LayNii: A software suite for layer-fMRI - Huber, Laurentius (Renzo); Poser, Benedikt A.; Bandettini, Peter A.; Arora, Kabir; Wagstyl, Konrad; Cho, Shinho; Goense, Jozien; Nothnagel, Nils; Morgan, Andrew Tyler; van den Hurk, Job; Müller, Anna K; Reynolds, Richard C.; et al. (NeuroImage (Orlando, Fla.), 2021)
•A new software toolbox is introduced for layer-specific functional MRI: LayNii.•LayNii is a suite of command-line executable C++ programs for Linux, Windows, and macOS.•LayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints.•LayNii performs layerification in the native voxel space of functional data.•LayNii performs layer-smoothing, GE-BOLD deveining, QA, and VASO analysis. 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. [Display omitted]