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27	27	There is also a fourth tutorial which describes application of a fast back-end implementation of the Montbrio-Pazo-Roxin model used for modeling [[resting state fMRI >>https://lab.jsc.ebrains.eu/hub/user-redirect/lab/tree/shared/TVB%20EBRAINS%20Baltic-Nordic%20school%202024/MPR_rs.ipynb]]. This is a based on a previous showcase [[Degeneracy in neuroscience>>https://wiki.ebrains.eu/bin/view/Collabs/sga3-d1-5-showcase-1/]],  which described the interpersonal variability analyzed by the [[Virtual Ageing Brain>>https://www.sciencedirect.com/science/article/pii/S1053811923005542?via%3Dihub]] study.
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29		-Lavanga, M., Stumme, J., Yalcinkaya, B. H., Fousek, J., Jockwitz, C., Sheheitli, H., Bittner, N., Hashemi, M., Petkoski, S., Caspers, S., & Jirsa, V. (2023). The virtual aging brain: Causal inference supports interhemispheric dedifferentiation in healthy aging. NeuroImage, 283, 120403.
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31		-Finally, the collab contains one tutorial where a [[Bayesian approach>>https://wiki.ebrains.eu/bin/view/Collabs/ebrains-task-3-3/Drive#notebooks/EITN_tutorial||style="background-color: rgb(255, 255, 255);"]] is used on synthetic data to infer the posterior of the parameters foir a single brain region.
	30	+Finally, the collab contains one tutorial where a [[Bayesian approach>>https://wiki.ebrains.eu/bin/view/Collabs/ebrains-task-3-3/Drive#notebooks/EITN_tutorial||style="background-color: rgb(255, 255, 255);"]] is used on synthetic data to infer the posterior of the parameters for a single brain region.
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34	34	These can all be found in the drive and accessed through the lab.