Taquet Maxime, Scherrer Benoît, Peters Jurriaan M, Prabhu Sanjay P, Warfield Simon K
Med Image Comput Comput Assist Interv. 2014;17(Pt 1):25-32. doi: 10.1007/978-3-319-10404-1_4.
Models of the diffusion-weighted signal are of strong interest for population studies of the brain microstructure. These studies are typically conducted by extracting a scalar property from the model and subjecting it to null hypothesis significance testing. This process has two major limitations: the reported p-value is a weak predictor of the reproducibility of findings and evidence for the absence of microstructural alterations cannot be gained. To overcome these limitations, this paper proposes a Bayesian framework for population studies of the brain microstructure represented by multi-fascicle models. A hierarchical model is built over the biophysical parameters of the microstructure. Bayesian inference is performed by Hamiltonian Monte Carlo sampling and results in a joint posterior distribution over the latent microstructure parameters for each group. Inference from this posterior enables richer analyses of the brain microstructure beyond the dichotomy of significance testing. Using synthetic and in-vivo data, we show that our Bayesian approach increases reproducibility of findings from population studies and opens new opportunities in the analysis of the brain microstructure.
扩散加权信号模型对于脑微观结构的群体研究具有重要意义。这些研究通常通过从模型中提取一个标量属性并对其进行零假设显著性检验来进行。这个过程有两个主要局限性：报告的p值对研究结果可重复性的预测能力较弱，并且无法获得微观结构无改变的证据。为了克服这些局限性，本文提出了一个用于以多纤维束模型表示的脑微观结构群体研究的贝叶斯框架。在微观结构的生物物理参数之上构建了一个层次模型。通过哈密顿蒙特卡洛采样进行贝叶斯推断，结果是每组潜在微观结构参数的联合后验分布。从这个后验进行推断能够对脑微观结构进行比显著性检验二分法更丰富的分析。使用合成数据和体内数据，我们表明我们的贝叶斯方法提高了群体研究结果的可重复性，并为脑微观结构分析开辟了新机会。
