The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.

/pdf/the-brain-imaging-data-structure-a-format-for-organizing-and-4cjk9fm8dr.pdf

The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments.

Given concerns about the reproducibility of scientific findings, neuroimaging must define best practices for data analysis, results reporting, and algorithm and data sharing to promote transparency, reliability and collaboration. We describe insights from developing a set of recommendations on behalf of the Organization for Human Brain Mapping and identify barriers that impede these practices, including how the discipline must change to fully exploit the potential of the world's neuroimaging data.

/pdf/best-practices-in-data-analysis-and-sharing-in-neuroimaging-iuotn6d5jz.pdf

Best practices in data analysis and sharing in neuroimaging using MRI.

In the last decade, major advances have been made in the availability of shared neuroimaging data, such that there are more than 8,000 shared MRI (magnetic resonance imaging) data sets available online. Here we outline the state of data sharing for task-based functional MRI (fMRI) data, with a focus on various forms of data and their relative utility for subsequent analyses. We also discuss challenges to the future success of data sharing and highlight the ethical argument that data sharing may be necessary to maximize the contribution of human subjects.

Making big data open: Data sharing in neuroimaging

Significant resources around the world have been invested in neuroimaging studies of brain function and disease. Easier access to this large body of work should have profound impact on research in cognitive neuroscience and psychiatry, leading to advances in the diagnosis and treatment of psychiatric and neurological disease. A trend toward increased sharing of neuroimaging data has emerged in recent years. Nevertheless, a number of barriers continue to impede momentum. Many researchers and institutions remain uncertain about how to share data or lack the tools and expertise to participate in data sharing. The use of electronic data capture (EDC) methods for neuroimaging greatly simplifies the task of data collection and has the potential to help standardize many aspects of data sharing. We review here the motivations for sharing neuroimaging data, the current data sharing landscape, and the sociological or technical barriers that still need to be addressed. The INCF Task Force on Neuroimaging Datasharing, in conjunction with several collaborative groups around the world, has started work on several tools to ease and eventually automate the practice of data sharing. It is hoped that such tools will allow researchers to easily share raw, processed, and derived neuroimaging data, with appropriate metadata and provenance records, and will improve the reproducibility of neuroimaging studies. By providing seamless integration of data sharing and analysis tools within a commodity research environment, the Task Force seeks to identify and minimize barriers to data sharing in the field of neuroimaging.

/pdf/data-sharing-in-neuroimaging-research-src1d3mkkp.pdf

Data sharing in neuroimaging research

Making data sharing work: the FCP/INDI experience

LORIS (Longitudinal Online Research and Imaging System) is a modular and extensible web-based data management system that integrates all aspects of a multi-center study: from heterogeneous data acquisition (imaging, clinical, behavior, genetics) to storage, processing and ultimately dissemination. It provides a secure, user-friendly, and streamlined platform to automate the flow of clinical trials and complex multi-center studies. A subject-centric internal organization allows researchers to capture and subsequently extract all information, longitudinal or cross-sectional, from any subset of the study cohort. Extensive error-checking and quality control procedures, security, data management, data querying and administrative functions provide LORIS with a triple capability (i) continuous project coordination and monitoring of data acquisition (ii) data storage/cleaning/querying, (iii) interface with arbitrary external data processing “pipelines”. LORIS is a complete solution that has been thoroughly tested through the full life cycle of a multi-center longitudinal project# and is now supporting numerous neurodevelopment and neurodegeneration research projects internationally.

/pdf/loris-a-web-based-data-management-system-for-multi-center-5g9o121fqk.pdf

Dario Vins

Papers

LORIS: a web-based data management system for multi-center studies.