The MNI data-sharing and processing ecosystem

AbstractNeuroimaging has been facing a data deluge characterized by the exponential growth of both raw and processed data. As a result, mining the massive quantities of digital data collected in these studies offers unprecedented opportunities and has become paramount for today's research. As the neuroimaging community enters the world of “Big Data”, there has been a concerted push for enhanced sharing initiatives, whether within a multisite study, across studies, or federated and shared publicly. This article will focus on the database and processing ecosystem developed at the Montreal Neurological Institute (MNI) to support multicenter data acquisition both nationally and internationally, create database repositories, facilitate data-sharing initiatives, and leverage existing software toolkits for large-scale data processing

Software architectures to integrate workflow engines in science gateways

International audienceScience gateways often rely on workflow engines to execute applications on distributed infrastructures. We investigate six software architectures commonly used to integrate workflow engines into science gateways. In tight integration, the workflow engine shares software components with the science gateway. In service invocation, the engine is isolated and invoked through a specific software interface. In task encapsulation, the engine is wrapped as a computing task executed on the infrastructure. In the pool model, the engine is bundled in an agent that connects to a central pool to fetch and execute workflows. In nested workflows, the engine is integrated as a child process of another engine. In workflow conversion, the engine is integrated through workflow language conversion. We describe and evaluate these architectures with metrics for assessment of integration complexity, robustness, extensibility, scalability and functionality. Tight integration and task encapsulation are the easiest to integrate and the most robust. Extensibility is equivalent in most architectures. The pool model is the most scalable one and meta-workflows are only available in nested workflows and workflow conversion. These results provide insights for science gateway architects and developers

Advancements in the CBRAIN Platform through Integration of Community-Based Tools and Standards

Introduction Since 2009, CBRAIN, a collaborative, web-based research platform, has served a broad international community of researchers in performing large-scale data and computational neuroscience (Sherif, 2014). With over 800 users at 193 sites in 32 different countries and hosting over 60 software pipelines, CBRAIN has provided over 35 million CPU hours on computing resources around the world, including Stampede2 at TACC, the world&#39;s largest supercomputer dedicated to academic research. CBRAIN is a central component in the Healthy Brains for Healthy Lives infrastructure (HBHL, 2018) and the Canadian Open Neuroscience Platform (CONP, 2018), which require new features connecting existing platforms (e.g. LORIS (Das, 2016), OpenNeuro (Gorgolewski, 2017), and BrainCode (Vaccarino, 2018), utilizing community-based standards (e.g. BIDS (Gorgolewski, 2016), Boutiques (Glatard, 2018), and CARMIN (Glatard, 2015)), and offering new usage modalities and interfaces. Methods CBRAIN&#39;s primary purpose is to provide an ecosystem that abstracts away the low-level details of data movement and computational execution on advanced research computing resources. CBRAIN provides an orchestration system consisting of a central control instrument, termed a Portal, which communicates and submit tasks to remote compute servers, called Bourreaux. Portals and Bourreaux access remote data resources through passive DataProviders.The CBRAIN platform provides a unifying service layer for access to remote computing resources around the world (e.g. Compute Canada, XSEDE, and the CCC-Axis). CBRAIN is a Ruby on Rails application, is completely open source (https://github.com/aces/cbrain) and provided as a service free of charge (https://portal.cbrain.mcgill.ca). Requirements from new national and international initiatives lead to developments increasing interoperability, functionality and usability supporting a wider community and integrating with a broader set of community-driven tools and standards. Results New features developed in the CBRAIN platform are: RESTful API: A fully documented and functional CBRAIN RESTful API is now available at https://app.swaggerhub.com/apis/prioux/CBRAIN/5.1.0 and allows projects to utilize CBRAIN as a backend technology. To promote a community standard, CBRAIN will support CARMIN, a common web API for remote pipeline execution, such that any CARMIN compliant tool can use CBRAIN as a backend without rewriting their package. Datalad and S3 Integration: A Datalad DataProvider provides an interface to move data from Datalad (Datalad, 2018) versioned resources into the CBRAIN ecosystem. A new S3 DataProvider provides data movement from cloud-based resources. BIDS Compatibility: Capitalizing on the BIDS standard, an automatic parallelization capability to ensure BIDSApps run efficiently. Provenance and error-handling are also available, and the user only needs to specify the BIDS-formatted input dataset and the pipeline to execute. Boutiques Integration: To enable computational pipelines to be discoverable and shareable, and ease the burden of integration and deployment, we have adopted the Boutiques JSON standard to define&nbsp;our computational pipelines and to pull new pipelines from the Boutiques Repository. New Interactive Interface: A user-focused, interactive and dynamic CBRAIN user-interface has been designed, built with React.js and GraphQL, interacting through the CBRAIN API to launch tasks and manage data, The UI is highly modular and can quickly be adapted to new user and visualization requirements over time. New modular visualizations have also been developed using React.js. Conclusions CBRAIN has for nearly a decade served as a platform for accomplishing large-scale, large-data neuroinformatics. The new implementations provide a connection to a larger community of neuroinformatics and scientific&nbsp;research and promote FAIR standards in computational science.</p

MINC 2.0: a flexible format for multi-modal images

It is often useful that an imaging data format can afford rich metadata, be flexible, scale to very large file sizes, support multi-modal data, and have strong inbuilt mechanisms for data provenance. Beginning in 1992, MINC was developed as a system for flexible, self-documenting representation of neuroscientific imaging data with arbitrary orientation and dimensionality. The MINC system incorporates three broad components: a file format specification, a programming library, and a growing set of tools.In the early 2000's the MINC developers created MINC 2.0, which added support for 64-bit file sizes, internal compression, and a number of other modern features. Because of its extensible design, it has been easy to incorporate details of provenance in the header metadata, including an explicit processing history, unique identifiers, and vendor-specific scanner settings. This makes MINC ideal for use in large scale imaging studies and databases. It also makes it easy to adapt to new scanning sequences and modalities

Muscarinic Acetylcholine Receptor M3 Mutation Causes Urinary Bladder Disease and a Prune-Belly-like Syndrome

Urinary bladder malformations associated with bladder outlet obstruction are a frequent cause of progressive renal failure in children. We here describe a muscarinic acetylcholine receptor M3 (CHRM3) (1q41-q44) homozygous frameshift mutation in familial congenital bladder malformation associated with a prune-belly-like syndrome, defining an isolated gene defect underlying this sometimes devastating disease. CHRM3 encodes the M3 muscarinic acetylcholine receptor, which we show is present in developing renal epithelia and bladder muscle. These observations may imply that M3 has a role beyond its known contribution to detrusor contractions. This Mendelian disease caused by a muscarinic acetylcholine receptor mutation strikingly phenocopies Chrm3 null mutant mice