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Fast joint detection-estimation of evoked brain activity in event-related fmri using a variational approach

机译:使用变分方法快速联合检测事件相关性fmri中诱发的大脑活动

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摘要

In standard within-subject analyses of event-related fMRI data, two steps are usually performed separately: detection of brain activity and estimation of the hemodynamic response. Because these two steps are inherently linked, we adopt the socalled region-based Joint Detection-Estimation (JDE) framework that addresses this joint issue using a multivariate inference for detection and estimation. JDE is built by making use of a regional bilinear generative model of the BOLD response and constraining the parameter estimation by physiological priors using temporal and spatial information in a Markovian model. In contrast to previous works that use Markov Chain Monte Carlo (MCMC) techniques to sample the resulting intractable posterior distribution, we recast the JDE into a missing data framework and derive a Variational Expectation-Maximization (VEM) algorithm for its inference. A variational approximation is used to approximate the Markovian model in the unsupervised spatially adaptive JDE inference, which allows automatic fine-tuning of spatial regularization parameters. It provides a new algorithm that exhibits interesting properties in terms of estimation error and computational cost compared to the previously used MCMC-based approach. Experiments on artificial and real data show that VEM-JDE is robust to model misspecification and provides computational gain while maintaining good performance in terms of activation detection and hemodynamic shape recovery.
机译:在事件相关的功能磁共振成像数据的标准受试者内部分析中,通常分别执行两个步骤:检测大脑活动和评估血流动力学反应。由于这两个步骤是固有联系在一起的,因此我们采用了所谓的基于区域的联合检测-估计(JDE)框架,该框架使用多变量推断进行检测和估计来解决此联合问题。通过使用BOLD响应的区域双线性生成模型并使用Markovian模型中的时间和空间信息通过生理先验约束参数估计来构建JDE。与以前使用马尔可夫链蒙特卡洛(MCMC)技术对所得的棘手的后验分布进行采样的工作相反,我们将JDE重铸为缺失的数据框架,并推导了变异期望最大化(VEM)算法以进行推断。在无监督的空间自适应JDE推理中,使用变分近似来近似马尔可夫模型,从而可以对空间正则化参数进行自动微调。它提供了一种新算法,与以前使用的基于MCMC的方法相比,该算法在估计误差和计算成本方面表现出令人感兴趣的特性。在人工和真实数据上的实验表明,VEM-JDE可以很好地建模误指定并提供计算增益,同时在激活检测和血液动力学形状恢复方面保持良好的性能。

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