MBIC, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands.
MBIC, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands.
Neuroimage. 2021 Aug 15;237:118091. doi: 10.1016/j.neuroimage.2021.118091. Epub 2021 May 12.
High-resolution fMRI in the sub-millimeter regime allows researchers to resolve brain activity across cortical layers and columns non-invasively. While these high-resolution data make it possible to address novel questions of directional information flow within and across brain circuits, the corresponding data analyses are challenged by MRI artifacts, including image blurring, image distortions, low SNR, and restricted coverage. These challenges often result in insufficient spatial accuracy of conventional analysis pipelines. Here we introduce a new software suite that is specifically designed for layer-specific functional MRI: LayNii. This toolbox is a collection of command-line executable programs written in C/C++ and is distributed opensource and as pre-compiled binaries for Linux, Windows, and macOS. LayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints and thus cannot be straightforwardly analyzed in alternative software packages. Some of the most popular programs of LayNii contain 'layerification' and columnarization in the native voxel space of functional data as well as many other layer-fMRI specific analysis tasks: layer-specific smoothing, model-based vein mitigation of GE-BOLD data, quality assessment of artifact dominated sub-millimeter fMRI, as well as analyses of VASO data.
在亚毫米分辨率下进行高分辨率 fMRI 可以使研究人员无创地解析跨皮质层和柱的大脑活动。虽然这些高分辨率数据使解决大脑回路内和跨大脑回路的定向信息流的新问题成为可能,但相应的数据分析受到 MRI 伪影的挑战,包括图像模糊、图像失真、低 SNR 和受限的覆盖范围。这些挑战通常导致传统分析管道的空间精度不足。这里我们引入了一个专门为层特异性功能磁共振成像设计的新软件套件:LayNii。该工具箱是一个用 C/C++编写的命令行可执行程序的集合,以开源形式和预编译的二进制文件形式提供,适用于 Linux、Windows 和 macOS。LayNii 专为那些受到 SNR 和覆盖范围限制的层 fMRI 数据而设计,因此不能在替代软件包中直接进行分析。LayNii 最受欢迎的程序之一是在功能数据的本地体素空间中进行“层分离”和“柱状化”,以及许多其他层 fMRI 特定的分析任务:特定于层的平滑、基于模型的 GE-BOLD 数据静脉减轻、受伪影主导的亚毫米 fMRI 的质量评估,以及 VASO 数据的分析。