Max-Planck Institute for Biological Cybernetics, Tuebingen, Germany.
Neuroimage. 2011 May 15;56(2):627-42. doi: 10.1016/j.neuroimage.2010.09.037. Epub 2010 Sep 22.
Multivariate machine learning algorithms applied to human functional MRI (fMRI) data can decode information conveyed by cortical columns, despite the voxel-size being large relative to the width of columns. Several mechanisms have been proposed to underlie decoding of stimulus orientation or the stimulated eye. These include: (I) aliasing of high spatial-frequency components, including the main frequency component of the columnar organization, (II) contributions from local irregularities in the columnar organization, (III) contributions from large-scale non-columnar organizations, (IV) functionally selective veins with biased draining regions, and (V) complex spatio-temporal filtering of neuronal activity by fMRI voxels. Here we sought to assess the plausibility of two of the suggested mechanisms: (I) aliasing and (II) local irregularities, using a naive model of BOLD as blurring and MRI voxel sampling. To this end, we formulated a mathematical model that encompasses both the processes of imaging ocular dominance (OD) columns and the subsequent linear classification analysis. Through numerical simulations of the model, we evaluated the distribution of functional differential contrasts that can be expected when considering the pattern of cortical columns, the hemodynamic point spread function, the voxel size, and the noise. We found that with data acquisition parameters used at 3 Tesla, sub-voxel supra-Nyquist frequencies, including frequencies near the main frequency of the OD organization (0.5 cycles per mm), cannot contribute to the differential contrast. The differential functional contrast of local origin is dominated by low-amplitude contributions from low frequencies, associated with irregularities of the cortical pattern. Realizations of the model with parameters that reflected best-case scenario and the reported BOLD point-spread at 3 Tesla (3.5mm) predicted decoding performances lower than those that have been previously obtained at this magnetic field strength. We conclude that low frequency components that underlie local irregularities in the columnar organization are likely to play a role in decoding. We further expect that fMRI-based decoding relies, in part, on signal contributions from large-scale, non-columnar functional organizations, and from complex spatio-temporal filtering of neuronal activity by fMRI voxels, involving biased venous responses. Our model can potentially be used for evaluating and optimizing data-acquisition parameters for decoding information conveyed by cortical columns.
应用于人类功能磁共振成像（fMRI）数据的多元机器学习算法可以解码皮质柱传递的信息，尽管体素大小相对于柱宽较大。已经提出了几种机制来解释刺激方向或受刺激眼睛的解码。这些机制包括：（I）高空间频率成分的混叠，包括柱状组织的主要频率成分，（II）柱状组织局部不规则性的贡献，（III）大规模非柱状组织的贡献，（IV）具有偏向引流区域的功能选择性静脉，以及（V） fMRI 体素对神经元活动的复杂时空滤波。在这里，我们试图使用 BOLD 作为模糊和 MRI 体素采样的简单模型来评估两种建议机制的合理性：（I）混叠和（II）局部不规则性。为此，我们提出了一个数学模型，该模型包含了成像眼优势（OD）柱的过程以及随后的线性分类分析。通过对模型的数值模拟，我们评估了当考虑皮质柱的模式、血流动力学点扩散函数、体素大小和噪声时，可以预期的功能差分对比度的分布。我们发现，使用 3T 时的采集参数，亚体素超奈奎斯特频率，包括接近 OD 组织的主要频率（0.5 个周期/毫米）的频率，不能对差分对比度做出贡献。局部起源的差分功能对比度主要由低频的低幅度贡献主导，与皮质模式的不规则性有关。在反映最佳情况的参数和报告的 3T 时的 BOLD 点扩散（3.5mm）的模型实现中，预测的解码性能低于在此磁场强度下先前获得的性能。我们得出的结论是，柱状组织局部不规则性的低频成分很可能在解码中发挥作用。我们进一步预计，基于 fMRI 的解码部分依赖于大规模、非柱状功能组织的信号贡献，以及 fMRI 体素对神经元活动的复杂时空滤波，涉及偏向性静脉反应。我们的模型可以潜在地用于评估和优化用于解码皮质柱传递信息的数据采集参数。
