Do alpha-power and SPEX topographical changes correlate between eyes-open and eyes-closed?

[MakotoMiyakoshi]

Posting my old unpublished observations.

I used Dortmund datasets by Edmund Wascher. n=608, 64 channels, eyes open and closed.
https://www.nature.com/articles/s41597-024-03797-w#libraryItemId=17697190

Hmm... they do not overlap. Interesting, isn't it?

Is there anyone who wants to write a paper on this?

[Contrerana]

I started to look into something similar (task vs resting state) with infants EEG data, but as maturation it is a big confound here it is difficult to make any conclusions. I will be happy to work with you in adults first, less variability than in infants EEG, and them move back to infants...

[MakotoMiyakoshi]

Hi @Contrerana,

There are multiple factors that affect the 1/f-ness, but as far as we know, myelination is one of the major ones. An unmyelinated axon is effectively treated the same as a dendrite and is therefore subject to the dendritic filter” so I see your concern. However, I do not think it means it is meaningless to study it--you can still study, for example, within-subject comparison, which Mate has been saying the most reliable method we can use.

Anyway, if you want to work on adult data, you are welcome.

[Contrerana]

Agreed, myelination and changes in the E/I balance are key factors during development that will affect 1/f. But also, how we preprocess the data (infant artefacts are very different to adults) and the bands of interest (e.g infant alpha is more in the range of 6 to 9Hz) also likely to affect how we calculate/ interpret the results. That is why I think it is easier first achieve a consensus in the meaning and "good practices" level in adults and then move towards infants. Infant data is messier and much more variability, even more than in adults. But I may be totally wrong too, lots of experience in EEG but newly arrived in the 1\f topic.

[dieloz]

Happy to chime in!
@MakotoMiyakoshi, if you could provide a bit more information about the power scale so we can properly interpret the negative values (and also about the SPEX) that would be great.

It seems that the Dortmund dataset may contain differences in sensorimotor mu that can be detected more or less easily depending on the magnitude of overlapping classical alpha. This relates to the additive vs. multiplicative signal-generation discussion raised in the email list. Alpha estimates during eyes-closed conditions may bias (or not) the 1/f estimation relative to eyes-open. Simply closing the eyes can shift the peak frequency in some cases (https://www.eneuro.org/content/7/1/ENEURO.0268-19.2019), so may be these are two different beasts.

Not relevant for the Dortmund dataset, but closing the eyes and performing a tactile task places participants in a very different brain state compared to performing the same task with eyes open (https://www.eneuro.org/content/9/1/ENEURO.0412-21.2021).

I’m very happy to contribute to study how these brain-state changes affect both oscillatory components and the 1/f exponent. There are open datasets with re-test measurements (cross-over designs) that we could use to examine how reliable these differences are (i.e., the trait vs. state debate).

[MakotoMiyakoshi]

The PSD color scales are in 10*log10 microV^2/Hz (dB). 0dB represents 1 microV^2/Hz in linear scale.

In fact, it is strange not to see 'red' in the eyes open condition. I'll ask Hyeonseok about it. Please wait for update.

[EugenMasherov]

1. Is the power in the figures on the left on a logarithmic scale?
2. What algorithm was used to remove the influence of the alpha rhythm peak with eyes closed?

And a simple slope estimation algorithm. Given a spectrum p(w), we calculate the slope for all possible frequency pairs s=(ln(p(w1)-ln(p(w2))/(ln(w1)-ln(w2)) and take the median of the resulting values. Peak values ​​will yield erroneous estimates, but the median, as a robust estimator, will discard them. It would be interesting to compare this approach with others.

[MakotoMiyakoshi]

1. Yes, 10*log10 microV^2/Hz (dB).
2. FOOOF with a single-peak detection (default is 3 peaks) See Donoghue et al. (2020)