An Exploratory Analysis of Pharmaceutical Price Disparities and Their Implications Among Six Developed Nations

In our study of 43 drugs, prescription drug prices in several wealthy nations (Australia, Canada, France, Germany, and the U.K.) were much lower than in the U.S. on average, well below relative per capita GDP. There was relatively little difference among the five foreign nations. All this is consistent with previous research. After separating less-unique from more unique drugs, however, important new findings emerged. Relative prices for less-unique drugs, which are subject to strong competition, were at about half the U.S. level. We suggest that this reflects the exercise of monopsony power that does not exist in the U.S., where buyers as well as sellers compete. On the other hand, relative prices for highly unique drugs tended to be approximately proportional to per capita GDP or higher. Remarkably, biotech drugs were priced at or above U.S. levels in Canada and France

The relationship between creativity and psychosocial development among college honors students and non-honors students

The purpose of this study was to determine if there was a difference in measures of creativity and psychosocial development in college Honors and Non-Honors students and also to determine interaction effects of demographic and academic background data. Additionally, another purpose was to establish any relationship between measures of creativity and psychosocial development. Of the 284 college students participating, 120 were honors students and 164 were non-honors students. Participants were administered the Torrance Tests of Creative Thinking (TTCT) Verbal Form B, Activities 4 and 5 and the Student Development Task and Lifestyle Assessment (SDTLA). The TTCT included scales of fluency, flexibility, originality, and average standard creativity score. The SDTLA includes the measurement of three developmental tasks, ten subtasks, and two scales. The participants were volunteers and were tested in four regularly scheduled classes during the 2006 spring and summer semesters. Two-tailed independent t-tests performed on the dependent variables of the TTCT indicated that the Non-Honors student’s scores were statistically significantly higher on fluency, originality, and the average standard creativity measures. On the average standard score, which is considered the best overall gauge of creative power, neither Non-Honors nor Honors student groups TTCT scores were considered higher than weak (0-16%) (Torrance, 1990). The results of the two-tailed independent t-tests performed on the dependent variables of the SDTLA resulted in the statistically significant higher development outcome scores in the Honors students. The mean SDTLA scores of both the Honors and Non-Honors scores were not outside of norm group average scores. The MANOVA data produced moderately statistically significant interaction effects between classification level and fluency. However, the post hoc tests did not confirm the difference in classification and fluency. Additional MANOVA data indicated a significant interaction effect between ethnicity and Lifestyle Planning (LP), and post hoc analysis confirmed the interaction with significant differences in Caucasian and “Other” students. Classification level significantly interacted with eight of the fourteen development outcomes, nevertheless the post hoc tests showed inconsistent differences between classification groups within the developmental outcomes. Correlations between the TTCT and SDTLA did not yield statistically significant relationships between the creativity and psychosocial development variables

Beyond advertising: New infrastructures for publishing integrated research objects

ABSTRACT: Moving beyond static text and illustrations is a central challenge for scientific publishing in the 21st century. As early as 1995, Donoho and Buckheit paraphrased John Claerbout that “an article about [a] computational result is advertising, not scholarship. The actual scholarship is the full software environment, code and data, that produced the result” [1]. Awareness of this problem has only grown over the last 25 years; nonetheless, scientific publishing infrastructures remain remarkably resistant to change [2]. Even as these infrastructures have largely stagnated, the internet has ushered in a transition “from the wet lab to the web lab” [3]. New expectations have emerged in this shift, but these expectations must play against the reality of currently available infrastructures and associated sociological pressures. Here, we compare current scientific publishing norms against those associated with online content more broadly, and we argue that meeting the “Claerbout challenge” of providing the full software environment, code, and data supporting a scientific result will require open infrastructure development to create environments for authoring, reviewing, and accessing interactive research objects

BRAIN Initiative: Cutting-Edge Tools and Resources for the Community.

The overarching goal of the NIH BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative is to advance the understanding of healthy and diseased brain circuit function through technological innovation. Core principles for this goal include the validation and dissemination of the myriad innovative technologies, tools, methods, and resources emerging from BRAIN-funded research. Innovators, BRAIN funding agencies, and non-Federal partners are working together to develop strategies for making these products usable, available, and accessible to the scientific community. Here, we describe several early strategies for supporting the dissemination of BRAIN technologies. We aim to invigorate a dialogue with the neuroscience research and funding community, interdisciplinary collaborators, and trainees about the existing and future opportunities for cultivating groundbreaking research products into mature, integrated, and adaptable research systems. Along with the accompanying Society for Neuroscience 2019 Mini-Symposium, "BRAIN Initiative: Cutting-Edge Tools and Resources for the Community," we spotlight the work of several BRAIN investigator teams who are making progress toward providing tools, technologies, and services for the neuroscience community. These tools access neural circuits at multiple levels of analysis, from subcellular composition to brain-wide network connectivity, including the following: integrated systems for EM- and florescence-based connectomics, advances in immunolabeling capabilities, and resources for recording and analyzing functional connectivity. Investigators describe how the resources they provide to the community will contribute to achieving the goals of the NIH BRAIN Initiative. Finally, in addition to celebrating the contributions of these BRAIN-funded investigators, the Mini-Symposium will illustrate the broader diversity of BRAIN Initiative investments in cutting-edge technologies and resources

Nilearn for new use cases: Scaling up computational and community efforts

Introduction Nilearn (https://nilearn.github.io) is a well-established Python package that provides statistical and machine learning tools for fast and easy analysis of brain images with instructive documentation and a friendly community. This focus has led to its current position as a crucial part of the neuroimaging community’s open-source software ecosystem, supporting efficient and reproducible science [1]. It has been continuously developed over the past 10 years, currently with 900 stars, 500 forks, and 176 contributors on GitHub. Nilearn leverages and builds upon other central Python machine learning packages, such as Scikit-Learn [2], that are extensively used, tested, and optimized by a large scientific and industrial community. In recent years, efforts in Nilearn have been focused on meeting evolving community needs by increasing General Linear Model (GLM) support, interfacing with initiatives like fMRIPrep and BIDS, and improving the user documentation. Here we report on progress regarding our current priorities. Methods Nilearn is developed to be accessible and easy-to-use for researchers and the open-source community. It features user-focused documentation that includes a user guide and an example gallery as well as comprehensive contribution guidelines. Nilearn is also presented in tutorials and workshops throughout the year including the Montreal Artificial Intelligence and Neuroscience (MAIN) Educational Workshop, the OHBM Brainhack event, and for the Chinese Open Science Network. The community is encouraged to ask questions, report bugs, make suggestions for improvements or new features, and make direct contributions to the source code. We use the platforms Neurostars, GitHub, and Discord to interact with contributors and users on a daily basis. Nilearn adheres to best practices in software development including using version control, unit testing, and requiring multiple reviews of contributions. We also have a continuous integration infrastructure set up to automate many aspects of our development process and make sure our code is continuously tested and up-to-date. Results Nilearn supports methods such as image manipulation and processing, decoding, functional connectivity analysis, GLM, multivariate pattern analysis, along with plotting volumetric and surface data. In the latest release, cluster-level and TFCE-based family-wise error rate (FWER) control have been added to support the mass univariate and GLM analysis modules, expanding from the already implemented voxel-level correction method (see Fig1). Optimizing Nilearn’s maskers is also underway such as the recently added classes for handling multi-subject 4D image data. These also provide the option to use parallelization to speed up computation. In addition, Nilearn has introduced a new theme to update the documentation making the website more readable and accessible (https://nilearn.github.io/). This change also sets the stage for further improvement and modernisation of several aspects of the documentation, like the user guide. Development on Nilearn’s interfaces module added a new function to write BIDS-compatible model results to disk. This and further development of the BIDS interface will facilitate interaction with other relevant community tools such as FitLins [3]. Finally, several surface plotting enhancements are in progress including improving the API for background maps (see Fig2). Conclusion Nilearn is extensively used by researchers of the neuroimaging community due to its implementations of well-founded methods and visualization tools which are often essential in brain imaging research for quality control and communicating results. Recent work has highlighted areas where more active work is needed to scale the project both technically and socially, including: working with large datasets, better supporting analyses on the cortical surface, and advancing standard practice in neuroimaging statistics through active community outreach. References [1] Poldrack, R., Gorgolewski, K., Varoquaux, G. (2019). Computational and Informatic Advances for Reproducible Data Analysis in Neuroimaging Annual Review of Biomedical Data Science 2(1), 119-138. https://dx.doi.org/10.1146/annurev-biodatasci-072018-021237 [2] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, 12, 2825-2830. [3] Markiewicz, C. J., De La Vega, A., Wagner, A., Halchenko, Y. O., Finc, K., Ciric, R., Goncalves, M., Nielson, D. M., Kent, J. D., Lee, J. A., Bansal, S., Poldrack, R. A., Gorgolewski, K. J. (2022). poldracklab/fitlins: 0.11.0 (0.11.0). Zenodo. https://doi.org/10.5281/zenodo.7217447This poster was presented at OHBM 2023

The past, present, and future of the Brain Imaging Data Structure (BIDS)

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS

Structural covariance across the lifespan, from 6-94 years of age

Structural covariance examines interindividual differences in the covariation of grey matter morphology between brain regions. Although structural covariance has been used to examine the development of brain networks in either adolescence or aging, no study to date has provided a lifespan perspective on the development of structural covariance networks, bridging childhood with early and late adulthood. Here, we investigate the lifespan trajectories of structural covariance in six canonical neurocognitive networks default, dorsal attention, frontoparietal control, somatomotor, ventral attention, and visual networks. By combining data from five open access data sources, we examine the structural covariance trajectories of these networks from 6-94 years of age in a sample of 1580 participants. Using partial least squares, we show that structural covariance patterns across the lifespan exhibit two significant, age-dependent trends. The first trend is a rapid decline that levels off in later life, suggestive of reduced within-network covariance. The second trend is an inverted-U that peaks in young adulthood, suggestive of the patterns of integration followed by de-differentiation as observed in functional brain networks. Hub regions, including posterior cingulate cortex and anterior insula, appear particularly influential in the expression of this second age-dependent trend. Overall, we suggest that these results provide evidence for the importance of a persistent pattern of functional coupling in the developmental trajectories of structural covariance networks

Healthy Brain Network - Serial Scanning Initiative (HBN-SSI) derivatives

Derivatives from the Healthy Brain Network - Serial Scanning Initative (HBN-SSI) for use in fmralig

ds1545 fmralign-tutorial

Pre-processed data for use in fmralign-tutorials