meditation effects on fMRI-based pain signatures
publication
@article{riegner2024mindfulness,
title={Mindfulness meditation and placebo modulate distinct multivariate neural signatures to reduce pain},
author={Riegner, Gabriel and Dean, Jon and Wager, Tor D and Zeidan, Fadel},
journal={Biological Psychiatry},
year={2024},
publisher={Elsevier}
}
project organization
.
├── README.md <- this README file
├── config
│ ├── bold-dem.csv <- study 1 demographics
│ ├── bold-filtered.csv <- study 1 bold runs to process
│ ├── cbf-dem.csv <- study 2 demographics
│ ├── cbf-filtered.csv <- study 2 cbf runs to process
│ └── smk-config.yml <- configuration file for workflow/snakemake
├── data
│ ├── bold-ps-agg.csv <- study 1 pain signature response data
│ ├── cbf-ps-agg.csv <- study 2 pain signature response data
│ ├── bold/ <- study 1 bold glm estimates [-]
│ ├── cbf/ <- study 2 cerebral blood flow estimates [-]
│ └── pain-sigs/ <- CANlab pain-predictive signatures [-]
├── figures/ <- figures derived from notebooks/
├── notebooks/
│ ├── fig01.ipynb <- plot neuroimaging data
│ ├── fig02to05.ipynb <- plot and analyze pain signature responses
│ ├── fig02to05.py <- helper functions for fig02to05.ipynb
│ └── sup01.ipynb <- plot and analyze supplementary data
├── results/ <- result tables derived from notebooks/
└── workflow
├── envs <- required python packages to reproduce full analysis pipeline
├── scripts
│ ├── 00_bold-to-pe.py <- calculate glm parameter estimates for study 1
│ ├── 00_cbf-to-ps.py <- apply pain signatures to study 2
│ ├── 01_bold-to-ps.py <- apply pain signatures to study 1
│ └── apply_ps.py
└── snakefile <- documentation of snakemake workflow
[-] not included in this repository
The data/*csv files contain the long-form pain signature and behavioral data, from which the figures/ and results/ can be reproduced with these notebooks/. Running the '*ipynb' files requires both Python and R packages, so a pre-installed distribution of the conda package manager (Anaconda, Miniconda, or Miniforge) is needed.
The pain signatures can be accessed on the CANLAB website.
The steps to reproduce the full analysis pipeline (from ASLPrep and fMRIPrep outputs) are outlined in workflow/snakefile, but access to the ~500GB of neuroimaging data is not included here.
installation
clone this repository
git clone https://github.com/griegner/med-pain-sig.git
cd med-pain-sig
install required dependencies
conda env create --file requirements.yml
conda activate med-pain-sig