spectrome-stability

Spectrome is a combination of the words "spectrum" and "connectome".

This repository is developed based on the original model's repository.

The spectral graph model (SGM) is a brain structure-function model that simulates brain activity power spectrum given a structural connectome. The model is linear, low-dimensional, and provides an analytical relationship between the brain's structural and functional patterns.

Citation:

Abstract:

We explore the stability and dynamic properties of a hierarchical, linearized, and analytic spectral graph model for neural oscillations that integrates the structuring wiring of the brain. Previously we have shown that this model can accurately capture the frequency spectra and the spatial patterns of the alpha and beta frequency bands obtained from magnetoencephalography recordings without regionally varying parameters. Here, we show that this macroscopic model based on long-range excitatory connections exhibits dynamic oscillations with a frequency in the alpha band even without any oscillations implemented at the mesoscopic level. We show that depending on the parameters, the model can exhibit combinations of damped oscillations, limit cycles, or unstable oscillations. We determined bounds on model parameters that ensure stability of the oscillations simulated by the model. Finally, we estimated time-varying model parameters to capture the temporal fluctuations in magnetoencephalography activity. We show that a dynamic spectral graph modeling framework with a parsimonious set of biophysically interpretable model parameters can thereby be employed to capture oscillatory fluctuations observed in electrophysiological data in various brain states and diseases.

Set-up:

Set up a conda environment if you do not have all the packages/compatible versions. The list of dependencies is listed in environment.yml.

Set-up environment using conda, detailed instructions can be found here. Or after cloning this repository, go to the repo by typing cd spectrome and then typing: conda env create -f environment.yml

If conda complains about not finding packages/libraries, make sure conda-forge is in the list of channels being searched by conda. You may add conda-forge to your list of channels with the command: conda config --add channels conda-forge.

The default name of the environment is spectrome, activate the environment with source activate spectrome, and deactivate with source deactivate or conda deactivate.

If you want to be able to run spectrome from anywhere, just add it's path to your PYTHONPATH. For instance, if you downloaded spectrome to ~/Documents/spectrome do export PYTHONPATH=$PYTHONPATH:~/Documents/spectrome. You may have to restart your terminal to make sure this change takes effect.

After completing the set-up for conda environment and spectrome path, you may go to the spectrome folder and type jupyter notebook or jupyter lab in your terminal to run the Jupyter notebooks.

Files:

• ../spectrome/notebooks: contains three jupyter notebooks, run_model_example.ipynb is the basic simulation of frequency spectrums with default parameters for the HCP template connectome. local_stability_freqspectra.ipynb generates the stability and frequency spectra plots for the local model, macro_stability_freqspectra.ipynb generates the stability and frequency spectra for the macroscopic model, local_LaplaceInverse.ipynb generates the inverse Laplace transform simulations for the local model, and dynamic_parameters.ipynb generates the dynamic model parameter plots.

• ../spectrome/data: contains intermediate data.

  • mean80_fibercount/length.csv: HCP template connectome and distance matrix.
  • individual_connectomes_reordered.nc: individual subject's connectivity matrices (N = 36).
  • individual_psd_reordered_matlab.nc: individual subject's MEG spectra (N = 36).
  • individual_wavelet_reordered_smooth.csv: individual subject's MEG Morlet wavelet decomposition (N = 36)