We address the issue of jointly detecting brain activity and estimating underlying brain hemodynamics from functional MRI data. We adopt the so-called Joint Detection Estimation (JDE) framework that takes spatial dependencies between voxels into account. We recast the JDE into a missing data framework and derive a Variational Expectation-Maximization (VEM) algorithm for its inference. It follows a new algorithm that has interesting advantages over the previously used intensive simulation methods (Markov Chain Monte Carlo, MCMC): tests on artificial data show that the VEM-JDE is more robust to model mis-specification while additional tests on real data confirm that it achieves similar performance in much less computation time.
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机译：我们解决了联合检测大脑活动并根据功能性MRI数据估算潜在的脑血流动力学的问题。我们采用了所谓的联合检测估计（JDE）框架，该框架考虑了体素之间的空间依赖性。我们将JDE重铸到丢失的数据框架中，并为其推导得出了变化期望最大化（VEM）算法。它遵循了一种新算法，该算法比以前使用的密集仿真方法（Markov Chain Monte Carlo，MCMC）具有有趣的优势：对人工数据的测试表明，VEM-JDE对模型错误指定的建模更加健壮，而对真实数据的其他测试则可以证实它以更少的计算时间实现了类似的性能。
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