Back

The FinnBrain Multimodal Neonatal Template and Atlas Collection: T1, T2, and DTI brain templates, and accompanying cortical and subcortical atlases

Tuulari, J. J.; Rosberg, A.; Pulli, E. P.; Hashempour, N.; Ukharova, E.; Lidauer, K.; Jolly, A.; Luotonen, S.; Audah, H. K.; Vartiainen, E.; Bano, W.; Suuronen, I.; Wigley, I. L. C.; Fonov, V.; Collins, D. L.; Merisaari, H.; Karlsson, L.; Karlsson, H.; Lewis, J. D.

2024-01-21 neuroscience
10.1101/2024.01.18.576325 bioRxiv
Show abstract

The accurate processing of neonatal and infant brain MRI data is crucially important for developmental neuroscience, but presents challenges that child and adult data do not. Tissue segmentation and image coregistration accuracy can be improved by optimizing template images and / or related segmentation procedures. Here, we describe the construction of the FinnBrain Neonate (FBN-125) template; a multi-contrast template with T1- and T2-weighted as well as diffusion tensor imaging derived fractional anisotropy and mean diffusivity images. The template is symmetric and aligned to the Talairach-like MNI 152 template and has high spatial resolution (0.5 mm3). In addition, we provide atlas labels, constructed from manual segmentations, for cortical grey matter, white matter, cerebrospinal fluid, brainstem, and cerebellum as well as the bilateral hippocampi, amygdalae, caudate nuclei, putamina, globi pallidi, and thalami. We provide this multi-contrast template along with the labelled atlases for the use of the neuroscience community in the hope that it will prove useful in advancing developmental neuroscience, for example, by helping to achieve reliable means for spatial normalization and measures of neonate brain structure via automated computational methods. Additionally, we provide standard co-registration files that will enable investigators to reliably transform their statistical maps to the adult MNI space, which has the potential to improve the consistency and comparability of neonatal studies or the use of adult MNI space atlases in neonatal neuroimaging.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.