LayNii: A software suite for layer-fMRI

Laurentius (Renzo) Huber,Benedikt A Poser,Peter A Bandettini,Kabir Arora,Konrad Wagstyl,Shinho Cho,Jozien Goense,Nils Nothnagel,Andrew Tyler Morgan,Job Van Den Hurk,Anna K Müller,Richard C Reynolds,Daniel R Glen,Rainer Goebel,Omer Faruk Gulban

doi:10.1016/j.neuroimage.2021.118091

Abstract

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.

Highlights

Functional Magnetic Resonance Imaging aims to measure correlates of brain activity at the level of individual voxels
If a layer-fMRI user is interested in alignment constraints that are specific to layer-fMRI data, we refer to the instructions of how to perform image alignment of layerfMRI data with ITK-snap (Yushkevich et al 2006) and ANTs, or with AFNI and (Navarro et al 2020)
E.g. the interested reader is referred to an instruction of how to use FreeSurfer, nighres and SUMA to generate an input rim file for LayNii here:, this segmentation can be further corrected with the semi-manual segmentation tool Segmentator (Gulban et al 2018)

Summary

Introduction

Functional Magnetic Resonance Imaging (fMRI) aims to measure correlates of brain activity at the level of individual voxels. Modern fMRI data from ultra-high fields (UHF) are approaching the spatial scale of cortical layers and columns (Bollmann and Barth 2020; Goense et al 2012a; Kuehn and Sereno 2018; Petro and Muckli 2017; van der Zwaag et al 2016). While the first decade of sub-millimeter fMRI research focused on data acquisition methods (Budde et al 2014; Goense et al 2010; Goense and Logothetis 2006; Goense et al 2007; Petridou et al 2013; Rua et al 2017; van der Zwaag et al 2009), the methodological research questions of the layerfMRI field have since shifted towards addressing analysis challenges. The specific shortcomings of common analysis approaches for high-resolution fMRI are listed in multiple review articles (Kemper et al 2018; Polimeni et al 2018), and there are many fMRI analysis software packages that are able to minimize these shortcomings to some degree: AFNI/SUMA (https://afni.nimh.nih.gov, (Cox 1996)), ANTs (http://stnava.github.io/ANTs/, (Avants et al 2008)), BrainVoyager (http://www.brainvoyager. com, (Goebel 2012), CBSTools/Nighres

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