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Author

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1		-XWiki.gorkazl
	1	+XWiki.fousekjan

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		-(% class="wikigeneratedid" %)
35		-== (a) Interpersonal variability—virtual ageing ==
	34	+== a. Interpersonal variability – virtual ageing ==
36	36
37	37	(% class="wikigeneratedid" %)
38	38	See the details of the first study in the following publication:
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52	52
53	53	* [[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]]
54	54
55		-[[image:image-20220103100841-2.png]]
56	56
	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:image-20220103101022-3.png]]
	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"]]
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:image-20220103104332-4.png||height="418" width="418"]]
	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"]]
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		-(% class="wikigeneratedid" %)
82		-== (b) Regional variability ==
	81	+== b. Regional variability – Receptor density maps ==
83	83
84	84	Aims at demonstrating the construction of whole-brain network models of the brain's activity, accounting for differences in receptor densities across cortical regions.
85	85
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91	91	1. GABAa and AMPA receptor densities for each brain region, and
92	92	1. empirical resting-state fMRI data for fitting and validation of the simulations.
93	93
94		-The three datasets shall be characterised in the same parcellation. Link to the notebook:
	93	+The three datasets are characterised in the same parcellation. Link to the notebook:
95	95
96	96	* [[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]]
97	97
98		-(% style="text-align:center" %)
99		-[[image:download - 2022-02-10T130815.902.png||alt="region-wise gene expression heterogeneity"]]
	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"]]
100	100
101		-=== Fitting model parameters for models with regional bias ===
	99	+=== Fitting model parameters with regional bias ===
102	102
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 mean-field AdEx population model; specifically modified to account for the regional densities of GABAa and AMPA neuroreceptors. See the details following document.
	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.
104	104
105	105	Link to the notebook:
106	106
107	107	* [[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]]
108	108
109		-[[image:download - 2022-02-10T131102.033.png||alt="regional bias vs goodness of fit"]]
	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"]]
110	110	)))
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112	112