A modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets

Year: 2024

Authors: Gutzen R., De Bonis G., De Luca C., Pastorelli E., Capone C., Mascaro ALA., Resta F., Manasanch A., Pavone FS., Sanchez-Vives MV., Mattia M., Gr’n S., Paolucci PS., Denker M.

Autors Affiliation: Inst Neurosci & Med INM 6, Julich Res Ctr, Julich, Germany; Julich Res Ctr, Inst Adv Simulat IAS 6, Julich, Germany; Julich Res Ctr, JARA Inst Brain Struct Funct Relationships INM 10, Julich, Germany; Rhein Westfal TH Aachen, Theoret Syst Neurobiol, Aachen, Germany; Ist Nazl Fis Nucl INFN, Sez Roma, Rome, Italy; Univ Zurich, Inst Neuroinformat, Zurich, Switzerland; Swiss Fed Inst Technol, Zurich, Switzerland; Univ Florence, European Lab Nonlinear Spect LENS, Florence, Italy; Neurosci Inst, Natl Res Council, Pisa, Italy; Univ Florence, Dept Phys & Astron, Florence, Italy; Inst Invest Biomed August Pi & Sunyer IDIBAPS, Barcelona, Spain; Natl Inst Opt, Natl Res Council, Sesto Fiorentino, Italy; Inst Catalana Recerca & Estudis Avancats ICREA, Barcelona, Spain; Ist Super Sanita ISS, Natl Ctr Radiat Protect & Computat Phys, Rome, Italy.

Abstract: Neuroscience is moving toward 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 electrocorticography (ECoG) and calcium imaging datasets. Journal/Review: CELL REPORTS METHODS

Volume: 4 (1)      Pages from: 100681-1  to: 100681-22

More Information: This research was funded by the European Union’s Horizon 2020 Frame-work Program for Research and Innovation under specific grant agreements nos. 785907 (HBP SGA2) and 945539 (HBP SGA3) ; the Joint Lab Supercomputing and Modeling for the Human Brain; the European Commission-NextGeneration EU (EBRAINS-Italy CUP B51E22000150006) ; and the Ministry of Culture and Science of the State of North Rhine-Westphalia, Germany, under NRW-network iBehavegrant number NW21-049. Open-access publication funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 491111487. This study was carried out in the framework of the Human Brain Project (HBP, https:// www.humanbrainproject.eu) and EBRAINS (https://ebrains.eu). We thank Andrew Davison for his continued advice and support in developing and integrating Cobrawap.
KeyWords: Propagating Waves; Neocortical Neurons; Network Mechanisms; Gamma Oscillations; Burst-suppression; Less-than-1 Hz; Sleep; Dynamics; Synchronization; Cortex
DOI: 10.1016/j.crmeth.2023.100681

Citations: 2
data from “WEB OF SCIENCE” (of Thomson Reuters) are update at: 2024-10-20
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