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
Change comment: There is no comment for this version
To version 25.1
edited by fousekjan
on 2022/02/18 08:19
Change comment: There is no comment for this version

Summary

Details

Page properties
Author
... ... @@ -1,1 +1,1 @@
1 -XWiki.gorkazl
1 +XWiki.fousekjan
Content
... ... @@ -18,13 +18,19 @@
18 18  
19 19  The Jupyter notebooks in this collab will load all required Python modules including Siibra and The Virtual Brain, and the interfaces for launching the computationally demanding parts in the HPC infrastructure.
20 20  
21 -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.
21 +(% class="box infomessage" %)
22 +(((
23 +(% class="box" %)
24 +(((
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—please make a private working copy 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, please contact [[The Virtual Brain Facility Hub>>mailto:jan.fousek@univ-amu.fr]].
26 +)))
27 +)))
22 22  
23 23  == Simulation of resting-state activity ==
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.
33 +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.
... ... @@ -37,7 +37,7 @@
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
46 +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.
... ... @@ -51,7 +51,7 @@
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.
60 +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  
... ... @@ -61,24 +61,28 @@
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.
70 +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  
72 +1. Structural connectivity matrices,
73 +1. GABA and AMPA receptor densities for each brain region, and
74 +1. empirical resting-state fMRI data for fitting and validation of the simulations. The three datasets shall be characterised in the same parcellation.
75 +
66 66  Link to the notebook:
67 67  
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]]
78 +* [[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]]
69 69  
70 70  (% style="text-align:center" %)
71 -[[image:image-20220103095424-1.png]]
81 +[[image:download - 2022-02-10T130815.902.png||alt="region-wise gene expression heterogeneity"]]
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.
85 +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  
79 -* [[regional_variability/notebooks/2_parameter_swep_proto.ipynb>>https://lab.ch.ebrains.eu/user-redirect/lab/tree/shared/SGA3%20D1.2%20Showcase%201/regional_variability/notebooks/2_parameter_swep_proto.ipynb]]
89 +* [[regional_variability/notebooks/2_parameter_swep.ipynb>>https://lab.ch.ebrains.eu/hub/user-redirect/lab/tree/shared/SGA3%20D1.2%20Showcase%201/regional_variability/notebooks/2_parameter_swep.ipynb]]
80 80  
81 -[[image:image-20220103095948-1.png]]
91 +[[image:download - 2022-02-10T131102.033.png||alt="regional bias vs goodness of fit"]]
82 82  )))
83 83  
84 84  
download - 2022-02-10T130815.902.png
Author
... ... @@ -1,0 +1,1 @@
1 +XWiki.fousekjan
Size
... ... @@ -1,0 +1,1 @@
1 +101.8 KB
Content
download - 2022-02-10T131102.033.png
Author
... ... @@ -1,0 +1,1 @@
1 +XWiki.fousekjan
Size
... ... @@ -1,0 +1,1 @@
1 +11.2 KB
Content