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25		-Running the notebooks requires an EBRANS account with permissions to access the Lab and programmatic access to the Knowledge Graph API. In addition, to interact with the HPC infrastructure, the user needs access to an active allocation on the corresponding FENIX site. Lastly, the virtual ageing brain notebooks write data to the Bucket storage.
	25	+Running the notebooks //**requires an EBRANS account**// with permissions to access the Lab and programmatic access to the Knowledge Graph API. In addition, to interact with the HPC infrastructure, the user needs access to an active allocation on the corresponding FENIX site. Lastly, the virtual ageing brain notebooks write data to the Bucket storage.
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27	27	Please, to avoid overwriting precomputed data, //**make first a private working duplicate**// of this Collab using the notebook [[copy_showcase1_collab.ipynb>>https://lab.ch.ebrains.eu/hub/user-redirect/lab/tree/shared/SGA3%20D1.2%20Showcase%201/copy_showcase1_collab.ipynb]]. If you encounter any issues running the notebooks, contact [[The Virtual Brain Facility Hub>>mailto:jan.fousek@univ-amu.fr]].
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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]]
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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]]
64	64
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]]
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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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