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Author

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1		-XWiki.fousekjan
	1	+XWiki.gorkazl

Content

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31	31	(% class="wikigeneratedid" %)
32	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.
33	33
34		-== a. Interpersonal variability – virtual ageing ==
	34	+(% class="wikigeneratedid" %)
	35	+== (a) Interpersonal variability—virtual ageing ==
35	35
36	36	(% class="wikigeneratedid" %)
37	37	See the details of the first study in the following publication:
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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
	55	+[[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"]]
56		-
57	57	=== Virtual ageing trajectories ===
58	58
59	59	The second steps shows the investigation of virtual ageing trajectory for each subject. In this context, we are going to show:
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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"]]
	69	+[[image:image-20220103101022-3.png]]
70	70
71	71	=== 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"]]
	75	+[[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 ==
	81	+(% class="wikigeneratedid" %)
	82	+== (b) Regional variability ==
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.
	84	+This part of the Showcase aims at demonstrating whole-brain network models of the brain's activity
84	84
85		-=== Loading the data from EBRAINS ===
	86	+=== Loading the data from EBRAINS via the //siibra// interface ===
86	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	+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:
88	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.
	91	+1. GABA and AMPA receptor densities for each brain region, and
	92	+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:
	94	+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"]]
	98	+(% style="text-align:center" %)
	99	+[[image:download - 2022-02-10T130815.902.png||alt="region-wise gene expression heterogeneity"]]
98	98
99		-=== Fitting model parameters with regional bias ===
	101	+== 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.
	103	+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"]]
	109	+[[image:download - 2022-02-10T131102.033.png||alt="regional bias vs goodness of fit"]]
108	108	)))
109	109
110	110