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29	29	)))
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31	31	(% class="wikigeneratedid" %)
32		-The virtual ageing study is described in detail in following publication:
	32	+The Showcase 1 aimed at investigations related to variability in neuroscience from two perspectives: (a) the interpersonal variability studied by the virtual ageing study, and (b) the variability across different cortical regions within an individual brain.
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	34	+(% class="wikigeneratedid" %)
	35	+== (a) Interpersonal variability—virtual ageing ==
	36	+
	37	+(% class="wikigeneratedid" %)
	38	+See the details of the first study in the following publication:
	39	+
34	34	M. Lavanga, J. Stumme, B. H. Yalcinkaya, J. Fousek, C. Jockwitz, H. Sheheitli, N. Bittner, M. Hashemi, S. Petkoski, S. Caspers, and V. Jirsa, [[The Virtual Aging Brain: A Model-Driven Explanation for Cognitive Decline in Older Subjects>>https://doi.org/10.1101/2022.02.17.480902]].
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36		-== Simulation of resting-state activity ==
	42	+=== Simulation of resting-state activity ===
37	37
38	38	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:
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49	49	[[image:image-20220103100841-2.png]]
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51		-== Virtual ageing trajectories ==
	57	+=== Virtual ageing trajectories ===
52	52
53	53	The second steps shows the investigation of virtual ageing trajectory for each subject. In this context, we are going to show:
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63	63	[[image:image-20220103101022-3.png]]
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65		-== Inference with SBI ==
	71	+=== Inference with SBI ===
66	66
67	67	The 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.
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73	73	* [[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]]
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75		-== Regional variability data ==
	81	+(% class="wikigeneratedid" %)
	82	+== (b) Regional variability ==
76	76
77		-The first step of the regional variability workflow consists in loading the data from the Knowledge Graph, including the regional bias on the model. In this case we require:
	84	+Aims at demonstrating the construction of whole-brain network models of the brain's activity accounting for differences in receptor densities across cortical regions.
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	86	+=== Loading the data from EBRAINS ===
	87	+
	88	+The first step of this workflow consists in loading the data from the Knowledge Graph via the //siibra interface//, including the regional bias on the model. In this case we require:
	89	+
79	79	1. Structural connectivity matrices,
80		-1. GABA and AMPA receptor densities for each brain region, and
	91	+1. GABAa and AMPA receptor densities for each brain region, and
81	81	1. 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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83	83	Link to the notebook: