A particular shoutout to Essa Yacoub, @drdamienfair.bsky.social, Alireza Sadeghi-Tarakameh, Jed Elison, @benediktramirez.bsky.social, @smnelson.bsky.social and many more for their important contributions!!

Our work, which was a huge collaborative effort between UMN CMRR and @umn-midb.bsky.social provides a first glance on what we can achieve with 7T MRI in infants and highlights its role as a promising new avenue for developmental cognitive neuroscience.

Leveraging this high-resolution data we were able to identify the Somato-Cognitive Action Network in a five week old infant, the earliest this network has been shown thus far, questioning the degree of ‘maturation’ we assign to infant networks.

Our 7T data showed an increase in spatial precision using a spatial resolution more appropriately matched to infants' small head sizes and improvements in reliability compared to 3T, reducing the need for long data acquisitions.

We implemented a subject specific safety assessment to ensure safe scanning within SAR limits on the Siemens Terra system using a commercially available head coil.

Thanks for all the contributions of @drdamienfair.bsky.social @catcamacho.bsky.social @roberthermosillo.bsky.social @gordonneuro.bsky.social @muriahwheelock.bsky.social @tu-jiaxin.bsky.social @koiralasanju.bsky.social @smnelson.bsky.social @crimoral.bsky.social and many many more!

Precision functional imaging in early development will allow us to uncover individual brain developmental trajectories and help tailor personalized interventions.

For these studies we acquired up to 154 min of low motion resting state fMRI data in neonates and up to 153 min with auditory oddball paradigm in 8 week old infants.

And that babies show individual specific nuances in their BOLD response patterns towards salient stimuli.

Using both resting state and task fMRI, we show that babies brain functional architecture can be divided into cortical areas that are individual specific and distinct from other babies.

This has been a huge group effort with contributions by @drdamienfair.bsky.social @markiewicz.bsky.social @russpoldrack.org @ericfeczko.bsky.social @oesteban.bsky.social @ted-satterthwaite.bsky.social @tsalo.bsky.social @smnelson.bsky.social @erikglee.bsky.social @koiralasanju.bsky.social and more!

Our paper additionally describes age independent upstream enhancements to the entire NiPreps suite like the implementation of a ‘fit’ and ‘transform’ model and convergence of processing with other popular workflows (ABCD-BIDS, HCP Pipelines). So check out fMRIPrep versions 23.2.1 and later!

fMRIPrep Lifespan produces high quality outputs for structural and functional images in a wide age range which we showed in a random sample of children from 1-43 months. The scalable, modular infrastructure of fMRIPrep Lifespan will ensure adaptability to data from birth to old age.