A NWB-based Dataset and Processing Pipeline of Human Single-Neuron Activity During a Declarative Memory Task

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.

在系统神经科学领域，数据共享的一大挑战在于数据格式的多样性。神经数据无国界计划：神经生理学2.0（NWB:N）已崭露头角，成为存储细胞级别数据及其元数据、刺激信息和行为的标准数据格式。为了促进NWB:N的采纳，关键的一步是提供易于使用的处理管道，以便从/到NWB:N导入/导出数据。在此，我们呈现了一个NWB格式的数据集，该数据集由来自59名受试者内侧颞叶的1863个单个神经元记录而成，这些受试者在进行识别记忆任务时接受了颅内监测。我们提供了用于分析并将刺激、行为和电生理记录导出/导入NWB的MATLAB和Python代码。数据文件符合NWB:N规范，这为编程语言和操作系统之间的互操作性提供了便利。这一数据和代码的结合发布是利用NWB:N进行人类单个神经元记录的案例研究，并使得这一难以获取的数据易于被用于教学和研究人类记忆机制。