An adaptable analysis pipeline makes cortical wave phenomena comparable across heterogeneous data sets

In the past decades, neuroscience excelled at accumulating a rich, heterogeneous landscape of datasets and methodologies. However, it is a challenge to effectively leverage the benefits of such diversity by combining the insights from across experiments, species, and measurement techniques in order to build a cumulative understanding of brain function. Integration of this heterogeneity requires rigorous analysis workflows that offer to distill consistent, reproducible, comparable, and reusable conclusions. Aligning data from various sources enables not only “big-data” analysis approaches, but also allows to produce a broader experimental basis for validating models and theories across the experimental and computational scales of description. Here, we explore such an approach in the context of slow cortical waves (up to 5 Hz). Slow waves are persistently observed in sleep and anesthesia across various species and various measurement techniques [1,2]. Furthermore, such activity is expressed by various models [3]. First, we address the issue of providing a comparable quantitative description of the wave dynamics to compare such heterogeneous data on a conceptual level by designing a general and modular analysis pipeline approach that is adaptable to a variety of applications. Here, the key objective is to interface existing methods, standards, and tools in a flexible manner in order to serve the requirements of a wide range of datasets and research questions using a common set of analysis components. Moreover, by building on and co-developing other specialized open-source tools, we further emphasize the reusability and extendability of each of the pipeline components.Based on this design, we present the Slow Wave Analysis Pipeline (SWAP) as a concrete, reusable implementation to perform broad, detailed, and rigorous comparisons of slow wave characteristics, such as velocities or directions. We apply SWAP to a range of openly available ECoG [4,5,6] and calcium imaging [7,8] datasets and investigate the influences of experimental parameters such as the mice’s genetic strain, the type and dosage of anesthetics, the measurement technique and the spatial resolution. We also demonstrate the pipeline’s capabilities to benchmark specific methods within the analysis by switching them with another method and by analyzing the same pool of data.Finally, we discuss how the premise of reusability and modularity helps to derive other analysis pipelines for similar research endeavors to amplify collaborative research.References 1. Adamantidis, A. R., Herrera C. G., and Gent T. C. (2019) "Oscillating circuitries in the sleeping brain." Nature Reviews Neuroscience 1-17. doi: 10.1038/s41583-019-0223-4 2. Alkire, M. T., Hudetz A. G. , and Tononi G. (2008) "Consciousness and anesthesia." Science 322.5903: 876-880. doi: 10.1126/science.1149213 3. Sanchez-Vives, Maria V., Marcello Massimini, and Maurizio Mattia. (2017) "Shaping the default activity pattern of the cortical network." Neuron 94.5: 993-1001. doi: 10.1016/j.neuron.2017.05.015 4. Dasilva et al. (2020) “Altered Neocortical Dynamics in a Mouse Model of Williams–Beuren Syndrome”. Molecular Neurobiology, vol. 57, no 2, p. 765-777. doi: 10.1007/s12035-019-01732-4 5. Sanchez-Vives, M. (2019) “Cortical activity features in transgenic mouse models of cognitive deficits (Williams Beuren Syndrome)” [Data set]. EBRAINS. doi: 10.25493/DZWT-1T8 6. De Bonis, G. et al. (2019) "Analysis pipeline for extracting features of cortical slow oscillations". Frontiers in Systems Neuroscience 13:70. doi: 10.3389/fnsys.2019.00070 7. Resta, F., Allegra Mascaro, A. L., and Pavone, F. (2020). Study of Slow Waves (SWs) propagation through wide-field calcium imaging of the right cortical hemisphere of GCaMP6f mice [Data set]. EBRAINS. doi: 10.25493/3E6Y-E8G 8. Celotto, M. et al. (2020) “Analysis and Model of Cortical Slow Waves Acquired with Optical Techniques”. Methods and Protocols 3.1:14. doi: 10.3390/mps301001