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Changes for page SGA3 D1.5 Showcase 1

Last modified by gorkazl on 2023/11/13 14:27
From version 15.1
edited by fousekjan
on 2023/10/31 09:33
Change comment: There is no comment for this version
To version 9.10
edited by gorkazl
on 2023/10/08 18:43
Change comment: There is no comment for this version

Summary

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29 29  )))
30 30  
31 31  (% class="wikigeneratedid" %)
32 -The Showcase 1 aimed at investigations related to variability in neuroscience from two perspectives: (a) the interpersonal variability studied by the virtual ageing study, and (b) the variability across different cortical regions within an individual brain.
32 +The virtual ageing study is described in detail in following publication:
33 33  
34 -== a. Interpersonal variability – virtual ageing ==
35 -
36 -(% class="wikigeneratedid" %)
37 -See the details of the first study in the following publication:
38 -
39 39  M. Lavanga, J. Stumme, B. H. Yalcinkaya, J. Fousek, C. Jockwitz, H. Sheheitli, N. Bittner, M. Hashemi, S. Petkoski, S. Caspers, and V. Jirsa, [[The Virtual Aging Brain: A Model-Driven Explanation for Cognitive Decline in Older Subjects>>https://doi.org/10.1101/2022.02.17.480902]].
40 40  
41 -=== Simulation of resting-state activity ===
36 +== Simulation of resting-state activity ==
42 42  
43 43  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:
44 44  
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51 51  
52 52  * [[virtual_ageing/notebooks/1_BNM_for_resting_state.ipynb>>https://lab.ch.ebrains.eu/user-redirect/lab/tree/shared/SGA3%20D1.2%20Showcase%201/virtual_ageing/notebooks/1_BNM_for_resting_state.ipynb]]
53 53  
49 +[[image:image-20220103100841-2.png]]
54 54  
55 -[[image:https://wiki.ebrains.eu/bin/download/Collabs/sga3-d1-2-showcase-1/WebHome/image-20220103100841-2.png?rev=1.1||alt="image-20220103100841-2.png"]]
51 +== Virtual ageing trajectories ==
56 56  
57 -=== Virtual ageing trajectories ===
58 -
59 59  The second steps shows the investigation of virtual ageing trajectory for each subject. In this context, we are going to show:
60 60  
61 61  1. What we mean by virtual ageing and what is the empirical basis to investigate this approach.
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66 66  
67 67  * [[virtual_ageing/notebooks/2_virtual_ageing_trajectories.ipynb>>https://lab.ch.ebrains.eu/user-redirect/lab/tree/shared/SGA3%20D1.2%20Showcase%201/virtual_ageing/notebooks/2_virtual_ageing_trajectories.ipynb]]
68 68  
69 -[[image:https://wiki.ebrains.eu/bin/download/Collabs/sga3-d1-2-showcase-1/WebHome/image-20220103101022-3.png?rev=1.1||alt="image-20220103101022-3.png"]]
63 +[[image:image-20220103101022-3.png]]
70 70  
71 -=== Inference with SBI ===
65 +== Inference with SBI ==
72 72  
73 73  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.
74 74  
75 -[[image:https://wiki.ebrains.eu/bin/download/Collabs/sga3-d1-2-showcase-1/WebHome/image-20220103104332-4.png?width=418&height=418&rev=1.1||alt="image-20220103104332-4.png"]]
69 +[[image:image-20220103104332-4.png||height="418" width="418"]]
76 76  
77 77  Link to the notebook:
78 78  
79 79  * [[virtual_ageing/notebooks/3_inference_with_SBI.ipynb>>https://lab.ch.ebrains.eu/user-redirect/lab/tree/shared/SGA3%20D1.2%20Showcase%201/virtual_ageing/notebooks/3_inference_with_SBI.ipynb]]
80 80  
81 -== b. Regional variability – Receptor density maps ==
75 +== Regional variability data ==
82 82  
83 -Aims at demonstrating the construction of whole-brain network models of the brain's activity, accounting for differences in receptor densities across cortical regions.
77 +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:
84 84  
85 -=== Loading the data from EBRAINS ===
86 -
87 -The first step of this workflow consists in loading the data from the Knowledge Graph via the //siibra interface//, including the regional bias on the model. In this case we require:
88 -
89 89  1. Structural connectivity matrices,
90 -1. GABAa and AMPA receptor densities for each brain region, and
91 -1. empirical resting-state fMRI data for fitting and validation of the simulations.
80 +1. GABA and AMPA receptor densities for each brain region, and
81 +1. empirical resting-state fMRI data for fitting and validation of the simulations. The three datasets shall be characterised in the same parcellation.
92 92  
93 -The three datasets are characterised in the same parcellation. Link to the notebook:
83 +Link to the notebook:
94 94  
95 95  * [[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]]
96 96  
97 -[[image:https://wiki.ebrains.eu/bin/download/Collabs/sga3-d1-2-showcase-1/WebHome/download%20-%202022-02-10T130815.902.png?rev=1.1||alt="region-wise gene expression heterogeneity"]]
87 +(% style="text-align:center" %)
88 +[[image:download - 2022-02-10T130815.902.png||alt="region-wise gene expression heterogeneity"]]
98 98  
99 -=== Fitting model parameters with regional bias ===
90 +== Fitting model parameters for models with regional bias ==
100 100  
101 -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 mean-field AdEx population model; specifically modified to account for the regional densities of GABAa and AMPA neuroreceptors. See the details in the following document.
92 +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.
102 102  
103 103  Link to the notebook:
104 104  
105 105  * [[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]]
106 106  
107 -[[image:https://wiki.ebrains.eu/bin/download/Collabs/sga3-d1-2-showcase-1/WebHome/download%20-%202022-02-10T131102.033.png?rev=1.1||alt="regional bias vs goodness of fit"]]
98 +[[image:download - 2022-02-10T131102.033.png||alt="regional bias vs goodness of fit"]]
108 108  )))
109 109  
110 110