed by a 4-week recovery phase. FOB was performed pre-dose, dosing day 27, and day 25 of recovery phase. Additional assessments included toxicological and toxicokinetic evaluations.ResultsReceptor binding assay confirmed MM120’s MOA is mediated by the serotonin system and hallucinogenic effects are driven by agonism at 5-HT2A and 5-HT2C receptors. MM120 demonstrated agonist activity at 5-HT1A, 5-HT1B, 5-HT1D, 5-HT5A, 5-HT6, and 5HT7 receptors and only nanomolar affinity at D2 dopaminergic receptors. Single dose MM120 at 0.5 mg/kg, had no effects on behavioral or physiological states. Following 6 mg/kg MM120, vocalization was recorded in 1/6 rats 2-4 h post dose, and incidences of mild piloerection were recorded in 3/6 rats from 4 to 24 h post dose. In the chronic study, there were no MM120-related changes in basic or fine movements, total ambulation, total rears, or total distance traveled.ConclusionsBinding studies suggest no indication for elements of abuse for MM120 even with binding at serotonergic and, to a smaller degree, dopaminergic receptors. in vivo studies with MM120 evaluated supratherapeutic doses and demonstrated no evidence of physical dependence or withdrawal after sustained, daily administration for four weeks.Disclosure of InterestJ. Tripp Employee of: Mind Medicine, Inc., G. Smagin Employee of: Mind Medicine, Inc.

The Antillean manatee (Trichechus manatus) is an endangered herbivorous aquatic mammal whose role as an ecological balancer and umbrella species underscores the importance of its conservation. An innovative approach to monitor manatee populations is passive acoustic monitoring (PAM), where vocalisations are extracted from submarine audio. We propose a novel end-to-end approach to detect manatee vocalisations building on the Audio Spectrogram Transformer (AST). In a transfer learning spirit, we fine-tune AST to detect manatee calls by redesigning its filterbanks and adapting a real-world dataset containing partial positive labels. Our experimental evaluation reveals the two key features of the proposed model: i) it performs on par with the state of the art without requiring hand-tuned denoising or detection stages, and ii) it can successfully identify missed vocalisations in the training dataset, thus reducing the workload of expert bioacoustic labellers. This work is a preliminary relevant step to develop novel, user-friendly tools for the conservation of the different species of manatees.

Large language models (LLMs) are a new asset class in the machine-learning landscape. Here we offer a primer on defining properties of these modeling techniques. We then reflect on new modes of investigation in which LLMs can be used to reframe classic neuroscience questions to deliver fresh answers. We reason that LLMs have the potential to (1) enrich neuroscience datasets by adding valuable meta-information, such as advanced text sentiment, (2) summarize vast information sources to overcome divides between siloed neuroscience communities, (3) enable previously unthinkable fusion of disparate information sources relevant to the brain, (4) help deconvolve which cognitive concepts most usefully grasp phenomena in the brain, and much more.

Recent large language models (LLMs) such as GPT, Bard and Claude have achieved impressive progress in comprehending and generating human language. We explore the potential for LLMs to automate markup of semi-structured texts into HTML, DITA, and other related languages. Automated markup could make it possible to move documents into XML at much lower cost. Currently, there is no standard benchmark for evaluating automatic markup systems. Establishing a benchmark for the understanding and generation of structured documents and markup languages can drive innovation, standardize evaluations, identify algorithm strengths and weaknesses, clarify the State of the Art and foster interdisciplinary collaborations. This paper introduces an early version of a benchmark for this purpose.