STAT+: FDA to speed up review of three psychedelics as mental health treatments
The Food and Drug Administration will accelerate its review of psychedelic drugs developed by Compass Pathways, the Usona Institute, and Transcend Therapeutics for mental health disorders, as part of the Trump administration’s plan to boost access to the controversial yet promising medications.
The agency will grant priority review vouchers specifically to Compass’ psilocybin product for treatment-resistant depression, Usona’s similar medicine for major depressive disorder, and an MDMA-like treatment for post-traumatic stress disorder from Transcend.
The FDA identified the medications receiving the vouchers, but not the companies developing them. Compass, Usona, and Transcend confirmed they received vouchers.
Sex-specific impact of vitamin D and B9 concentrations on neuroticism: a polygenic score-based study
Lithuanian children’s trauma characteristics and correlates: comparison of clinical and non-clinical samples
A longitudinal inquiry into the vicious cycle of social media addiction and self-injury: the moderating role of resilience
Impact of extremely low frequency electromagnetic fields exposure on sleep quality and mental health in a Tunisian power plant: a cross-sectional study
Mental health in the time of polycrisis: geopolitical determinants and modern psychiatry
Transcriptomic and phenotypic convergence of neurodevelopmental disorder risk genes in vitro and in vivo
Nature Neuroscience, Published online: 24 April 2026; doi:10.1038/s41593-026-02247-7
By studying 23 neurodevelopmental disorder genes across model systems and brain cell types, the authors uncovered shared downstream effects that converge on synaptic biology, epigenetic regulation and mitochondrial function.
Rational causal induction from events in time.
Psychological Review, Vol 133(3), Apr 2026, 584-618; doi:10.1037/rev0000570
A longstanding focus in the causal learning literature has been on inferring causal relations from contingencies, where these abstract away from time by collating independent instances or by aggregating over regularly demarcated trials. In contrast, individual causal learners encounter events in their daily lives that occur in a continuous temporal flow with no such demarcation. Consequently, the process of learning causal relationships in naturalistic environments is comparatively less understood. In this article, we lay out a rational framework that foregrounds the role of time in causal learning. We work within the Bayesian rational analysis tradition, starting by considering how causal relations induce dependence between events in continuous time and how this can be modeled by stochastic processes from the Poisson–Gamma distribution family. We derive the qualitative signatures of causal influence and the general computations needed to infer structure from temporal patterns. We show that this rational account can parsimoniously explain the human preference for causal models that invoke shorter, more reliable, and more predictable causal influences. Furthermore, we show this provides a unifying explanation for human judgments across a wide variety of tasks in the reanalysis of seven experimental data sets. We anticipate the framework will help researchers better understand the many manifestations of continuous-time causal learning across human cognition and the tasks that probe it, from explicit causal structure induction settings to implicit associative or reinforcement learning settings. (PsycInfo Database Record (c) 2026 APA, all rights reserved)
Building a Science-Driven Business: How National Institutes of Health Funding Enabled an Evidence-Based Approach to Maternal Mental Health Innovation
The digital mental health (DMH) industry has grown drastically over the last decade; yet, many DMH products have failed to demonstrate meaningful clinical outcomes, in large part due to lack of scientific evidence. This viewpoint paper highlights an example of how early-stage DMH companies can prioritize science as a strategic advantage. We discuss Moment for Parents, an artificial intelligence–driven maternal mental health app built entirely with support from the National Institutes of Health (NIH) Small Business Innovation Research (SBIR) program. We illustrate the advantages and challenges of building a science-backed product with federal funding. Benefits include credible evidence generation, independence in product development, and enhanced market differentiation. We also discuss the challenges of navigating the SBIR ecosystem, including grant writing and administrative demands, and aligning business objectives with federal research priorities. By showcasing both the promise and complexity of SBIR funding, this viewpoint paper offers actionable insights for founders and chief executive officers who aim to prioritize science in the DMH space.
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