Early functional magnetic resonance imaging activations predict language outcome after stroke
Early functional magnetic resonance imaging activations predict language outcome after stroke
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DOI：
10.1093/brain/awq021
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发表时间：
2010-04-10
期刊：
影响因子：
14.5
通讯作者：
Kloeppel, Stefan
中科院分区：
文献类型：
--
作者：
Saur, Dorothee;Ronneberger, Olaf;Kloeppel, Stefan
准确预测卒中后特定系统的恢复对于根据个体需求提供康复治疗至关重要。我们从听觉语言理解实验中探索了功能磁共振成像扫描在预测21名失语症中风患者个体语言恢复方面的有用性。至少有中度语言障碍的受试者在左半球中风后2周和6个月接受广泛的语言测试。使用多变量机器学习技术来预测中风后6个月的语言结果。此外，我们的目标是预测6个月后语言进步的程度。根据语言相关区域的功能磁共振成像数据，76%的患者在卒中后6个月正确地划分为语言能力良好和语言能力差的患者。当年龄和语言分数与功能磁共振成像数据一起输入全自动分类器时，准确率进一步提高(86%正确分配)。在根据想象、年龄和语言表现预测语言进步程度时，也达到了类似的准确性。在探讨卒中后两天获得的弥散加权成像和功能磁共振成像的有用性时，没有比机会水平更好的预测。这项研究证明了当前的机器学习技术在预测系统特定的临床结果方面的高潜力，即使是对于像中风这样的异质性疾病。当在中风后的第二周评估系统特定刺激后的大脑激活潜力时，语言恢复的最佳预测达到了。对于那些预计恢复不佳的人，可以提供更密集的早期康复，并将其扩展到其他系统，例如运动和注意力似乎是可行的。
An accurate prediction of system-specific recovery after stroke is essential to provide rehabilitation therapy based on the individual needs. We explored the usefulness of functional magnetic resonance imaging scans from an auditory language comprehension experiment to predict individual language recovery in 21 aphasic stroke patients. Subjects with an at least moderate language impairment received extensive language testing 2 weeks and 6 months after left-hemispheric stroke. A multivariate machine learning technique was used to predict language outcome 6 months after stroke. In addition, we aimed to predict the degree of language improvement over 6 months. 76% of patients were correctly separated into those with good and bad language performance 6 months after stroke when based on functional magnetic resonance imaging data from language relevant areas. Accuracy further improved (86% correct assignments) when age and language score were entered alongside functional magnetic resonance imaging data into the fully automatic classifier. A similar accuracy was reached when predicting the degree of language improvement based on imaging, age and language performance. No prediction better than chance level was achieved when exploring the usefulness of diffusion weighted imaging as well as functional magnetic resonance imaging acquired two days after stroke. This study demonstrates the high potential of current machine learning techniques to predict system-specific clinical outcome even for a disease as heterogeneous as stroke. Best prediction of language recovery is achieved when the brain activation potential after system-specific stimulation is assessed in the second week post stroke. More intensive early rehabilitation could be provided for those with a predicted poor recovery and the extension to other systems, for example, motor and attention seems feasible.