# Synchrony, oscillations, and phase relationships in collective neuronal activity: A highly comparative overview of methods

## TL;DR

This paper compares many methods for analyzing synchronized brain activity patterns to help researchers choose the best tools for their studies.

## Contribution

The paper introduces a comprehensive comparison of 122+ methods for analyzing neuronal synchrony and oscillations, revealing their strengths and limitations.

## Key findings

- Most methods are affected by factors like firing rate and population frequency, but to varying degrees.
- The highly comparative approach enables detailed quantification of collective neural activity patterns in both synthetic and biological data.
- There is no single best method; the choice depends on specific research criteria and experimental conditions.

## Abstract

Neuronal activity is organized in collective patterns that are critical for information coding, generation, and communication between neural populations. These patterns are often described in terms of synchrony, oscillations, and phase relationships. Many methods have been proposed for the quantification of these collective states of dynamic neuronal organization. However, it is difficult to determine which method is best suited for which experimental setting and research question. This choice is further complicated by the fact that most methods are sensitive to a combination of synchrony, oscillations, and other factors; in addition, some of them display systematic biases that can complicate their interpretation. To address these challenges, we adopt a highly comparative approach, whereby spike trains are represented by a diverse library of measures. This enables unsupervised or supervised analysis in the space of measures, or in that of spike trains. We compile a battery of 122 measures of synchrony, oscillations, and phase relationships, complemented with 9 measures of spiking intensity and variability. We first apply them to sets of synthetic spike trains with known statistical properties, and show that all measures are confounded by extraneous factors such as firing rate or population frequency, but to different extents. Then, we analyze spike trains recorded in different species—rat, mouse, and monkey—and brain areas—primary sensory cortices and hippocampus—and show that our highly comparative approach provides a high-dimensional quantification of collective network activity that can be leveraged for both unsupervised and supervised characterization of firing patterns. Overall, the highly comparative approach provides a detailed description of the empirical properties of multineuron spike train analysis methods, including practical guidelines for their use in experimental settings, and advances our understanding of neuronal coordination and coding.

Cognition and brain–body regulation rely on collective patterns of neural activity, which are typically described in terms of synchrony, oscillations and phase relationships. Many methods have been proposed for measuring these properties, and selecting the most appropriate method for a given research question can be a daunting task. To address this issue, we assembled a broad range of statistical measures and tested them on both synthetic and biological spike trains. Our analyses indicate that there is no overall “best” measure, and inform on the relative advantages and drawbacks of a broad range of measures with respect to several criteria of interest for their empirical application, including their modulation by firing rate or spike failures, population frequency, sequentialness and rhythmicity, as well as their bias and precision resulting from finite time window length and number of neurons. Our results provide a comprehensive picture of the range of available methods for the quantification of collective patterns of neural activity, enabling researchers to make better informed decisions and avoid interpretational pitfalls.