Neuroimaging and Stroke Recovery Laboratory, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School Boston, MA, USA.
Front Hum Neurosci. 2013 Dec 10;7:831. doi: 10.3389/fnhum.2013.00831. eCollection 2013.
There is a need to identify biomarkers that predict degree of chronic speech fluency/language impairment and potential for improvement after stroke. We previously showed that the Arcuate Fasciculus lesion load (AF-LL), a combined variable of lesion site and size, predicted speech fluency in patients with chronic aphasia. In the current study, we compared lesion loads of such a structural map (i.e., AF-LL) with those of a functional map [i.e., the functional gray matter lesion load (fGM-LL)] in their ability to predict speech fluency and naming performance in a large group of patients. The fGM map was constructed from functional brain images acquired during an overt speaking task in a group of healthy elderly controls. The AF map was reconstructed from high-resolution diffusion tensor images also from a group of healthy elderly controls. In addition to these two canonical maps, a combined AF-fGM map was derived from summing fGM and AF maps. Each canonical map was overlaid with individual lesion masks of 50 chronic aphasic patients with varying degrees of impairment in speech production and fluency to calculate a functional and structural lesion load value for each patient, and to regress these values with measures of speech fluency and naming. We found that both AF-LL and fGM-LL independently predicted speech fluency and naming ability; however, AF lesion load explained most of the variance for both measures. The combined AF-fGM lesion load did not have a higher predictability than either AF-LL or fGM-LL alone. Clustering and classification methods confirmed that AF lesion load was best at stratifying patients into severe and non-severe outcome groups with 96% accuracy for speech fluency and 90% accuracy for naming. An AF-LL of greater than 4 cc was the critical threshold that determined poor fluency and naming outcomes, and constitutes the severe outcome group. Thus, surrogate markers of impairments have the potential to predict outcomes and can be used as a stratifier in experimental studies.
需要确定能够预测慢性言语流畅度/语言障碍程度以及中风后潜在改善程度的生物标志物。我们之前的研究表明，弓状束病变负荷（AF-LL），即病变部位和大小的综合变量，可预测慢性失语症患者的言语流畅度。在当前的研究中，我们比较了这种结构图谱（即 AF-LL）的病变负荷与功能图谱（即功能灰质病变负荷（fGM-LL））的病变负荷，以评估它们在大量患者中预测言语流畅度和命名表现的能力。fGM 图谱是从一组健康老年对照组在进行显性说话任务时的功能脑图像构建的。AF 图谱是从另一组健康老年对照组的高分辨率弥散张量图像重建的。除了这两个典型图谱外，还从 fGM 和 AF 图谱的总和中得出了一个组合的 AF-fGM 图谱。每个典型图谱都与 50 名慢性失语症患者的个体病变掩模叠加，这些患者的言语产生和流畅度受损程度不同，以便为每位患者计算功能和结构病变负荷值，并将这些值与言语流畅度和命名能力进行回归。我们发现，AF-LL 和 fGM-LL 都可以独立地预测言语流畅度和命名能力；然而，AF 病变负荷解释了这两个测量值的大部分方差。组合的 AF-fGM 病变负荷的预测能力并不优于单独的 AF-LL 或 fGM-LL。聚类和分类方法证实，AF 病变负荷最适合将患者分为严重和非严重结局组，对言语流畅度的准确率为 96%，对命名的准确率为 90%。AF-LL 大于 4cc 是决定语言流畅度和命名结果不良的关键阈值，构成严重结局组。因此，损伤的替代标志物有可能预测结局，并可作为实验研究的分层因素。
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