Over the years the lab has generated a large database of mouse rsfMRI scans that are either freely downloadable or available for researchers on a collaborative basis. Requests for datasets not directly downloadable here should be addressed to: Alessandro.Gozzi@iit.it
Much of our code is also uploaded to our lab Github Repository:
Rocchi, F., Canella, C., Noei, S., Gutierrez-Barragan, D., Coletta, L., Galbusera, A., Stuefer, A., Vassanelli, S., Pasqualetti, M., Iurilli, G., Panzeri, S., & Gozzi, A. (2022). Nature Communications 2022 13:1, 13(1), 1–15.
Paper: https://doi.org/10.1038/s41467-022-28591-3
Code: https://github.com/functional-neuroimaging/PFC-chemo-inhib
Gutierrez-Barragan, D., Singh, N. A., Alvino, F. G., Coletta, L., Rocchi, F., de Guzman, E., Galbusera, A., Uboldi, M., Panzeri, S., & Gozzi, A. (2022). Current Biology, 32(3), 631-644.e6.
Paper: https://doi.org/10.1016/J.CUB.2021.12.015
Data:
Pagani, M., Barsotti, N., Bertero, A., Trakoshis, S., Ulysse, L., Locarno, A., Miseviciute, I., de Felice, A., Canella, C., Supekar, K., Galbusera, A., Menon, V., Tonini, R., Deco, G., Lombardo, M. v., Pasqualetti, M., & Gozzi, A. (2021).
Paper: https://doi.org/10.1038/s41467-021-26131-z
Data: https://data.mendeley.com/datasets/2b3n86gr25/1
Code:
Coletta, L., Pagani, M., Whitesell, J. D., Harris, J. A., Bernhardt, B., & Gozzi, A. (2020).
Paper: https://advances.sciencemag.org/content/6/51/eabb7187
Data: https://data.mendeley.com/datasets/dxtzpvv83k/3
This resource contains the resampled, symmetrized version of the original voxel-wise connectome that we used for our analysis. We also provide the nifti masks of all the network properties descried in the manuscript, together with a set of simple matlab routines to probe incoming/outcoming/total axonal connectivity of any arbitrarily shaped region of interest.
Grandjean, J., Canella, C., Anckaerts, C., Ayrancı, G., Bougacha, S., Bienert, T., Buehlmann, D., Coletta, L., Gallino, D., Gass, N., Garin, C. M., Nadkarni, N. A., Hübner, N. S., Karatas, M., Komaki, Y., Kreitz, S., Mandino, F., Mechling, A. E., Sato, C., … Gozzi, A. (2020).
Paper: https://www.sciencedirect.com/science/article/pii/S1053811919308699
Datasets: https://openneuro.org/datasets/ds001720/versions/1.0.2
The collection includes resting-state fMRI scans of over 600 mice of 20 distinct autism etiologies. Twelve cohorts were collected at the IIT functional neuroimaging lab and eight at ETH Zürich (Switzerland). Here is a full list of the mouse cohorts:
Paper: https://www.biorxiv.org/content/10.1101/2020.10.15.340588v1.full.pdf
C-PAC is a software developed at Child Mind Institute to preprocess and de-noise human MRI scans. A IIT-CMI intergroup collaboration allowed to extend the capabilities of the software to preprocess and de-noise mouse rsfMRI time-series (C-PAC v1.6.0 Jan3, 2020).
https://fcp-indi.github.io/docs/latest/user/index#the-c-pac-mission