Heads in the Cloud: A Primer on Neuroimaging Applications of High Performance Computing

Bridging the Brain and Data Sciences

Brain scientists are now capable of collecting more data in a single experiment than researchers a generation ago might have collected over an entire career. Indeed, the brain itself seems to thirst for more and more data. Such digital information not only comprises individual studies but is also increasingly shared and made openly available for secondary, confirmatory, and/or combined analyses. Numerous web resources now exist containing data across spatiotemporal scales. Data processing workflow technologies running via cloud-enabled computing infrastructures allow for large-scale processing. Such a move toward greater openness is fundamentally changing how brain science results are communicated and linked to available raw data and processed results. Ethical, professional, and motivational issues challenge the whole-scale commitment to data-driven neuroscience. Nevertheless, fueled by government investments into primary brain data collection coupled with increased sharing and community pressure challenging the dominant publishing model, large-scale brain and data science is here to stay.

A Model for Examining Challenges and Opportunities in Use of Cloud Computing for Health Information Systems

Health Information Systems (HIS) are becoming crucial for health providers, not only for keeping Electronic Health Records (EHR) but also because of the features they provide that can be lifesaving, thanks to the advances in Information Technology (IT). These advancements have led to increasing demands for additional features to these systems to improve their intelligence, reliability, and availability. All these features may be provisioned through the use of cloud computing in HIS. This study arrives at three dimensions pertinent to adoption of cloud computing in HIS through extensive interviews with experts, professional expertise and knowledge of one of the authors working in this area, and review of academic and practitioner literature. These dimensions are financial performance and cost; IT operational excellence and DevOps; and security, governance, and compliance. Challenges and drivers in each of these dimensions are detailed and operationalized to arrive at a model for HIS adoption. This proposed model detailed in this study can be employed by executive management of health organizations, especially senior clinical management positions like Chief Technology Officers (CTOs), Chief Information Officers (CIOs), and IT managers to make an informed decision on adoption of cloud computing for HIS. Use of cloud computing to support operational and financial excellence of healthcare organizations has already made some headway in the industry, and its use in HIS would be a natural next step. However, due to the mission′s critical nature and sensitivity of information stored in HIS, the move may need to be evaluated in a holistic fashion that can be aided by the proposed dimensions and the model. The study also identifies some issues and directions for future research for cloud computing adoption in the context of HIS.

Running Neuroimaging Applications on Amazon Web Services: How, When, and at What Cost?

The contribution of this paper is to identify and describe current best practices for using Amazon Web Services (AWS) to execute neuroimaging workflows “in the cloud.” Neuroimaging offers a vast set of techniques by which to interrogate the structure and function of the living brain. However, many of the scientists for whom neuroimaging is an extremely important tool have limited training in parallel computation. At the same time, the field is experiencing a surge in computational demands, driven by a combination of data-sharing efforts, improvements in scanner technology that allow acquisition of images with higher image resolution, and by the desire to use statistical techniques that stress processing requirements. Most neuroimaging workflows can be executed as independent parallel jobs and are therefore excellent candidates for running on AWS, but the overhead of learning to do so and determining whether it is worth the cost can be prohibitive. In this paper we describe how to identify neuroimaging workloads that are appropriate for running on AWS, how to benchmark execution time, and how to estimate cost of running on AWS. By benchmarking common neuroimaging applications, we show that cloud computing can be a viable alternative to on-premises hardware. We present guidelines that neuroimaging labs can use to provide a cluster-on-demand type of service that should be familiar to users, and scripts to estimate cost and create such a cluster.

Crowdsourcing Precision Cerebrovascular Health: Imaging and Cloud Seeding A Million Brains Initiative™

Crowdsourcing, an unorthodox approach in medicine, creates an unusual paradigm to study precision cerebrovascular health, eliminating the relative isolation and non-standardized nature of current imaging data infrastructure, while shifting emphasis to the astounding capacity of big data in the cloud. This perspective envisions the use of imaging data of the brain and vessels to orient and seed A Million Brains Initiative™ that may leapfrog incremental advances in stroke and rapidly provide useful data to the sizable population around the globe prone to the devastating effects of stroke and vascular substrates of dementia. Despite such variability in the type of data available and other limitations, the data hierarchy logically starts with imaging and can be enriched with almost endless types and amounts of other clinical and biological data. Crowdsourcing allows an individual to contribute to aggregated data on a population, while preserving their right to specific information about their own brain health. The cloud now offers endless storage, computing prowess, and neuroimaging applications for postprocessing that is searchable and scalable. Collective expertise is a windfall of the crowd in the cloud and particularly valuable in an area such as cerebrovascular health. The rise of precision medicine, rapidly evolving technological capabilities of cloud computing and the global imperative to limit the public health impact of cerebrovascular disease converge in the imaging of A Million Brains Initiative™. Crowdsourcing secure data on brain health may provide ultimate generalizability, enable focused analyses, facilitate clinical practice, and accelerate research efforts.