Intracortical smoothing of small-voxel fMRI data can provide increased detection power without spatial resolution losses compared to conventional large-voxel fMRI data
Intracortical smoothing of small-voxel fMRI data can provide increased detection power without spatial resolution losses compared to conventional large-voxel fMRI data
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DOI：
10.1016/j.neuroimage.2019.01.054
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发表时间：
2019-04-01
期刊：
影响因子：
5.7
通讯作者：
Polimeni, Jonathan R.
中科院分区：
文献类型：
--
作者：
Blazejewska, Anna, I;Fischl, Bruce;Polimeni, Jonathan R.
MRI 采集技术的不断改进使得各向同性体素尺寸小至 1 毫米及以下的功能性 MRI (fMRI) 更加普遍。尽管许多传统的 fMRI 研究试图研究皮质激活的区域模式，其中 3 毫米及更大的传统体素尺寸可提供足够的空间分辨率，但较小的体素可以帮助避免邻近白质 (WM) 和脑脊液 (CSF) 的污染，从而提高 fMRI 对灰质内信号变化的特异性。不幸的是，时间信噪比（tSNR）（fMRI 灵敏度的指标）在高分辨率采集中会降低，从而抵消了小体素的优势。在这里，我们介绍了一个框架，它将在 7 T 场强下获取的小型各向同性 fMRI 体素与新颖的解剖学信息、表面网格导航空间平滑相结合，可以提供比传统体素尺寸更高的检测能力和更高分辨率。我们的平滑方法使用一系列皮质内表面网格，并允许使用各种形状和大小的内核，包括适应和跟踪皮质折叠模式的弯曲 3D 内核。我们的目标是限制对皮质灰质带的平滑，并通过部分体积效应避免来自 CSF 的噪声污染和来自 WM 的信号稀释。我们发现最大化 tSNR 的皮质内内核并不能最大化信号变化百分比 (Delta S/S)，因此优化检测能力的内核配置不能仅从 tSNR 考虑因素来确定。然而，多种内核配置在提高 tSNR 和 Delta S/S 之间提供了良好的平衡，并且与未平滑的 3.0-mm 各向同性 fMRI 采集相比，平滑后的 1.1-mm 各向同性 fMRI 采集具有更高的性能（在检测能力和空间分辨率方面）。总体而言，本研究的结果支持获取小于皮质厚度的体素的策略，即使对于不需要高空间分辨率的研究也是如此，并用适当形状的内核在皮质带内平滑它们以实现最佳性能，从而将 fMRI 体素大小的选择与特定研究的空间分辨率要求脱钩。对于传统分辨率，这种新的皮质内平滑方法相对于传统的基于表面的平滑的改进预计是适度的，但是随着分辨率的提高，改进预计会增加。该框架还可以应用于具有激活空间结构先验信息的研究中高分辨率数据（例如沿列和层）的解剖学信息皮质内平滑。
Continued improvement in MRI acquisition technology has made functional MRI (fMRI) with small isotropic voxel sizes down to 1 mm and below more commonly available. Although many conventional fMRI studies seek to investigate regional patterns of cortical activation for which conventional voxel sizes of 3 mm and larger provide sufficient spatial resolution, smaller voxels can help avoid contamination from adjacent white matter (WM) and cerebrospinal fluid (CSF), and thereby increase the specificity of fMRI to signal changes within the gray matter. Unfortunately, temporal signal-to-noise ratio (tSNR), a metric of fMRI sensitivity, is reduced in high-resolution acquisitions, which offsets the benefits of small voxels. Here we introduce a framework that combines small, isotropic fMRI voxels acquired at 7 T field strength with a novel anatomically-informed, surface mesh-navigated spatial smoothing that can provide both higher detection power and higher resolution than conventional voxel sizes. Our smoothing approach uses a family of intracortical surface meshes and allows for kernels of various shapes and sizes, including curved 3D kernels that adapt to and track the cortical folding pattern. Our goal is to restrict smoothing to the cortical gray matter ribbon and avoid noise contamination from CSF and signal dilution from WM via partial volume effects. We found that the intracortical kernel that maximizes tSNR does not maximize percent signal change (Delta S/S), and therefore the kernel configuration that optimizes detection power cannot be determined from tSNR considerations alone. However, several kernel configurations provided a favorable balance between boosting tSNR and Delta S/S, and allowed a 1.1-mm isotropic fMRI acquisition to have higher performance after smoothing (in terms of both detection power and spatial resolution) compared to an unsmoothed 3.0-mm isotropic fMRI acquisition. Overall, the results of this study support the strategy of acquiring voxels smaller than the cortical thickness, even for studies not requiring high spatial resolution, and smoothing them down within the cortical ribbon with a kernel of an appropriate shape to achieve the best performance-thus decoupling the choice of fMRI voxel size from the spatial resolution requirements of the particular study. The improvement of this new intracortical smoothing approach over conventional surface-based smoothing is expected to be modest for conventional resolutions, however the improvement is expected to increase with higher resolutions. This framework can also be applied to anatomically-informed intracortical smoothing of higher-resolution data (e.g. along columns and layers) in studies with prior information about the spatial structure of activation.