University of California, Berkeley, California, USA.
VA Northern California Health Care System, Martinez, California, USA.
Hum Brain Mapp. 2021 Mar;42(4):1070-1101. doi: 10.1002/hbm.25278. Epub 2020 Nov 20.
Lesion symptom mapping (LSM) tools are used on brain injury data to identify the neural structures critical for a given behavior or symptom. Univariate lesion symptom mapping (ULSM) methods provide statistical comparisons of behavioral test scores in patients with and without a lesion on a voxel by voxel basis. More recently, multivariate lesion symptom mapping (MLSM) methods have been developed that consider the effects of all lesioned voxels in one model simultaneously. In the current study, we provide a much-needed systematic comparison of several ULSM and MLSM methods, using both synthetic and real data to identify the potential strengths and weaknesses of both approaches. We tested the spatial precision of each LSM method for both single and dual (network type) anatomical target simulations across anatomical target location, sample size, noise level, and lesion smoothing. Additionally, we performed false positive simulations to identify the characteristics associated with each method's spurious findings. Simulations showed no clear superiority of either ULSM or MLSM methods overall, but rather highlighted specific advantages of different methods. No single method produced a thresholded LSM map that exclusively delineated brain regions associated with the target behavior. Thus, different LSM methods are indicated, depending on the particular study design, specific hypotheses, and sample size. Overall, we recommend the use of both ULSM and MLSM methods in tandem to enhance confidence in the results: Brain foci identified as significant across both types of methods are unlikely to be spurious and can be confidently reported as robust results.
病灶症状映射（LSM）工具用于脑损伤数据，以识别对于特定行为或症状至关重要的神经结构。单变量病灶症状映射（ULSM）方法提供了基于体素的患者有病变和无病变的行为测试分数的统计比较。最近，已经开发了多变量病灶症状映射（MLSM）方法，该方法同时考虑了一个模型中所有病变体素的影响。在当前的研究中，我们使用合成和真实数据对几种 ULSM 和 MLSM 方法进行了急需的系统比较，以确定这两种方法的潜在优势和弱点。我们测试了每种 LSM 方法在单个和双重（网络类型）解剖目标模拟中的空间精度，跨越解剖目标位置、样本大小、噪声水平和病变平滑。此外，我们进行了假阳性模拟，以确定与每种方法的虚假发现相关的特征。模拟结果表明，总体而言，ULSM 或 MLSM 方法都没有明显的优势，而是强调了不同方法的特定优势。没有一种单一的方法可以生成一个阈值 LSM 图，专门描绘与目标行为相关的大脑区域。因此，根据特定的研究设计、具体假设和样本大小，需要使用不同的 LSM 方法。总的来说，我们建议同时使用 ULSM 和 MLSM 方法，以增强对结果的信心：两种类型的方法都确定为显著的大脑焦点不太可能是虚假的，可以自信地报告为可靠的结果。
