P177 Cobrawap: from a specific use-case to a more general scientifically-technologically co-designed tool for neuroscience
Cosimo Lupo1,*, Robin Gutzen2, Federico Marmoreo1, Alessandra Cardinale1,3, Michael Denker4, Pier Stanislao Paolucci1, Giulia De Bonis1
1Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, Italy
2Dept. of Psychology and Center for Data Science, New York University, New York, USA
3Università Campus Bio-Medico di Roma, Rome, Italy
4Institute for Advanced Simulation (IAS-6), Jülich Research Centre, Jülich, Germany
*Email:cosimo.lupo89@gmail.com
Introduction
Cobrawap (Collaborative Brain Wave Analysis Pipeline) [1-3] is an open-source, modular and customizable data analysis tool designed and implemented by INFN (Italy) and Jülich Research Centre (Germany) in the context of the Human Brain Project, further enhanced within the EBRAINS and EBRAINS-Italy initiatives. Its foundational goal was to enable standardized quantitative descriptions of cortical wave dynamics observed in heterogeneous data sources, both experimental and simulated, also allowing for validation and calibration of brain simulation models (Fig. 1). The current directions of development aim at enhancing generalizability beyond the set of originally considered use cases.
Methods
Intercepting the increasing demand by the Neuroscience community for reusability and reproducibility, Cobrawap provides a framework suitable for collecting generalized implementations of established methods and algorithms. Inspired by FAIR principles and leveraging the latest software solutions, Cobrawap is structured as a collection of modular Python3 building blocks that can be flexibly arranged along sequential stages, implementing data processing steps and analysis methods, directed by workflow managers (Snakemake or CWL). The collaborative approach behind the whole software allows users to seamlessly enrich its scope, by co-designing and implementing new processing or visualization blocks with the support of the Cobrawap “core team”.
Results
Cobrawap has been successfully appliedon murine data and data-driven simulations, for multi-scale quantitative comparisons of heterogeneous experimental datasets [4] and for validation and calibration of simulation models [5], in the specific use-case of cortical slow wave data analysis in low-consciousness brain states. Later applications on non-human primate experimental data, and on increasing levels of consciousness, have proven the robustness and the versatility of the approach, paving the way to the crucial extension toward human data. A fundamental step is represented by the comparison with simulations, e.g. via TheVirtualBrain [6,7], which allow both to benchmark the new algorithms, and to validate and calibrate such models [8,9].
Discussion
Cobrawap has proven to be effective in the analysis of both synthetic and experimental data of different origin, representing a FAIR-compliant collaborative framework for the scientific and technological co-design. Together with the appealing extension to experimental human data, in both physiological and pathological conditions, further lines of enhancement involve the analysis of the output from a variety of theoretical models, also including the outcomes of artificial neural networks; this makes it eligible for addressing the explainability of AI solutions in bio-inspired systems that incorporate the emulation of brain states as a key element for the implementation of efficient incremental learning and cognition [10,11].
Figure 1. Cobrawap offers standardized quantitative descriptions of brain wave dynamics observed in heterogeneous data sources, both experimental and simulated (top right panel), via a set of sequential stages featuring modular and flexible sets of processing and visualization blocks (bottom panel, for two different recording techniques on anesthetized mice), each easily customizable by the user.
Acknowledgements
Research co-funded by: European Union’s Horizon Europe Programme under Specific Grant Agreement No. 101147319 (EBRAINS 2.0); European Commission NextGeneration EU through Italian Grant MUR-CUP-B51E22000150006 EBRAINS-Italy PNRR.
References
[1]github.com/NeuralEnsemble/cobrawap
[2]cobrawap.readthedocs.io
[3]doi.org/10.5281/zenodo.10198748
[4] Gutzen, et al. (2024)doi.org/10.1016/j.crmeth.2023.100681
[5] Capone, De Luca, et al. (2023)doi.org/10.1038/s42003-023-04580-0
[6] Sanz Leon, et al. (2013)doi.org/10.3389/fninf.2013.00010
[7]www.thevirtualbrain.org
[8] Gaglioti, et al. (2024)doi.org/10.3390/app14020890
[9] Cardinale, Gaglioti, et al. (2025)in preparation
[10] Capone, et al. (2019)doi.org/10.1038/s41598-019-45525-0
[11] Golosio, De Luca, et al. (2021)doi.org/10.1371/journal.pcbi.1009045
