Introduction

Several recent studies indicate that deep gray matter or thalamic volume loss (VL) might be promising surrogate markers of disease activity in multiple sclerosis (MS) patients. To allow applying these markers to individual MS patients in clinical routine, age-dependent cut-offs distinguishing physiological from pathological VL and an estimation of the measurement error, which provides the confidence of the result, are to be defined.

Methods

Longitudinal MRI scans of the following cohorts were analyzed in this study: 189 healthy controls (HC) (mean age 54 years, 22% female), 98 MS patients from Zurich university hospital (mean age 34 years, 62% female), 33 MS patients from Dresden university hospital (mean age 38 years, 60% female), and publicly available reliability data sets consisting of 162 short-term MRI scan-rescan pairs with scan intervals of days or few weeks. Percentage annualized whole brain volume loss (BVL), gray matter (GM) volume loss (GMVL), deep gray matter volume loss (deep GMVL), and thalamic volume loss (ThalaVL) were computed deploying the Jacobian integration (JI) method. BVL was additionally computed using Siena, an established method used in many Phase III drug trials. A linear mixed effect model was used to estimate the measurement error as the standard deviation (SD) of model residuals of all 162 scan-rescan pairs For estimation of age-dependent cut-offs, a quadratic regression function between age and the corresponding annualized VL values of the HC was computed. The 5^th percentile was defined as the threshold for pathological VL per year since 95% of HC subjects exhibit a less pronounced VL for a given age. For the MS patients BVL, GMVL, deep GMVL, and ThalaVL were mutually compared and a paired t-test was used to test whether there are systematic differences in VL between these brain regions.

Results

Siena and JI showed a high agreement for BVL measures, with a median absolute difference of 0.1% and a correlation coefficient of r=0.78. Siena and GMVL showed a similar standard deviation (SD) of the scan-rescan error of 0.28% and 0.29%, respectively. For deep GMVL, ThalaVL the SD of the scan-rescan error was slightly higher (0.43% and 0.5%, respectively). Among the HC the thalamus showed the highest mean VL (-0.16 %, -0.39%, and -0.59% at ages 35, 55, and 75, respectively). Corresponding cut-offs for a pathological VL/year were -0.68%, -0.91%, and -1.11%. The MS cohorts did not differ in BVL and GMVL. However, both MS cohorts showed a significantly (p=0.05) stronger deep GMVL than BVL per year.

Conclusion

It might be methodologically feasible to assess deep GMVL using JI in individual MS patients. However, age and the measurement error need to be taken into account. Furthermore, deep GMVL may be used as a complementary marker to BVL since MS patients exhibit a significantly stronger deep GMVL than BVL.

中文翻译：

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使用雅可比积分的病理学深灰质和丘脑体积损失的年龄依赖性临界值

介绍

最近的一些研究表明，深层灰质或丘脑体积损失（VL）可能是多发性硬化症（MS）患者疾病活动的有前途的替代标志。为了在临床常规中将这些标记物应用于个别MS患者，需要定义区分生理学与病理学VL的年龄相关的临界值和测量误差的估计值，以提供结果的可信度。

方法

在这项研究中分析了以下队列的纵向MRI扫描：189名健康对照者（HC）（平均年龄54岁，女性22％），来自苏黎世大学医院的98名MS患者（平均年龄34岁，女性62％），33 MS来自德累斯顿大学医院的患者（平均年龄38岁，女性占60％），以及公开的可靠性数据集，其中包括162个短期MRI扫描-再扫描对，扫描间隔为几天或几周。应用雅可比积分法（JI）计算了全脑体积损失（BVL），灰质（GM）体积损失（GMVL），深灰质物质体积损失（deep GMVL）和丘脑体积损失（ThalaVL）的百分比。另外，还使用Siena计算了BVL，Siena是许多III期药物试验中使用的既定方法。使用线性混合效应模型来估计测量误差，作为所有162次扫描-再扫描对的模型残差的标准偏差（SD）。为了估计年龄相关的临界值，年龄与相应的年化VL之间的二次回归函数计算HC的值。5^第百分之一百被定义为每年病理性VL的阈值，因为在给定年龄的95％的HC受试者表现出不太明显的VL。对于MS患者，将BVL，GMVL，深层GMVL和ThalaVL相互比较，并使用配对t检验测试这些脑区之间VL是否存在系统性差异。

结果

锡耶纳（Siena）和吉恩（JI）对BVL测度显示出高度一致性，中位数绝对差为0.1％，相关系数为r = 0.78。锡耶纳（Siena）和GMVL的扫描再扫描误差的相似标准偏差（SD）分别为0.28％和0.29％。对于深层GMVL，ThalaVL的扫描-再扫描误差的SD稍高（分别为0.43％和0.5％）。在HC中，丘脑显示出最高的平均VL（分别在35、55和75岁时分别为-0.16％，-0.39％和-0.59％）。病理性VL /年的相应截止值为-0.68％，-0.91％和-1.11％。MS队列在BVL和GMVL中没有差异。但是，两个MS队列均显示每年比BVL的深层GMVL显着强（p = 0.05）。

结论

在个别MS患者中使用JI评估深层GMVL可能在方法上可行。但是，需要考虑年龄和测量误差。此外，深部GMVL可以用作BVL的补充标记，因为MS患者的深部GMVL比BVL强得多。