Building adaptable and reusable pipelines for investigating the features of slow cortical rhythms across scales, methods, and species
Creators: name: Gutzen, Robin
name: De Bonis, Giulia
name: Pastorelli, Elena
name: Capone, Cristiano
name: De Luca, Chiara
name: Mattheisen, Glynis
name: Allegra Mascaro, Anna Letizia
name: Resta, Francesco
name: Pavone, Francesco Saverio
name: Sanchez-Vives, Maria V.
name: Mattia, Maurizio
name: Grün, Sonja
name: Davison, Andrew
name: Paolucci, Pier Stanislao
name: Denker, Michael
Publisher: name: Zenodo
Year: 2020

description: Making progress in neuroscience is increasingly a collaborative effort that requires insights obtained across data, methodologies, and models. Individual puzzle pieces must eventually be properly related to one another and integrated to frame the research in a coherent picture. In this process, often three questions arise: How to make results comparable? How to relate complementary, yet different methodological approaches to one another? How to incorporate and validate models based on these findings? A prominent example of such a scenario is slow cortical waves which are persistently observed in sleeping and anesthetized subjects of various species and across various measurement techniques [1,2], as well as being expressed by various models. While slow waves are a ubiquitous phenomenon with a rich literature basis and have been proposed to be the default activity pattern of the cortical network [3], the diversity of the experimental data and the numerous different analytical methods, tools, and even terminologies makes it difficult to rigorously compare and interrelate results from the various sources to form a coherent understanding. Here, we present an analysis pipeline for the study of slow wave activity to support quantitative comparisons of analysis results across data sources and algorithms. A key concept of the pipeline is modularity at the correct level of granularity in order to flexibly adapt, reuse, and extend it to a wide range of datasets and research questions. In the spirit of reproducibility, individual analysis blocks are built on a number of community-driven, open-source software tools, e.g., the workflow manager Snakemake (RRID:SCR_003475), the Neo (RRID:SCR_000634) library for data representation [4], the Elephant (RRID:SCR_003833) analysis toolbox, and the pipeline execution can be captured in the EBRAINS Knowledge Graph (https://kg.ebrains.eu). This pipeline design enables multi-scale analyses of measured slow wave activity, which we demonstrate using ECoG [5,6,7] and Calcium Imaging [8,9] data of anesthetized mice. Furthermore, the integration of model data provides a basis for rigorous validation testing [10,11]. While the ‘same method - different data’ approach enables fair comparisons, the pipeline equally enables ‘same data - different methods’ benchmarking. Finally, we discuss how the re-usable and adaptable conceptual design helps to derive analysis pipelines for similar research endeavors to amplify collaborative research. Acknowledgements This project has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation, Specific Grant Agreement No. 785907 (Human Brain Project SGA2) and 945539 (Human Brain Project SGA3), and by the Helmholtz Association Initiative and Networking Fund ZT-I-0003. References Adamantidis, A. R., Herrera C. G., and Gent T. C. (2019) "Oscillating circuitries in the sleeping brain." Nature Reviews Neuroscience 1-17., 10.1038/s41583-019-0223-4 Alkire, M. T., Hudetz A. G. , and Tononi G. (2008) "Consciousness and anesthesia." Science 322.5903: 876-880., 10.1126/science.1149213 Sanchez-Vives, M. V., Massimini, M., and Mattia, M. (2017) “Shaping the default activity pattern of the cortical network”. Neuron, 94(5), 993-1001., 10.1016/j.neuron.2017.05.015 Garcia, S., Guarino, D., Jaillet, F., Jennings, T., Pröpper, R., Rautenberg, P.L., Rodgers, C.C., Sobolev, A., Wachtler, T., Yger, P., et al. (2014). Neo: an object model for handling electrophysiology data in multiple formats. Frontiers in Neuroinformatics 8, 10., 10.3389/fninf.2014.00010 Dasilva et al. (2020) “Altered Neocortical Dynamics in a Mouse Model of Williams–Beuren Syndrome”. Molecular Neurobiology, vol. 57, no 2, p. 765-777., 10.1007/s12035-019-01732-4 Sanchez-Vives, M. (2019) “Cortical activity features in transgenic mouse models of cognitive deficits (Williams Beuren Syndrome)” [Data set]. EBRAINS., 10.25493/DZWT-1T8 De Bonis, G. et al. (2019) "Analysis pipeline for extracting features of cortical slow oscillations". Frontiers in Systems Neuroscience 13:70., 10.3389/fnsys.2019.00070 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., 10.25493/3E6Y-E8G Celotto, M. et al. (2020) “Analysis and Model of Cortical Slow Waves Acquired with Optical Techniques”. Methods and Protocols 3.1:14., 10.3390/mps3010014 Gutzen, R. et al. (2018) "Reproducible neural network simulations: statistical methods for model validation on the level of network activity data." Frontiers in Neuroinformatics 12:90., 10.3389/fninf.2018.00090 Trensch, G. et al. (2018) "Rigorous neural network simulations: a model substantiation methodology for increasing the correctness of simulation results in the absence of experimental validation data." Frontiers in Neuroinformatics 12:81., 10.3389/fninf.2018.00090
description: Also funded by: Helmholtz Association Initiative and Networking Fund ZT-I-0003.