Revealing the neurobiology underlying interpersonal neural synchronization with multimodal data fusion

This repository accompanies our publication:

Leon D. Lotter, Simon H. Kohl, Christian Gerloff, Laura Bell, Alexandra Niephaus, Jana A. Kruppa, Juergen Dukart, Martin Schulte-Rüther, Vanessa Reindl, & Kerstin Konrad (2023). Revealing the neurobiology underlying interpersonal neural synchronization with multimodal data fusion. Neuroscience & Biobehavioral Reviews.

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All data and code in this repository are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Please cite both the publication and the dataset (Zenodo) when using data from this repository.

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Main analyses and results:

• MAsync_analyses.ipynb: Jupyter notebook to reproduce all analyses except for resting-state data processing. Contains general descriptions of methods and results.
• data/datasets/fmri_coordinates.csv: Coordinates and associated study information from fMRI interpersonal neural synchrony studies
• data/datasets/fnirs_coordinates.csv: Coordinates and associated study information from fNIRS interpersonal neural synchrony studies

All data, code, figures, & literature search result:

/data

Contains all results, see separate README file.

/fig

Contains code to reproduce figures and the associated files.

/lit

Contains results from different stages of the literature search as *.ris files.

/src

Contains all functions called in the analysis notebook. Also includes a development version of the JuSpyce toolbox.

• ale.py: Wrapper for Activation Likelihood Estimation workflow in NiMARE
• fnirs.py: Quantitative analysis of fNIRS data
• fsn.py: Fail-Safe-N validation analysis
• loeo.py: Leave-one-experiment-out validation analysis
• neurosynth.py: Get Neurosynth data and generate meta-analytic maps
• overlap.py: Quantify overlap between cluster volumes using relative and absolute distributions
• parcellate_pet.py: Obtain parcellated PET data from volumes (volumes not in this repo)
• rsfc_resample_hcp.py: Resample HCP-data to 3mm voxel-resolution
• utils_image.py: Image manipulation functions
• utils_io.py: NiMARE dataset input/output
• utils_plot.py: Plotting functions used within analysis notebook
• CONNbatch_processHCP.m: Matlab: Batch script to generate resting-state functional connectivity data from FIX-preprocessed HCP-data

Reproduce the analyses

To ease the reproduction of our analyses, we added dependency management via poetry to this repository. To create a virtual python environment with all the package versions we used, perform the steps below. However, you can also install all required packages manually in a python 3.8.8 environment.

Poetry on osx/ linux

Install via curl:

curl -sSL https://install.python-poetry.org | python -

Poetry on windows

Install via powershell:

(Invoke-WebRequest -Uri https://install.python-poetry.org -UseBasicParsing).Content | python -

Test if poetry was correctly installed:

poetry --version

If the command is not found, add poetry to the PATH environment variable via:

export PATH=$PATH:$HOME/.poetry/bin

Run virtual environment

Go to the folder you downloaded the repository to, start a new terminal, and get synchronized 😎

poetry shell  
poetry install

Further resources

Here, we provide an interactive version of the citation network included in the manuscript (Figure 2B).
Three toolboxes were developed partly in the context of this paper:

• SetYouFree: A tool for automated literature retrieval from API-accessible databases
• JuSpyce: A - wait for it - spyced up Python adaptation of the JuSpace toolbox, incorporating numerous strategies to test for alignment between multimodal neuroimaging data
• ABAnnotate: A MATLAB toolbox for ensemble-based multimodal gene-category enrichment analysis of human neuroimaging data

Contact

If you have any problems, questions, comments or suggestions, feel free to open an issue or contact me!