Tools for working with Allen Institute for Brain Science voxel-scale connectivity data.

Requirements:

• numpy
• scipy
• h5py
• allensdk (tested with 0.13.1, NOT guaranteed to work with later versions)
• skimage, mayavi (optional, for visualization)

To fit connectomes:

• spatial-network-regression

1. Edit run_setup.py. This sets which structures will be included, the values of the regularization parameter, etc.
2. python create_visual_matrices.py. This will create a hierarchy of directories for model fitting with nested cross-validation.
3. Run the commands in model_fitting_cmds (located in the project directory) to perform the model fits.
4. Run python model_select_and_fit.py. In the inner cross-validation loop, evaluate the errors and perform model selection.
5. Run the commands in model_fitting_after_selection_cmds. This will fit the selected models.
6. Run python region_model_fits_and_voxel_errors.py. This will both evaluate the errors of the voxel models as well as fit regional models and compare their errors to the voxel models.

1. Run python voxel_model_visualizations.py. This performs fake injections into VISp, plotting the results. Also saves volumetric data & region labeled plot.
2. You can turn the virtual injection pictures into a movie easily: avconv -q 4 -r 7 -b 9600 -i int_virt_inj%d.png movie.mp4
3. You can visualize the volumetric data in VTK format (.vti files). Use Paraview.

First edit the following scripts to set the data and output directories, then run:

 python create_regional_matrices.py
 python run_new_regional_model.py

If you want to compare the output of this model to that from Oh et al. (2014), this can be accomplished with compare_new_old.py.

Run in this order:

get_2d_connectivity.py
create_2d_matrices.py

Note that the streamlines needed to generate top view and flatmap 2-D cortical projections are available from CCF 2017 informatics.