Spatially sparse source cluster modeling by compressive neuromagnetic tomography.
Spatially sparse source cluster modeling by compressive neuromagnetic tomography.
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DOI：
10.1016/j.neuroimage.2010.05.013
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发表时间：
2010-10-15
期刊：
影响因子：
5.7
通讯作者：
Lin, Fa-Hsuan
中科院分区：
文献类型：
--
作者：
Chang, Wei-Tang;Nummenmaa, Aapo;Hsieh, Jen-Chuen;Lin, Fa-Hsuan
脑磁图通过利用超导量子干涉装置（SQUID）实现对弱脑磁场的非侵入性检测。解决脑磁图逆问题需要重建的位置和方向的基础上颅外测量的基础上的神经元电流源。大多数逆问题求解器明确地支持空间上更集中或扩散的电流源模式。自然地，在存在焦点源和空间扩展源两者的情况下，这样的重建方法可能产生不准确的估计。为了解决这个问题，我们提出了一种新的压缩神经磁断层成像（CENT）方法的基础上的假设，电流源是可压缩的。压缩性由源表示在标准源空间和变换域中的联合稀疏性来量化。变换稀疏性约束的目的是通过利用变换域中源配置的自然冗余来自适应地结合局部空间结构。通过结合这些互补的标准和变换域稀疏性的约束，我们得到的源估计，这不仅是局部光滑和定期，但也形成全球可分离的集群。在这项工作中，我们使用的稀疏性和凸优化的措施，以产生压缩估计在计算上易于处理的方式的101-范数。我们研究了拉普拉斯矩阵（CENTL）和球面小波（CENTW）作为压缩约束下的变换的替代方案。除了对源的两个先验约束外，我们还通过将残差的功率限制在指定值以下来控制建模数据和测量数据之间的差异。结果表明，CENTL和CENTW都能够产生强大的空间规则源估计与高计算效率。对于模拟的焦点源、漫射源和混合源，CENT方法在估计源的位置和空间范围方面比最小范数或最小范数约束的逆解具有更好的精度。不同的转换产生不同的好处：通过利用CENT与拉普拉斯矩阵，它可以抑制生理上的非典型激活延伸到两个相对的银行的深沟。与球面小波变换CENT可以提高检测两个相邻但不直接连接的源。正如模拟所示，CENT能够反映焦点和空间扩展电流源的空间范围。CENT对体内MEG数据的分析在体感和听觉MEG测量中产生较少的生理不一致的“杂波”电流源。总体而言，CENT方法被证明是一个很有前途的工具，与认知任务相关的分布式神经元电流的自适应建模。
Magnetoencephalography enables non-invasive detection of weak cerebral magnetic fields by utilizing super-conducting quantum interference devices (SQUIDs). Solving the MEG inverse problem requires reconstructing the locations and orientations of the underlying neuronal current sources based on the extracranial measurements. Most inverse problem solvers explicitly favor either spatially more focal or diffuse current source patterns. Naturally, in a situation where both focal and spatially extended sources are present, such reconstruction methods may yield inaccurate estimates. To address this problem, we propose a novel ComprEssive Neuromagnetic Tomography (CENT) method based on the assumption that the current sources are compressible. The compressibility is quantified by the joint sparsity of the source representation in the standard source space and in a transformed domain. The purpose of the transformation sparsity constraint is to incorporate local spatial structure adaptively by exploiting the natural redundancy of the source configurations in the transform domain. By combining these complementary constraints of standard and transformed domain sparsity we obtain source estimates, which are not only locally smooth and regular but also form globally separable clusters. In this work, we use the ℓ1-norm as a measure of sparsity and convex optimization to yield compressive estimates in a computationally tractable manner. We study the Laplacian matrix (CENTL) and spherical wavelets (CENTW) as alternatives for the transformation in the compression constraint. In addition to the two prior constraints on the sources, we control the discrepancy between the modeled and measured data by restricting the power of residual error below a specified value. The results show that both CENTL and CENTW are capable of producing robust spatially regular source estimates with high computational efficiency. For simulated sources of focal, diffuse, or combined types, the CENT method shows better accuracy on estimating the source locations and spatial extents than the minimum ℓ1-norm or minimum ℓ2-norm constrained inverse solutions. Different transformations yield different benefits: By utilizing CENT with the Laplacian matrix it is possible to suppress physiologically atypical activations extending across two opposite banks of a deep sulcus. With the spherical wavelet transform CENT can improve the detection of two nearby yet not directly connected sources. As demonstrated by simulations, CENT is capable of reflecting the spatial extent for both focal and spatially extended current sources. The analysis of in vivo MEG data by CENT produces less physiologically inconsistent “clutter” current sources in somatosensory and auditory MEG measurements. Overall, the CENT method is demonstrated to be a promising tool for adaptive modeling of distributed neuronal currents associated with cognitive tasks.
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