Melbourne Brain Centre Imaging Unit, Department of Anatomy and Neuroscience, The University of Melbourne, Kenneth Myer Building 30 Royal Parade, Parkville, Victoria, Australia; Vascular Bionics Laboratory, Melbourne Brain Centre, Department of Medicine, The University of Melbourne, Victoria, Australia.
Department of Electrical & Electronic Engineering, The University of Melbourne, Victoria, Australia; Vascular Bionics Laboratory, Melbourne Brain Centre, Department of Medicine, The University of Melbourne, Victoria, Australia; The Florey Institute of Neuroscience and Mental Health, 30 Royal Parade, Parkville, Victoria, Australia.
Neuroimage. 2018 Jan 1;164:214-229. doi: 10.1016/j.neuroimage.2017.03.002. Epub 2017 Mar 8.
Recent developments in accelerated imaging methods allow faster acquisition of high spatial resolution images. This could improve the applications of functional magnetic resonance imaging at 7 Tesla (7T-fMRI), such as neurosurgical planning and Brain Computer Interfaces (BCIs). However, increasing the spatial and temporal resolution will both lead to signal-to-noise ratio (SNR) losses due to decreased net magnetization per voxel and T-relaxation effect, respectively. This could potentially offset the SNR efficiency gains made with increasing temporal resolution. We investigated the effects of varying spatial and temporal resolution on fMRI sensitivity measures and their implications on fMRI-based BCI simulations. We compared temporal signal-to-noise ratio (tSNR), observed percent signal change (%∆S), volumes of significant activation, Z-scores and decoding performance of linear classifiers commonly used in BCIs across a range of spatial and temporal resolution images acquired during an ankle-tapping task. Our results revealed an average increase of 22% in %∆S (p=0.006) and 9% in decoding performance (p=0.015) with temporal resolution only at the highest spatial resolution of 1.5×1.5×1.5mm, despite a 29% decrease in tSNR (p<0.001) and plateaued Z-scores. Further, the volume of significant activation was indifferent (p>0.05) across spatial resolution specifically at the highest temporal resolution of 500ms. These results demonstrate that the overall BOLD sensitivity can be increased significantly with temporal resolution, granted an adequately high spatial resolution with minimal physiological noise level. This shows the feasibility of diffuse motor-network imaging at high spatial and temporal resolution with robust BOLD sensitivity with 7T-fMRI. Importantly, we show that this sensitivity improvement could be extended to an fMRI application such as BCIs.
加速成像方法的最新进展使得能够更快地获取高空间分辨率的图像。这可能会改善 7 特斯拉（7T-fMRI）下功能磁共振成像的应用，例如神经外科规划和脑机接口（BCI）。然而，增加空间和时间分辨率都会导致由于每个体素的净磁化率降低和 T 弛豫效应分别导致信号噪声比（SNR）损失。这可能会抵消随着时间分辨率增加而获得的 SNR 效率提高。我们研究了空间和时间分辨率变化对 fMRI 灵敏度测量的影响及其对基于 fMRI 的 BCI 模拟的影响。我们比较了在脚踝敲击任务期间获取的一系列空间和时间分辨率图像的时间信号到噪声比（tSNR）、观察到的信号变化百分比（%∆S）、显著激活体积、Z 分数和线性分类器的解码性能，这些分类器常用于 BCI 中。我们的结果表明，在最高空间分辨率为 1.5×1.5×1.5mm 时，仅通过时间分辨率就可以平均提高 22%的%∆S（p=0.006）和 9%的解码性能（p=0.015），尽管 tSNR 下降了 29%（p<0.001）且 Z 分数趋于平稳。此外，在最高时间分辨率为 500ms 时，特定于空间分辨率的显著激活体积没有差异（p>0.05）。这些结果表明，只要具有足够高的空间分辨率且生理噪声水平最低，就可以通过时间分辨率显著提高整体 BOLD 灵敏度。这表明在 7T-fMRI 下具有高空间和时间分辨率的稳健 BOLD 灵敏度进行弥散运动网络成像具有可行性。重要的是，我们表明这种灵敏度的提高可以扩展到 fMRI 应用，例如 BCI。
