In this blog post I want to go through the analysis pipeline of layer-dependent VASO.
I will go through the all the analysis steps that need to be done to go from raw data from the scanner to final layer profiles. The entire thing will take about 30 min (10 min analysis and 20 min explaining and browsing through data).
During the entire analysis pipeline I am using the following software packages: SPM, AFNI, and LAYNII, and gnuplot (if you want fancier plotting tools)
In this blog post, I want to share my thoughts on the number of layers that should be extracted from any given dataset. I will try to give an overview of how many layers are usually extracted in the field, I’ll describe my personal choices of layer numbers, and I will try to discuss the challenges of layer signal extraction along the way.
What’s the best name of our field and what’s the best attributing term for our data? There are many competing options: “Layer fMRI”, “mesoscopic fMRI”, “sub-millimeter fMRI”, “ultra-high resolution fMRI”, “laminar fMRI”, “cortical depth-dependent fMRI”. They differ with respect to how flashy they are, how scientifically appropriate they are, and how popular they are.
In this blog post, I want to review which ones are the most popular ones in the field and also share some thoughts on my favourite candidates.
Edits on March 3rd 2019 with contributions and clarifications taken from Kamil Uludağ, Sri Kashyap and Faruk Gulban.
CBV-fMRI with VASO is highly dependent on a good inversion contrast. It gives it its CBV sensitivity and is also responsible for most of the VASO specific pitfalls (e.g. inflow, CSF etc. ). And thus, it should be optimized as much as possible.
In this blog post, I want to describe the most important features of a reliable inversion pulse for the application of VASO at 7T with a head transmit coil.
In the last years, I and multiple other VASO users have encountered many occasions of voxels that show negative CBV change (positive VASO signal change) while the BOLD suggests that the activation should be positive. In this blog post, I want to list potential sources of this surprising effect.
In this blog post, I want to write about pipelines on how to prepare Nifti-brain data and make them printable by a 3D-printer.
Two pipelines are shown. One pipeline describes the 3D-printing the cortical folding structure that is estimated with Freesurfer and subsequently corrected with Meshlab. And another pipeline describes how you can 3D-print any binary nii-volume by using the AFNI-program IsoSurface and correct the output with netfabb. Continue reading “3D-printing nii data”→
Often we would like to normalize depth-dependent fMRI signals and assign it to specific cytoarchitectonially defined cortical layers. However, we often only have access to cytoarchitectonial histology data in the form to figures in papers. But since we only have the web-view or the PDF available, we cannot easily extract those data as a layer-profile. Since most layering tools are designed for nii data only, paper figures (e.g. jpg or GNP) are not straight-forwardly transformed to layer profiles.
In this blob post, I describe a set of steps on how to convert any paper figure into a nii-file that allows the extraction of layer profiles.
In this blog post I want to discuss how the tSNR in sub-millimeter fMRI can be substantially improved by optimizing the GRAPPA regularization. Adjusting one single GRAPPA reconstruction parameter can almost double the tSNR of your fMRI time series. With almost no penalty.
This post documents the installation of an IDEA VE11 virtual box on a mac as done on May 14th 2018
Big thanks to Andy for figuring out how this works
Here I start with a already built images of IDEA on windows vista and mars on Ubuntu. the images from FMRIF can be taken from erbium.nimh.nih.gov:/fmrif/projects/SiemensIdea/virtual_machines/OVF/): IDEA_ve11c-mars.ova and IDEA_ve11c+vd13d+vd13a.ova
At high resolution EPI, the gradients are pushed to their limits and the ramp sampling ratio is particularly large. This means that the ghosting is increased and the Nyquist ghost correction is getting more important. In this post, I describe how to change the Nyquist ghost correction algorithm.
With respect to high-resolution VASO application, visual cortex is very unique. I found it to be a challenging area. However, because of its high demand, I have been working on is with multiple collaborators. The most important pitfalls of SS-SI VASO in visual cortex that I came across in these collaborations are discussed below.
The take home message tat I learned from manny experiments is:
Use axial slices with the phase encoding direction A>>P.
Watch out for negative voxels.
Invest a lot of effort in optimizing GRAPPA parameters, its worth it.