Last modified by robing on 2022/03/25 09:55
From version 69.1
edited by robing
on 2020/04/30 14:26
Change comment: There is no comment for this version
To version 74.1
edited by robing
on 2020/05/08 16:06
Change comment: There is no comment for this version

Summary

Details

Page properties
Content
... ... @@ -9,11 +9,11 @@
9 9  
10 10  Robin Gutzen^^1^^, Giulia De Bonis^^2^^, Elena Pastorelli^^2,3^^, Cristiano Capone^^2^^,
11 11  
12 -Chiara De Luca^^2,3^^, Michael Denker^^1^^, Sonja Grün^^1^^,
12 +Chiara De Luca^^2,3^^, Michael Denker^^1^^, Sonja Grün^^1,4^^,
13 13  
14 -Pier Stanislao Paolucci^^2^^, Andrew Davison^^4^^
14 +Pier Stanislao Paolucci^^2^^, Andrew Davison^^5^^
15 15  
16 -Experiments: Anna Letizia Allegra Mascaro^^5,6^^, Francesco Resta^^5^^, Francesco Saverio Pavone^^5^^, Maria-Victoria Sanchez-Vives^^7,8^^
16 +Experiments: Anna Letizia Allegra Mascaro^^6,7^^, Francesco Resta^^6^^, Francesco Saverio Pavone^^6^^, Maria-Victoria Sanchez-Vives^^8,9^^
17 17  
18 18  ,,1) Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany,,
19 19  
... ... @@ -21,15 +21,17 @@
21 21  
22 22  ,,3) Ph.D. Program in Behavioural Neuroscience, “Sapienza” University of Rome, Rome, Italy,,
23 23  
24 -,,4) Unité de Neurosciences, Information et Complexité, Neuroinformatics Group, CNRS FRE 3693, Gif-sur-Yvette, France,,
24 +,,4) Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany,,
25 25  
26 -,,5) European Laboratory for Non-linear Spectroscopy (LENS), (% style="--darkreader-inline-color:inherit; color:inherit" %)University of Florence, Florence, Italy(%%),,
26 +,,5) Unité de Neurosciences, Information et Complexité, Neuroinformatics Group, CNRS FRE 3693, Gif-sur-Yvette, France,,
27 27  
28 -,,6) Istituto di Neuroscienze, CNR, Pisa, Italy,,
28 +,,6) European Laboratory for Non-linear Spectroscopy (LENS), (% style="--darkreader-inline-color:inherit; color:inherit" %)University of Florence, Florence, Italy(%%),,
29 29  
30 -,,7) Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain,,
30 +,,7) Istituto di Neuroscienze, CNR, Pisa, Italy,,
31 31  
32 -,,8) Institució Catalana de Recerca i Estudis Avanc ̨ats (ICREA), Barcelona, Spain,,
32 +,,8) Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain,,
33 +
34 +,,9) Institució Catalana de Recerca i Estudis Avanc ̨ats (ICREA), Barcelona, Spain,,
33 33  )))
34 34  )))
35 35  
... ... @@ -39,11 +39,8 @@
39 39  (((
40 40  == Flexible workflows to generate multi-scale analysis scenarios ==
41 41  
42 -This Collab is aimed at experimental and computational neuroscientists interested in the usage of the [[Neo>>https://neo.readthedocs.io/en/stable/]] and [[Elephant>>https://elephant.readthedocs.io/en/latest/]] tools in performing data analysis of spiking data.
43 -Here, the collab illustrates the tool usage with regards to the SGA2-SP3-UC002 KR3.2, investigating sleep, anesthesia, and the transition to wakefulness: see Chapter 1 and Figure 2  of SGA2[[ Deliverable D3.2.1.>>https://drive.ebrains.eu/smart-link/17ac0d6e-e050-4a49-8ca2-e223b70a3121/]], for an overview of the scientific motivations and a description of the UseCase workflow; Chapter 2 (same document) for an introduction to KR3.2; Chapter 3, for a description of the mice ECoG data sets; Chapter 5, about the Slow Wave Analysis Pipeline and Chapter 6 for the mice wide-field GECI data)
44 +This collab illustrates the usage of the [[Neo>>https://neo.readthedocs.io/en/stable/]] and [[Elephant>>https://elephant.readthedocs.io/en/latest/]] tools in performing data analysis with regards to the SGA2-SP3-UC002 KR3.2, investigating sleep, anesthesia, and the transition to wakefulness: see Chapter 1 and Figure 2  of SGA2[[ Deliverable D3.2.1.>>https://drive.ebrains.eu/smart-link/17ac0d6e-e050-4a49-8ca2-e223b70a3121/]], for an overview of the scientific motivations and a description of the UseCase workflow; Chapter 2 (same document) for an introduction to KR3.2; Chapter 3, for a description of the mice ECoG data sets; Chapter 5, about the Slow Wave Analysis Pipeline and Chapter 6 for the mice wide-field GECI data). For details on the datasets used in this collab, please see the References below.
44 44  
45 -[[image:https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png||height="35" width="35"]][[INM-6/wavescalephant>>https://github.com/INM-6/wavescalephant]]
46 -
47 47  == How the pipeline works ==
48 48  
49 49  The design of the pipeline aims at interfacing a variety of general and specific analysis and processing steps in a flexible modular manner. Hence, it enables the pipeline to adapt to diverse types of data (e.g., electrical ECoG, or optical Calcium Imaging recordings) and to different analysis questions. This makes the analyses a) more reproducible and b) comparable amongst each other since they rely on the same stack of algorithms and any differences in the analysis are fully transparent.
... ... @@ -114,8 +114,20 @@
114 114  
115 115  All results are stored in the path specified in the //settings.py// file. The folder structure reflects the structuring of the pipeline into stages and blocks. All intermediate results are stored as //.nix// files using the [[Neo data format>>https://neo.readthedocs.io/en/stable/]] and can be loaded with ##neo.NixIO('/path/to/file.nix').read_block()##. Additionally, most blocks produce a figure, and each stage a report file, to give an overview of the execution log, parameters, intermediate results, and to help with debugging. The final stage (//stage05_wave_characterization//) stores the results as[[ //pandas.DataFrames//>>https://pandas.pydata.org/]] in //.csv// files, separately for each measure as well as in a combined dataframe for all measures.
116 116  
117 -== Outlook ==
116 +**Examples of the output figures (for IDIBAPS dataset)**
118 118  
118 +* Stage 01 - [[example signal traces and metadata>>https://drive.ebrains.eu/smart-link/cf2fa914-260d-4d61-a2da-03ea07b7f9be/]]
119 +* Stage 02 - [[background substraction>>https://drive.ebrains.eu/smart-link/586d2f3c-591b-4dfb-94ee-8c0e28050dc4/]]
120 +* Stage 02 - [[logMUA estimation>>https://drive.ebrains.eu/smart-link/c92e4b0c-0938-44e8-9f8d-00522796b2fd/]]
121 +* Stage 02 - [[processed signal trace>>https://drive.ebrains.eu/smart-link/26ed27c6-de56-4b48-a57b-f70aab629197/]]
122 +* Stage 03 - [[amplitude distribution>>https://drive.ebrains.eu/smart-link/8ba80293-ba75-4a37-8a8f-05d44cf6f65c/]]
123 +* Stage 03 - [[UP state detection>>https://drive.ebrains.eu/smart-link/ab172be0-178e-4153-a3e6-b4bace32dd50/]]
124 +* Stage 04 - [[trigger clustering>>https://drive.ebrains.eu/smart-link/4a1f0169-8b43-49ce-80c8-f2fa0f4d50d3/]]
125 +* Stage 05 - [[planar velocities>>https://drive.ebrains.eu/smart-link/f4de8073-cb40-47a7-bc82-f97d36dbae25/]]
126 +* Stage 05 - [[directionality>>https://drive.ebrains.eu/smart-link/5485032d-0121-4cde-9ea2-3e0af3f12178/]]
127 +
128 +=== Outlook ===
129 +
119 119  * Using the **KnowledgeGraph API **to insert data directly from the Knowledge Graph into the pipeline and also register and store the corresponding results as Analysis Objects. Such Analysis Objects are to incorporate **Provenance Tracking, **using [[fairgraph>>https://github.com/HumanBrainProject/fairgraph]],** **to record the details of the processing and analysis steps.
120 120  * Adding support for the pipeline to make use of **HPC** resources when running on the collab.
121 121  * Further extending the available **methods** to address a wider variety of analysis objectives and support the processing of other datatypes. Additional documentation and guides should also make it easier for non-developers to contribute new method blocks.
... ... @@ -130,14 +130,21 @@
130 130  * [[Sanchez-Vives, M. (2020). "Propagation modes of slow waves in mouse cortex".  //EBRAINS//>>https://doi.org/10.25493/WKA8-Q4T]]
131 131  * [[Sanchez-Vives, M. (2019). "Cortical activity features in transgenic mouse models of cognitive deficits (Fragile X Syndrome).//" EBRAINS//>>https://doi.org/10.25493/ANF9-EG3]]
132 132  
133 -== License (to discuss) ==
144 +Code developed at:
134 134  
135 -All text and example data in this collab is licensed under Creative Commons CC-BY 4.0 license. Software code is licensed under GNU General Public License v3.0.
146 +[[image:https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png||height="35" width="35"]][[INM-6/wavescalephant>>https://github.com/INM-6/wavescalephant]]
136 136  
148 +== License ==
149 +
150 +Text is licensed under the Creative Commons CC-BY 4.0 license. LENS data is licensed under the Creative Commons CC-BY-NC-ND 4.0 license. IDIBAPS data is licensed under the Creative Commons CC-BY-NC-SA 4.0 license. Software code is licensed under GNU General Public License v3.0.
151 +
137 137  [[image:https://i.creativecommons.org/l/by/4.0/88x31.png||style="float:left"]]
138 138  
139 -== ==
154 +[[image:https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png||alt="https://i.creativecommons.org/l/by/4.0/88x31.png" style="float:left"]]
140 140  
156 +[[image:https://licensebuttons.net/l/by-nc-nd/4.0/88x31.png||alt="https://i.creativecommons.org/l/by/4.0/88x31.png" style="float:left"]]
157 +
158 +
141 141  == Acknowledgments ==
142 142  
143 143  This open source software code was developed in part or in whole in the Human Brain Project, funded from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2).
Collaboratory.Apps.Collab.Code.CollabClass[0]
Description
... ... @@ -1,1 +1,1 @@
1 -Space for developing and hosting a showcase pipeline for performing reproducible and adaptable analysis with the focus of slow cortical waves.
1 +Use Case SGA2-SP3-002 KR3.2: Integrating multi-scale data and the output of simulations in a reproducible and adaptable pipeline