We propose applications to physiological networks, climate, and finance

Unlike traditional methods treating measurements as independent snapshots, PIRD captures temporal structure to reveal whether multiple factors work independently, provide overlapping information, or only show influence when considered together over time.

We developed a mathematical framework called Partial Information Rate Decomposition (PIRD) that accounts for how information evolves in dynamic systems by recognizing that measurements depend on past conditions.

Totally my experience too

It's just yet another journal.. The board moved because of high APC, not because of being Elsevier. Then the same community pays much more to publish in Nature Communications

Indeed comments are clunky, cc-ing @richardsever.bsky.social .

And FC is dominated by 1/f, with a comment of mine mentioning that issue, which of course remained unanswered 😭
www.biorxiv.org/content/10.1...

Well but also amplitude modulation = E/I balance 😂😂😂.
Also, brain dark matter!

See for example www.sciencedirect.com/science/arti...

pubpeer.com/publications...

We should definitely not blindly talk about "beta connectivity" without asking ourselves whether there's beta activity in the first place, and even more so when there isn't, 1/f is prominent (as it is to be expected). This was (at least I think now 😅) my comment

Weird, is the comment to the preprint
www.biorxiv.org/content/10.1...

At short scales all is aperiodic, at long scales, all is periodic
disq.us/p/30ouev0

Some see these as hallmark of AI generated text, but apparently is not necessarily the case
www.nytimes.com/2025/09/18/m...

You could have rather put the quotation marks in "arousal", given the last sentence of the paper.
Nice and impressive, but it's basically saying that there is a low-dimensional descriptor. Now, it could have been reversed having latent brain dynamics as a predictor, and pupillometry as the target

There are so many assumptions we make, and so many mechanisms that could originate the same spectral features (both "periodic" and "aperiodic"), that pretending to "separate" them (are they separable?) is super difficult if not ill-posed.

thanks!. I appreciate your effort, and I have been thinking a lot about this "separation" too. But outside the framework of a (biased imo) generative model of "pure oscillations" plus 1/f, how can this be applied to real data?

ha! you wouldn't tell..
I always use this slide

Congratulations, you'll do great things

It's a self answering question, those are defined by the association, so if they do stuff together, call them the same. Or do as some smart asses who talk about "coactivation"