Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands.
Department of Physics and Astronomy, Dartmouth College, Hanover, NH, USA.
J Neurosci Methods. 2021 Apr 1;353:109095. doi: 10.1016/j.jneumeth.2021.109095. Epub 2021 Feb 5.
The specificity of gradient echo (GE)-BOLD laminar fMRI activation profiles is degraded by intracortical veins that drain blood from lower to upper cortical layers, propagating activation signal in the same direction. This work describes an approach to obtain layer specific profiles by deconvolving the measured profiles with a physiological Point Spread Function (PSF).
It is shown that the PSF can be characterised by a TE-dependent peak to tail (p2t) value that is independent of cortical depth and can be estimated by simulation. An experimental estimation of individual p2t values and the sensitivity of the deconvolved profiles to variations in p2t is obtained using laminar data measured with a multi-echo 3D-FLASH sequence. These profiles are echo time dependent, but the underlying neuronal response is the same, allowing a data-based estimation of the PSF.
The deconvolved profiles are highly similar to the gold-standard obtained from extremely high resolution 3D-EPI data, for a range of p2t values of 5-9, which covers both the empirically determined value (6.8) and the value obtained by simulation (6.3). -Comparison with Existing Method(s) Corrected profiles show a flatter shape across the cortex and a high level of similarity with the gold-standard, defined as a subset of profiles that are unaffected by intracortical veins.
We conclude that deconvolution is a robust approach for removing the effect of signal propagation through intracortical veins. This makes it possible to obtain profiles with high laminar specificity while benefitting from the higher efficiency of GE-BOLD sequences.
梯度回波（GE）-BOLD 层 fMRI 激活轮廓的特异性会因从下至上皮质层引流血液的皮质内静脉而降低，从而沿相同方向传播激活信号。本研究描述了一种通过反卷积测量轮廓来获得层特异性轮廓的方法，该方法使用生理点扩散函数（PSF）。
结果表明，PSF 可以通过 TE 依赖性峰到尾（p2t）值来描述，该值与皮质深度无关，可以通过模拟进行估计。通过使用多回波 3D-FLASH 序列测量的层状数据，获得了个体 p2t 值的实验估计值以及反卷积轮廓对 p2t 变化的敏感性。这些轮廓随回波时间变化，但潜在的神经元响应是相同的，允许基于数据估计 PSF。
反卷积轮廓与从极高分辨率 3D-EPI 数据获得的金标准高度相似，p2t 值范围为 5-9，涵盖了经验确定的值（6.8）和模拟获得的值（6.3）。与现有方法的比较校正后的轮廓在皮质上的形状更平坦，与金标准高度相似，金标准定义为不受皮质内静脉影响的一组轮廓。
我们得出的结论是，反卷积是一种可靠的方法，可以去除通过皮质内静脉传播信号的影响。这使得获得具有高层特异性的轮廓成为可能，同时受益于 GE-BOLD 序列的更高效率。
