Hauptseite > Publikationsdatenbank > A modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets > print |
001 | 1020500 | ||
005 | 20250225084318.0 | ||
024 | 7 | _ | |a 10.1016/j.crmeth.2023.100681 |2 doi |
024 | 7 | _ | |a 10.34734/FZJ-2024-00219 |2 datacite_doi |
024 | 7 | _ | |a 38183979 |2 pmid |
024 | 7 | _ | |a WOS:001171298600001 |2 WOS |
037 | _ | _ | |a FZJ-2024-00219 |
041 | _ | _ | |a English |
082 | _ | _ | |a 610 |
100 | 1 | _ | |a Gutzen, Robin |0 P:(DE-Juel1)171572 |b 0 |e Corresponding author |
245 | _ | _ | |a A modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets |
260 | _ | _ | |a Cambridge, MA |c 2024 |b Cell Press |
336 | 7 | _ | |a article |2 DRIVER |
336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1710500822_28984 |2 PUB:(DE-HGF) |
336 | 7 | _ | |a ARTICLE |2 BibTeX |
336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
520 | _ | _ | |a 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. |
536 | _ | _ | |a 5235 - Digitization of Neuroscience and User-Community Building (POF4-523) |0 G:(DE-HGF)POF4-5235 |c POF4-523 |f POF IV |x 0 |
536 | _ | _ | |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) |0 G:(EU-Grant)785907 |c 785907 |f H2020-SGA-FETFLAG-HBP-2017 |x 1 |
536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |f H2020-SGA-FETFLAG-HBP-2019 |x 2 |
536 | _ | _ | |a JL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027) |0 G:(DE-Juel1)JL SMHB-2021-2027 |c JL SMHB-2021-2027 |x 3 |
536 | _ | _ | |a DFG project 491111487 - Open-Access-Publikationskosten / 2022 - 2024 / Forschungszentrum Jülich (OAPKFZJ) (491111487) |0 G:(GEPRIS)491111487 |c 491111487 |x 4 |
536 | _ | _ | |a Algorithms of Adaptive Behavior and their Neuronal Implementation in Health and Disease (iBehave-20220812) |0 G:(DE-Juel-1)iBehave-20220812 |c iBehave-20220812 |x 5 |
588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
700 | 1 | _ | |a De Bonis, Giulia |0 P:(DE-HGF)0 |b 1 |
700 | 1 | _ | |a De Luca, Chiara |0 P:(DE-HGF)0 |b 2 |
700 | 1 | _ | |a Pastorelli, Elena |0 P:(DE-HGF)0 |b 3 |
700 | 1 | _ | |a Capone, Cristiano |0 P:(DE-HGF)0 |b 4 |
700 | 1 | _ | |a Allegra Mascaro, Anna Letizia |0 P:(DE-HGF)0 |b 5 |
700 | 1 | _ | |a Resta, Francesco |0 P:(DE-HGF)0 |b 6 |
700 | 1 | _ | |a Manasanch, Arnau |0 P:(DE-HGF)0 |b 7 |
700 | 1 | _ | |a Pavone, Francesco Saverio |0 P:(DE-HGF)0 |b 8 |
700 | 1 | _ | |a Sanchez-Vives, Maria V. |0 P:(DE-HGF)0 |b 9 |
700 | 1 | _ | |a Mattia, Maurizio |0 P:(DE-HGF)0 |b 10 |
700 | 1 | _ | |a Grün, Sonja |0 P:(DE-Juel1)144168 |b 11 |
700 | 1 | _ | |a Paolucci, Pier Stanislao |0 P:(DE-HGF)0 |b 12 |
700 | 1 | _ | |a Denker, Michael |0 P:(DE-Juel1)144807 |b 13 |
773 | _ | _ | |a 10.1016/j.crmeth.2023.100681 |g p. 100681 - |0 PERI:(DE-600)3091714-1 |n 1 |p 100681 |t Cell reports / Methods |v 4 |y 2024 |x 2667-2375 |
856 | 4 | _ | |u https://juser.fz-juelich.de/record/1020500/files/1-s2.0-S266723752300365X-main-1.pdf |y OpenAccess |
856 | 4 | _ | |u https://juser.fz-juelich.de/record/1020500/files/1-s2.0-S266723752300365X-main-1.gif?subformat=icon |x icon |y OpenAccess |
856 | 4 | _ | |u https://juser.fz-juelich.de/record/1020500/files/1-s2.0-S266723752300365X-main-1.jpg?subformat=icon-1440 |x icon-1440 |y OpenAccess |
856 | 4 | _ | |u https://juser.fz-juelich.de/record/1020500/files/1-s2.0-S266723752300365X-main-1.jpg?subformat=icon-180 |x icon-180 |y OpenAccess |
856 | 4 | _ | |u https://juser.fz-juelich.de/record/1020500/files/1-s2.0-S266723752300365X-main-1.jpg?subformat=icon-640 |x icon-640 |y OpenAccess |
909 | C | O | |o oai:juser.fz-juelich.de:1020500 |p openaire |p open_access |p OpenAPC |p driver |p VDB |p ec_fundedresources |p openCost |p dnbdelivery |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)171572 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 11 |6 P:(DE-Juel1)144168 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 13 |6 P:(DE-Juel1)144807 |
913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-523 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Neuromorphic Computing and Network Dynamics |9 G:(DE-HGF)POF4-5235 |x 0 |
914 | 1 | _ | |y 2024 |
915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1190 |2 StatID |b Biological Abstracts |d 2023-10-27 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0501 |2 StatID |b DOAJ Seal |d 2022-04-27T10:46:44Z |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0500 |2 StatID |b DOAJ |d 2022-04-27T10:46:44Z |
915 | _ | _ | |a Fees |0 StatID:(DE-HGF)0700 |2 StatID |d 2023-10-27 |
915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b DOAJ : Anonymous peer review |d 2022-04-27T10:46:44Z |
915 | _ | _ | |a Article Processing Charges |0 StatID:(DE-HGF)0561 |2 StatID |d 2023-10-27 |
915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b CELL REP METHODS : 2022 |d 2024-12-16 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2024-12-16 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2024-12-16 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2024-12-16 |
915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0112 |2 StatID |b Emerging Sources Citation Index |d 2024-12-16 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2024-12-16 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |d 2024-12-16 |
915 | _ | _ | |a IF < 5 |0 StatID:(DE-HGF)9900 |2 StatID |d 2024-12-16 |
915 | p | c | |a APC keys set |2 APC |0 PC:(DE-HGF)0000 |
915 | p | c | |a Local Funding |2 APC |0 PC:(DE-HGF)0001 |
915 | p | c | |a DFG OA Publikationskosten |2 APC |0 PC:(DE-HGF)0002 |
915 | p | c | |a DEAL: Elsevier 09/01/2023 |2 APC |0 PC:(DE-HGF)0125 |
915 | p | c | |a DOAJ Journal |2 APC |0 PC:(DE-HGF)0003 |
920 | 1 | _ | |0 I:(DE-Juel1)INM-6-20090406 |k INM-6 |l Computational and Systems Neuroscience |x 0 |
920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Computational and Systems Neuroscience |x 1 |
920 | 1 | _ | |0 I:(DE-Juel1)INM-10-20170113 |k INM-10 |l Jara-Institut Brain structure-function relationships |x 2 |
980 | 1 | _ | |a FullTexts |
980 | _ | _ | |a journal |
980 | _ | _ | |a VDB |
980 | _ | _ | |a I:(DE-Juel1)INM-6-20090406 |
980 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
980 | _ | _ | |a I:(DE-Juel1)INM-10-20170113 |
980 | _ | _ | |a UNRESTRICTED |
980 | _ | _ | |a APC |
981 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
Library | Collection | CLSMajor | CLSMinor | Language | Author |
---|