> For the complete documentation index, see [llms.txt](https://time-series-features.gitbook.io/pyspi/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://time-series-features.gitbook.io/pyspi/information-about-pyspi/publications-using-pyspi.md). # 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

Introduced pyspi as a comprehensive library of statistics of pairwise interactions and assessed their behaviour on a range of real-world and simulated systems.

📕 Nature Computational Science (2023).

📙 arXIv preprint (2023).

💻 GitHub repository.

Screenshot 2024-05-11 at 7.35.36 am.pnghttps://www.nature.com/articles/s43588-023-00519-x
*** ## Applications Papers The following publications have utilised *pyspi* for a variety of real-world applications including:

Extracting interpretable signatures of whole-brain dynamics through systematic comparison.

📗PLOS Computational Biology (2024).

📙 bioRxiv (2024).

💻 Code.

Screenshot 2024-05-11 at 7.41.53 am.pnghttps://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012692

How to measure functional connectivity using resting-state fMRI? A comprehensive empirical exploration of different connectivity metrics.

📗NeuroImage (2025).

📙 bioRxiv (2024).

💻 Data.

Screenshot 2024-05-11 at 7.44.07 am.pnghttps://www.sciencedirect.com/science/article/pii/S1053811925001983?via%3Dihub

Benchmarking methods for mapping functional connectivity in the brain.

📗 Nature Methods (2025).

📙 bioRxiv (2024). 💻 Code.

Screenshot 2024-05-11 at 7.45.46 am.pnghttps://www.nature.com/articles/s41592-025-02704-4

Identifying and evaluating connectivity-based neural correlates of consciousness.

📗 Neuroscience of Consciousness (2026).

💻 Code.

methods_overview.pnghttps://doi.org/10.1093/nc/niag029
*in addition to:* * Capture the functional interaction structure of the brain using feature similarity, compared against 46 of 49 SPIs. * [📗 Wang et al., *Communications Biology* (2025).](https://doi.org/10.1038/s42003-025-09165-7) --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://time-series-features.gitbook.io/pyspi/information-about-pyspi/publications-using-pyspi.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.