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32	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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35		-== (a) Interpersonal variability—virtual ageing ==
	34	+== a. Interpersonal variability—virtual ageing ==
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38	38	See the details of the first study in the following publication:
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40	40	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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42		-=== Simulation of resting-state activity ===
	41	+==== Simulation of resting-state activity ====
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44	44	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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55	55	[[image:image-20220103100841-2.png]]
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57		-=== Virtual ageing trajectories ===
	56	+==== Virtual ageing trajectories ====
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59	59	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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69	69	[[image:image-20220103101022-3.png]]
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71		-=== Inference with SBI ===
	70	+==== Inference with SBI ====
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73	73	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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79	79	* [[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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82		-== (b) Regional variability ==
	80	+== b. Regional variability ==
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84	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 ===
	84	+==== Loading the data from EBRAINS ====
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88	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:
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99	99	[[image:download - 2022-02-10T130815.902.png||alt="region-wise gene expression heterogeneity"]]
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101		-=== Fitting model parameters for models with regional bias ===
	99	+==== Fitting model parameters for models with regional bias ====
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103	103	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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