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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	+
33	33	[[image:image-20220103100841-2.png]]
34	34
	39	+== Virtual ageing trajectories ==
35	35
	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	+
36	36	== Regional variability data ==
37	37
38	38	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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	1	+XWiki.fousekjan

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