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半自动化海马亚区分割方法在儿科样本中跨年龄段的性能。

Performance of semi-automated hippocampal subfield segmentation methods across ages in a pediatric sample.

机构信息

Center for Learning and Memory, The University of Texas at Austin, USA; Department of Psychology, University of Toronto, Canada.

Center for Learning and Memory, The University of Texas at Austin, USA; Department of Psychology, University of Toronto, Canada.

出版信息

Neuroimage. 2019 May 1;191:49-67. doi: 10.1016/j.neuroimage.2019.01.051. Epub 2019 Feb 5.

Abstract

Episodic memory function has been shown to depend critically on the hippocampus. This region is made up of a number of subfields, which differ in both cytoarchitectural features and functional roles in the mature brain. Recent neuroimaging work in children and adolescents has suggested that these regions may undergo different developmental trajectories-a fact that has important implications for how we think about learning and memory processes in these populations. Despite the growing research interest in hippocampal structure and function at the subfield level in healthy young adults, comparatively fewer studies have been carried out looking at subfield development. One barrier to studying these questions has been that manual segmentation of hippocampal subfields-considered by many to be the best available approach for defining these regions-is laborious and can be infeasible for large cross-sectional or longitudinal studies of cognitive development. Moreover, manual segmentation requires some subjectivity and is not impervious to bias or error. In a developmental sample of individuals spanning 6-30 years, we assessed the degree to which two semi-automated segmentation approaches-one approach based on Automated Segmentation of Hippocampal Subfields (ASHS) and another utilizing Advanced Normalization Tools (ANTs)-approximated manual subfield delineation on each individual by a single expert rater. Our main question was whether performance varied as a function of age group. Across several quantitative metrics, we found negligible differences in subfield validity across the child, adolescent, and adult age groups, suggesting that these methods can be reliably applied to developmental studies. We conclude that ASHS outperforms ANTs overall and is thus preferable for analyses carried out in individual subject space. However, we underscore that ANTs is also acceptable and may be well-suited for analyses requiring normalization to a single group template (e.g., voxelwise analyses across a wide age range). Previous work has supported the use of such methods in healthy young adults, as well as several special populations such as older adults and those suffering from mild cognitive impairment. Our results extend these previous findings to show that ASHS and ANTs can also be used in pediatric populations as young as six.

摘要

情景记忆功能已被证明严重依赖于海马体。这个区域由许多子区域组成,这些子区域在成熟大脑中的细胞结构特征和功能作用上有所不同。最近对儿童和青少年的神经影像学研究表明,这些区域可能经历不同的发育轨迹——这一事实对我们如何思考这些人群的学习和记忆过程具有重要意义。尽管在健康的年轻成年人中,对海马体结构和功能的子区域的研究兴趣日益增加,但对这些子区域的发育研究相对较少。研究这些问题的一个障碍是,手动分割海马体子区域——许多人认为这是定义这些区域的最佳方法——既费力,又不适合认知发展的大型横断面或纵向研究。此外,手动分割需要一些主观性,并且不能避免偏见或错误。在一个跨越 6 至 30 岁个体的发育样本中,我们评估了两种半自动分割方法(一种基于自动海马体子区域分割(ASHS)的方法,另一种利用高级归一化工具(ANTs)的方法)在个体专家评分者对每个人的手动子区域描绘的程度。我们的主要问题是,性能是否随年龄组而变化。通过几个定量指标,我们发现子区域有效性在儿童、青少年和成年年龄组之间几乎没有差异,这表明这些方法可以可靠地应用于发育研究。我们的结论是,ASHS 总体上优于 ANTs,因此更适合在个体主体空间中进行分析。然而,我们强调 ANTs 也是可以接受的,并且可能非常适合需要归一化为单个组模板的分析(例如,在广泛的年龄范围内进行体素分析)。以前的工作已经支持在健康的年轻成年人以及其他特殊人群(如老年人和轻度认知障碍患者)中使用这些方法。我们的结果扩展了这些先前的发现,表明 ASHS 和 ANTs 也可以在 6 岁以下的儿科人群中使用。

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