DataSheet1.pdf

Relating disease status to imaging data stands to increase the clinical significance of neuroimaging studies. Many neurological and psychiatric disorders involve complex, systems-level alterations that manifest in functional and structural properties of the brain and possibly other clinical and biologic measures. We propose a Bayesian hierarchical model to predict disease status, which is able to incorporate information from both functional and structural brain imaging scans. We consider a two-stage whole brain parcellation, partitioning the brain into 282 subregions, and our model accounts for correlations between voxels from different brain regions defined by the parcellations. Our approach models the imaging data and uses posterior predictive probabilities to perform prediction. The estimates of our model parameters are based on samples drawn from the joint posterior distribution using Markov Chain Monte Carlo (MCMC) methods. We evaluate our method by examining the prediction accuracy rates based on leave-one-out cross validation, and we employ an importance sampling strategy to reduce the computation time. We conduct both whole-brain and voxel-level prediction and identify the brain regions that are highly associated with the disease based on the voxel-level prediction results. We apply our model to multimodal brain imaging data from a study of Parkinson's disease. We achieve extremely high accuracy, in general, and our model identifies key regions contributing to accurate prediction including caudate, putamen, and fusiform gyrus as well as several sensory system regions.

将疾病状态与影像数据相关联，有望提升神经影像学研究的临床应用价值。诸多神经及精神类疾病均存在复杂的系统级改变，这类改变会体现在大脑的功能与结构特性中，同时也可能反映在其他临床及生物学指标上。本研究提出一种贝叶斯分层模型（Bayesian hierarchical model）用于疾病状态预测，该模型可整合来自功能性与结构性脑影像扫描的信息。本研究采用两阶段全脑分区（whole brain parcellation）策略，将大脑划分为282个亚区，且模型可考虑由分区定义的不同脑区体素（voxel）间的相关性。本方法对影像数据进行建模，并利用后验预测概率开展预测。模型参数的估计基于通过马尔可夫链蒙特卡洛（Markov Chain Monte Carlo, MCMC）方法从联合后验分布中抽取的样本。本研究通过基于留一法交叉验证（leave-one-out cross validation）的预测准确率评估模型性能，并采用重要性采样（importance sampling）策略以降低计算耗时。我们同时开展全脑级别与体素级别的预测，并基于体素层面的预测结果识别出与疾病高度相关的脑区。我们将所提模型应用于一项帕金森病（Parkinson's disease）研究的多模态脑影像数据集。总体而言，本模型取得了极高的预测准确率，且识别出了对精准预测具有关键作用的脑区，包括尾状核（caudate）、壳核（putamen）以及梭状回（fusiform gyrus），同时还包括数个感觉系统相关脑区。