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Brain networks

Complex topology meets simple statistics

Nature Neuroscience volume 26, pages 732–734 (2023)Cite this article

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In this issue, Shinn et al. demonstrate a close relationship between complex brain network topology and lower-level statistical properties of neuroimaging data. They also highlight the potential of these statistical measures, which capture similarity in space and time, to provide imaging-based markers of aging and pharmacological states.

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Fig. 1: Illustration of spatial and temporal autocorrelation in fMRI measurements of the human brain.

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Acknowledgements

The authors acknowledge support from NIH RF1MH125931.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA

    Shiyu Wang & Catie Chang

  2. Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA

    Catie Chang

  3. Department of Computer Science, Vanderbilt University, Nashville, TN, USA

    Catie Chang

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  1. Shiyu Wang
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  2. Catie Chang
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Correspondence to Catie Chang.

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Wang, S., Chang, C. Complex topology meets simple statistics. Nat Neurosci 26, 732–734 (2023). https://doi.org/10.1038/s41593-023-01295-7

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