Predicting language outcomes after stroke: Is structural disconnection a useful predictor?
Predicting language outcomes after stroke: Is structural disconnection a useful predictor?
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DOI：
10.1016/j.nicl.2018.03.037
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Price CJ
中科院分区：
文献类型：
--
作者：
Hope TMH;Leff AP;Price CJ
多年来，研究人员一直试图了解患有获得性语言障碍（失语症）的中风幸存者是否以及何时会康复。有广泛的共识，病变位置信息应该在这些预测中发挥一定的作用，但仍然没有达成共识的最佳或正确的方式来编码的信息。在这里，我们解决了在这项工作中出现的结构连接体的重点-特别是声称中断白色物质连接传达重要的，独特的中风失语症幸存者的预后信息。我们的样本包括从PLORAS数据库中提取的818例卒中患者，该数据库将卒中患者的结构MRI与综合失语症测试（CAT）和基本人口统计学的语言评估评分相关联。当患者的病变太扩散或太小（<1 cm 3）而无法被自动病变识别工具箱检测到时，将其排除在外，我们使用该工具箱将患者的病变编码为标准空间中的二进制病变图像。使用自动解剖标记图谱定义的116个区域对病变进行编码。我们检查了由这些区域中的“病变负荷”（即每个患者病变破坏的每个区域的比例）和通过网络修改工具箱计算的它们之间的白色连接的断开驱动的预后模型。使用这些数据，我们建立了一系列预测模型来预测第一个（“命名”），然后是CAT定义的所有语言分数。我们没有发现一致的证据表明这些模型中的连接中断数据提高了我们预测任何语言分数的能力。这可能是因为连接中断变量与病变负荷变量密切相关：我们测量原始形式的变量对之间以及两个数据集的主成分之间的相关性。我们的结论是，虽然这两种类型的结构脑数据确实传达了有用的，在这个领域的预测信息，他们也似乎传达了基本相同的方差。我们的结论是，连接中断变量并不能帮助我们更准确地预测患者的语言技能比病变位置（负载）数据。结构连接对语言和语言障碍很重要。我们询问是否可以使用连接变量更准确地预测中风后的语言结果。我们没有发现任何一致的证据表明任何语言结果的预测优势
For many years, researchers have sought to understand whether and when stroke survivors with acquired language impairment (aphasia) will recover. There is broad agreement that lesion location information should play some role in these predictions, but still no consensus on the best or right way to encode that information. Here, we address the emerging emphasis on the structural connectome in this work – specifically the claim that disrupted white matter connectivity conveys important, unique prognostic information for stroke survivors with aphasia. Our sample included 818 stroke patients extracted from the PLORAS database, which associates structural MRI from stroke patients with language assessment scores from the Comprehensive Aphasia Test (CAT) and basic demographic. Patients were excluded when their lesions were too diffuse or small (<1 cm3) to be detected by the Automatic Lesion Identification toolbox, which we used to encode patients' lesions as binary lesion images in standard space. Lesions were encoded using the 116 regions defined by the Automatic Anatomical Labelling atlas. We examined prognostic models driven by both “lesion load” in these regions (i.e. the proportion of each region destroyed by each patient's lesion), and by the disconnection of the white matter connections between them which was calculated via the Network Modification toolbox. Using these data, we build a series of prognostic models to predict first one (“naming”), and then all of the language scores defined by the CAT. We found no consistent evidence that connectivity disruption data in these models improved our ability to predict any language score. This may be because the connectivity disruption variables are strongly correlated with the lesion load variables: correlations which we measure both between pairs of variables in their original form, and between principal components of both datasets. Our conclusion is that, while both types of structural brain data do convey useful, prognostic information in this domain, they also appear to convey largely the same variance. We conclude that connectivity disruption variables do not help us to predict patients' language skills more accurately than lesion location (load) data alone. Structural connectivity appears important to language and disorders of language We asked whether we could use connectivity variables to predict language outcomesafter stroke more accurately We found no consistent evidence of any predictive advantage for any language outcome
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DOI：
10.1093/brain/awx086
发表时间：
2017-06-01
期刊：
Brain : a journal of neurology
影响因子：
--
作者：
Hope TMH;Leff AP;Prejawa S;Bruce R;Haigh Z;Lim L;Ramsden S;Oberhuber M;Ludersdorfer P;Crinion J;Seghier ML;Price CJ
通讯作者：
Price CJ
神经影像学中脑部疾病的单受试者预测：前景和陷阱
影响因子：
5.7
作者：
Arbabshirani MR;Plis S;Sui J;Calhoun VD
通讯作者：
Calhoun VD
影响因子：
3.4
作者：
Del Gaizo, John;Fridriksson, Julius;Bonilha, Leonardo
通讯作者：
Bonilha, Leonardo
影响因子：
8.3
作者：
Koenig, Inke R.;Ziegler, Andreas;Weimar, Christian
通讯作者：
Weimar, Christian
影响因子：
14.5
作者：
Fridriksson, Julius;Guo, Dazhou;Rorden, Chris
通讯作者：
Rorden, Chris