Changes for page SGA3 D1.2 Showcase 1

Last modified by fousekjan on 2022/07/04 18:31
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edited by fousekjan
on 2022/02/10 12:08
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edited by gorkazl
on 2022/01/31 20:10
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1 -XWiki.fousekjan
1 +XWiki.gorkazl
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24 24  
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:
26 26  
27 -1. The neural-mass model by Montbrió, Pazó and Roxin.
27 +1. The neural mass model by Montbrio, Pazo 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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37 37  
38 38  == Virtual ageing trajectories ==
39 39  
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
41 41  
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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51 51  
52 52  == Inference with SBI ==
53 53  
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.
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.
55 55  
56 56  [[image:image-20220103104332-4.png||height="418" width="418"]]
57 57  
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61 61  
62 62  == Regional variability data ==
63 63  
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:
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.
65 65  
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.
69 -
70 70  Link to the notebook:
71 71  
72 72  * [[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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76 76  
77 77  == Fitting model parameters for models with regional bias ==
78 78  
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.
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.
80 80  
81 81  Link to the notebook:
82 82  
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