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31	31	(% class="wikigeneratedid" %)
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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34		-== a. Interpersonal variability—virtual ageing ==
	34	+(% class="wikigeneratedid" %)
	35	+== (a) Interpersonal variability—virtual ageing ==
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36	36	(% class="wikigeneratedid" %)
37	37	See the details of the first study in the following publication:
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38	38
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 ====
	42	+=== Simulation of resting-state activity ===
42	42
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 ====
	57	+=== Virtual ageing trajectories ===
57	57
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 ====
	71	+=== Inference with SBI ===
71	71
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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78	78	* [[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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80		-== b. Regional variability ==
	81	+(% class="wikigeneratedid" %)
	82	+== (b) Regional variability ==
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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.
	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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84		-==== Loading the data from EBRAINS ====
	86	+=== Loading the data from EBRAINS via the //siibra// interface ===
85	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:
	88	+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:
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88	88	1. Structural connectivity matrices,
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.
	91	+1. GABA and AMPA receptor densities for each brain region, and
	92	+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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92		-The three datasets shall be characterised in the same parcellation. Link to the notebook:
	94	+Link to the notebook:
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94	94	* [[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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96	96	(% style="text-align:center" %)
97	97	[[image:download - 2022-02-10T130815.902.png||alt="region-wise gene expression heterogeneity"]]
98	98
99		-==== Fitting model parameters for models with regional bias ====
	101	+== Fitting model parameters for models with regional bias ==
100	100
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.
	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 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.
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103	103	Link to the notebook:
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