Changes for page SGA3 D1.2 Showcase 1

Last modified by fousekjan on 2022/07/04 18:31
From version 16.2
edited by gorkazl
on 2022/01/31 20:10
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To version 19.1
edited by fousekjan
on 2022/02/10 12:08
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1 -XWiki.gorkazl
1 +XWiki.fousekjan
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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 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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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 -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.
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 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:
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 +
66 66  Link to the notebook:
67 67  
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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72 72  
73 73  == Fitting model parameters for models with regional bias ==
74 74  
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.
76 76  
77 77  Link to the notebook:
78 78  
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