Tadayon Ehsan, Moret Beatrice, Sprugnoli Giulia, Monti Lucia, Pascual-Leone Alvaro, Santarnecchi Emiliano
Berenson-Allen Center for Noninvasive Brain Stimulation and Division for Cognitive Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Department of General Psychology, University of Padova, Padova, Italy.
J Alzheimers Dis. 2020;74(4):1057-1068. doi: 10.3233/JAD-190706.
Recent studies have revealed the possible role of choroid plexus (ChP) in Alzheimer's disease (AD). T1-weighted MRI is the modality of choice for the segmentation of ChP in humans. Manual segmentation is considered the gold-standard technique, but given its time-consuming nature, large-scale neuroimaging studies of ChP would be impossible. In this study, we introduce a lightweight segmentation algorithm based on the Gaussian Mixture Model (GMM). We compared its performance against manual segmentation as well as automated segmentation by Freesurfer in three separate datasets: 1) patients with structural MRIs enhanced with contrast (n = 19), 2) young healthy subjects (n = 20), and 3) patients with AD (n = 20). GMM outperformed Freesurfer and showed high similarity with manual segmentation. To further assess the algorithm's performance in large scale studies, we performed GMM segmentations in young healthy subjects from the Human Connectome Project (n = 1,067), as well as healthy controls, mild cognitive impairment (MCI), and AD patients from the Alzheimer's Disease Neuroimaging Initiative (n = 509). In both datasets, GMM segmented ChP more accurately than Freesurfer. To show the clinical importance of accurate ChP segmentation, total AV1451 (tau) PET binding to ChP was measured in 108 MCI and 32 AD patients. GMM was able to reveal the higher AV1451 binding to ChP in AD compared with MCI. Our results provide evidence for the utility of the GMM in accurately segmenting ChP and show its clinical relevance in AD. Future structural and functional studies of ChP will benefit from GMM's accurate segmentation.
最近的研究揭示了脉络丛（ChP）在阿尔茨海默病（AD）中的可能作用。T1加权磁共振成像（MRI）是人类脉络丛分割的首选方式。手动分割被认为是金标准技术，但鉴于其耗时的特性，对脉络丛进行大规模神经影像学研究将是不可能的。在本研究中，我们引入了一种基于高斯混合模型（GMM）的轻量级分割算法。我们在三个独立的数据集中将其性能与手动分割以及Freesurfer自动分割进行了比较：1）有增强结构MRI的患者（n = 19），2）年轻健康受试者（n = 20），以及3）AD患者（n = 20）。GMM的表现优于Freesurfer，并且与手动分割显示出高度相似性。为了进一步评估该算法在大规模研究中的性能，我们对来自人类连接组计划的年轻健康受试者（n = 1067）以及来自阿尔茨海默病神经影像学倡议的健康对照、轻度认知障碍（MCI）和AD患者（n = 509）进行了GMM分割。在这两个数据集中，GMM对脉络丛的分割比Freesurfer更准确。为了展示准确的脉络丛分割的临床重要性，我们在108名MCI患者和32名AD患者中测量了总AV1451（tau）正电子发射断层扫描（PET）与脉络丛的结合情况。与MCI相比，GMM能够揭示AD中更高的AV1451与脉络丛的结合。我们的结果为GMM在准确分割脉络丛方面的效用提供了证据，并显示了其在AD中的临床相关性。未来对脉络丛的结构和功能研究将受益于GMM的准确分割。
