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基于 UNC/UMN 婴儿连接组计划 (BCP) 队列的 4D 婴儿脑容量图谱。

A 4D infant brain volumetric atlas based on the UNC/UMN baby connectome project (BCP) cohort.

机构信息

Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium.

Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium.

出版信息

Neuroimage. 2022 Jun;253:119097. doi: 10.1016/j.neuroimage.2022.119097. Epub 2022 Mar 14.

Abstract

Spatiotemporal (four-dimensional) infant-dedicated brain atlases are essential for neuroimaging analysis of early dynamic brain development. However, due to the substantial technical challenges in the acquisition and processing of infant brain MR images, 4D atlases densely covering the dynamic brain development during infancy are still scarce. Few existing ones generally have fuzzy tissue contrast and low spatiotemporal resolution, leading to degraded accuracy of atlas-based normalization and subsequent analyses. To address this issue, in this paper, we construct a 4D structural MRI atlas for infant brains based on the UNC/UMN Baby Connectome Project (BCP) dataset, which features a high spatial resolution, extensive age-range coverage, and densely sampled time points. Specifically, 542 longitudinal T1w and T2w scans from 240 typically developing infants up to 26-month of age were utilized for our atlas construction. To improve the co-registration accuracy of the infant brain images, which typically exhibit dynamic appearance with low tissue contrast, we employed the state-of-the-art registration method and leveraged our generated reliable brain tissue probability maps in addition to the intensity images to improve the alignment of individual images. To achieve consistent region labeling on both infant and adult brain images for facilitating region-based analysis across ages, we mapped the widely used Desikan cortical parcellation onto our atlas by following an age-decreasing mapping manner. Meanwhile, the typical subcortical structures were manually delineated to facilitate the studies related to the subcortex. Compared with the existing infant brain atlases, our 4D atlas has much higher spatiotemporal resolution and preserves more structural details, and thus can boost accuracy in neurodevelopmental analysis during infancy.

摘要

时空(四维)婴儿专用脑图谱对于早期动态脑发育的神经影像学分析至关重要。然而,由于婴儿脑磁共振成像采集和处理方面存在重大技术挑战,密集覆盖婴儿期动态脑发育的 4D 图谱仍然稀缺。现有的少数图谱通常存在组织对比度模糊和时空分辨率低的问题,导致基于图谱的归一化和后续分析的准确性降低。为了解决这个问题,在本文中,我们基于 UNC/UMN 婴儿连接组计划(BCP)数据集构建了一个用于婴儿大脑的 4D 结构磁共振成像图谱,该图谱具有高空间分辨率、广泛的年龄范围覆盖和密集采样的时间点。具体来说,我们利用了 240 名正常发育婴儿的 542 个纵向 T1w 和 T2w 扫描,年龄范围从出生到 26 个月。为了提高婴儿脑图像的配准精度,我们采用了最先进的配准方法,并利用我们生成的可靠的脑组织概率图,除了强度图像外,还提高了个体图像的对齐度。为了在婴儿和成人脑图像上实现一致的区域标记,以便在不同年龄进行基于区域的分析,我们通过按年龄递减的映射方式将广泛使用的 Desikan 皮质分区映射到我们的图谱上。同时,手动勾勒出典型的皮质下结构,以促进与皮质下相关的研究。与现有的婴儿脑图谱相比,我们的 4D 图谱具有更高的时空分辨率,保留了更多的结构细节,因此可以提高婴儿期神经发育分析的准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c14e/9155180/a886ad01f2ce/nihms-1809167-f0001.jpg

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