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 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 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 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 From BIDS-Formatted EEG Data to Sensor-Space Group Results: A Fully Reproducible Workflow With EEGLAB and LIMO EEG

2021 ◽  
Vol 14 ◽  
Author(s):  
Cyril R. Pernet ◽  
Ramon Martinez-Cancino ◽  
Dung Truong ◽  
Scott Makeig ◽  
Arnaud Delorme
Keyword(s):  
Brain Imaging ◽  
Repetition Effect ◽  
Modern Human ◽  
Imaging Data ◽  
Eeg Data ◽  
Regression Approach ◽  
Share Data ◽  
Community Recommendations ◽  
Brain Imaging Data ◽  
Analytical Complexity

Reproducibility is a cornerstone of scientific communication without which one cannot build upon each other’s work. Because modern human brain imaging relies on many integrated steps with a variety of possible algorithms, it has, however, become impossible to report every detail of a data processing workflow. In response to this analytical complexity, community recommendations are to share data analysis pipelines (scripts that implement workflows). Here we show that this can easily be done using EEGLAB and tools built around it. BIDS tools allow importing all the necessary information and create a study from electroencephalography (EEG)-Brain Imaging Data Structure compliant data. From there preprocessing can be carried out in only a few steps using EEGLAB and statistical analyses performed using the LIMO EEG plug-in. Using Wakeman and Henson (2015) face dataset, we illustrate how to prepare data and build different statistical models, a standard factorial design (faces ∗ repetition), and a more modern trial-based regression approach for the stimulus repetition effect, all in a few reproducible command lines.

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

2021 ◽  
Author(s):  
Elise Bannier ◽  
Gareth Barker ◽  
Valentina Borghesani ◽  
Nils Broeckx ◽  
Patricia Clement ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Imaging Data ◽  
Research Participants ◽  
Brain Imaging Data

Learning from Enhanced Contextual Similarity in Brain Imaging Data for Classification of Schizophrenia

2017 ◽  
pp. 265-275 ◽  
Author(s):  
Tewodros Mulugeta Dagnew ◽  
Letizia Squarcina ◽  
Massimo W. Rivolta ◽  
Paolo Brambilla ◽  
Roberto Sassi
Keyword(s):  
Brain Imaging ◽  
Imaging Data ◽  
Brain Imaging Data

scholarly journals Chronic subdural hematomas: challenges and solutions. Part I. Clinical variants and diagnosis

2021 ◽  
Vol 2 (2) ◽  
pp. 21-27
Author(s):  
Leonid B. Likhterman ◽  
◽  
Aleksandr D. Kravchuk ◽  
Vladimir A. Okhlopkov ◽  
◽  
...  
Keyword(s):  
Brain Imaging ◽  
Intracranial Hemorrhage ◽  
Subdural Hematoma ◽  
Clinical Course ◽  
Chronic Subdural Hematoma ◽  
Imaging Data ◽  
Clinical Variants ◽  
Brain Compression ◽  
Brain Imaging Data ◽  
Ct And Mri

Chronic subdural hematoma (cSDH) is a multifactorial extensive intracranial hemorrhage, causing the local and/or general brain compression. Hematoma has a delimiting capsule, which defines all pathophysiological features, clinical course and treatment tactics. The paper reports contemporary views on ethiology and clinical course of cSDH. Emphasis is placed on the diagnosis. Based on the analysis of 558 verified cSDH observations, the phasal course and brain imaging data are reported. CT and MRI signs of cSDH are defined.

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