Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Canada.
Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada; Schulich School of Medicine and Dentistry, Western University, London, Canada.
Neuroimage. 2018 Apr 15;170:132-150. doi: 10.1016/j.neuroimage.2016.10.027. Epub 2016 Oct 18.
Recently, much attention has been focused on the definition and structure of the hippocampus and its subfields, while the projections from the hippocampus have been relatively understudied. Here, we derive a reliable protocol for manual segmentation of hippocampal white matter regions (alveus, fimbria, and fornix) using high-resolution magnetic resonance images that are complementary to our previous definitions of the hippocampal subfields, both of which are freely available at https://github.com/cobralab/atlases. Our segmentation methods demonstrated high inter- and intra-rater reliability, were validated as inputs in automated segmentation, and were used to analyze the trajectory of these regions in both healthy aging (OASIS), and Alzheimer's disease (AD) and mild cognitive impairment (MCI; using ADNI). We observed significant bilateral decreases in the fornix in healthy aging while the alveus and cornu ammonis (CA) 1 were well preserved (all p's<0.006). MCI and AD demonstrated significant decreases in fimbriae and fornices. Many hippocampal subfields exhibited decreased volume in both MCI and AD, yet no significant differences were found between MCI and AD cohorts themselves. Our results suggest a neuroprotective or compensatory role for the alveus and CA1 in healthy aging and suggest that an improved understanding of the volumetric trajectories of these structures is required.
最近,人们对海马体及其亚区的定义和结构给予了高度关注,而对其投射的研究相对较少。在这里,我们提出了一种可靠的使用高分辨率磁共振图像手动分割海马白质区域(包括侧脑室下角、穹窿和穹窿连合)的方案,该方案与我们之前对海马亚区的定义相辅相成,这两个定义都可以在 https://github.com/cobralab/atlases 上免费获取。我们的分割方法具有较高的组内和组间可靠性,可作为自动分割的输入进行验证,并用于分析这些区域在健康老化(OASIS)、阿尔茨海默病(AD)和轻度认知障碍(MCI;使用 ADNI)中的轨迹。我们观察到健康老化时双侧穹窿明显减少,而侧脑室下角和 CA1 区域保持完好(所有 p 值<0.006)。MCI 和 AD 患者的穹窿连合和穹窿明显减少。在 MCI 和 AD 患者中,许多海马亚区的体积都减少了,但 MCI 和 AD 队列之间没有发现显著差异。我们的结果表明,侧脑室下角和 CA1 在健康老化中具有神经保护或代偿作用,这表明需要更好地了解这些结构的体积变化轨迹。