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Transcranial Ultrasound Stimulation (TUS) Whole-Brain Modeling

Python code to reproduce the computational model of transcranial ultrasound stimulation (TUS) at different stimulation intensities, as proposed in the preprint:

Gatica et al., 2025 - "Differential impact of transcranial ultrasound stimulation intensities on whole-brain dynamics"

🧠 Overview

This repository contains the simulation pipeline to model TUS-driven changes in brain dynamics using a whole-brain Hopf model, incorporating:

  • Homogeneous simulations for control conditions
  • Fitting procedures for global coupling
  • Heterogeneous modeling based on communicability and distance metrics
  • Intensity-dependent stimulation of brain regions

📂 Pipeline: How to Run

Scripts are available in the scripts folder.

Follow the steps below to reproduce the model results.
  1. 01_main_runHopf_CN_homogeneous
    Running the simulations for control conditions and several global coupling parameters (G).
  2. 02_fitting_CN
    Fitting the global coupling parameter (G = 0.16).
  3. 03main_runHopf_CN_heterogeneous
    Adding heterogeneity based on communicability (CMY) or distance vector, testing several bias and scale parameters.
  4. 04_fitting_stim_het
    Fitting the bias and scale parameters for the heterogeneous model.
  5. 05_main_runHopf_sameparams_alpha
    Running the dynamic stimulation model based on communicability or distance, at several stimulation intensities (modulated by alpha).
    The stimulated targets are the thalamus ("thalamusproper") and the inferior frontal cortex ("parstriangularis").

📄 Original Hopf Model

This model is based on and adapted from the Hopf whole-brain simulation framework available at:
https://github.com/carlosmig/StarCraft-2-Modeling

Coronel-Oliveros C, Medel V, Orellana S, et al. (2024). Gaming expertise induces meso-scale brain plasticity and efficiency mechanisms as revealed by whole-brain modeling. NeuroImage, 293:120633.
https://doi.org/10.1016/j.neuroimage.2024.120633

📊 Datasets

Data is available in the datasets folder.

The model uses 84 brain regions, parcellated using the Desikan-Killiany atlas, and functional and structural data from 19 subjects.

The following datasets are provided:

  • Empirical functional data:

    • Functional connectivity matrices (upper triangular part), shape: 3486 x 19
    • Kuramoto Order Parameter (KOP) values, shape: 1 x 19
    • Filenames: triu_emp_subset.npy, kop_emp_subset.npy

  • Structural connectomes:

    • Structural connectivity matrices per subject, shape: 84 x 84 x 19
    • Filename: sc_subset.npy

  • Heterogeneity vectors:

    • Global communicability: global_CMY.mat, shape: 1 x 84
    • Euclidean distance vector: global_distance.mat, shape: 1 x 84

  • Stimulated targets (binary mask):

    • Binary vectors (1 = stimulated region, 0 = others), shape: 1 x 84
    • Filenames:
      • discrete_local_thalamusproper.mat
      • discrete_local_parstriangularis.mat

📬 Contact

For questions or collaboration inquiries, contact:

Marilyn Gatica
Email: marilyn.gatica@nulondon.ac.uk

📜 License

Distributed for academic, non-commercial use. Please cite the relevant papers when using this code.

🧾 Citation

If you use this code, please cite both of the following works:

Gatica M, Atkinson-Clement C, Coronel-Oliveros C, et al. (2025). Differential impact of transcranial ultrasound stimulation intensities on whole-brain dynamics. bioRxiv.
https://doi.org/10.1101/2025.01.11.632528v1

Coronel-Oliveros C, Medel V, Orellana S, et al. (2024). Gaming expertise induces meso-scale brain plasticity and efficiency mechanisms as revealed by whole-brain modeling. NeuroImage, 293:120633.
https://doi.org/10.1016/j.neuroimage.2024.120633