Thank you so much @cedricboeckx.bsky.social
More than happy to keep discussing implications of brain #shape and #complexity (across species?)
08.01.2026 19:40
👍 2
🔁 0
💬 0
📌 0
Thank you so much @cedricboeckx.bsky.social
More than happy to keep discussing implications of brain #shape and #complexity (across species?)
Happy to have been able to contribute to this great work 👶🗻⛰️🏔️
Thanks for making it all the way here!❤️Huge thanks to the amazing team Amy Romanello Nina von Schwanenflug Jerod M Rasmussen Claudia Buss @sofievalk.bsky.social @engra.me @carstenfinke.bsky.social Charité Berlin @humboldtuni.bsky.social @bihatcharite.bsky.social
More than happy to chat, of course!
So what to make of this? Our results suggest that the formation of brain shape is a fundamental aspect of early-life brain maturation (only in humans?). FD captured key information over earlier brain measures, warranting further exploration of inferential, predictive, and clinical utility.
Here again, FD was systematically more sensitive to genetic similarity than all other studied brain measures. Notably, FD was able to predict which babies are twin siblings from their brain scans with high accuracy (77% overall, 97% identical), outperforming all other measures.
Finally, we conducted individual brain-to-brain comparisons and found that the brains of genetically related infants are more similar in shape compared to age-matched unrelated babies. Identical twins (~100% genes shared) were even more similar than fraternal twins (~50% shared).
Moreover, brain shape as measured with FD captured morphological signatures of premature birth that were not detected with brain size.
Corroborating the strong age-FD associations, we were able to predict the age of the babies from the shape of their brains with high accuracy (mean error ~4 days), again outperforming age prediction from brain size.
Cross-sectional age-FD effects were strongly confirmed by longitudinal effects in individual infants. Shape development was fastest in cortical gray matter and slowest in white matter. Interestingly, prematurely born infants showed a higher rate of change than term-born babies.
With regard to age-related differences of the cerebral cortex, FD not only outperformed volume as a measure of brain size but also many earlier morphological measures, including cortical thickness, curvature, gyrification, surface area, sulcation, and the T1/T2 ratio.
Brain shape was strongly related to the infant age at the time of scanning. In many regions - especially the cerebral cortex and white matter - FD tracked infant age more strongly than brain size (measured by volume).
In the first few weeks after birth, the human brain increases rapidly in size. Here, we studied how the brain develops in *shape*. For this, we used fractal dimensionality (FD) - a measure of structural complexity with high sensitivity to age-related changes of brain morphology.
Thrilled to share our paper on the formation of brain shape in human newborns, just out @natneuro.nature.com: tinyurl.com/2ty4ef43
Using #fractal analysis of #MRI data from the developing Human Connectome Project (lnkd.in/dxeHbJX6), we show that brain shape closely captures infant age and genetics ⬇️
Very happy to see this great study by @skrohn.bsky.social @carstenfinke.bsky.social @engra.me @sofievalk.bsky.social on brain shape & human brain development, out in @natneuro.nature.com
🧪🧠
www.nature.com/articles/s41...
The brain’s action-mode network — a Perspective by Nico U. F. Dosenbach, Marcus E. Raichle & Evan M. Gordon
@ndosenbach.bsky.social @gordonneuro.bsky.social
www.nature.com/articles/s41...
The majority of people who responded to a poll in Nature say they’re now using Bluesky. They’re using it to connect with other scientists, keep up to date with other research or researchers, and promote their own research. 🧪
Hello #world! Fresh on here today after friends told me it's great for #science in general and #neuroscience in particular. Where my brainies at?
