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39	39	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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41		-==== Simulation of resting-state activity ====
	41	+=== Simulation of resting-state activity ===
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43	43	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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54	54	[[image:image-20220103100841-2.png]]
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56		-==== Virtual ageing trajectories ====
	56	+=== Virtual ageing trajectories ===
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58	58	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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68	68	[[image:image-20220103101022-3.png]]
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70		-==== Inference with SBI ====
	70	+=== Inference with SBI ===
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72	72	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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82	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 ====
	84	+=== Loading the data from EBRAINS ===
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86	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:
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96	96	(% style="text-align:center" %)
97	97	[[image:download - 2022-02-10T130815.902.png||alt="region-wise gene expression heterogeneity"]]
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99		-==== Fitting model parameters for models with regional bias ====
	99	+=== Fitting model parameters for models with regional bias ===
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101	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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