scholarly journals OpenNeuro: An open resource for sharing of neuroimaging data

2021 ◽  
Author(s):  
Christopher J Markiewicz ◽  
Krzysztof Jacek Gorgolewski ◽  
Franklin Feingold ◽  
Ross Blair ◽  
Yaroslav O Halchenko ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Open Science ◽  
Imaging Data ◽  
Data Types ◽  
Public Investments ◽  
Neuroimaging Data ◽  
Share Data ◽  
Brain Imaging Data ◽  
Multiple Species ◽  
The Impact

The sharing of research data is essential to ensure reproducibility and maximize the impact of public investments in scientific research. Here we describe OpenNeuro, a BRAIN Initiative data archive that provides the ability to openly share data from a broad range of brain imaging data types following the FAIR principles for data sharing. We highlight the importance of the Brain Imaging Data Structure (BIDS) standard for enabling effective curation, sharing, and reuse of data. The archive presently shares more than 500 datasets including data from more than 18,000 participants, comprising multiple species and measurement modalities and a broad range of phenotypes. The impact of the shared data is evident in a growing number of published reuses, currently totalling more than 150 publications. We conclude by describing plans for future development and integration with other ongoing open science efforts.

scholarly journals The OpenNeuro resource for sharing of neuroscience data

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Christopher J Markiewicz ◽  
Krzysztof J Gorgolewski ◽  
Franklin Feingold ◽  
Ross Blair ◽  
Yaroslav O Halchenko ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Open Science ◽  
Imaging Data ◽  
Data Types ◽  
Public Investments ◽  
Share Data ◽  
Fair Principles ◽  
Brain Imaging Data ◽  
Multiple Species ◽  
The Impact

The sharing of research data is essential to ensure reproducibility and maximize the impact of public investments in scientific research. Here we describe OpenNeuro, a BRAIN Initiative data archive that provides the ability to openly share data from a broad range of brain imaging data types following the FAIR principles for data sharing. We highlight the importance of the Brain Imaging Data Structure (BIDS) standard for enabling effective curation, sharing, and reuse of data. The archive presently shares more than 600 datasets including data from more than 20,000 participants, comprising multiple species and measurement modalities and a broad range of phenotypes. The impact of the shared data is evident in a growing number of published reuses, currently totalling more than 150 publications. We conclude by describing plans for future development and integration with other ongoing open science efforts.

scholarly journals COINSTAC: Decentralizing the future of brain imaging analysis

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1512 ◽  
Author(s):  
Jing Ming ◽  
Eric Verner ◽  
Anand Sarwate ◽  
Ross Kelly ◽  
Cory Reed ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Data Sharing ◽  
Large Scale ◽  
Data Transfer ◽  
Imaging Data ◽  
Sensitive Data ◽  
Large Scale Data ◽  
Decentralized Algorithms ◽  
Neuroimaging Data ◽  
Brain Imaging Data

In the era of Big Data, sharing neuroimaging data across multiple sites has become increasingly important. However, researchers who want to engage in centralized, large-scale data sharing and analysis must often contend with problems such as high database cost, long data transfer time, extensive manual effort, and privacy issues for sensitive data. To remove these barriers to enable easier data sharing and analysis, we introduced a new, decentralized, privacy-enabled infrastructure model for brain imaging data called COINSTAC in 2016. We have continued development of COINSTAC since this model was first introduced. One of the challenges with such a model is adapting the required algorithms to function within a decentralized framework. In this paper, we report on how we are solving this problem, along with our progress on several fronts, including additional decentralized algorithms implementation, user interface enhancement, decentralized regression statistic calculation, and complete pipeline specifications.

scholarly journals Assessment of the impact of shared brain imaging data on the scientific literature

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Michael P. Milham ◽  
R. Cameron Craddock ◽  
Jake J. Son ◽  
Michael Fleischmann ◽  
Jon Clucas ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Scientific Literature ◽  
Imaging Data ◽  
Brain Imaging Data ◽  
The Impact

scholarly journals Capturing the nature of events and event context using Hierarchical Event Descriptors (HED)

2021 ◽  
Author(s):  
Kay A. Robbins ◽  
Dung Truong ◽  
Stefan Appelhoff ◽  
Arnaud Delorme ◽  
Scott Makeig
Keyword(s):  
Time Series Data ◽  
Full Range ◽  
Series Data ◽  
Imaging Data ◽  
Related Data ◽  
Annotated Edition ◽  
Neuroimaging Data ◽  
Brain Imaging Data ◽  
Event Annotation ◽  
The Impact

Because of the central role that event-related data analysis plays in EEG and MEG (MEEG) experiments, choices about which events to report and how to annotate their full natures can significantly influence the reliability, reproducibility, and value of MEEG datasets for further analysis. Current, more powerful annotation strategies combine robust event description with details of experiment design and metadata in a human-readable as well as machine-actionable form, making event annotation relevant to the full range of neuroimaging and other time series data. This paper dissects the event design and annotation process using as a case study the well-known multi-subject, multimodal dataset of Wakeman and Henson (openneuro.org, ds000117) shared by its authors using Brain Imaging Data Structure (BIDS) formatting (bids.neuroimaging.io). We propose a set of best practices and guidelines for event handling in MEEG research, examine the impact of various design decisions, and provide a working template for organizing events in MEEG and other neuroimaging data. We demonstrate how annotations using the new third-generation formulation of the Hierarchical Event Descriptors (HED-3G) framework and tools (hedtags.org) can document events occurring during neuroimaging experiments and their interrelationships, providing machine-actionable annotation enabling automated both within- and across-study comparisons and analysis, and point to a more complete BIDS formatted, HED-3G annotated edition of the MEEG portion of the Wakeman and Henson dataset (OpenNeuro ds003645).

scholarly journals BIDScoin: A User-Friendly Application to Convert Source Data to Brain Imaging Data Structure

2022 ◽  
Vol 15 ◽  
Author(s):  
Marcel Peter Zwiers ◽  
Stefano Moia ◽  
Robert Oostenveld
Keyword(s):  
Data Structure ◽  
Brain Imaging ◽  
Imaging Data ◽  
Data Set ◽  
Source Data ◽  
Neuroimaging Data ◽  
Programming Skills ◽  
Brain Imaging Data ◽  
Set Up ◽  
User Friendly

Analyses of brain function and anatomy using shared neuroimaging data is an important development, and have acquired the potential to be scaled up with the specification of a new Brain Imaging Data Structure (BIDS) standard. To date, a variety of software tools help researchers in converting their source data to BIDS but often require programming skills or are tailored to specific institutes, data sets, or data formats. In this paper, we introduce BIDScoin, a cross-platform, flexible, and user-friendly converter that provides a graphical user interface (GUI) to help users finding their way in BIDS standard. BIDScoin does not require programming skills to be set up and used and supports plugins to extend their functionality. In this paper, we show its design and demonstrate how it can be applied to a downloadable tutorial data set. BIDScoin is distributed as free and open-source software to foster the community-driven effort to promote and facilitate the use of BIDS standard.

scholarly journals Brain Imaging Data Structure - a new standard for describing and organizing human neuroimaging data

2015 ◽  
Vol 9 ◽  
Author(s):  
Gorgolewski Krzysztof ◽  
Poline Jean-Baptiste ◽  
Keator David ◽  
Nichols B ◽  
Auer Tibor ◽  
...  
Keyword(s):  
Data Structure ◽  
Brain Imaging ◽  
Imaging Data ◽  
Neuroimaging Data ◽  
Brain Imaging Data ◽  
Human Neuroimaging

scholarly journals PET-BIDS, an extension to the brain imaging data structure for positron emission tomography

2021 ◽  
Author(s):  
Martin Norgaard ◽  
Granville James Matheson ◽  
Hanne D Hansen ◽  
Adam G Thomas ◽  
Graham Searle ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
Data Structure ◽  
Brain Imaging ◽  
Emission Tomography ◽  
Imaging Data ◽  
New Methods ◽  
Positron Emission ◽  
Neuroimaging Data ◽  
Brain Imaging Data ◽  
The Brain

The Brain Imaging Data Structure (BIDS) is a standard for organizing and describing neuroimaging datasets. It serves not only to facilitate the process of data sharing and aggregation, but also to simplify the application and development of new methods and software for working with neuroimaging data. Here, we present an extension of BIDS to include positron emission tomography (PET) data (PET-BIDS). We describe the PET-BIDS standard in detail and share several open-access datasets curated following PET-BIDS. Additionally, we highlight several tools which are already available for converting, validating and analyzing PET-BIDS datasets.

scholarly journals Survey on Open Science Practices in Functional Neuroimaging

2021 ◽  
Author(s):  
Christian Paret ◽  
Nike Unverhau ◽  
Franklin Feingold ◽  
Russell A. Poldrack ◽  
Madita Stirner ◽  
...  
Keyword(s):  
Data Structure ◽  
Functional Neuroimaging ◽  
Open Science ◽  
Imaging Data ◽  
Data Governance ◽  
Science Practices ◽  
Neuroimaging Data ◽  
Pubmed Search ◽  
Medical Faculties ◽  
Brain Imaging Data

Replicability and reproducibility of scientific findings is paramount for sustainable progress in neuroscience. Preregistration of the hypotheses and methods of an empirical study before analysis, the sharing of primary research data, and compliance with data standards such as the Brain Imaging Data Structure (BIDS), are considered effective practices to secure progress and to substantiate quality of research. We investigated the current level of adoption of open science practices in neuroimaging and the difficulties that prevent researchers from using them. Email invitations to participate in the survey were sent to addresses received through a PubMed search of human functional magnetic resonance imaging studies between 2010 and 2020. 283 persons completed the questionnaire. Although half of the participants were experienced with preregistration, the willingness to preregister studies in the future was modest. The majority of participants had experience with the sharing of primary neuroimaging data. Most of the participants were interested in implementing a standardized data structure such as BIDS in their labs. Based on demographic variables, we compared participants on seven subscales, which had been generated through factor analysis. It was found that experienced researchers at lower career level had higher fear of being transparent, researchers with residence in the EU had a higher need for data governance, and researchers at medical faculties as compared to other university faculties reported a higher need for data governance and a more unsupportive environment. The results suggest growing adoption of open science practices but also highlight a number of important impediments.

scholarly journals Optogenetic assessment of VIP, PV, SOM and NOS inhibitory neuron activity and cerebral blood flow regulation in mouse somato-sensory cortex

2019 ◽  
Vol 40 (7) ◽  
pp. 1427-1440 ◽  
Author(s):  
Michael B Krawchuk ◽  
Catherine F Ruff ◽  
Xiaoling Yang ◽  
Sarah E Ross ◽  
Alberto L Vazquez
Keyword(s):  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Brain Imaging ◽  
Inhibitory Neuron ◽  
Neuron Activity ◽  
Imaging Data ◽  
Stimulus Pulse ◽  
Doppler Flowmetry ◽  
Brain Imaging Data ◽  
The Impact

The impact of different neuronal populations on local cerebral blood flow (CBF) regulation is not well known and insight into these relationships could enhance the interpretation of brain function and dysfunction from brain imaging data. We investigated the role of sub-types of inhibitory neuron activity on the regulation of CBF using optogenetics, laser Doppler flowmetry and different transgenic mouse models (parvalbumin (PV), vasoactive intestinal peptide (VIP), somatostatin (SOM) and nitric oxide synthase (NOS)). Whisker stimulation was used to verify that typical CBF responses were obtained in all mice. Photo-stimulation of SOM-cre and NOS-cre mice produced significant increases in CBF that were similar to whisker responses. In NOS-cre mice, CBF responses scaled with the photo-stimulus pulse duration and frequency. In SOM-cre mice, CBF increases were followed by decreases. In VIP-cre mice, photo-stimulation did not consistently produce significant changes in CBF, while slower increases in CBF that peaked 14–18 s after stimulation onset were observed in PV-cre mice. Control experiments performed in non-expressing regions showed no changes in CBF. These findings suggest that dysfunction in NOS or SOM neurons can have a significant impact on vascular responses that are detected by brain imaging methods like functional magnetic resonance imaging (fMRI).

scholarly journals BIDSonym - a BIDSapp for the pseudo-anonymization of neuroimaging datasets

2021 ◽  
Author(s):  
Peer Herholz ◽  
Rita M. Ludwig ◽  
Jean-Baptiste Poline
Keyword(s):  
Data Structure ◽  
Brain Imaging ◽  
Data Sharing ◽  
Sensitive Information ◽  
Imaging Data ◽  
Manual Intervention ◽  
Neuroimaging Data ◽  
Brain Imaging Data ◽  
The Brain ◽  
Infor Mation

The amount of neuroimaging data being shared increased exponentially in recent years. While thisdevelopment introduces prominent advantages concerning open, reproducible and sustainable neu-roimaging, the process of data sharing must ensure the privacy of participant data. A requirement fromboth, Ethics Review Boards and data sharing resources, datasets need to be (pseudo-) anonymized priorto sharing in order to limit participant re-identification. Depending on the dataset at hand, this processcan however become cumbersome and prone to errors. Here we introduce BIDSonym, a tool for auto-mated pseudo-anonymization of neuroimaging datasets. BIDSonym supports multiple de-identificationprocedures and operates on neuroimaging, as well as metadata files. In addition, all metadata infor-mation present in the respective files is gathered and evaluated. Its outputs furthermore allow usersto conduct a more in-depth assessment of potentially sensitive information present in a given dataset.Through its workflow and utilization of the Brain Imaging Data Structure (BIDS), BIDSonym’s appli-cation is reproducible, requires no manual intervention and is agnostic to idiosyncrasies of small andlarge scale datasets.

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