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.
In this blog post Sri Kashyap and I describe how to deal with the registration of high-resolution datasets across days, across different resolutions, and across different sequences.
I am particularly fond of the following two tools: Firstly, ITK-SNAP for visually-guided manual alignment and secondly, using ANTs programs: antsRegistration and antsApplyTransforms.
Maximum intensity projection and minimum intensity projection can be insightful for mapping of vessels in 3D-slabs. In this post, I describe the application of intensity projections with LAYNII.
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.
ISIS-conv is a very useful dicom to nii converter from Enrico Reimer. ISIS-conv gets along with a lot of challenging data sets that no other converter (that I know of) can handle so conveniently:
- SMS-data, where individual slices have a non-constant inter-slice distances.
- VASO data with non-constant TRs
- Multi-echo, multi-coil, and Magnitude/Phase data.
There is a Mac-installation package of ISIS-conv. Unfortunately, however, with every IOS update, it has become more complicated to install it.
Since, I spend too much time figuring out how to install it after every update, I am collecting the necessary steps in this Blog post for future reference:
In this blog post, I want to describe the application and working principle of a few spatial smoothing algorithms that are implemented in LAYNII.
- Confined smoothing along similar anatomical structures with
- Smoothing along the layers with
- Smoothing within columns with
- Smoothing across specific spatial dimensions only with
- Smoothing long the time domain with