Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA.
Neuroimage. 2010 Jan 1;49(1):597-602. doi: 10.1016/j.neuroimage.2009.07.061. Epub 2009 Jul 30.
Most existing voxel-based lesion-symptom mapping methods are based on the same statistical foundation: null hypothesis significance testing (NHST). The two major limitations of these methods are the inability to infer that there is no difference in lesion proportions, and a requirement for multiple-comparison correction. We propose a Bayesian approach that directly models the posterior distribution of lesion-proportion difference, and makes decisions based on inference on this posterior distribution. Compared to NHST-based approaches, our Bayesian approach yields inference results with clearer semantics, and does not require multiple-comparison correction. We evaluated our Bayesian method using simulated data, and data from a study of acute ischemic left-hemispheric stroke. Results of both experiments indicate that the Bayesian approach is sensitive in detecting regions that characterize group differences.
大多数现有的基于体素的病灶-症状映射方法都基于相同的统计基础：零假设显著性检验（NHST）。这些方法的两个主要局限性是无法推断病灶比例没有差异，并且需要进行多次比较校正。我们提出了一种贝叶斯方法，该方法直接对病灶比例差异的后验分布进行建模，并基于对该后验分布的推断做出决策。与基于 NHST 的方法相比，我们的贝叶斯方法得出的推断结果具有更清晰的语义，并且不需要多次比较校正。我们使用模拟数据和急性缺血性左半球中风研究的数据来评估我们的贝叶斯方法。这两个实验的结果都表明，贝叶斯方法在检测能体现组间差异的区域时很敏感。
