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Functional Network Connectivity Interpolation

Code for the preprint: Brain functional network connectivity interpolation characterizes neuropsychiatric continuum and heterogeneity (under review)

image

Environment setup

  1. Clone the repository
git clone https://github.com/XinhuiLi/interpolation.git
cd interpolation
  1. Create a conda environment
conda create --name interp python=3.12
conda activate interp
  1. Install the required packages
pip install -r requirements.txt

Experiments

Static functional network connectivity (sFNC) interpolation

FBIRN data: interp_sfnc_sz.ipynb

ABIDE I data: interp_sfnc_asd.ipynb

Dynamic functional network connectivity (dFNC) interpolation

FBIRN data: interp_dfnc_sz.ipynb

ABIDE I data: interp_dfnc_asd.ipynb

Visualization

Figure 2: plot_comparison.ipynb

Figure 3: interp_sfnc_sz.ipynb

Figure 4: interp_sfnc_asd.ipynb

Figure 5: plot_subject_measure.ipynb

Figure 6: plot_correlation.ipynb

Figure 7: interp_dfnc_sz.ipynb

Figure 8: interp_dfnc_asd.ipynb

Figure 9: plot_dynamic_metrics.ipynb

Figure 10: plot_sfnc_latent_space.ipynb

Figure 11: plot_dfnc_latent_space.ipynb

Figure 12: plot_dfnc_latent_space.ipynb

Figure A.1: plot_subject_measure.ipynb

Figure E.1a: plot_hypopt_sfnc_vae.ipynb

Figure E.1b: plot_hypopt_dfnc_vae.ipynb

Figure E.2: plot_hypopt_sfnc_ivae.ipynb

Figure G.1: plot_mse.ipynb

Figure H.1: plot_kmeans.ipynb

Acknowledgement

We thank the authors of identifiable variational autoencoders for making the code publicly available.

Citation

If you find this repository useful, please cite the following paper:

@article{li2024brain,
	author = {Li, Xinhui and Geenjaar, Eloy and Fu, Zening and Pearlson, Godfrey and Calhoun, Vince},
	title = {Brain functional network connectivity interpolation characterizes neuropsychiatric continuum and heterogeneity},
	elocation-id = {2024.11.13.623318},
	year = {2024},
	doi = {10.1101/2024.11.13.623318},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2024/11/14/2024.11.13.623318},
	eprint = {https://www.biorxiv.org/content/early/2024/11/14/2024.11.13.623318.full.pdf},
	journal = {bioRxiv}
}

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