ABSTRACT
Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.
Subject(s)
Cloud Computing , Neurosciences , Neurosciences/methods , Humans , Neuroimaging/methods , Reproducibility of Results , Software , Brain/physiology , Brain/diagnostic imagingABSTRACT
Medical imaging has become a fascinating field with detailed visualizations of the body's internal environments. Although the field has grown fast and is sensitive to new technologies, it does not use the latest rendering techniques available in other domains, such as day-to-day movie production or game development. In this work, we bring forward Horizon, a new engine that provides cinematic rendering capabilities in real-time for quality controlling medical data. In addition, Horizon is provided as free, open-source software to be used as a foundation stone for building the next generation of medical imaging applications. In this introductory paper, we focus on the extensive development of advanced shaders, which can be used to highlight untapped features of the data and allow fast interaction with machine learning algorithms. In addition, Horizon provides physically-based rendering capabilities, the epitome of advanced visualization, adapted for the needs of medical imaging analysis practices.
ABSTRACT
Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research.
ABSTRACT
In this paper, we present a novel approach for functional network connectivity in fMRI resting activity using distance correlation. The proposed method accounts for nonlinear relationships between the resting state networks and can be used for both single subject and group level analyses. We showed that the new strategy improves the capacity of characterization of pathological populations, such as, patients with disorder of consciousness, when compared to related approaches.
