netneurotools.metrics.simulate_atrophy

netneurotools.metrics.simulate_atrophy(SC_den, SC_len, seed, roi_sizes, T_total=1000, dt=0.1, p_stay=0.5, v=1, trans_rate=1, init_number=1, GBA=None, SNCA=None, k1=0.5, k=0, FC=None)[source]

Simulate atrophy on a specified network.

This is a python version of SIRsimulator, by Ying-Qiu Zheng: https://github.com/yingqiuz/SIR_simulator [1]. This python version was first used in [2].

Parameters:

  • SC_den ((n, n) ndarray) – Structural connectivity matrix (strength)

  • SC_len ((n, n) ndarray) – Structural connectivity matrix (len)

  • seed (int) – ID of the node to be used as a seed for the atrophy process

  • roi_sizes ((n,) ndarray:) – Size of each ROIs in the parcellation

  • T_total (int) – Total time steps of the function

  • dt (float) – Size of each time step

  • p_stay (float) – The probability of staying in the same region per unit time

  • v (float) – Speed of the atrophy process

  • trans_rate (float) – A scalar value controlling the baseline infectivity

  • init_number (int) – Number of injected misfolded protein

  • GBA ((n,) ndarray) – GBA gene expression (clearance of misfolded protein)

  • SNCA ((n,) ndarray) – SNCA gene expression (synthesis of misfolded protein)

  • k1 (float) – Ratio between weight of atrophy accrual due to accumulation of misfolded agends vs. weight of atrophy accrual due to deafferation. Must be between 0 and 1

  • FC ((n, n) ndarray) – Functional connectivity

  • k (float) – weight of functional connectivity

Returns:

simulated_atrophy – Trajectory matrix of the simulated atrophy in individual brain regions.

Return type:

(n_regions, T_total) ndarray

References