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| Home > Publications database > Blocks instead of puzzles pieces - analyzing cortical wave activity across scales in an adaptable framework |
| Home > Publications database > Blocks instead of puzzles pieces - analyzing cortical wave activity across scales in an adaptable framework |
| Talk (non-conference) (Invited) | FZJ-2022-02625 |
2022
Abstract: The 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.
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