Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1118, 1081 HV Amsterdam, The Netherlands.
Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1118, 1081 HV Amsterdam, The Netherlands.
Neuroimage Clin. 2021;29:102549. doi: 10.1016/j.nicl.2020.102549. Epub 2020 Dec 25.
Thalamus atrophy has been linked to cognitive decline in multiple sclerosis (MS) using various segmentation methods. We investigated the consistency of the association between thalamus volume and cognition in MS for two common automated segmentation approaches, as well as fully manual outlining.
Standardized neuropsychological assessment and 3-Tesla 3D-T1-weighted brain MRI were collected (multi-center) from 57 MS patients and 17 healthy controls. Thalamus segmentations were generated manually and using five automated methods. Agreement between the algorithms and manual outlines was assessed with Bland-Altman plots; linear regression assessed the presence of proportional bias. The effect of segmentation method on the separation of cognitively impaired (CI) and preserved (CP) patients was investigated through Generalized Estimating Equations; associations with cognitive measures were investigated using linear mixed models, for each method and vendor.
In smaller thalami, automated methods systematically overestimated volumes compared to manual segmentations [ρ=(-0.42)-(-0.76); p-values < 0.001). All methods significantly distinguished CI from CP MS patients, except manual outlines of the left thalamus (p = 0.23). Poorer global neuropsychological test performance was significantly associated with smaller thalamus volumes bilaterally using all methods. Vendor significantly affected the findings.
Automated and manual thalamus segmentation consistently demonstrated an association between thalamus atrophy and cognitive impairment in MS. However, a proportional bias in smaller thalami and choice of MRI acquisition system might impact the effect size of these findings.
使用各种分割方法，丘脑萎缩与多发性硬化症（MS）患者的认知能力下降有关。我们通过两种常见的自动分割方法（即手动分割和全自动分割）来研究MS 患者的丘脑体积与认知之间的相关性的一致性。
从 57 名 MS 患者和 17 名健康对照者中收集了标准化神经心理学评估和 3-Tesla 3D-T1 加权脑 MRI（多中心）。手动和使用五种自动方法生成了丘脑分割。使用 Bland-Altman 图评估算法和手动轮廓之间的一致性；线性回归评估是否存在比例偏差。通过广义估计方程（GEE）研究分割方法对认知障碍（CI）和认知正常（CP）患者的分离效果；通过线性混合模型（LMM），针对每种方法和供应商，研究了与认知测量的关联。
与手动分割相比，自动方法在较小的丘脑区域中系统地高估了体积[ρ=（-0.42）-（-0.76）；p 值均<0.001）。除了左丘脑的手动轮廓（p=0.23）之外，所有方法均能显著区分 CI 和 CP 的 MS 患者。使用所有方法，双侧丘脑体积较小与全球神经心理学测试表现较差均呈显著相关。供应商显著影响了研究结果。
自动和手动丘脑分割均一致表明 MS 患者的丘脑萎缩与认知障碍之间存在相关性。然而，在较小的丘脑区域中存在比例偏差以及 MRI 采集系统的选择可能会影响这些发现的效应大小。
