Improving the precision of fMRI BOLD signal deconvolution with implications for connectivity analysis
Improving the precision of fMRI BOLD signal deconvolution with implications for connectivity analysis
复制标题
DOI：
10.1016/j.mri.2015.07.007
复制
发表时间：
2015-12-01
影响因子：
2.5
通讯作者：
Kilts, Clint
中科院分区：
文献类型：
--
作者：
Bush, Keith;Cisler, Josh;Kilts, Clint
神经影像学分析中一个重要的开放性问题是开发分析方法，确保精确推断fMRI BOLD信号背后的神经活动，尽管已知存在混淆。在这里，我们开发并测试了一种新的元算法，用于进行半盲（即，不知道刺激定时）对BOLD信号进行去卷积，其经由自举估计驱动BOLD的潜在神经事件以及这些估计的置信度。我们的方法包括对当前性能最好的去卷积方法的两个改进; 1）我们优化了去卷积特征空间的参数形式; 2）我们在进行神经事件分类之前，基于估计的置信度，将神经事件估计预分类为已知或未知的两个子组。这种知道它知道的方法显着提高了神经事件分类在当前最好的性能算法，在一个详细的计算机模拟高度混淆的fMRI BOLD信号测试。然后，我们实现了基于引导的反卷积算法的大规模并行化版本，并在高性能计算机上执行它以进行大规模（即，voxelwise）估计一组17个人类受试者的神经事件。我们表明，通过限制区域间相关性的计算，仅包括那些高置信度估计的神经事件，该方法似乎具有更高的灵敏度，用于识别默认模式网络相比，一个标准的BOLD信号相关性分析时，跨学科比较。(C)2015爱思唯尔公司All rights reserved.
An important, open problem in neuroimaging analyses is developing analytical methods that ensure precise inferences about neural activity underlying fMRI BOLD signal despite the known presence of confounds. Here, we develop and test a new meta-algorithm for conducting semi-blind (i.e., no knowledge of stimulus timings) deconvolution of the BOLD signal that estimates, via bootstrapping, both the underlying neural events driving BOLD as well as the confidence of these estimates. Our approach includes two improvements over the current best performing deconvolution approach; 1) we optimize the parametric form of the deconvolution feature space; and, 2) we pre-classify neural event estimates into two subgroups, either known or unknown, based on the confidence of the estimates prior to conducting neural event classification. This knows-what-it-knows approach significantly improves neural event classification over the current best performing algorithm, as tested in a detailed computer simulation of highly-confounded fMRI BOLD signal. We then implemented a massively parallelized version of the bootstrapping-based deconvolution algorithm and executed it on a high-performance computer to conduct large scale (i.e., voxelwise) estimation of the neural events for a group of 17 human subjects. We show that by restricting the computation of inter-regional correlation to include only those neural events estimated with high-confidence the method appeared to have higher sensitivity for identifying the default mode network compared to a standard BOLD signal correlation analysis when compared across subjects. (C) 2015 Elsevier Inc. All rights reserved.