Comparing apples to apples -- Using a modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets

TL;DR

This paper introduces a flexible analysis pipeline and software tool for comparing slow wave brain activity across diverse datasets, facilitating integrative neuroscience research.

Contribution

It presents a modular, adaptable pipeline and the Cobrawap software for consistent comparison of slow wave activity across heterogeneous experimental data.

Findings

Effective comparison of slow wave characteristics across datasets

Demonstrated pipeline's adaptability to various data types

Enabled rigorous cross-study analysis of brain rhythms

Abstract

Neuroscience is moving towards a more integrative discipline, where understanding brain function requires consolidating the accumulated evidence seen across experiments, species, and measurement techniques. A remaining challenge on that path is integrating such heterogeneous data into analysis workflows such that consistent and comparable conclusions can be distilled as an experimental basis for models and theories. Here, we propose a solution in the context of slow wave activity ($<1$ Hz), which occurs during unconscious brain states like sleep and general anesthesia, and is observed across diverse experimental approaches. We address the issue of integrating and comparing heterogeneous data by conceptualizing a general pipeline design that is adaptable to a variety of inputs and applications. Furthermore, we present the Collaborative Brain Wave Analysis Pipeline (Cobrawap) as a concrete, reusable software implementation to perform broad, detailed, and rigorous comparisons of slow wave characteristics across multiple, openly available ECoG and calcium imaging datasets.