🔥OK I'm editing a bunch of papers for tomorrow's ISBI deadline, and somehow everyone knows about this cool paper [1] but I didn't as I must have not been paying attention:
🔥Raut et al. found a sort of "Mexican wave" (like people standing up in a football stadium in a propagating/travelling wave) in the resting brain. A slow wave (on resting state) fMRI that is linked to arousal sweeps across cortex every so often, roughly from sensory regions to association regions (principal gradient, see the movie).
🔥SO if the standard (Yeo-7) resting state functional networks sit at different places along that path, when you average each network and compare them, you will see consistent lags between networks (even if lots of other stuff is going on and this is not the "main thing").
🔥SO, networks far apart on the path, like visual and DMN, would show the biggest phase lags. OK so that is "Raut's wave" in the movie here from his paper, and assuming that the brain could have several, you can use dynamic mode decomposition (DMD) that includes Raut’s wave as a special case. so a puzzle... can a handful of whole-brain modes explain the 7-network lags and cross-spectra?
🔥And (my usual obsession)= how much data do we need to stably identify and estimate these components with a known accuracy? There isn't an analog of Davis Kahan (which we use in the zeta law) for DMD models as the eigenvectors of the transition operator are not normal.
🔥[1] Raut RV, Snyder AZ, Mitra A, Yellin D, Fujii N, Malach R, Raichle ME. Global waves synchronize the brain’s functional systems with fluctuating arousal. Sci Adv. 2021.
🔥[2] on DMD: Song Y, Kim PS, Philip BA, Kim T. Large-scale signal propagation modes in the human brain. Neuroimage. 2025 Sep.