Thank you so much to the brilliant @sylvainbaillet.bsky.social and @alexwiesman.bsky.social for all their support. And to our wonderful collaborators at @sickkidsto.bsky.social Margot Taylor! (6/6)🌈

Our findings suggest a growing influence of genetic factors on neurophysiological traits across the lifespan and advance our understanding of the evolving biological foundations of inter-individual differences. (5/6) 🧬🧬🧠🧠

The cortical regions most critical for determining individuality shift across the lifespan🧠, with sensorimotor cortices becoming increasingly prominent in adulthood. These changes closely align with the expression of cortical genetic systems involved in ion transport and neurotransmission. (4/6)

Children 👦👧 were more difficult to distinguish from one another based on their neurophysiology than adults. This is because children's neurophysiological traits are more similar to one another. Arrhythmic brain activity drives this effect—children are differentiable from rhythmic brain traits. (3/6)

People’s brain activity is much like a fingerprint and can distinguish individuals from one another. But all of this work has been done, for the most part, in adults. What about children and older adults? (2/6)

It does not, however, speak to the origins of the 1/f. I agree that many mechanisms could produce the same spectrum. More work is needed to clarify these neural mechanisms.

This is a great question! Our paper is grounded in the 'specparam' framework and aims to test whether current approaches yield biased and/or correlated estimates of spectral parameters. I think this is an important question given the widespread use of these methods.

Using an empirical dataset, we show that analytical choice in quantifying brain rhythms impacts the interpretation of your results! 😰 Lastly, we find that resting state alpha power and arrhythmic slope are positively correlated at rest, which dovetails with the cortical inhibition account.

(4/4)

We show that spectral detrending methods produce biased estimates of rhythmic power that introduce spurious correlations between brain rhythms and arrhythmic brain activity. Modelled Gaussian power, on the other hand, does not. 🤯

(3/4)

Brain rhythms are important! 🚨Peristimulus alpha power predicts participants’ subjective experience. 👀 Arrhythmic brain activity is equally important! Both are theorized to be markers of cortical inhibition⚡️. But can we measure the two independently of one another with our current tools?

(2/4)

-Sensorimotor cortices 🏋️‍♀️🚴‍♀️ become increasingly prominent for differentiating individuals across neurodevelopment and aging.
-These changes become increasingly aligned with cortical gene expression🧬.

🧵 (3/3)