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Raffaeli and colleagues present a systematic comparison of linear versus nonlinear bivariate dependency structures in the brain across spatial and temporal scales in both human and mouse.

Major issues

• An old and heuristic method for computing mutual information is used (equiprobable binning) despite fast, sophisticated modern methods exist for continuous data, but these are not quantitatively compared to their binning approach. cf. https://github.com/jlizier/jidt for an open implementation.

• It’s unclear whether the heuristic to decompose MI through ratios is theoretically valid.

• Previous work has indeed compared linear vs. nonlinear coupling metrics in fMRI and other modalities, contrary to the statement “A principled approach for its quantification in terms of extra-Gaussian information has so far only been applied to a single modality ([10])” cf.:

  • Nozari et al. (2023) https://www.nature.com/articles/s41551-023-01117-y

  • Blinowski & Malinowski (1991): https://link.springer.com/article/10.1007/BF00243291

  • Zhao et al. (2013): https://ieeexplore.ieee.org/document/6542649/

• Important work explaining why measured brain dynamics may become more linear (e.g., when averaging across space/time) is missing: e.g.:

  • Nozari et al. (2023) https://www.nature.com/articles/s41551-023-01117-y

  • Messe et al. (2015) http://dx.doi.org/10.1016/j.neuroimage.2015.02.001

• Important context on methods for computing pairwise dependence (beyond MI and Pearson correlation) is missing, e.g.,:

  • Smith et al. (2011): http://linkinghub.elsevier.com/retrieve/pii/S1053811910011602

  • Liu et al. (2024): https://www.biorxiv.org/content/10.1101/2024.05.07.593018v1

  • Mohanty et al. (2020): https://www.nature.com/articles/s41598-020-57915-w

  • Prado et al. (2023): https://www.sciencedirect.com/science/article/pii/S096999612300061X

  • Cliff et al. (2023): https://www.nature.com/articles/s43588-023-00519-x

Minor issues

• Concepts of “Gaussianity” and “linearity” are distinct but are not clearly distinguished (~L178), when it is important to do so.

Competing interests

The authors declare that they have no competing interests.