A NWB-based dataset and processing pipeline of human single-neuron activity during a declarative memory task

Creators: Chandravadia, N.; Liang, D.; Schjetnan, A. G. P.; Carlson, A.; Faraut, M.; Chung, J. M.; Reed, C. M.; Dichter, B.; Maoz, U.; Kalia, S. K.; Valiante, T. A.; Mamelak, A. N.; Rutishauser, U.

Abstract

A challenge for data sharing in systems neuroscience is the multitude of different data formats used. Neurodata Without Borders: Neurophysiology 2.0 (NWB:N) has emerged as a standardized data format for the storage of cellular-level data together with meta-data, stimulus information, and behavior. A key next step to facilitate NWB:N adoption is to provide easy to use processing pipelines to import/export data from/to NWB:N. Here, we present a NWB-formatted dataset of 1863 single neurons recorded from the medial temporal lobes of 59 human subjects undergoing intracranial monitoring while they performed a recognition memory task. We provide code to analyze and export/import stimuli, behavior, and electrophysiological recordings to/from NWB in both MATLAB and Python. The data files are NWB:N compliant, which affords interoperability between programming languages and operating systems. This combined data and code release is a case study for how to utilize NWB:N for human single-neuron recordings and enables easy re-use of this hard-to-obtain data for both teaching and research on the mechanisms of human memory.

Additional Information

© 2020 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article. Received 08 October 2019; Accepted 07 February 2020; Published 04 March 2020. We thank all patients and their families for their participation, Vijay Iyer and Mark Cafaro (MathWorks) for their help with MatNWB, and the organizers and participants of the 6th NWB:N Developer Hackathon and User Days hosted by Janelia/HHMI for their feedback. Acquisition of this dataset has been supported by the National Institute of Neurological Disorders and Stroke (U01NS103792 to UR, TV), the National Science Foundation (1554105 to UR), the National Institute of Mental Health (R01MH110831 to UR), the McKnight Endowment for Neuroscience (to UR), a NARSAD Young Investigator grant from the Brain & Behavior Research Foundation (to UR). Preparation of this dataset was made possible by a seed grant from the Kavli Foundation (to UR) and a grant from the BRAIN initiative (U19 NS104590). Code availability: All code associated with this project is available as open source. The code is available on GitHub under the BSD license (https://github.com/rutishauserlab/recogmem-release-NWB). Both Python and MATLAB scripts are included in this repository along with the matNWB API. We also provide a streamlined workflow as a Jupyter Notebook. Note, we tested our code with the following versions of the Python Packages: numpy (1.17.2), pandas (0.23.0), scipy (1.1.0), matplotlib (2.2.2), pynwb (1.1.0), hdmf (1.2.0), and seaborn (0.9.0). Detailed instructions on installing and running the code in this repository are found in our online documentation on GitHub. Author Contributions: Performed experiments (U.R., M.F. and A.G.P.S.), data processing and analysis (N.C., A.C. and U.R.), development of code/analytical tools (N.C., D.L., U.R. and B.D.), performed surgery (T.A.V., A.M. and S.K.K.), patient care and seizure localization (J.C. and C.R.), experimental design (U.R. and A.M.), conception and initiation of project (U.R. and U.M.), writing of the paper (N.C. and U.R.). The authors declare no competing interests.

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