Changes for page SGA3 D1.2 Showcase 1

Last modified by fousekjan on 2022/07/04 18:31
From version 15.1
edited by fousekjan
on 2022/01/03 10:01
Change comment: There is no comment for this version
To version 12.1
edited by fousekjan
on 2022/01/03 09:10
Change comment: Uploaded new attachment "image-20220103101022-3.png", version {1}

Summary

Details

Page properties
Content
... ... @@ -20,7 +20,8 @@
20 20  
21 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.
22 22  
23 -== Simulation of resting state ==
23 +==
24 +Simulation of resting state ==
24 24  
25 25  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:
26 26  
... ... @@ -29,36 +29,9 @@
29 29  1. Dynamics of the model, and execution of a parameter study
30 30  1. Summary of the simulation results for the whole cohort
31 31  
32 -Link to the notebook:
33 -
34 -* [[virtual_ageing/notebooks/1_BNM_for_resting_state.ipynb>>https://lab.ch.ebrains.eu/user-redirect/lab/tree/shared/SGA3%20D1.2%20Showcase%201/virtual_ageing/notebooks/1_BNM_for_resting_state.ipynb]]
35 -
36 36  [[image:image-20220103100841-2.png]]
37 37  
38 -== Virtual ageing trajectories ==
39 39  
40 -The second steps shows the investigation of virtual ageing trajectory for each subject. In this context, we are going to show
41 -
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
45 -
46 -Link to the notebook:
47 -
48 -* [[virtual_ageing/notebooks/2_virtual_ageing_trajectories.ipynb>>https://lab.ch.ebrains.eu/user-redirect/lab/tree/shared/SGA3%20D1.2%20Showcase%201/virtual_ageing/notebooks/2_virtual_ageing_trajectories.ipynb]]
49 -
50 -[[image:image-20220103101022-3.png]]
51 -
52 -== Inference with SBI ==
53 -
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.
55 -
56 -[[image:image-20220103104332-4.png||height="418" width="418"]]
57 -
58 -Link to the notebook:
59 -
60 -* [[virtual_ageing/notebooks/3_inference_with_SBI.ipynb>>https://lab.ch.ebrains.eu/user-redirect/lab/tree/shared/SGA3%20D1.2%20Showcase%201/virtual_ageing/notebooks/3_inference_with_SBI.ipynb]]
61 -
62 62  == Regional variability data ==
63 63  
64 64  The first step of the regional variability workflow loads the data from the Knowledge Graph and defines the regional bias on the model. In this case we require: 1) structural connectivity matrices, 2) GABA and AMPA receptor densities at each region, and 3) empirical resting-state fMRI data for fitting and validation of the simulations. The three datasets shall be characterised in the same parcellation.
image-20220103104332-4.png
Author
... ... @@ -1,1 +1,0 @@
1 -XWiki.fousekjan
Size
... ... @@ -1,1 +1,0 @@
1 -50.6 KB
Content