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Review
. 2023 Oct 19;10(1):719.
doi: 10.1038/s41597-023-02614-0.

A comparison of neuroelectrophysiology databases

Priyanka Subash  1 , Alex Gray  1 , Misque Boswell  1 , Samantha L Cohen  1 , Rachael Garner  1 , Sana Salehi  1 , Calvary Fisher  1 , Samuel Hobel  1 , Satrajit Ghosh  2 , Yaroslav Halchenko  3 , Benjamin Dichter  4 , Russell A Poldrack  5 , Chris Markiewicz  5 , Dora Hermes  6 , Arnaud Delorme  7 , Scott Makeig  7 , Brendan Behan  8 , Alana Sparks  9 , Stephen R Arnott  10 , Zhengjia Wang  11 , John Magnotti  11 , Michael S Beauchamp  11 , Nader Pouratian  12 , Arthur W Toga  1 , Dominique Duncan  13
Affiliations
Review

A comparison of neuroelectrophysiology databases

Priyanka Subash et al. Sci Data. .

Abstract

As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration. This paper compares four freely available intracranial neuroelectrophysiology data repositories: Data Archive for the BRAIN Initiative (DABI), Distributed Archives for Neurophysiology Data Integration (DANDI), OpenNeuro, and Brain-CODE. The aim of this review is to describe archives that provide researchers with tools to store, share, and reanalyze both human and non-human neurophysiology data based on criteria that are of interest to the neuroscientific community. The Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB) are utilized by these archives to make data more accessible to researchers by implementing a common standard. As the necessity for integrating large-scale analysis into data repository platforms continues to grow within the neuroscientific community, this article will highlight the various analytical and customizable tools developed within the chosen archives that may advance the field of neuroinformatics.

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Conflict of interest statement

DABI Affiliated Researchers: Priyanka Subash, Alex Gray, Misque Boswell, Samantha L. Cohen, Rachael Garner, Sana Salehi, Calvary Fisher, Samuel Hobel, Nader Pouratian, Arthur W. Toga, and Dominique Duncan; DANDI and NWB Affiliated Researchers: Satrajit Ghosh, Yaroslav Halchenko, Benjamin Dichter; OpenNeuro Affiliated Researchers: Russell A. Poldrack, Chris Markiewicz; BIDS Affiliated Researchers: Dora Hermes; NEMAR Affiliated Researchers: Arnaud Delorme, Scott Makeig; Brain-CODE Affiliated Researchers: Brendan Behan, Alana Sparks; RAVE Affiliated Researchers: Zhengjia Wang, John Magnotti, Michael Beauchamp.

Figures

Fig. 1
Fig. 1
iEEG-BIDS folder structure. (a) BIDS structure contains folders for each subject and one folder for stimuli. Within a subject folder, an /anat/ folder contains structural images alongside iEEG data. (b) _ieeg.json file stores iEEG data containing information on acquisition systems and their parameters. (c) _channels.tsv file stores metadata about channel-specific information, such as hardware filters or electrophysiological units. (d) _events.tsv TSV file contains event timing data. (e) _electrodes.tsv files store electrode coordinates. (f) _coordsystem.json file stores the coordinate system information. (g) Other images relevant for iEEG, such as surface models and 2-D images can be stored in a systematic manner. Optional folders and labels, such as the session folder and space- label, are mostly left out of this example.
Fig. 2
Fig. 2
NWB Data Types.
Fig. 3
Fig. 3
DABI Feature Architecture.
Fig. 4
Fig. 4
DANDI Feature Architecture.
Fig. 5
Fig. 5
OpenNeuro Data Uploading Process Flow.
Fig. 6
Fig. 6
Brain-CODE Feature Architecture.
See this image and copyright information in PMC

Update of

References

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