Explore the vast range of titles available.

Psilocybin (PSIL) is a psychedelic drug and a promising experimental therapeutic for many psychiatric conditions. Precision functional mapping (PFM) combines densely repeated resting state fMRI sampling and individual-specific network mapping to improve signal-to-noise ratio (SNR) and effect size in brain imaging research. We present a randomized cross-over study in which PFM was used to characterize acute and persistent effects of psilocybin or methylphenidate (MTP) on brain networks. Seven healthy volunteers (mean age 34.1 years, SD = 9.8; n = 3 females, n = 6 Caucasians) underwent (1) extensive baseline imaging, (2) imaging beginning 60–90 minutes after drug exposure, and (3) longitudinal imaging for up to two weeks after drug exposure. Four individuals also participated in an open-label PSIL replication protocol over 6 months later. This dataset includes resting state (using advanced high-resolution multi-echo fMRI), task fMRI, structural, and diffusion basis spectral imaging as well as assessments of subjective experience. We are releasing this unique dataset as a resource for neuroscientists to study the acute and persistent effects of PSIL and MTP on brain networks.

A single dose of psilocybin, a psychedelic that acutely causes distortions of space–time perception and ego dissolution, produces rapid and persistent therapeutic effects in human clinical trials 1 – 4 . In animal models, psilocybin induces neuroplasticity in cortex and hippocampus 5 – 8 . It remains unclear how human brain network changes relate to subjective and lasting effects of psychedelics. Here we tracked individual-specific brain changes with longitudinal precision functional mapping (roughly 18 magnetic resonance imaging visits per participant). Healthy adults were tracked before, during and for 3 weeks after high-dose psilocybin (25 mg) and methylphenidate (40 mg), and brought back for an additional psilocybin dose 6–12 months later. Psilocybin massively disrupted functional connectivity (FC) in cortex and subcortex, acutely causing more than threefold greater change than methylphenidate. These FC changes were driven by brain desynchronization across spatial scales (areal, global), which dissolved network distinctions by reducing correlations within and anticorrelations between networks. Psilocybin-driven FC changes were strongest in the default mode network, which is connected to the anterior hippocampus and is thought to create our sense of space, time and self. Individual differences in FC changes were strongly linked to the subjective psychedelic experience. Performing a perceptual task reduced psilocybin-driven FC changes. Psilocybin caused persistent decrease in FC between the anterior hippocampus and default mode network, lasting for weeks. Persistent reduction of hippocampal-default mode network connectivity may represent a neuroanatomical and mechanistic correlate of the proplasticity and therapeutic effects of psychedelics. Healthy adults were tracked before, during and after high doses of psilocybin and methylphenidate to assess how psychedelics can change human brain networks, and psilocybin was found to massively disrupt functional connectivity in cortex and subcortex with some changes persisting for weeks.

High‐throughput phenotyping (HTP) techniques have brought new opportunities to understand and evaluate key traits in plant breeding programs. Combining multiple measures through time and random regression models permits a more comprehensive understanding of the genetic and environmental effects on trait expression over time. This study aims to understand the genetic basis of biomass accumulation in winter wheat and how this biomass is related to grain yield using unmanned aerial vehicle (UAV)‐based vegetation indices. A large panel of 596 soft red winter wheat genotypes was evaluated for agronomic performance in six environments to verify the ability of HTPs to predict grain yield using multivariate genomic prediction and random regression with Legendre polynomials to model growth through time. An additional set of 22 breeding lines was directly measured for above‐ground biomass, serving as a ground truth for the HTP‐derived biomass estimates. Cumulative vegetation indices were found to be a reliable method to infer biomass accumulation. Vegetation indices capture reliable phenotypes but exhibit low and inconsistent genetic correlation to grain yield, especially when incorporating residual covariance between traits. Predictive abilities of grain yield increased when using vegetation indices as a secondary trait in a multi‐trait genomic prediction model, but increases were highly variable across environments and growing stages, which may be confounded by micro‐environmental variation and lead to biased estimates of true genetic merit. Our results suggest that UAV‐based vegetation indices can be used to understand genetic parameters of biomass accumulation, but wheat breeders should use caution in their use as proxies for grain yield. Cumulative vegetation indices derived from UAV images can be used to model and evaluate genetic parameters of biomass accumulation in wheat. Vegetation indices may increase the predictive ability of grain yield in multi‐trait genomic prediction models, but performance is highly variable across environments. Wheat breeders should be cautious in employing vegetation indices in wheat breeding programs. Normalized difference vegetation index is not a reliable vegetation index to estimate biomass accumulation and has a poor genetic relationship with grain yield. New technologies, such as drone images, have been used to evaluate the performance of plants. These images are used to estimate values, called vegetative indices, that are related to photosynthesis and, theoretically, to growth and yield. Drone images of several wheat varieties grown in field experiments were taken in different environments throughout the entire growing season. Vegetative indices were calculated and used to build up growth curves for tracking the biomass accumulation of each variety. We observed that drone images are a reliable method to estimate growth curves and the genetic variability of biomass accumulation. However, vegetative indices are not a reliable proxy for grain yield because they present lower genetic correlation with grain yield and high inconsistency over time and across environments. Due to that, wheat breeders need to be cautious when employing vegetative indices to select the best varieties in breeding programs.