spectrome-revisited

Spectrome is a combination of the words "spectrum" and "connectome". This package is the collection of codes that constructed the analysis for the preprint "Spectral graph theory of brain oscillations - revisited and improved". 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:

The code in this repository is used for the analysis as shown in: Parul Verma, Srikantan Nagarajan, and Ashish Raj. “Spectral Graph Theory of Brain Oscillations - revisited and improved” (https://www.biorxiv.org/content/10.1101/2021.09.28.462078v1). If you found this useful, please cite the following:

@article {verma2021spectral,
	author = {Verma, Parul and Nagarajan, Srikantan and Raj, Ashish},
	title = {Spectral graph theory of brain oscillations -- revisited and improved},
	elocation-id = {2021.09.28.462078},
	year = {2021},
	doi = {10.1101/2021.09.28.462078},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2021/09/30/2021.09.28.462078},
	eprint = {https://www.biorxiv.org/content/early/2021/09/30/2021.09.28.462078.full.pdf},
	journal = {bioRxiv}
}

Abstract:

Mathematical modeling of the relationship between the functional activity and the structural wiring of the brain has largely been undertaken using non-linear and biophysically detailed mathematical models with regionally varying parameters. While this approach provides us a rich repertoire of multistable dynamics that can be displayed by the brain, it is computationally demanding. Moreover, although neuronal dynamics at the microscopic level are nonlinear and chaotic, it is unclear if such detailed nonlinear models are required to capture the emergent meso- (regional population ensemble) and macro-scale (whole brain) behavior, which is largely deterministic and reproducible across individuals. Indeed, recent modeling effort based on spectral graph theory has shown that an analytical model without regionally varying parameters can capture the empirical magnetoencephalography frequency spectra and the spatial patterns of the alpha and beta frequency bands accurately.

In this work, we demonstrate an improved hierarchical, linearized, and analytic spectral graph theory-based model that can capture the frequency spectra obtained from magnetoencephalography recordings of resting healthy subjects. We reformulated the spectral graph theory model in line with classical neural mass models, therefore providing more biologically interpretable parameters, especially at the local scale. We demonstrated that this model performs better than the original model when comparing the spectral correlation of modeled frequency spectra and that obtained from the magnetoencephalography recordings. This model also performs equally well in predicting the spatial patterns of the empirical alpha and beta frequency bands.

Set-up:

First clone the environment to your computer, either download this repo as a .zip file or git clone https://github.com/Raj-Lab-UCSF/spectrome-revisited.git.

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. spatialcorrelation.ipynb looks at the spatial correlations between the eigenmodes and the empirical spectra, and reproduce_MEG_modeled_spectra.ipynb compares optimized modeled spectra with the MEG spectra.

• ../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).
  • MSGM_Reordered_matlab_500iter.csv: optimized model parameters for the 36 subjects.