Dynamic Functional Connectivity Better Predicts Disability Than Structural and Static Functional Connectivity in People With Multiple Sclerosis.
Dynamic Functional Connectivity Better Predicts Disability Than Structural and Static Functional Connectivity in People With Multiple Sclerosis.
复制标题
DOI：
10.3389/fnins.2021.763966
复制
发表时间：
2021
影响因子：
4.3
通讯作者：
Kuceyeski A
中科院分区：
文献类型：
--
作者：
Tozlu C;Jamison K;Gauthier SA;Kuceyeski A
背景：扩散和功能 MRI 等先进成像技术可用于识别大脑结构和功能连接（SC 和 FC）网络的病理相关变化，并将这些变化映射到多发性硬化症 (pwMS) 患者的残疾和代偿机制。迄今为止，还没有研究进行比较研究来调查哪种连接类型（SC、静态或动态 FC）能够更好地区分健康对照 (HC) 和 pwMS 和/或根据残疾状态对 pwMS 进行分类。目的：我们的目的是比较 SC、静态 FC 和动态 FC (dFC) 在分类 (a) HC 与 pwMS 和 (b) 无残疾与残疾的 pwMS 方面的表现。该研究的次要目标是确定哪些大脑区域的连接组测量对分类任务贡献最大。材料和方法：包括 100 个 pwMS 和 19 个 HC。扩展残疾状态量表（EDSS）用于评估残疾，其中 67 名 EDSS<2 的 pwMS 被认为没有残疾。扩散和静息态功能 MRI 分别用于计算 SC 和 FC 矩阵。进行了岭正则化的逻辑回归，其中模型包括人口统计/临床信息以及来自以下矩阵之一的成对条目或区域摘要：SC、FC 和 dFC。使用受试者工作曲线下面积（AUC）评估模型的性能。结果：在对 HC 与 pwMS 进行分类时，区域 SC 模型显着优于其他模型，中位 AUC 为 0.89（p <0.05）。在按残疾状态对 pwMS 进行分类时，区域 dFC 和 dFC 指标模型显着优于其他模型，中位 AUC 分别为 0.65 和 0.61 (p < 0.05)。背侧注意力、皮质下和小脑网络中的区域 SC 是 HC 与 pwMS 分类任务中最重要的变量。在某些 DFC 状态中，背侧注意力和视觉网络中的区域 dFC 增加以及额顶和小脑网络中的区域 dFC 减少与处于有残疾证据的 PWMS 组相关。讨论：SC 损伤是 MS 的一个标志，并且毫不奇怪，它是对患者和对照进行分类的最准确的连接组学测量方法。另一方面，动态 FC 指标对于确定 pwMS 的残疾水平最为重要，并且可以代表针对 pwMS 中白质病理的功能补偿。
Background: Advanced imaging techniques such as diffusion and functional MRI can be used to identify pathology-related changes to the brain's structural and functional connectivity (SC and FC) networks and mapping of these changes to disability and compensatory mechanisms in people with multiple sclerosis (pwMS). No study to date performed a comparison study to investigate which connectivity type (SC, static or dynamic FC) better distinguishes healthy controls (HC) from pwMS and/or classifies pwMS by disability status. Aims: We aim to compare the performance of SC, static FC, and dynamic FC (dFC) in classifying (a) HC vs. pwMS and (b) pwMS who have no disability vs. with disability. The secondary objective of the study is to identify which brain regions' connectome measures contribute most to the classification tasks. Materials and Methods: One hundred pwMS and 19 HC were included. Expanded Disability Status Scale (EDSS) was used to assess disability, where 67 pwMS who had EDSS<2 were considered as not having disability. Diffusion and resting-state functional MRI were used to compute the SC and FC matrices, respectively. Logistic regression with ridge regularization was performed, where the models included demographics/clinical information and either pairwise entries or regional summaries from one of the following matrices: SC, FC, and dFC. The performance of the models was assessed using the area under the receiver operating curve (AUC). Results: In classifying HC vs. pwMS, the regional SC model significantly outperformed others with a median AUC of 0.89 (p <0.05). In classifying pwMS by disability status, the regional dFC and dFC metrics models significantly outperformed others with a median AUC of 0.65 and 0.61 (p < 0.05). Regional SC in the dorsal attention, subcortical and cerebellar networks were the most important variables in the HC vs. pwMS classification task. Increased regional dFC in dorsal attention and visual networks and decreased regional dFC in frontoparietal and cerebellar networks in certain dFC states was associated with being in the group of pwMS with evidence of disability. Discussion: Damage to SCs is a hallmark of MS and, unsurprisingly, the most accurate connectomic measure in classifying patients and controls. On the other hand, dynamic FC metrics were most important for determining disability level in pwMS, and could represent functional compensation in response to white matter pathology in pwMS.
登录
查看更多内容
影响因子：
4.8
作者：
Bonkhoff AK;Schirmer MD;Bretzner M;Etherton M;Donahue K;Tuozzo C;Nardin M;Giese AK;Wu O;D Calhoun V;Grefkes C;Rost NS
通讯作者：
Rost NS
DOI：
10.1073/pnas.1110024108
发表时间：
2011-11-22
影响因子：
11.1
作者：
Hawellek, David J.;Hipp, Joerg F.;Engel, Andreas K.
通讯作者：
Engel, Andreas K.
影响因子：
3.7
作者：
Daselaar, Sander M.;Iyengar, Vijeth;Cabeza, Roberto E.
通讯作者：
Cabeza, Roberto E.
影响因子：
3.7
作者：
Allen, Elena A.;Damaraju, Eswar;Calhoun, Vince D.
通讯作者：
Calhoun, Vince D.
影响因子：
4.3
作者：
Muthuraman, Muthuraman;Fleischer, Vinzenz;Groppa, Sergiu
通讯作者：
Groppa, Sergiu