Background

Multiple Sclerosis (MS), a neurodegenerative and neuroinflammatory disease, causing lesions that disrupt the brain’s anatomical and physiological connectivity networks, resulting in cognitive, visual and/or motor disabilities. Advanced imaging techniques like diffusion and functional MRI allow measurement of the brain’s structural connectivity (SC) and functional connectivity (FC) networks, and can enable a better understanding of how their disruptions cause disability in people with MS (pwMS). However, advanced MRI techniques are used mainly for research purposes as they are expensive, time-consuming and require high-level expertise to acquire and process. As an alternative, the Network Modification (NeMo) Tool can be used to estimate SC and FC using lesion masks derived from pwMS and a reference set of controls' connectivity networks.

Objective

Here, we test the hypothesis that estimated SC and FC (eSC and eFC) from the NeMo Tool, based only on an individual’s lesion masks, can be used to classify pwMS into disability categories just as well as SC and FC extracted from advanced MRI directly in pwMS. We also aim to find the connections most important for differentiating between no disability vs evidence of disability groups.

Materials and Methods

One hundred pwMS (age:45.5 ± 11.4 years, 66% female, disease duration: 12.97 ± 8.07 years) were included in this study. Expanded Disability Status Scale (EDSS) was used to assess disability, 67 pwMS had no disability (EDSS < 2). Observed SC and FC were extracted from diffusion and functional MRI directly in pwMS, respectively. The NeMo Tool was used to estimate the remaining structural connectome (eSC), by removing streamlines in a reference set of tractograms that intersected the lesion mask. The NeMo Tool's eSC was used then as input to a deep neural network to estimate the corresponding FC (eFC). Logistic regression with ridge regularization was used to classify pwMS into disability categories (no disability vs evidence of disability), based on demographics/clinical information (sex, age, race, disease duration, clinical phenotype, and spinal lesion burden) and either pairwise entries or regional summaries from one of the following matrices: SC, FC, eSC, and eFC. The area under the ROC curve (AUC) was used to assess the classification performance. Both univariate statistics and parameter coefficients from the classification models were used to identify features important to differentiating between the groups.

Results

The regional eSC and eFC models outperformed their observed FC and SC counterparts (p-value < 0.05), while the pairwise eSC and SC performed similarly (p = 0.10). Regional eSC and eFC models had higher AUC (0.66–0.68) than the pairwise models (0.60–0.65), with regional eFC having highest classification accuracy across all models. Ridge regression coefficients for the regional eFC and regional observed FC models were significantly correlated (Pearson's r = 0.52, p-value < 10e-7). Decreased estimated SC node strength in default mode and ventral attention networks and increased eFC node strength in visual networks was associated with evidence of disability.

Discussion

Here, for the first time, we use clinically acquired lesion masks to estimate both structural and functional connectomes in patient populations to better understand brain lesion-dysfunction mapping in pwMS. Models based on the NeMo Tool’s estimates of SC and FC better classified pwMS by disability level than SC and FC observed directly in the individual using advanced MRI. This work provides a viable alternative to performing high-cost, advanced MRI in patient populations, bringing the connectome one step closer to the clinic.

中文翻译：

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将多发性硬化症患者分为残疾组时，估计的连接网络优于观察到的连接网络

 背景

多发性硬化症 (MS) 是一种神经退行性和神经炎症性疾病，会引起破坏大脑解剖和生理连接网络的病变，导致认知、视觉和/或运动障碍。扩散和功能 MRI 等先进成像技术可以测量大脑的结构连接 (SC) 和功能连接 (FC) 网络，并可以更好地了解它们的破坏如何导致多发性硬化症 (pwMS) 患者残疾。然而，先进的 MRI 技术主要用于研究目的，因为它们昂贵、耗时，并且需要高水平的专业知识来获取和处理。作为替代方案，网络修改 (NeMo) 工具可用于使用从 pwMS 导出的病变掩模和控制连接网络的参考集来估计 SC 和 FC。

 客观的

在这里，我们测试了这样的假设：仅基于个人病变掩模，从 NeMo 工具估计的 SC 和 FC（eSC 和 eFC）可用于将 pwMS 分类为残疾类别，就像直接从高级 MRI 提取的 SC 和 FC 一样在 pwMS 中。我们还旨在找到区分无残疾与残疾群体证据最重要的联系。

 材料和方法

本研究纳入了 100 名 pwMS（年龄：45.5 ± 11.4 岁，66% 女性，病程：12.97 ± 8.07 年）。采用扩展残疾状态量表（EDSS）来评估残疾，67 名 pwMS 没有残疾（EDSS < 2）。观察到的 SC 和 FC 分别直接在 pwMS 中从扩散和功能 MRI 中提取。 NeMo Tool 用于通过删除与病变掩模相交的参考束图集中的流线来估计剩余的结构连接组 (eSC)。然后使用 NeMo Tool 的 eSC 作为深度神经网络的输入来估计相应的 FC (eFC)。基于人口统计/临床信息（性别、年龄、种族、疾病持续时间、临床表型和脊柱病变负担）和任一配对条目，使用岭正则化的 Logistic 回归将 pwMS 分类为残疾类别（无残疾与残疾证据）或来自以下矩阵之一的区域摘要：SC、FC、eSC 和 eFC。 ROC 曲线下面积 (AUC) 用于评估分类性能。分类模型中的单变量统计数据和参数系数都用于识别对区分各组很重要的特征。

 结果

区域 eSC 和 eFC 模型的表现优于观察到的 FC 和 SC 模型（p 值 < 0.05），而成对的 eSC 和 SC 表现相似（p = 0.10）。区域 eSC 和 eFC 模型的 AUC (0.66–0.68) 高于成对模型 (0.60–0.65)，区域 eFC 在所有模型中具有最高的分类精度。区域 eFC 和区域观测 FC 模型的岭回归系数显着相关（Pearson r = 0.52，p 值 < 10e-7）。默认模式和腹侧注意网络中估计的 SC 节点强度降低以及视觉网络中 eFC 节点强度的增加与残疾证据相关。

 讨论

在这里，我们首次使用临床获得的病变掩模来估计患者群体中的结构和功能连接组，以更好地了解 pwMS 中的脑病变-功能障碍图谱。基于 NeMo Tool 对 SC 和 FC 估计的模型比使用先进 MRI 在个体中直接观察到的 SC 和 FC 更好地按残疾水平对 pwMS 进行了分类。这项工作为在患者群体中进行高成本、先进的 MRI 提供了一种可行的替代方案，使连接组离临床又近了一步。