Changes for page SGA3 D1.2 Showcase 1

Last modified by fousekjan on 2022/07/04 18:31
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edited by gorkazl
on 2022/01/31 20:06
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To version 24.1
edited by fousekjan
on 2022/02/18 08:18
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14 14  (((
15 15  The Showcase is implemented as a series of interactive Jupyter notebooks covering the individual logical steps and can be accessed in a dedicated public EBRAINS collab.
16 16  
17 -
18 18  The EBRAINS collab is a virtual environment that interlinks the Drive, Bucket, Wiki, and Lab services. The Drive provides storage for small files. It contains the notebooks and all the supporting code. The Bucket is a storage service for larger files. It holds the pre-computed results from the extensive parameter sweeps and model optimizations to allow skipping the computationally demanding steps. The documentation of the showcase implementation is collected in the Wiki. The Lab service is an instance of JupyterLab—an interactive computing environment where the notebooks can be run and worked with.
19 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 -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. Running the notebooks requires an EBRANS account with permissions to access the Lab and the Knowledge Graph API. In addition, to be able to interact with the HPC infrastructure, the user has to have access to an active allocation on the corresponding FENIX site.
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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—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]]
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27 +)))
22 22  
23 -== Simulation of resting state ==
29 +== .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
28 -1. Construction of the TVB model for a particular subject
29 -1. Dynamics of the model, and execution of a parameter study
30 -1. Summary of the simulation results for the whole cohort
33 +1. The neural-mass model by Montbrió, Pazó and Roxin.
34 +1. Construction of the TVB model for a particular subject.
35 +1. Dynamics of the model, and execution of a parameter study.
36 +1. Summary of the simulation results for the whole cohort.
31 31  
32 32  Link to the notebook:
33 33  
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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
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 -1. What we mean by virtual ageing and what is the empirical basis to investigate this approach
43 -1. How we can virtually age a subject using whole-brain modelling
44 -1. How the increase structure-function relationship relates to virtual ageing
48 +1. What we mean by virtual ageing and what is the empirical basis to investigate this approach.
49 +1. How we can virtually age a subject using whole-brain modelling.
50 +1. How the increase structure-function relationship relates to virtual ageing.
45 45  
46 46  Link to the notebook:
47 47  
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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.
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  
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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.
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]]
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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]]
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81 -[[image:image-20220103095948-1.png]]
91 +[[image:download - 2022-02-10T131102.033.png||alt="regional bias vs goodness of fit"]]
82 82  )))
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