scholarly journals The Open Brain Consent: Informing research participants and obtaining consent to share brain imaging data

2020 ◽  
Author(s):  
Elise Bannier ◽  
Gareth Barker ◽  
Valentina Borghesani ◽  
Nils Broeckx ◽  
Patricia Clement ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Modern Science ◽  
Imaging Data ◽  
General Data Protection Regulation ◽  
Data User ◽  
History Of ◽  
General Data ◽  
Brain Imaging Data ◽  
The Brain ◽  
The Eu

Having the means to share research data openly is essential to modern science. For human research, a key aspect in this endeavour is obtaining consent from participants, not just to take part in a study, which is a basic ethical principle, but also to share their data with the scientific community. To ensure that the participants’ privacy is respected, national and/or supranational regulations and laws are in place. It is, however, not always clear to researchers what the implications of those are, nor how to comply with them. The Open Brain Consent (https://open-brain-consent.readthedocs.io) is an international initiative that aims to provide researchers in the brain imaging community with information about data sharing options and tools. We present here a short history of this project and its latest developments, and share pointers to consent forms, including a template consent form that is compliant with the EU General Data Protection Regulation. We also share pointers to an associated data user agreement that is not only useful in the EU context, but also for any researchers dealing with personal (clinical) data elsewhere.

Supercomputing in the Study and Stimulation of the Brain

2021 ◽  
pp. 290-300
Author(s):  
Laura Dipietro ◽  
Seth Elkin-Frankston ◽  
Ciro Ramos-Estebanez ◽  
Timothy Wagner
Keyword(s):  
High Performance ◽  
Neurological Diseases ◽  
Data Sets ◽  
Imaging Data ◽  
Building Simulations ◽  
History Of ◽  
Brain Imaging Data ◽  
Evolution Of Science ◽  
Computational Systems ◽  
The Brain

The history of neuroscience has tracked with the evolution of science and technology. Today, neuroscience's trajectory is heavily dependent on computational systems and the availability of high-performance computing (HPC), which are becoming indispensable for building simulations of the brain, coping with high computational demands of analysis of brain imaging data sets, and developing treatments for neurological diseases. This chapter will briefly review the current and potential future use of supercomputers in neuroscience.

scholarly journals Integrating the Brain Imaging Data Structure (BIDS) standard into C-PAC

GigaScience ◽  
2016 ◽  
Vol 5 (suppl_1) ◽  
Author(s):  
Daniel Clark ◽  
Krzysztof J. Gorgolewski ◽  
R. Cameron Craddock
Keyword(s):  
Data Structure ◽  
Brain Imaging ◽  
Imaging Data ◽  
Brain Imaging Data ◽  
The Brain

scholarly journals Analysis of task-based functional MRI data preprocessed with fMRIPrep

2019 ◽  
Author(s):  
Oscar Esteban ◽  
Rastko Ciric ◽  
Karolina Finc ◽  
Ross Blair ◽  
Christopher J. Markiewicz ◽  
...  
Keyword(s):  
Ad Hoc ◽  
Brain Activity ◽  
Imaging Data ◽  
Modeling And Analysis ◽  
Manual Intervention ◽  
Standard Tool ◽  
History Of ◽  
Brain Imaging Data ◽  
Many Sources ◽  
The Brain

Functional magnetic resonance imaging (fMRI) is a standard tool to investigate the neural correlates of cognition. fMRI noninvasively measures brain activity, allowing identification of patterns evoked by tasks performed during scanning. Despite the long history of this technique, the idiosyncrasies of each dataset have led to the use of ad-hoc preprocessing protocols customized for nearly every different study. This approach is time-consuming, error-prone, and unsuitable for combining datasets from many sources. Here we showcase fMRIPrep (http://fmriprep.org), a robust tool to prepare human fMRI data for statistical analysis. This software instrument addresses the reproducibility concerns of the established protocols for fMRI preprocessing. By leveraging the Brain Imaging Data Structure (BIDS) to standardize both the input datasets —MRI data as stored by the scanner— and the outputs —data ready for modeling and analysis—, fMRIPrep is capable of preprocessing a diversity of datasets without manual intervention. In support of the growing popularity of fMRIPrep, this protocol describes how to integrate the tool in a task-based fMRI investigation workflow.

scholarly journals EEG-BIDS, an extension to the brain imaging data structure for electroencephalography

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Cyril R. Pernet ◽  
Stefan Appelhoff ◽  
Krzysztof J. Gorgolewski ◽  
Guillaume Flandin ◽  
Christophe Phillips ◽  
...  
Keyword(s):  
Data Structure ◽  
Brain Imaging ◽  
Imaging Data ◽  
Brain Imaging Data ◽  
The Brain

scholarly journals qMRI-BIDS: An extension to the brain imaging data structure for quantitative magnetic resonance imaging data

2021 ◽  
Author(s):  
Agah Karakuzu ◽  
Stefan Appelhoff ◽  
Tibor Auer ◽  
Mathieu Boudreau ◽  
Franklin Feingold ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Data Structure ◽  
Magnetic Resonance ◽  
Brain Imaging ◽  
Common Ground ◽  
Quantitative Mri ◽  
Imaging Data ◽  
Resonance Imaging ◽  
Brain Imaging Data ◽  
The Brain

The Brain Imaging Data Structure (BIDS) established community consensus on the organization of data and metadata for several neuroimaging modalities. Traditionally, BIDS had a strong focus on functional magnetic resonance imaging (MRI) datasets and lacked guidance on how to store multimodal structural MRI datasets. Here, we present and describe the BIDS Extension Proposal 001 (BEP001), which adds a range of quantitative MRI (qMRI) applications to the BIDS. In general, the aim of qMRI is to characterize brain microstructure by quantifying the physical MR parameters of the tissue via computational, biophysical models. By proposing this new standard, we envision standardization of qMRI which makes multicenter dissemination of interoperable data possible. As a result, BIDS can act as a catalyst of convergence between qMRI methods development and application-driven neuroimaging studies that can help develop quantitative biomarkers for neural tissue characterization. Finally, our BIDS extension offers a common ground for developers to exchange novel imaging data and tools, reducing the practical barriers to standardization that is currently lacking in the field of neuroimaging.

scholarly journals BIDS-iEEG: an extension to the brain imaging data structure (BIDS) specification for human intracranial electrophysiology

2018 ◽  
Author(s):  
Christopher Holdgraf ◽  
Stefan Appelhoff ◽  
Stephan Bickel ◽  
Kristofer Bouchard ◽  
Sasha D'Ambrosio ◽  
...  
Keyword(s):  
Data Structure ◽  
Human Brain ◽  
Brain Imaging ◽  
Common Ground ◽  
Data Transfer ◽  
Open Data ◽  
Imaging Data ◽  
Data Repositories ◽  
Brain Imaging Data ◽  
The Brain

Intracranial electroencephalography (iEEG) data offer a unique combination of high spatial and temporal resolution measures of the living human brain. However, data collection is limited to highly specialized clinical environments. To improve internal (re)use and external sharing of these unique data, we present a structure for storing and sharing iEEG data: BIDS-iEEG, an extension of the Brain Imaging Data Structure (BIDS) specification, along with freely available examples and a bids-starter-kit. BIDS is a framework for organizing and documenting data and metadata with the aim to make datasets more transparent and reusable and to improve reproducibility of research. It is a community-driven specification with an inclusive decision-making process. As an extension of the BIDS specification, BIDS-iEEG facilitates integration with other modalities such as fMRI, MEG, and EEG. As the BIDS-iEEG extension has received input from many iEEG researchers, it provides a common ground for data transfer within labs, between labs, and in open-data repositories. It will facilitate reproducible analyses across datasets, experiments, and recording sites, allowing scientists to answer more complex questions about the human brain. Finally, the cross-modal nature of BIDS will enable efficient consolidation of data from multiple sites for addressing questions about generalized brain function.

scholarly journals The Brain Imaging Data Structure: a standard for organizing and describing outputs of neuroimaging experiments

2015 ◽  
Author(s):  
Krzysztof J. Gorgolewski ◽  
Tibor Auer ◽  
Vince D. Calhoun ◽  
R. Cameron Craddock ◽  
Samir Das ◽  
...  
Keyword(s):  
Data Structure ◽  
Brain Imaging ◽  
Imaging Data ◽  
Non Invasive ◽  
File Formats ◽  
Neuroscience Research ◽  
Imaging Experiment ◽  
Magnetic Resonance Imaging Mri ◽  
Brain Imaging Data ◽  
The Brain

AbstractThe 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.

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