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
Clinical Neurosciences, University of Turku, Turku, Finland.
Neurocenter, Turku University Hospital, Turku, Finland.
Commun Biol. 2025 Apr 11;8(1):600. doi: 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 mm³). 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.
新生儿和婴儿脑MRI数据的精确处理对发育神经科学至关重要，但也带来了儿童和成人数据所没有的独特挑战。通过优化模板图像和相关分割程序，可以提高组织分割和图像配准的准确性。在此，我们描述了FinnBrain新生儿（FBN - 125）模板的构建，这是一个具有T1加权、T2加权以及扩散张量成像衍生的分数各向异性和平均扩散率图像的多对比度模板。该模板是对称的，与类似Talairach的MNI - 152模板对齐，并且具有高空间分辨率（0.5 mm³）。此外，我们提供了由手动分割构建的图谱标签，用于皮质灰质、白质、脑脊液、脑干、小脑以及双侧海马体、杏仁核、尾状核、壳核、苍白球和丘脑。这个多对比度模板和带标签的图谱旨在通过实现可靠的空间归一化方法以及通过自动化计算方法测量新生儿脑结构，来推动发育神经科学的发展。我们还提供了标准的体积和表面配准文件，以使研究人员能够将他们的统计图转换到成人MNI空间，提高新生儿研究的一致性和可比性，或者在新生儿神经成像中使用成人MNI空间图谱。
