A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
The FinnBrain multimodal neonatal template and atlas collection
Tekijät: Tuulari Jetro J.; Rosberg Aylin; Pulli Elmo P.; Hashempour Niloofar; Ukharova Elena; Lidauer Kristian; Jolly Ashmeet; Luotonen Silja; Audah Hilyatushalihah K.; Vartiainen Elena; Bano Wajiha; Suuronen Ilkka; Mariani Wigley Isabella L. C.; Fonov Vladimir S.; Collins D. Louis; Merisaari Harri; Karlsson Linnea; Karlsson Hasse; Lewis John D.
Kustantaja: Springer Science and Business Media LLC
Kustannuspaikka: BERLIN
Julkaisuvuosi: 2025
Journal: Communications Biology
Tietokannassa oleva lehden nimi: Communications Biology
Lehden akronyymi: COMMUN BIOL
Artikkelin numero: 600
Vuosikerta: 8
Numero: 1
Sivujen määrä: 14
eISSN: 2399-3642
DOI: https://doi.org/10.1038/s42003-025-07963-7
Verkko-osoite: https://doi.org/10.1038/s42003-025-07963-7
The accurate processing of neonatal and infant brain MRI data is crucial for developmental neuroscience but presents unique challenges that child and adult data do not. Tissue segmentation and image coregistration accuracy can be improved by optimizing template images and 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, aligned to the Talairach-like MNI-152 template, and has high spatial resolution (0.5 mm(3)). Additionally, we provide atlas labels, constructed from manual segmentations, for cortical grey matter, white matter, cerebrospinal fluid, brainstem, cerebellum as well as the bilateral hippocampi, amygdalae, caudate nuclei, putamina, globi pallidi, and thalami. This multi-contrast template and labelled atlases aim to advance developmental neuroscience by achieving reliable means for spatial normalization and measures of neonate brain structure via automated computational methods. We also provide standard volumetric and surface co-registration files to enable investigators to transform their statistical maps to the adult MNI space, improving the consistency and comparability of neonatal studies or the use of adult MNI space atlases in neonatal neuroimaging.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
