Vrije Universiteit Brussel, Center for Neurosciences, Laarbeeklaan 103, 1090 Brussels, Belgium; Radiology, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium.
Reference Center for Biological Markers of Dementia (BIODEM), University of Antwerp, Universiteitsplein 1, 2610 Antwerpen, Belgium.
Neuroimage Clin. 2018 May 19;19:516-526. doi: 10.1016/j.nicl.2018.05.018. eCollection 2018.
Although brain atrophy is considered to be a downstream marker of Alzheimer's disease (AD), subtle changes may allow to identify healthy subjects at risk of developing AD. As the ability to select at-risk persons is considered to be important to assess the efficacy of drugs and as MRI is a widely available imaging technique we have recently developed a reliable segmentation algorithm for the corpus callosum (CC). Callosal atrophy within AD has been hypothesized to reflect both myelin breakdown and Wallerian degeneration.
We applied our fully automated segmentation and feature extraction algorithm to two datasets: the OASIS database consisting of 316 healthy controls (HC) and 100 patients affected by either mild cognitive impairment (MCI) or Alzheimer's disease dementia (ADD) and a second database that was collected at the Memory Clinic of Hospital Network Antwerp and consists of 181 subjects, including healthy controls, subjects with subjective cognitive decline (SCD), MCI, and ADD. All subjects underwent (among others) neuropsychological testing including the Mini-Mental State Examination (MMSE). The extracted features were the callosal area (CCA), the circularity (CIR), the corpus callosum index (CCI) and the thickness profile.
CIR and CCI differed significantly between most groups. Furthermore, CIR allowed us to discriminate between SCD and HC with an accuracy of 77%. The more detailed callosal thickness profile provided little added value towards the discrimination of the different AD stages. The largest effect of normal ageing on callosal thickness was found in the frontal callosal midbody.
To the best of our knowledge, this is the first study investigating changes in corpus callosum morphometry in normal ageing and AD by exploring both summarizing features (CCA, CIR and CCI) and the complete CC thickness profile in two independent cohorts using a completely automated algorithm. We showed that callosal circularity allows to discriminate between an important subgroup of the early AD spectrum (SCD) and age and sex matched healthy controls.
尽管脑萎缩被认为是阿尔茨海默病（AD）的下游标志物，但细微的变化可能有助于识别有患 AD 风险的健康受试者。由于识别高危人群的能力被认为对于评估药物的疗效很重要，并且 MRI 是一种广泛可用的成像技术，我们最近开发了一种可靠的胼胝体分割算法。AD 中的胼胝体萎缩被假设反映了髓鞘的破坏和沃勒变性。
我们将我们的全自动分割和特征提取算法应用于两个数据集：OASIS 数据库，其中包含 316 名健康对照（HC）和 100 名患有轻度认知障碍（MCI）或阿尔茨海默病痴呆（ADD）的患者；以及第二个数据库，该数据库是在安特卫普医院网络的记忆诊所收集的，包含 181 名受试者，包括健康对照、主观认知下降（SCD）、MCI 和 ADD。所有受试者均接受了神经心理学测试（包括 Mini-Mental State Examination，MMSE）等测试。提取的特征包括胼胝体面积（CCA）、圆度（CIR）、胼胝体指数（CCI）和厚度分布。
大多数组之间的 CIR 和 CCI 差异显著。此外，CIR 可以将 SCD 和 HC 区分开来，准确率为 77%。更详细的胼胝体厚度分布对不同 AD 阶段的区分几乎没有增加价值。正常老化对胼胝体厚度的影响最大的部位是额部胼胝体中间部分。
据我们所知，这是第一项通过探索两个独立队列中使用完全自动化算法的总结特征（CCA、CIR 和 CCI）和完整的胼胝体厚度分布，研究正常老化和 AD 中胼胝体形态变化的研究。我们表明，胼胝体的圆度可以区分 AD 谱的一个重要亚组（SCD）和年龄、性别匹配的健康对照者。
