Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
Neuroradiology. 2021 Sep;63(9):1489-1500. doi: 10.1007/s00234-021-02685-z. Epub 2021 Mar 26.
fMRI is increasingly used for presurgical language mapping, but lack of standard methodology has made it difficult to combine/compare data across institutions or determine the relative efficacy of different approaches. Here, we describe a quantitative analytic framework for determining language laterality in clinical fMRI that addresses these concerns.
We retrospectively analyzed fMRI data from 59 patients who underwent presurgical language mapping at our institution with identical imaging and behavioral protocols. First, we compared the efficacy of different regional masks in capturing language activations. Then, we systematically explored how laterality indices (LIs) computed from these masks vary as a function of task and activation threshold. Finally, we determined the percentile threshold that maximized the correlation between the results of our LI approach and the laterality assessments from the original clinical radiology reports.
First, we found that a regional mask derived from a meta-analysis of the fMRI literature better captured language task activations than masks based on anatomically defined language areas. Then, we showed that an LI approach based on this functional mask and percentile thresholding of subject activation can quantify the relative ability of different language tasks to lateralize language function at the population level. Finally, we determined that the 92nd percentile of subject-level activation provides the optimal LI threshold with which to reproduce the original clinical reports.
A quantitative framework for determining language laterality that uses a functionally-derived language mask and percentile thresholding of subject activation can combine/compare results across tasks and patients and reproduce clinical assessments of language laterality.
功能磁共振成像（fMRI）越来越多地用于术前语言定位，但由于缺乏标准方法，使得难以在不同机构之间合并/比较数据，也难以确定不同方法的相对效果。在这里，我们描述了一种用于确定临床 fMRI 中语言侧化的定量分析框架，该框架解决了这些问题。
我们回顾性分析了 59 名在我们机构接受术前语言定位的患者的 fMRI 数据，这些患者采用了相同的成像和行为方案。首先，我们比较了不同区域掩模在捕捉语言激活方面的效果。然后，我们系统地探索了从这些掩模计算出的侧化指数（LI）如何随任务和激活阈值的变化而变化。最后，我们确定了使我们的 LI 方法的结果与原始临床放射学报告中的侧化评估之间的相关性最大化的百分位数阈值。
首先，我们发现，基于 fMRI 文献的荟萃分析得出的区域掩模比基于解剖定义的语言区域的掩模更好地捕获语言任务激活。然后，我们表明，基于此功能掩模和对受试者激活进行百分位阈值处理的 LI 方法可以在群体水平上量化不同语言任务使语言功能侧化的相对能力。最后，我们确定，受试者水平激活的第 92 百分位数提供了最佳的 LI 阈值，可用于重现原始临床报告。
一种用于确定语言侧化的定量框架，该框架使用功能上得出的语言掩模和对受试者激活进行百分位阈值处理，可以合并/比较不同任务和患者的结果，并重现语言侧化的临床评估。
