Code to reproduce all network analyses in Lubben et al. "LRRK2 kinase inhibition reverses G2019S mutation-dependent effects on tau pathology spread". Scripts are modified versions of those found in https://github.com/ejcorn/tau-spread.

• Tested with R 4.2.1
  • Requisite packages are listed in code/packages.R

For a general overview of linear diffusion modeling in neurodegenerative disease, see tutorial in example folder here: https://github.com/ejcorn/tau-spread.

Master branch contains 2 major folders:

• code contains folders with scripts organized by analysis, i.e. code/diffmodel contains code that uses linear diffusion models to predict spread of protein through structural connectome.
• data contains input structural data and pathology data.

The file pipeline.R is located in the main directory. This file will coordinate the sequential execution of all scripts within the code/ folder, generating all the figures in the paper and more. Custom specification of the following inputs at the top of pipeline.R is required:

• basedir: path to the main directory containing the code and data folders
• matlab.path: path to MATLAB binary. only needed for one supplemental analysis.
• injection.site: vector of character names of brain regions in ABA to inject pathology into
• grps: character vector containing the name of genotypes in data file to test. For our dataset, these were 'NTG' and 'G20'.
• treatments: character vector containing the names of treatment groups in the data file to test. For our dataset, these were '0 MLi-2', '75 MLi-2', and '450 MLi-2'.
• opdir: here you can add some extra custom label for your output folder given a particular configuration at the top of the script

Please contact Kate Brynildsen (jbryn AT seas.upenn.edu) with any questions regarding network analysis and code, and contact Mike Henderson (Michael DOT Henderson AT vai.org) with any questions regarding experiments and data.