Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.
Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.
Neuroimage. 2018 Mar;168:332-344. doi: 10.1016/j.neuroimage.2017.05.022. Epub 2017 May 12.
Functional MRI at ultra-high magnetic fields (≥ 7T) provides the opportunity to probe columnar and laminar processing in the human brain in vivo at sub-millimeter spatial scales. However, fMRI data only indirectly reflects the neuronal laminar profile due to a bias to ascending and pial veins inherent in gradient- and spin-echo BOLD fMRI. In addition, accurate delineation of the cortical depths is difficult, due to the relatively large voxel sizes and lack of sufficient tissue contrast in the functional images. In conventional depth-dependent fMRI studies, anatomical and functional data are acquired with different image read-out modules, the fMRI data are distortion-corrected and vascular biases are accounted for by subtracting the depth-dependent activation profiles of different stimulus conditions. In this study, using high-resolution gradient-echo fMRI data (0.7 mm isotropic) of the human visual cortex, we propose instead, that depth-dependent functional information is best preserved if data analysis is performed in the original functional data space. To achieve this, we acquired anatomical images with high tissue contrast and similar distortion to the functional images using multiple inversion-recovery time EPI, thereby eliminating the need to un-distort the fMRI data. We demonstrate higher spatial accuracy for the cortical layer definitions of this approach as compared to the more conventional approach using MP2RAGE anatomy. In addition, we provide theoretical arguments and empirical evidence that vascular biases can be better accounted for using division instead of subtraction of the depth-dependent profiles. Finally, we show that the hemodynamic response of grey matter has relatively stronger post-stimulus undershoot than the pial vein voxels. In summary, we show that the choice of fMRI data acquisition and processing can impact observable differences in the cortical depth profiles and present evidence that cortical depth-dependent modulation of the BOLD signal can be resolved using gradient-echo imaging.
超高磁场（≥7T）下的功能磁共振成像（fMRI）提供了在亚毫米空间尺度上活体探测人类大脑柱和层处理的机会。然而，由于梯度回波和自旋回波 BOLD fMRI 固有的对上升和脑皮层静脉的偏向，fMRI 数据仅间接反映神经元的层状轮廓。此外，由于功能图像中相对较大的体素大小和缺乏足够的组织对比度，皮层深度的精确描绘是困难的。在传统的依赖深度的 fMRI 研究中，解剖学和功能数据是使用不同的图像读出模块获取的，fMRI 数据通过减去不同刺激条件下的深度依赖激活轮廓来进行失真校正和血管偏差校正。在这项研究中，我们使用人类视觉皮层的高分辨率梯度回波 fMRI 数据（0.7mm 各向同性），而不是使用不同的图像读出模块获取解剖学和功能数据，我们提出，如果在原始功能数据空间中进行数据分析，那么深度依赖的功能信息可以得到最好的保留。为了实现这一点，我们使用多次反转恢复时间 EPI 获得具有高组织对比度和与功能图像相似失真的解剖图像，从而无需对 fMRI 数据进行去失真。与使用 MP2RAGE 解剖学的更传统方法相比，我们证明了这种方法对皮层层定义的空间精度更高。此外，我们提供了理论论据和经验证据，表明使用除法而不是减法可以更好地解释深度依赖的轮廓的血管偏差。最后，我们表明灰质的血流动力学响应在刺激后具有比脑皮层静脉体素更强的下冲。总之，我们表明 fMRI 数据采集和处理的选择会影响皮层深度轮廓的可观察差异，并提供证据表明可以使用梯度回波成像来解决 BOLD 信号的皮层深度依赖性调制。
