Changes for page SGA3 D1.2 Showcase 1

Last modified by fousekjan on 2022/07/04 18:31
From version 12.2
edited by fousekjan
on 2022/01/03 09:17
Change comment: There is no comment for this version
To version 16.2
edited by gorkazl
on 2022/01/31 20:10
Change comment: There is no comment for this version

Summary

Details

Page properties
Author
... ... @@ -1,1 +1,1 @@
1 -XWiki.fousekjan
1 +XWiki.gorkazl
Content
... ... @@ -14,21 +14,20 @@
14 14  (((
15 15  The Showcase is implemented as a series of interactive Jupyter notebooks covering the individual logical steps and can be accessed in a dedicated public EBRAINS collab.
16 16  
17 +The EBRAINS collab is a virtual environment that interlinks the Drive, Bucket, Wiki, and Lab services. The Drive provides storage for small files. It contains the notebooks and all the supporting code. The Bucket is a storage service for larger files. It holds the pre-computed results from the extensive parameter sweeps and model optimizations to allow skipping the computationally demanding steps. The documentation of the showcase implementation is collected in the Wiki. The Lab service is an instance of JupyterLab—an interactive computing environment where the notebooks can be run and worked with.
17 17  
18 -The EBRAINS collab consists of interlinked Drive, Bucket, Wiki, and Lab. The Drive provides small file storage and contains the notebooks and all supporting code. The Bucket is a large file storage service and holds the pre-computed results of the extensive parameter sweeps and model optimizations to allow skipping the computationally demanding steps. The documentation of the showcase implementation is collected in the Wiki. The Lab service is an instance of JupyterLab—an interactive computing environment where the notebooks can be run and worked with.
19 +The Jupyter notebooks in this collab will load all required Python modules including Siibra and The Virtual Brain, and the interfaces for launching the computationally demanding parts in the HPC infrastructure.
19 19  
21 +Running the notebooks requires an EBRANS account with permissions to access the Lab and programmatic access to the Knowledge Graph API. In addition, to interact with the HPC infrastructure, the user needs access to an active allocation on the corresponding FENIX site.
20 20  
21 -The notebooks in this collab will load all required Python modules including Siibra and The Virtual Brain, and the interfaces for launching the computationally demanding parts in the HPC infrastructure. Running the notebooks requires an EBRANS account with permissions to access the Lab and the Knowledge Graph API. In addition, to be able to interact with the HPC infrastructure, the user has to have access to an active allocation on the corresponding FENIX site.
23 +== Simulation of resting-state activity ==
22 22  
23 -==
24 -Simulation of resting state ==
25 -
26 26  The fist notebook in the inter-individual variability workflow explores the resting-state simulation for a subject of the 1000BRAINS dataset. Functional data are simulated by means of a brain network model implemented in TVB, which is an ensemble of neural mass models linked via the weights of the structural connectivity (SC) matrix. Following topics are covered:
27 27  
28 -1. The neural mass model by Montbrio, Pazo and Roxin
29 -1. Construction of the TVB model for a particular subject
30 -1. Dynamics of the model, and execution of a parameter study
31 -1. Summary of the simulation results for the whole cohort
27 +1. The neural mass model by Montbrio, Pazo and Roxin.
28 +1. Construction of the TVB model for a particular subject.
29 +1. Dynamics of the model, and execution of a parameter study.
30 +1. Summary of the simulation results for the whole cohort.
32 32  
33 33  Link to the notebook:
34 34  
... ... @@ -40,9 +40,9 @@
40 40  
41 41  The second steps shows the investigation of virtual ageing trajectory for each subject. In this context, we are going to show
42 42  
43 -1. What we mean by virtual ageing and what is the empirical basis to investigate this approach
44 -1. How we can virtually age a subject using whole-brain modelling
45 -1. How the increase structure-function relationship relates to virtual ageing
42 +1. What we mean by virtual ageing and what is the empirical basis to investigate this approach.
43 +1. How we can virtually age a subject using whole-brain modelling.
44 +1. How the increase structure-function relationship relates to virtual ageing.
46 46  
47 47  Link to the notebook:
48 48  
... ... @@ -50,11 +50,11 @@
50 50  
51 51  [[image:image-20220103101022-3.png]]
52 52  
53 -(% class="wikigeneratedid" %)
54 54  == Inference with SBI ==
55 55  
56 -Last step of the inter-individual variability workflow
54 +Last step of the inter-individual variability workflow employs Simulation Based Inference for estimation of the full posterior values of the parameters. Here, a deep neural estimator is trained to provide a relationship between the parameters of a model (black box simulator) and selected descriptive statistics of the observed data.
57 57  
56 +[[image:image-20220103104332-4.png||height="418" width="418"]]
58 58  
59 59  Link to the notebook:
60 60  
image-20220103104332-4.png
Author
... ... @@ -1,0 +1,1 @@
1 +XWiki.fousekjan
Size
... ... @@ -1,0 +1,1 @@
1 +50.6 KB
Content