# Publications using pyspi

This page lists scientific research publications that have used *pyspi*. In cases where journal articles are not open access (marked by 📗), we also provide a link to the freely accessible preprint (marked by 📙). Links to GitHub code repositories (marked by :computer:) are also provided where possible. To summarise:

* :closed\_book: = Journal is ***not***** open access**.
* 📗 = Journal is **open access**.
* 📙 = **Freely** accessible pre-print.
* :computer:  = **Code/Data available**.

## Original Paper 

<table data-view="cards"><thead><tr><th align="center"></th><th data-hidden data-card-cover data-type="files"></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td align="center"><p><strong>Introduced </strong><em><strong>pyspi</strong></em><strong> as a comprehensive library of statistics of pairwise interactions and assessed their behaviour on a range of real-world and simulated systems.</strong></p><p><span data-gb-custom-inline data-tag="emoji" data-code="1f4d5">📕</span> <a href="https://doi.org/10.1038/s43588-023-00519-x"><em><mark style="color:green;">Nature Computational Science</mark></em><mark style="color:green;"> (2023)</mark><em><mark style="color:green;">.</mark></em></a></p><p>📙 <a href="https://arxiv.org/abs/2201.11941"><em><mark style="color:orange;">arXIv preprint</mark></em><mark style="color:orange;"> (2023)</mark></a><mark style="color:orange;">.</mark></p><p> <span data-gb-custom-inline data-tag="emoji" data-code="1f4bb">💻</span> <a href="https://github.com/DynamicsAndNeuralSystems/pyspi">GitHub repository</a>. </p></td><td><a href="/files/Kg0kdfagCZa7C2aNoeJf">/files/Kg0kdfagCZa7C2aNoeJf</a></td><td><a href="https://www.nature.com/articles/s43588-023-00519-x">https://www.nature.com/articles/s43588-023-00519-x</a></td></tr></tbody></table>

***

## Applications Papers&#x20;

The following publications have utilised *pyspi* for a variety of real-world applications including:

<table data-view="cards"><thead><tr><th align="center"></th><th data-hidden data-card-cover data-type="files"></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td align="center"><p>Extracting interpretable signatures of whole-brain dynamics through systematic comparison.</p><p>📗<a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012692"><em><mark style="color:green;">PLOS Computational Biology (2024).</mark></em></a></p><p>📙 <a href="https://www.biorxiv.org/content/10.1101/2024.01.10.573372v2"><em><mark style="color:orange;">bioRxiv</mark></em> <mark style="color:orange;">(2024).</mark></a></p><p> <span data-gb-custom-inline data-tag="emoji" data-code="1f4bb">💻</span> <a href="https://github.com/DynamicsAndNeuralSystems/fMRI_FeaturesDisorders">Code</a>.</p></td><td><a href="/files/1F4jA2nhAR9tSbYYsBaa">/files/1F4jA2nhAR9tSbYYsBaa</a></td><td><a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012692">https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012692</a></td></tr><tr><td align="center"><p>How to measure functional connectivity using resting-state fMRI? A comprehensive empirical exploration of different connectivity metrics.</p><p>📗<a href="https://www.sciencedirect.com/science/article/pii/S1053811925001983?via%3Dihub"><em><mark style="color:green;">NeuroImage (2025)</mark></em></a><em><mark style="color:green;">.</mark></em></p><p>📙 <a href="https://www.biorxiv.org/content/10.1101/2024.03.18.585458v1.abstract"><em><mark style="color:orange;">bioRxiv</mark></em><mark style="color:orange;"> (2024).</mark></a></p><p><span data-gb-custom-inline data-tag="emoji" data-code="1f4bb">💻</span> <a href="https://openneuro.org/datasets/ds000221/versions/00002">Data</a>.</p></td><td><a href="/files/eJycoAANcfNe5pUTH4YD">/files/eJycoAANcfNe5pUTH4YD</a></td><td><a href="https://www.sciencedirect.com/science/article/pii/S1053811925001983?via%3Dihub">https://www.sciencedirect.com/science/article/pii/S1053811925001983?via%3Dihub</a></td></tr><tr><td align="center"><p>Benchmarking methods for mapping functional connectivity in the brain.</p><p><span data-gb-custom-inline data-tag="emoji" data-code="1f4d7">📗</span> <a href="https://www.nature.com/articles/s41592-025-02704-4"><em><mark style="color:green;">Nature Methods (2025).</mark></em></a></p><p>📙<a href="https://www.biorxiv.org/content/10.1101/2024.05.07.593018v1"> <em><mark style="color:orange;">bioRxiv</mark></em><mark style="color:orange;"> (2024)</mark></a><mark style="color:orange;">.</mark> <span data-gb-custom-inline data-tag="emoji" data-code="1f4bb">💻</span> <a href="https://github.com/netneurolab/liu_fc-pyspi">Code</a>.</p></td><td><a href="/files/oX35WXi3UjgAjW9BKbam">/files/oX35WXi3UjgAjW9BKbam</a></td><td><a href="https://www.nature.com/articles/s41592-025-02704-4">https://www.nature.com/articles/s41592-025-02704-4</a></td></tr><tr><td align="center"><p>Identifying and evaluating connectivity-based neural correlates of consciousness.</p><p> <span data-gb-custom-inline data-tag="emoji" data-code="1f4d9">📙</span> <a href="https://www.biorxiv.org/content/10.1101/2025.04.06.646695v1.full.pdf"><em><mark style="color:orange;">bioRxiv (2025).</mark></em></a> </p><p><span data-gb-custom-inline data-tag="emoji" data-code="1f4bb">💻</span> <a href="https://github.com/anniegbryant/MEG_functional_connectivity">Code</a>.</p></td><td><a href="/files/8KNuGvtH0XyAxmOkeaJm">/files/8KNuGvtH0XyAxmOkeaJm</a></td><td><a href="https://www.biorxiv.org/content/10.1101/2025.04.06.646695v1.full.pdf">https://www.biorxiv.org/content/10.1101/2025.04.06.646695v1.full.pdf</a></td></tr><tr><td align="center"></td><td></td><td></td></tr></tbody></table>


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