Hope Thomas M H, Parker Jones 'Ōiwi, Grogan Alice, Crinion Jenny, Rae Johanna, Ruffle Louise, Leff Alex P, Seghier Mohamed L, Price Cathy J, Green David W
1 Wellcome Trust Centre for Neuroimaging, University College London, UK
1 Wellcome Trust Centre for Neuroimaging, University College London, UK 2 Wolfson College, University of Oxford, UK.
Brain. 2015 Apr;138(Pt 4):1070-83. doi: 10.1093/brain/awv020. Epub 2015 Feb 13.
Post-stroke prognoses are usually inductive, generalizing trends learned from one group of patients, whose outcomes are known, to make predictions for new patients. Research into the recovery of language function is almost exclusively focused on monolingual stroke patients, but bilingualism is the norm in many parts of the world. If bilingual language recruits qualitatively different networks in the brain, prognostic models developed for monolinguals might not generalize well to bilingual stroke patients. Here, we sought to establish how applicable post-stroke prognostic models, trained with monolingual patient data, are to bilingual stroke patients who had been ordinarily resident in the UK for many years. We used an algorithm to extract binary lesion images for each stroke patient, and assessed their language with a standard tool. We used feature selection and cross-validation to find 'good' prognostic models for each of 22 different language skills, using monolingual data only (174 patients; 112 males and 62 females; age at stroke: mean = 53.0 years, standard deviation = 12.2 years, range = 17.2-80.1 years; time post-stroke: mean = 55.6 months, standard deviation = 62.6 months, range = 3.1-431.9 months), then made predictions for both monolinguals and bilinguals (33 patients; 18 males and 15 females; age at stroke: mean = 49.0 years, standard deviation = 13.2 years, range = 23.1-77.0 years; time post-stroke: mean = 49.2 months, standard deviation = 55.8 months, range = 3.9-219.9 months) separately, after training with monolingual data only. We measured group differences by comparing prediction error distributions, and used a Bayesian test to search for group differences in terms of lesion-deficit associations in the brain. Our models distinguish better outcomes from worse outcomes equally well within each group, but tended to be over-optimistic when predicting bilingual language outcomes: our bilingual patients tended to have poorer language skills than expected, based on trends learned from monolingual data alone, and this was significant (P < 0.05, corrected for multiple comparisons) in 13/22 language tasks. Both patient groups appeared to be sensitive to damage in the same sets of regions, though the bilinguals were more sensitive than the monolinguals. media-1vid1 10.1093/brain/awv020_video_abstract awv020_video_abstract.
中风后的预后通常是归纳性的,即从一组已知预后结果的患者中总结出趋势,以此来预测新患者的情况。语言功能恢复的研究几乎完全集中在单语中风患者身上,但在世界许多地区,双语是常态。如果双语在大脑中招募的神经网络在性质上有所不同,那么为单语者开发的预后模型可能无法很好地推广到双语中风患者身上。在这里,我们试图确定,用单语患者数据训练的中风后预后模型,对于长期居住在英国的双语中风患者的适用性如何。我们使用一种算法为每位中风患者提取二元病变图像,并使用标准工具评估他们的语言能力。我们仅使用单语数据(174名患者;112名男性和62名女性;中风时年龄:平均 = 53.0岁,标准差 = 12.2岁,范围 = 17.2 - 80.1岁;中风后时间:平均 = 55.6个月,标准差 = 62.6个月,范围 = 3.1 - 431.9个月),通过特征选择和交叉验证,为22种不同的语言技能分别找到“良好”的预后模型,然后仅用单语数据训练后,分别对单语者和双语者(33名患者;18名男性和15名女性;中风时年龄:平均 = 49.0岁,标准差 = 13.2岁,范围 = 23.1 - 77.0岁;中风后时间:平均 = 49.2个月,标准差 = 55.8个月,范围 = 3.9 - 219.9个月)进行预测。我们通过比较预测误差分布来测量组间差异,并使用贝叶斯检验来寻找大脑中病变 - 缺陷关联方面的组间差异。我们的模型在每组中都能同样好地区分较好和较差的预后结果,但在预测双语语言结果时往往过于乐观:基于仅从单语数据中得出的趋势,我们的双语患者的语言技能往往比预期的要差,在22项语言任务中的13项中,这种情况具有统计学意义(P < 0.05,经多重比较校正)。尽管双语者比单语者更敏感,但两组患者似乎对相同区域的损伤都很敏感。media - 1vid1 10.1093/brain/awv020_video_abstract awv020_video_abstract