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16		-== Siibra - Python client for accessing the human atlas ==
	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.
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18		-The multilevel atlas interface called siibra (formerly known as Brainscapes) is designed to allow safe and convenient interaction with brain definitions from different parcellations, facilitating the implementation of reproducible neuroscience workflows on the basis of brain atlases. It allows to work with reference brain templates both at millimeter and micrometer resolutions and provides streamlined access to multimodal data features linked to brain regions. In particular in this demonstrator it is used to retrieve the dataset of receptor density spatial maps.
	17	+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.
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20		-The Showcase 1a is built on the [[connectivity data from the 1000BRAINS study>>https://doi.org/10.25493%2F6640-3XH]] available in the Knowledge graph as a dataset with protected access available to EBRAINS users. The access to protected datasets is provided in EBRAINS through the [[Human Data Gateway>>https://wiki.ebrains.eu/bin/view/Collabs/data-proxy/Human%20Data%20Gateway/]] which allows full access once the user has validated the terms of use.
	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.
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22		-== Brain Network Modeling: TVB ==
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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]].
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24		-The second major component of the showcase is The Virtual Brain (TVB) simulation platform. TVB is a simulation platform that uses empirical structural and functional data to build whole brain models of individual subjects. For convenient model construction, the system is based on a processing pipeline for structural, functional, and diffusion-weighted magnetic resonance imaging (MRI) data. The pipeline combines several state-of-the-art neuroinformatics tools to generate subject-specific cortical and subcortical parcellations, surface-tessellations, structural and functional connectomes, and region-wise aggregated blood oxygen level-dependent (BOLD) functional MRI (fMRI) time-series.
	29	+== Simulation of resting-state activity ==
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26		-== Virtual ageing trajectories: 1000BRAINS cohort ==
	31	+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:
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28		-(% style="font-size: 11pt; font-variant: normal; white-space: pre-wrap; font-family: Arial; color: rgb(0, 0, 0); font-weight: 400; font-style: normal; text-decoration: none" %)Beyond the general common architecture, the human brain is characterized by a high interindividual variability with regard to its structure and functional abilities, including measurable cognitive outcome. Disentangling the sources and consequences of this variability is key to understanding the capabilities of our brains and any pathologies occurring over the lifespan. This also includes implementing measures of variability into simulations and modelling approaches to make them biologically plausible and as realistic as possible.
	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.
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30		-(% style="line-height:1.38; margin-top:13px" %)
31		-(% style="font-size: 11pt; font-variant: normal; white-space: pre-wrap; font-family: Arial; color: rgb(0, 0, 0); font-weight: 400; font-style: normal; text-decoration: none" %)The aging brain is a particularly remarkable use case in this respect as the interindividual variability of brains and cognitive abilities of older subjects from the general population is very pronounced. The relevant factors influencing this variability, be it genetics or environmental and lifestyle factors, are manifold and barely understood yet. Similarly, the different levels within the organization of the brain, from the molecular, cellular to the systems level, contribute to these effects to varying degrees. The aim of the first branch of Showcase 1 is thus to model brain aging with biologically plausible assumptions about the architecture of the brain, using the EBRAINS platform and integrating information from the different organizational levels of the brain, and to create a virtually aged cohort of brain models which allow testing of hypotheses and causal linkage of potential sources and effects of the empirically observed variability.
	38	+Link to the notebook:
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33		-=== Technical specification ===
	40	+* [[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]]
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	42	+[[image:image-20220103100841-2.png]]
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36		-== Brain region variance: incorporating receptor densities ==
	44	+== Virtual ageing trajectories ==
37	37
38		-(% style="font-size: 11pt; font-variant: normal; white-space: pre-wrap; font-family: Arial; color: rgb(0, 0, 0); font-weight: 400; font-style: normal; text-decoration: none" %)The second branch of the Showcase 1 focuses on the impact of variability of the properties of brain regions on the expressed dynamics. Over the last two decades, whole-brain network models have proven useful instruments to describe the brain's resting-state activity: from explaining the intrinsic dynamical behavior of the brain to the classification of brain states in disorders of consciousness. These whole-brain models are built at the resolution of interconnected cortical regions and subcortical brain areas, with each region simulated at the population levels. So far, a major limitation of these models lies in the assumption that all brain regions are of identical characteristics while it is well-known that brain areas differ in a variety of properties, e.g., neuronal densities, local cytoarchitecture, and types of neuroreceptors [Deco2021]. Accounting for these regional variances is crucial for translational purposes as for example differences in neuroreceptor densities determine the impact of pharmacological agents on particular brain regions and thus their potential to modulate the whole-brain dynamics. The purpose of Showcase #1(b) is, therefore, to illustrate how whole-brain network models can be adapted to include such regional variances and to develop workflows to run the simulations taking full advantage of the HBP's EBRAINS infrastructure, leading to workflows that are accessible and re-usable by the scientific community.
	46	+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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	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.
40	40
41		-=== Technical specification ===
	52	+Link to the notebook:
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43		-(% style="line-height:1.38; margin-top:13px" %)
44		-(% style="font-size: 11pt; font-variant: normal; white-space: pre-wrap; font-family: Arial; color: rgb(0, 0, 0); font-weight: 400; font-style: normal; text-decoration: none" %)In implementation of Showcase 1b, the simulation is based on a network of nodes that represent brain ROI, each running the (% style="font-size: 11pt; font-variant: normal; white-space: pre-wrap; font-family: Arial; color: rgb(0, 0, 0); font-weight: 700; font-style: normal; text-decoration: none" %)**BEI**(% style="font-size: 11pt; font-variant: normal; white-space: pre-wrap; font-family: Arial; color: rgb(0, 0, 0); font-weight: 400; font-style: normal; text-decoration: none" %) (Balanced Excitation-Inhibition) model [Deco2014], which defines two neuronal subpopulations, an excitatory and an inhibitory one, both interacting through neurochemical (e.g., AMPA, GABA, NMDA, etc.) currents. The objective of the Showcase 1b is to demonstrate and find out the more precise mechanism through which regional neurotransmitter density affects this excitation-inhibition balance. Numerically, the goal is to minimize the difference in sliding window Functional Connectivity Dynamics ((% style="font-size: 11pt; font-variant: normal; white-space: pre-wrap; font-family: Arial; color: rgb(0, 0, 0); font-weight: 700; font-style: normal; text-decoration: none" %)**swFCD**(% style="font-size: 11pt; font-variant: normal; white-space: pre-wrap; font-family: Arial; color: rgb(0, 0, 0); font-weight: 400; font-style: normal; text-decoration: none" %)) between a reference empirical signal and a simulated one. The swFCD subdivides the time-series into successive windows and, for each one, computes its corresponding Functional Connectivity through the usual Pearson correlation, resulting in a series of NxN matrices. Two swFCD can be compared by means of the Kolmogorov-Smirnov statistic.
	54	+* [[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]]
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	56	+[[image:image-20220103101022-3.png]]
	57	+
	58	+== Inference with SBI ==
	59	+
	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.
	61	+
	62	+[[image:image-20220103104332-4.png||height="418" width="418"]]
	63	+
	64	+Link to the notebook:
	65	+
	66	+* [[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]]
	67	+
	68	+== Regional variability data ==
	69	+
	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:
	71	+
	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	+
	76	+Link to the notebook:
	77	+
	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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	80	+(% style="text-align:center" %)
	81	+[[image:download - 2022-02-10T130815.902.png||alt="region-wise gene expression heterogeneity"]]
	82	+
	83	+== Fitting model parameters for models with regional bias ==
	84	+
	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.
	86	+
	87	+Link to the notebook:
	88	+
	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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	91	+[[image:download - 2022-02-10T131102.033.png||alt="regional bias vs goodness of fit"]]
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