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...	...	@@ -29,11 +29,16 @@
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	+== a. Interpersonal variability—virtual ageing ==
	35	+
	36	+(% class="wikigeneratedid" %)
	37	+See the details of the first study in the following publication:
	38	+
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 ==
	41	+==== 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]]
50	50
51		-== Virtual ageing trajectories ==
	56	+==== 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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62	62
63	63	[[image:image-20220103101022-3.png]]
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65		-== Inference with SBI ==
	70	+==== 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]]
74	74
75		-== Regional variability data ==
	80	+== 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:
	82	+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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	84	+==== Loading the data from EBRAINS ====
	85	+
	86	+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:
	87	+
79	79	1. Structural connectivity matrices,
80		-1. GABA and AMPA receptor densities for each brain region, and
81		-1. empirical resting-state fMRI data for fitting and validation of the simulations. The three datasets shall be characterised in the same parcellation.
	89	+1. GABAa and AMPA receptor densities for each brain region, and
	90	+1. empirical resting-state fMRI data for fitting and validation of the simulations.
82	82
83		-Link to the notebook:
	92	+The three datasets shall be characterised in the same parcellation. Link to the notebook:
84	84
85	85	* [[regional_variability/notebooks/1_load_data.ipynb>>https://lab.ch.ebrains.eu/hub/user-redirect/lab/tree/shared/SGA3%20D1.2%20Showcase%201/regional_variability/notebooks/1_load_data.ipynb]]
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87	87	(% style="text-align:center" %)
88	88	[[image:download - 2022-02-10T130815.902.png||alt="region-wise gene expression heterogeneity"]]
89	89
90		-== Fitting model parameters for models with regional bias ==
	99	+==== Fitting model parameters for models with regional bias ====
91	91
92		-A series of simulations of the whole-brain network model are launched in the EBRAINS HPC facilities in order to identify the optimal model parameters leading to simulated resting-state brain activity that best resembles the empirically observed activity. In this branch of the showcase, brain regions are simulated using the Balanced Excitation-Inhibition model which allows to tune the E-I balance for every region individually, according to the empirically observed GABA and AMPA neuroreceptor densities.
	101	+A series of simulations of the whole-brain network model are launched in the EBRAINS HPC facilities in order to identify the optimal model parameters leading to simulated resting-state brain activity that best resembles the empirically observed activity. In this branch of the showcase, brain regions are simulated using the mean-field AdEx population model; specifically modified to account for the regional densities of GABAa and AMPA neuroreceptors. See the details following document.
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94	94	Link to the notebook:
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