000908472 001__ 908472
000908472 005__ 20240313094917.0
000908472 037__ $$aFZJ-2022-02625
000908472 041__ $$aEnglish
000908472 1001_ $$0P:(DE-Juel1)171572$$aGutzen, R. $$b0$$eCorresponding author$$ufzj
000908472 1112_ $$aBrain Activity across Scales and Species: Analysis of Experiments and Simulations$$cRome$$d2022-06-13 - 2022-06-15$$gBASSES$$wItaly
000908472 245__ $$aBlocks instead of puzzles pieces - analyzing cortical wave activity across scales in an adaptable framework$$f2022-06-14 - 
000908472 260__ $$c2022
000908472 3367_ $$033$$2EndNote$$aConference Paper
000908472 3367_ $$2DataCite$$aOther
000908472 3367_ $$2BibTeX$$aINPROCEEDINGS
000908472 3367_ $$2ORCID$$aLECTURE_SPEECH
000908472 3367_ $$0PUB:(DE-HGF)31$$2PUB:(DE-HGF)$$aTalk (non-conference)$$btalk$$mtalk$$s1661147239_5165$$xInvited
000908472 3367_ $$2DINI$$aOther
000908472 520__ $$aThe expanding availability and variety of data and methodologies represent a great opportunity to access neural processes in finer detail. Leveraging the complementary insights from across experiments, species, and measurement techniques, however, poses a challenge as the data is too heterogeneous and the corresponding analyses too specific to allow for rigorous quantitative comparisons of the results. However, this challenge also promises new avenues of scientific progress. By aligning existing data and analyses from different sources in a reusable workflow we can build a broader basis for meta-studies, contextualization of individual studies, and model validation. Here, we showcase such an analysis pipeline with the application to cortical wave activity in the delta (‘slow waves’) and beta range, integrating capabilities to process diverse data and topical analytical methods within a consistent framework: the ‘collaborative brain wave analysis pipeline’ (Cobrawap).The pipeline design is based on modular building blocks that provide implementations of analysis methods and processing steps. The components are matched by their input-output relations and can be flexibly combined and arranged into a workflow to fit the requirements of the data and the scientific question. In this framework, by reusing the identical methods and implementations and by converging the heterogeneous data to a common descriptive level of wave activity, we are in a situation where analysis outcomes can be quantitatively compared using common characteristic measures.We demonstrate the versatility of the pipeline by analyzing slow wave activity in ECoG and calcium imaging recordings to evaluate the influence of dataset-specific parameters on the wave characteristics such as the type and dose of anesthesia or the measurement modality and their temporal and spatial resolution, and show that we can replicate corresponding findings from the literature. Furthermore, we show how the pipeline enables the benchmarking of methods by analyzing the same data with different method blocks and how the individual pipeline elements can be reused, rearranged, or extended to help derive analysis workflows for similar research endeavors and amplify collaborative research.
000908472 536__ $$0G:(DE-HGF)POF4-5235$$a5235 - Digitization of Neuroscience and User-Community Building (POF4-523)$$cPOF4-523$$fPOF IV$$x0
000908472 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x1
000908472 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x2
000908472 536__ $$0G:(DE-HGF)ZT-I-0003$$aHAF - Helmholtz Analytics Framework (ZT-I-0003)$$cZT-I-0003$$x3
000908472 909CO $$ooai:juser.fz-juelich.de:908472$$pec_fundedresources$$pVDB$$popenaire
000908472 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171572$$aForschungszentrum Jülich$$b0$$kFZJ
000908472 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5235$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
000908472 9141_ $$y2022
000908472 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000908472 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x1
000908472 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x2
000908472 980__ $$atalk
000908472 980__ $$aVDB
000908472 980__ $$aI:(DE-Juel1)INM-6-20090406
000908472 980__ $$aI:(DE-Juel1)INM-10-20170113
000908472 980__ $$aI:(DE-Juel1)IAS-6-20130828
000908472 980__ $$aUNRESTRICTED
000908472 981__ $$aI:(DE-Juel1)IAS-6-20130828