Changes for page SGA3 D1.2 Showcase 1

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30 30  1. Dynamics of the model, and execution of a parameter study
31 31  1. Summary of the simulation results for the whole cohort
32 32  
33 -Link to the notebook:
34 -
35 -* [[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]]
36 -
37 37  [[image:image-20220103100841-2.png]]
38 38  
39 -== Virtual ageing trajectories ==
40 40  
41 -The second steps shows the investigation of virtual ageing trajectory for each subject. In this context, we are going to show
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
46 -
47 -Link to the notebook:
48 -
49 -* [[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]]
50 -
51 -[[image:image-20220103101022-3.png]]
52 -
53 -(% class="wikigeneratedid" %)
54 -== Inference with SBI ==
55 -
56 -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 -
58 -[[image:image-20220103104332-4.png||height="418" width="418"]]
59 -
60 -Link to the notebook:
61 -
62 -* [[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]]
63 -
64 64  == Regional variability data ==
65 65  
66 66  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.
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