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| Hauptseite > Publikationsdatenbank > Building adaptable and reusable pipelines for investigating the features of slow cortical rhythms across scales, methods, and species > print |
| Hauptseite > Publikationsdatenbank > Building adaptable and reusable pipelines for investigating the features of slow cortical rhythms across scales, methods, and species > print |
| 001 | 885608 | ||
| 005 | 20240313094845.0 | ||
| 024 | 7 | _ | |a 10.12751/NNCN.BC2020.0030 |2 doi |
| 037 | _ | _ | |a FZJ-2020-03960 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Gutzen, Robin |0 P:(DE-Juel1)171572 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a Bernstein Conference |c online |d 2020-09-29 - 2020-10-29 |w Germnay |
| 245 | _ | _ | |a Building adaptable and reusable pipelines for investigating the features of slow cortical rhythms across scales, methods, and species |
| 260 | _ | _ | |c 2020 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a CONFERENCE_POSTER |2 ORCID |
| 336 | 7 | _ | |a Output Types/Conference Poster |2 DataCite |
| 336 | 7 | _ | |a Poster |b poster |m poster |0 PUB:(DE-HGF)24 |s 1602677158_19471 |2 PUB:(DE-HGF) |x After Call |
| 520 | _ | _ | |a Making progress in neuroscience is increasingly a collaborative effort that requires insights obtained across data, methodologies, and models. For various spatial and temporal scales, there exist sophisticated analysis approaches capturing the observed phenomena. These puzzle pieces must eventually be properly related to one another and integrated to frame the research in a coherent picture. In this process, often three questions arise: How to make results comparable? How to relate complementary, yet different methodological approaches to one another? How to incorporate and validate models based on these findings?A prominent example of such a scenario are slow cortical waves which are persistently observed in sleeping and anesthetized subjects of various species and across various measurement techniques [1,2], as well as being expressed by various models. While slow waves are an ubiquitous phenomenon with a rich literature basis, and have been proposed to be the default activity pattern of the cortical network [3], the diversity of the experimental data and the numerous different analytical methods, tools, and even terminologies makes it next to impossible to rigorously compare and interrelate results from the various sources to form a coherent understanding.Here, we present an analysis pipeline for the study of slow wave dynamics to support quantitative, statistical comparisons of analysis results across data sources and algorithms. A key concept of the pipeline is modularity at the correct level of granularity to flexibly adapt, reuse, and extend it to a wide range of datasets, and research questions. In the spirit of reproducibility, individual analysis blocks are built on open-source software tools, e.g., the workflow manager Snakemake (RRID:SCR_003475), the Neo (RRID:SCR_000634) library for data representation [4], and the Elephant (RRID:SCR_003833) analysis toolbox.This pipeline design enables multi-scale analyses of measured slow wave activity, which we demonstrate using ECoG [5,6] and Calcium Imaging [7] data of anesthetized mice. Furthermore, the integration of model data provides a basis for rigorous validation testing [8,9]. While the ‘same method - different data’ approach enables fair comparisons, the pipeline equally enables ‘same data - different methods’ benchmarking. Finally, we discuss how the re-usable and adaptable conceptual design helps to derive analysis pipelines for similar research endeavours to amplify collaborative research.References 1. De Bonis, G. et al. (2019) "Analysis pipeline for extracting features of cortical slow oscillations". Frontiers in Systems Neuroscience 13:70. doi: 10.3389/fnsys.2019.00070 2. Celotto, M. et al. (2020) “Analysis and Model of Cortical Slow Waves Acquired with Optical Techniques”. Methods and Protocols 3.1:14. doi: 10.3390/mps3010014 3. Sanchez-Vives, M. V., Massimini, M., and Mattia, M. (2017) “Shaping the default activity pattern of the cortical network”. Neuron, 94(5), 993-1001. doi: 10.1016/j.neuron.2017.05.015 4. Grewe, J., Wachtler T, and Benda J. (2011) "A bottom-up approach to data annotation in neurophysiology." Frontiers in Neuroinformatics 5:16. doi: 10.3389/fninf.2011.00016 5. Dasilva et al. (2020) “Altered Neocortical Dynamics in a Mouse Model of Williams–Beuren Syndrome”. Molecular Neurobiology, vol. 57, no 2, p. 765-777. doi: 10.1007/s12035-019-01732-4 6. Sanchez-Vives, M. (2019) “Cortical activity features in transgenic mouse models of cognitive deficits (Williams Beuren Syndrome)” [Data set]. EBRAINS. doi: 10.25493/DZWT-1T8 7. Resta, F., Allegra Mascaro, A. L., and Pavone, F. (2020). Study of Slow Waves (SWs) propagation through wide-field calcium imaging of the right cortical hemisphere of GCaMP6f mice [Data set]. EBRAINS. doi: 10.25493/3E6Y-E8G 8. Gutzen, R. et al. (2018) "Reproducible neural network simulations: statistical methods for model validation on the level of network activity data." Frontiers in Neuroinformatics 12:90. doi: 10.3389/fninf.2018.00090 9. Trensch, G. et al. (2018) "Rigorous neural network simulations: a model substantiation methodology for increasing the correctness of simulation results in the absence of experimental validation data." Frontiers in Neuroinformatics 12:81. doi: 10.3389/fninf.2018.00090 |
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| 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 2 |
| 536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |x 3 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 700 | 1 | _ | |a De Bonis, Giulia |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Pastorelli, Elena |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Capone, Cristiano |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a De Luca, Chiara |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Mattheisen, Glynis |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Allegra Mascaro, Anna Letizia |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Resta, Francesco |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 |u fzj |
| 700 | 1 | _ | |a Davison, Andrew |0 P:(DE-HGF)0 |b 12 |
| 700 | 1 | _ | |a Paolucci, Pier Stanislao |0 P:(DE-HGF)0 |b 13 |
| 700 | 1 | _ | |a Denker, Michael |0 P:(DE-Juel1)144807 |b 14 |u fzj |
| 773 | _ | _ | |a 10.12751/NNCN.BC2020.0030 |
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