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Neuroimaging Resources Registry
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Featured tool/resource

Graphic for EEG2BIDS Wizard

EEG2BIDS Wizard

EEG2BIDS can be downloaded from GitHub and installed within a few minutes. Its sequenced workflow walks users through data labelling and anonymization steps, and the embedding of supplemental metadata into a complete BIDS dataset. MNE-BIDS libraries provide standardization as well as anonymization functionality, and as a final step, the app runs the BIDS Validator locally to ensure data outputs are BIDS compliant.
The app’s GUI components are written using React and Node.js, supported by a Python server facilitated by a Socket.io library. The app can connect securely to any LORIS database instance to automate and validate data entry (Das 2012), and securely stores LORIS user credentials using the local system keychain. Its flexible open design optimizes customizability, extensibility and interoperability with other formats and data platforms.
This tool was released open-source in 2021 has been included in the BIDS Neuroimaging list of community tools.

Featured tool/resource

Graphic for Machine Learning in NeuroImaging (MALINI)

Machine Learning in NeuroImaging (MALINI)

Machine Learning in NeuroImaging (MALINI) is a MATLAB-based toolbox used for feature extraction and disease classification using resting-state functional magnetic resonance imaging (rs-fMRI) data. Eighteen different popular classifiers are presented. With slight modifications, it can also be used for any classification problem using any set of features.

Featured tool/resource

Graphic for C57BL anatomical mouse brain MRI atlases

C57BL anatomical mouse brain MRI atlases

In-vivo (N=11) and ex-vivo (N=10) C57BL anatomical mouse brain atlases are created by Brookhaven National Laboratory and National High Magnetic Field Laboratory research collaboration. 3D volumetric anatomical MRI reference data and corresponding manually delineated labels are shared in nifti image format.

The work has been published (PMID: 16165303 and 18958199) and the authors would like to acknowledge the financial support of the National Institute of Health through grants R01 EB 00233–04 and P41 RR16105 and the National High Magnetic Field Laboratory.

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National Institutes of HealthFunded by NIH Grant number: 5R24EB029173