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25	25	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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27		-1. The neural mass model by Montbrio, Pazo and Roxin.
	27	+1. The neural-mass model by Montbrió, Pazó and Roxin.
28	28	1. Construction of the TVB model for a particular subject.
29	29	1. Dynamics of the model, and execution of a parameter study.
30	30	1. Summary of the simulation results for the whole cohort.
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38	38	== Virtual ageing trajectories ==
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40		-The second steps shows the investigation of virtual ageing trajectory for each subject. In this context, we are going to show
	40	+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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42	42	1. What we mean by virtual ageing and what is the empirical basis to investigate this approach.
43	43	1. How we can virtually age a subject using whole-brain modelling.
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52	52	== Inference with SBI ==
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54		-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.
	54	+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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56	56	[[image:image-20220103104332-4.png||height="418" width="418"]]
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62	62	== Regional variability data ==
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64		-The first step of the regional variability workflow loads the data from the Knowledge Graph and defines the regional bias on the model. In this case we require: 1) structural connectivity matrices, 2) GABA and AMPA receptor densities at each region, and 3) empirical resting-state fMRI data for fitting and validation of the simulations. The three datasets shall be characterised in the same parcellation.
	64	+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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	66	+1. Structural connectivity matrices,
	67	+1. GABA and AMPA receptor densities for each brain region, and
	68	+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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66	66	Link to the notebook:
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68	68	* [[regional_variability/notebooks/1_data_setup.ipynb>>https://lab.ch.ebrains.eu/user-redirect/lab/tree/shared/SGA3%20D1.2%20Showcase%201/regional_variability/notebooks/1_data_setup.ipynb]]
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73	73	== Fitting model parameters for models with regional bias ==
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75		-A series of simulations of the whole-brain network model is 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.
	79	+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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77	77	Link to the notebook:
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