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# Neurophysiological signatures of cortical micro-architecture | ||
This repository contains processing scripts and data in support of the preprint "Neurophysiological signatures of cortical micro-architecture" on biorxiv. | ||
This repository contains processing scripts and data in support of the preprint "Neurophysiological signatures of cortical micro-architecture" on biorxiv. [doi: https://doi.org/10.1101/2023.01.23.525101](https://doi.org/10.1101/2023.01.23.525101) | ||
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## `code` | ||
The [code](code/) folder contains all the code used to run the analyses and generate the figures. | ||
All code in [preprocessing](code/preprocessing/) folder was written in Matlab and was used to preprocess resting-state MEG HCP data using [Brainstorm](https://neuroimage.usc.edu/brainstorm/Introduction). Time-series features of MEG data were calculated using [hctsa](https://hctsa-users.gitbook.io/hctsa-manual/) toolbox. | ||
All code in [analysis](code/analysis/) folder was written in Python and was used to analyze the preprocessed data. | ||
I regularly use [netneurotools](https://github.com/netneurolab/netneurotools), a handy Python package developed in-house. | ||
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<!-- The [preprocessing](code/preprocessing/) folder contains the following files: | ||
- [fcn_hcp_meg_process_connectivity.m](code/preprocessing/fcn_hcp_meg_process_connectivity.m) is the main function used to preprocess MEG HCP data. It relies on [Brainstorm](https://neuroimage.usc.edu/brainstorm/Introduction) and is a modified version of [Brainstorm tutorial](https://neuroimage.usc.edu/brainstorm/Tutorials/HCP-MEG) to preprocess resting-state MEG data from HCP. | ||
- [scpt_runBrainstorm.m](code/preprocessing/scpt_runBrainstorm.m) is the script that runs [fcn_hcp_meg_process_connectivity.m](code/preprocessing/fcn_hcp_meg_process_connectivity.m). | ||
- [resolution_metric.py](code/preprocessing/resolution_metric.py) is the script that is used to estimate MEG source localization error. | ||
The [analysis](code/analysis/) folder contains the following files: | ||
- [fcn_megfmri.py](code/analysis/fcn_megfmri.py) contains all the functions used in main analysis ([scpt_megfmri.py](code/analysis/scpt_megfmri.py)) | ||
- [scpt_megfmri.py](code/analysis/scpt_megfmri.py) contains the script to run the main analyses and generate the figures of the manuscript. | ||
- [scpt_Fig1.py](code/analysis/scpt_Fig1.py), [scpt_Fig2.py](code/analysis/scpt_Fig2.py), and so on contain the scripts to regenerate the main and supplementary figures of the manuscript. | ||
- [scpt_prepare_plotting_main.py](code/analysis/scpt_prepare_plotting_main.py) and [scpt_prepare_plotting_suppl.py](code/analysis/scpt_prepare_plotting_suppl.py) contain the scripts that were used to prepare summary data to plot the main and supplementary figures (available in [figures_data](data/figures_data/)). --> | ||
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## `data` | ||
The [data](data/) folder contains the data used to run the analyses. Specifically, it containes the parcellated group-average MEG time-series feature matrices from 33 unrelated subjects in HCP. Note that HCP data redistribution must follow their [data terms](https://www.humanconnectome.org/study/hcp-young-adult/document/wu-minn-hcp-consortium-open-access-data-use-terms). If you use any of the HCP data, please note that you must register with ConnectomeDB, agree to their terms and sign up for Open Access Data [here](https://www.humanconnectome.org/study/hcp-young-adult/data-use-terms). Please also cite relevant publications as mentioned [here](https://www.humanconnectome.org/study/hcp-young-adult/document/wu-minn-hcp-consortium-open-access-data-use-terms). | ||
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The [data](data/) folder also contains micro-architectural feature maps that were compiled using [neurompas](https://netneurolab.github.io/neuromaps/index.html), [abagen](https://abagen.readthedocs.io/en/stable/), and [BigBrainWarp](https://bigbrainwarp.readthedocs.io/en/latest/) tools. If you use any of these data, please cite relevant publications as mentioned in their corresponding documentation. | ||
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<!-- The [figures_data](data/figures_data/) folder contains the summary data that can be directly used to regenerate the figures. --> | ||
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The [data](data/) folder also contains required files to use and plot brain maps with [Schaefer atlas](https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal). |