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001006775 005__ 20240315203645.0
001006775 0247_ $$2doi$$a10.48550/ARXIV.2211.08527
001006775 0247_ $$2doi$$a10.48550/arXiv.2211.08527
001006775 0247_ $$2Handle$$a2128/34376
001006775 037__ $$aFZJ-2023-01831
001006775 1001_ $$0P:(DE-Juel1)171572$$aGutzen, Robin$$b0$$eCorresponding author$$ufzj
001006775 245__ $$aComparing apples to apples -- Using a modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets
001006775 260__ $$barXiv$$c2022
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001006775 3367_ $$2BibTeX$$aARTICLE
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001006775 520__ $$aNeuroscience 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.
001006775 536__ $$0G:(DE-HGF)POF4-5235$$a5235 - Digitization of Neuroscience and User-Community Building (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001006775 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x1
001006775 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x2
001006775 536__ $$0G:(DE-Juel-1)iBehave-20220812$$aAlgorithms of Adaptive Behavior and their Neuronal Implementation in Health and Disease (iBehave-20220812)$$ciBehave-20220812$$x3
001006775 588__ $$aDataset connected to DataCite
001006775 650_7 $$2Other$$aNeurons and Cognition (q-bio.NC)
001006775 650_7 $$2Other$$aQuantitative Methods (q-bio.QM)
001006775 650_7 $$2Other$$aFOS: Biological sciences
001006775 7001_ $$0P:(DE-HGF)0$$aDe Bonis, Giulia$$b1
001006775 7001_ $$0P:(DE-HGF)0$$aDe Luca, Chiara$$b2
001006775 7001_ $$0P:(DE-HGF)0$$aPastorelli, Elena$$b3
001006775 7001_ $$0P:(DE-HGF)0$$aCapone, Cristiano$$b4
001006775 7001_ $$0P:(DE-HGF)0$$aMascaro, Anna Letizia Allegra$$b5
001006775 7001_ $$0P:(DE-HGF)0$$aResta, Francesco$$b6
001006775 7001_ $$0P:(DE-HGF)0$$aManasanch, Arnau$$b7
001006775 7001_ $$0P:(DE-HGF)0$$aPavone, Francesco Saverio$$b8
001006775 7001_ $$0P:(DE-HGF)0$$aSanchez-Vives, Maria V.$$b9
001006775 7001_ $$0P:(DE-HGF)0$$aMattia, Maurizio$$b10
001006775 7001_ $$0P:(DE-Juel1)144168$$aGrün, Sonja$$b11$$ufzj
001006775 7001_ $$0P:(DE-HGF)0$$aPaolucci, Pier Stanislao$$b12
001006775 7001_ $$0P:(DE-Juel1)144807$$aDenker, Michael$$b13$$ufzj
001006775 773__ $$a10.48550/arXiv.2211.08527
001006775 8564_ $$uhttps://juser.fz-juelich.de/record/1006775/files/Gutzen%20et%20al_2022_Comparing%20apples%20to%20apples%20--%20Using%20a%20modular%20and%20adaptable%20analysis%20pipeline.pdf$$yOpenAccess
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001006775 9141_ $$y2023
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001006775 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
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