Neuroelectrophysiology Analysis Ontology

The Neuroelectrophysiology Analysis Ontology (NEAO) defines a controlled vocabulary and conceptual representations of the typical processes involved in the analysis of neural activity data acquired using electrophysiology techniques.

Electrophysiology is a branch of physiology that studies the electrical properties of biological entities. The studies involve measurements of electric potentials and/or currents, as well as electrical manipulations (e.g. stimuli with electric pulses). Neuroelectrophysiology is the application of electrophysiology techniques to investigate the function of neural tissue.

Neuroelectrophysiology recording is the process of data acquisition, and it usually involves placing electrodes (of several configurations and types) into a preparation of neural tissue. The data will contain a representation of the voltages or currents in the preparation during the recording session, usually as a time series. The analysis requires specific methods to progressively process and transform the recorded data, which generate results and insights.

The scope of the NEAO aims to provide a comprehensive representation of neuroelectrophysiology data and analysis processes, standardizing the description of their properties and relationships. The NEAO does not provide a detailed representation of electrophysiology recording techniques, data acquisition methods/equipment, and experimental settings/subjects. The goal is to ensure a common representation of data analysis that can be used by tools to provide a detailed and semantically-enriched description of the processes involved.

The NEAO design assumes that neuroelectrophysiology data analysis consists of a sequence of small, atomic steps, each performing a specific action to generate, transform, or characterize data.

For example, consider the example below illustrating the sequence of steps to plot the power spectral density (PSD) of a local field potential (LFP) time series recorded by an extracellular electrode implanted in a brain area and saved to a data file.

First, the raw data is loaded from the file into a data structure containing the voltage time series acquired by the recording equipment. The LFP is the low-frequency component of the extracellular signal (e.g., below 250 Hz), so a low-pass filter with a 250 Hz cutoff is applied to obtain the LFP time series. The PSD is then computed from the filtered data, resulting in an array of power density estimates for various frequency values. This power spectrum can be plotted and saved to a file. In this example, each step takes input data, processes it, and produces output data, with parameters (e.g., the low-pass cutoff frequency for filtering) controlling each step.

We propose the NEAO to describe such scenarios:

NEAO is organized around the central AnalysisStep class to model the atomic steps of the analysis. This class represents any process that generates new data entities (e.g., generating artificial LFP data) or performs specific operations to extract additional information from existing data entities. These operations include data transformations (e.g., filtering the raw signal into the LFP) or computations of new, derived data (e.g., obtaining the PSD from the LFP signal). Thus, each analysis step either acts on existing data entities or produces new data.

Two additional classes complete the core of the ontology model: Data and AnalysisParameter.

The Data class represents any entity containing information used during the analysis and serves as the input and output for analysis steps. It can represent data obtained from biological electrophysiology recordings or data generated or transformed during analysis steps. In the example of computing the PSD from an electrode signal, the raw signal time series, the filtered LFP time series, the resulting PSD estimate array, and the plot are all instances of the Data class.

The AnalysisParameter class represents information entities that control the behavior of an analysis step. These parameters do not provide data to the step but influence its output. In the example above, the filter step uses a 250 Hz low-frequency cutoff parameter to set the bandwidth of the output signal.

This work was performed as part of the Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE) and received funding from the Helmholtz Association of German Research Centres. This project has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreement No. 945539 (Human Brain Project SGA3), the European Union’s Horizon Europe Programme under the Specific Grant Agreement No. 101147319 (EBRAINS 2.0 Project), the Ministry of Culture and Science of the State of North Rhine-Westphalia, Germany (NRW-network "iBehave", grant number: NW21-049), and the Joint Lab "Supercomputing and Modeling for the Human Brain."

The authors would like to thank Silvio Peroni for developing LODE, a Live OWL Documentation Environment, which is used for representing the Cross Referencing Section of this document and Daniel Garijo for developing Widoco, the program used to create the template used in this documentation.