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Nothnagel, N.

Publications and source records attributed to Nothnagel, N..

2 recordsLinked to original sources

High-resolution line-scanning reveals distinct visual response properties across human cortical layers

Our understanding of the human brain relies on advancing noninvasive brain imaging approaches. Characterization of the function of brain circuitry depends on the spatiotemporal correspondence at which recorded signals can be mapped onto underlying neuronal structures and processes. Here we aimed to address key first-stage questions of feasibility, reliability, and utility of line-scanning fMRI as a next generation non-invasive imaging method for human neuroscience research at the mesoscopic scale. Line-scanning can achieve high spatial resolution by employing anisotropic voxels aligned to cortical layers. The method can simultaneously achieve high temporal resolution by limiting acquisition to a very small patch of cortex which is repeatedly acquired as a single frequency-encoded k-space line. We developed multi-echo line-scanning procedures to record cortical layers in humans at high spatial (200 m) and temporal resolution (100 ms) using ultra high-field 7T fMRI. Quantitative mapping allowed us to identify cortical layers in primary visual cortex (V1) and record functional signals from them while participants viewed movie clips. Analysis of these recordings revealed layer-specific V1 spatial and orientation tuning properties analogous to those previously observed in electrophysiological recordings of non-human primates. We have consequently demonstrated that line-scanning is a powerful non-invasive imaging technique for investigating mesoscopic functional circuits in human cortex. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/179762v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@c314f9org.highwire.dtl.DTLVardef@1960fd4org.highwire.dtl.DTLVardef@e95d73org.highwire.dtl.DTLVardef@f6680f_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience

LAYNII: A software suite for layer-fMRI

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 open-source 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. HighlightsO_LIA new software toolbox is introduced for layer-specific functional MRI: LayNii. C_LIO_LILayNii is a suite of command-line executable C++ programs for Linux, Windows, and macOS. C_LIO_LILayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints. C_LIO_LILayNii performs layerification in the native voxel space of functional data. C_LIO_LILayNii performs layer-smoothing, GE-BOLD deveining, QA, and VASO analysis. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/148080v3_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@e12e9org.highwire.dtl.DTLVardef@1fbb564org.highwire.dtl.DTLVardef@41da13org.highwire.dtl.DTLVardef@1545106_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

neuroscience