KuramotoOptimization.zip: README.txt

This is a code package developed for the optimization of Kuramoto model simulations by iteratively adjusting the underlying frequency distribution, reducing the mean squared error between the simulated FC and a target empirical FC.

Software Requirements: MATLAB 2022a or later recommended
Dependencies:
1. Brain Connectivity Toolbox (https://sites.google.com/site/bctnet/home)
   - Files used: strengths_und_sign.m
2. Braun et al., 2021 (https://github.com/ursbraun/network_control_and_dopamine)
   - Original publication: Brain network dynamics during working memory are modulated by dopamine and diminished in      schizophrenia (https://www.nature.com/articles/s41467-021-23694-9)
   - Files used: optim_fun.m

Includes:
1. optimizeKuO.m
   a. strengths_Kuo.m
      i. Kuramoto_fun.m
2. postProcessing.m

File 1 (optimizeKuO.m) is a function which uses functions from the files classified under it and other dependencies to run 100 iterative simulations and save the results to a file named by an input parameter. File 2 (postProcessing.m) is a script which uses the simulated data and empirical (target) FC to determine each iteration's MSE and Predictive Power.

This code package can be accessed at https://www.seas.upenn.edu/~molneuro/

Code authored by Adam C Rayfield (adamra@seas.upenn.edu) and Taotao Wu (taotao.wu@uga.edu)

Citation:
Rayfield AC, Wu T, Rifkin JA, Meaney DF. 2024.