Dear All,
I am a mathematician working on numerical methods for brain dynamics and, while I am numerically trained and can program in several languages, I have never worked first hand with a concrete dataset.
I must now test a numerical scheme on a model, using a realistic cortex, and with a somewhat realistic structural connectivity matrix. What I need is:
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A triangulated surface mesh for a cortex with n vertices (so x,y,z coordinates for the vertices, and corresponding triangulations)
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An accompanying structural connectivity matrix. Ideally, this would be an n-by-n matrix, potentially sparse, expressing some form of functional connectivity between each pair of vertices.
Do you know of any dataset giving access to the above in some standard format? I am aware that connectivity matrices can be hugely expensive to store, which is why I could compromise on resolution (n need not be huge).
Even synthetic data would be helpful for what I must do.
Also, I am aware that connectivities are sometimes expressed from region to region, rather than vertex to vertex. I would be happy with that, provided it would also be possible, with little (or possibly no) effort, to associate nodes to regions: in this way, I could take a “constant” connectivity matrix between group of nodes that correspond to the same region.
This seems quite a nontrivial but possibly standard thing to do, and I wonder if there is anything that I could use out of the box, or some dataset that serves as a benchmark. There is a plethora of available codes for displaying and manipulating this type of dataset, and I would not want to invest time into learning one specific tool if I can’t extract the dataset I need above.
Best
Daniele Avitabile
Associate Professor in Applied Mathematics
danieleavitabile.com