A Recurrent Cooperative/Competitive Field for Segmentation of Magnetic Resonance Brain Imagery

The Grey-White Decision Network is introduced as an application of an on-center, off-surround recurrent cooperative/competitive network for segmentation of magnetic resonance imaging (MRI) brain images. The three layer dynamical system relaxes into a solution where each pixel is labeled as either grey matter, white matter, or "other" matter by considering raw input intensity, edge information, and neighbor interactions. This network is presented as an example of applying a recurrent cooperative/competitive field (RCCF) to a problem with multiple conflicting constraints. Simulations of the network and its phase plane analysis are presented

Introduction to the special issue on reproducibility in neuroimaging

The last decade has seen increasing attention to the problem of scientific reproducibility, across a broad range of scientific fields (Camerer et al., 2016;Morrison, 2014;Open Science Collaboration, 2015). Within the field of neuroimaging, there has been a particular focus on issues of analytic variability (Bowring et al., 2019;Carp, 2012) statistical power (Button et al., 2013;Poldrack et al., 2017), and test-retest reliability (Bennett and Miller, 2013), all of which have raised alarms regarding the potential for irreproducible results. In addition, failed replications (Boekel et al., 2015;Dinga et al., 2019) and meta-analytic null results (Müller et al., 2017) have raised particular concern about studies of group and individual differences. This special issue was developed in light of these emerging concerns, with the goal of highlighting and encouraging work that aims to both quantify and improve the reproducibility of neuroimaging research. Here we provide a brief overview of the papers within this special issue

Evaluation of FAST TCP in Low-Speed DOCSIS-based Access Networks

There is strong evidence that the efficiency of the Internet is limited by its existing TCP congestion control system. A replacement, FAST, has been shown to improve performance in high-speed networks. In order to achieve widespread acceptance and standardisation, it must also be tested in environments more typical of the existing Internet. This paper experimentally evaluates the performance of FAST over a typical access link, with bandwidths of around 0.5-3 Mbps. Links both using the DOCSIS cable modem medium access control (MAC) protocol and simple low rate links were investigated. It is shown that the random delay introduced by MAC protocol of the cable modem does not appear to interfere significantly with FAST's ability to set the congestion window size to its target. However, the cable modem does appear to introduce consistent additional delays when the link is highly, but not fully, utilised. These unexplained delays mean that a larger congestion window is required, and must be taken into account when setting FAST's parameters, notably the target queue size, alpha

Data Citation in Neuroimaging: Proposed Best Practices for Data Identification and Attribution

Data sharing and reuse, while widely accepted as good ideas, have been slow to catch on in any concrete and consistent way. One major hurdle within the scientific community has been the lack of widely accepted standards for citing that data, making it difficult to track usage and measure impact. Within the neuroimaging community, there is a need for a way to not only clearly identify and cite datasets, but also to derive new aggregate sets from multiple sources while clearly maintaining lines of attribution. This work presents a functional prototype of a system to integrate Digital Object Identifiers (DOI) and a standardized metadata schema into a XNAT-based repository workflow, allowing for identification of data at both the project and image level. These item and source level identifiers allow any newly defined combination of images, from any number of projects, to be tagged with a new group-level DOI that automatically inherits the individual attributes and provenance information of its constituent parts. This system enables the tracking of data reuse down to the level of individual images. The implementation of this type of data identification system would impact researchers and data creators, data hosting facilities, and data publishers, but the benefit of having widely accepted standards for data identification and attribution would go far toward making data citation practical and advantageous

Conversation among Physical Chemists: Strategies and Resources for Remote Teaching and Learning Catalyzed by a Global Pandemic

In the midst of a global pandemic in spring 2020, physical chemistry faculty gathered to share strategies and resources for teaching remotely. During this conversation, instructors created a shared document compiling the challenges they faced in spring 2020 and ways to improve teaching and learning in the physical chemistry classroom and laboratory when institutions reopened in the fall. We present a content analysis of the shared document that provides a snapshot of physical chemists’ thoughts at that moment in June 2020. The themes that emerged from our analysis are assessment, choice of learning objectives, course management, opportunities, resources, student motivation, and wellbeing. We have summarized the numerous strategies, resources, and implementation ideas that were shared by participants, many of which we believe will remain in use when traditional in-person instruction resumes. Finally, the conversation connected physical chemists, strengthening our community. Continued community engagement has occurred through further synchronous conversations, asynchronous conversations on our Slack workspace, and the creation of the repository PChem Inspired Pedagogical Electronic Resource (PIPER)

Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging

There has been a recent major upsurge in the concerns about reproducibility in many areas of science. Within the neuroimaging domain, one approach is to promote reproducibility is to target the re-executability of the publication. The information supporting such re-executability can enable the detailed examination of how an initial finding generalizes across changes in the processing approach, and sampled population, in a controlled scientific fashion. ReproNim: A Center for Reproducible Neuroimaging Computation is a recently funded initiative that seeks to facilitate the last mile implementations of core re-executability tools in order to reduce the accessibility barrier and increase adoption of standards and best practices at the neuroimaging research laboratory level. In this report, we summarize the overall approach and tools we have developed in this domain

Meaningful associations in the adolescent brain cognitive development study

The Adolescent Brain Cognitive Development (ABCD) Study is the largest single-cohort prospective longitudinal study of neurodevelopment and children\u27s health in the United States. A cohort of n = 11,880 children aged 9-10 years (and their parents/guardians) were recruited across 22 sites and are being followed with in-person visits on an annual basis for at least 10 years. The study approximates the US population on several key sociodemographic variables, including sex, race, ethnicity, household income, and parental education. Data collected include assessments of health, mental health, substance use, culture and environment and neurocognition, as well as geocoded exposures, structural and functional magnetic resonance imaging (MRI), and whole-genome genotyping. Here, we describe the ABCD Study aims and design, as well as issues surrounding estimation of meaningful associations using its data, including population inferences, hypothesis testing, power and precision, control of covariates, interpretation of associations, and recommended best practices for reproducible research, analytical procedures and reporting of results

Impact of Canadian tobacco packaging policy on quitline reach and reach equity

AbstractObjectiveTo examine the impact of the new Canadian tobacco package warning labels with a quitline toll-free phone number for seven provincial quitlines, focusing on treatment reach and reach equity in selected vulnerable groups.MethodsA quasi-experimental design assessed changes in new incoming caller characteristics, treatment reach for selected vulnerable sub-populations and the extent to which this reach is equitable, before and after the introduction of the labels in June, 2012. Administrative call data on smokers were collected at intake. Pre- and post-label treatment reach and reach equity differences were analysed by comparing the natural logarithms of the reach and reach equity statistics.ResultsDuring the six months following the introduction of the new warning labels, 86.4% of incoming new callers indicated seeing the quitline number on the labels. Treatment reach for the six-month period significantly improved compared to the same six-month period the year before from .042% to .114% (p<.0001) and reach equity significantly improved for young males (p<.0001) and those with high school education or less (p=.004).ConclusionsThe introduction of the new tobacco warning labels with a quitline toll-free number in Canada was associated with an increase in treatment reach. The toll-free number on tobacco warning labels aided in reducing tobacco related inequalities, such as improved reach equity for young males and those with high school or less education

CANDI Store: An Infrastructure for Neuroimage Storage and Processing

In order to support the local data management need for neuroimaging researchers at UMass Medical School within the Child and Adolescent NeuroDevelopment Initiative (CANDI) and beyond, we have implemented a XNAT (xnat.org) instance called CANDIStore. XNAT is an open source imaging informatics platform, developed by the Neuroinformatics Research Group at Washington University. It facilitates common management, productivity, and quality assurance tasks for imaging and associated data. Located securely within the medical school firewall, CANDIStore offers a comprehensive set of image management tools. Users can be authenticated based against their UMass credentials, create private projects, manage research team access, DICOM \u27push\u27 directly to CANDIStore from the MRI imaging console, manage demographic and additional subject variables, and perform automated analysis and processing pipelines. CANDIStore is an essential adjunct to the daily operations of neuroimaging research