This post summarizes the presentation, tutorials, and discussions of the 2019 UHF Minnesota Workshop on Cortical Depth-Resolved fMRI Methods, Nov 12th-Nov 13th.
Author: renzohuber
Layer-fMRI Jobs
This page lists all open layer-fMRI job opportunities. This page is created with respect to efforts of the layer-fMRI network meeting in 2019 in Minnesota.
Suggestions are welcome to layerfMRI@gmail.com.
- Lausanne, Switzerland, with Ileana Jelescu on diffusion functional fMRI at 7T
- Brisbane, Australia, with Alex Puckett, Post-doc and PhD positions, on layer-fMRI
- Trondheim, in Systems Neuroscience, Faculty Associate Professor with 7T
- Glasgow, with Jozien Goense on layer-fMRI applications, two post-docs and a faculty position.
- Suwon, Korea, with Kamil Uludag, Post-doc position on layer-fMRI methods.
- The Veterans Health Research Institute, with An Vu, about microscale fMRI
- Maastricht, with Benedikt Poser, Post-doc, on UHF advanced imaging.
- Glasgow, with Lars Muckli, on layerfMRI and computational modeling
- Magdeburg, with layerfMRI-expert Esther Kuehn, PdH-Position on Neuronal Mechanisms of Autosuggestion
- NIH, Bethesda, with Peter Bandettini, multiple positions on fMRI methods, including layerfMRI.
- UCSF, with Duan Xu, on advances imaging methods at with a GE 7T scanner, Post-doc position.
- Aalto, Finland, Faculty position on NeuroImaging (7T Terra might come soon).
- Montreal, with Rick Hoge at their new 7T Terra, Research Associate position on advanced UHF functional imaging.
- Liege, with 7T Terra, Post-doc MRI physicist
- Maastricht, with Federiko de Martino, on layer-fMRI, Post-doc
Tutorial on scanning high-resolution EPI on SIEMENS scanners
This post lists the background material of the hands-on tutorial about high-resolution EPI on SIEMENS scanners.
Continue reading “Tutorial on scanning high-resolution EPI on SIEMENS scanners”
layer-fMRI abstracts
The following is a personal selection of my must-see layer-fMRI abstracts presented at SFN/OHBM/ISMRM. If I missed something, please let us know (layerfMRI@gmail.com).
SFN 2019 in Chicago
Saturday
- 4:00 pm – 5:00 pm, 092.12/ BB63. Poster, O. STANLEY, Phase-based macrovascular filtering from gradient echo bold fMRI reduces orientation dependence.
Sunday
- 8:00 am – 8:15 am, 111.01/Room S402. Nanosymposium talk, J. TOWNSEND: Non-invasive mapping of acoustic-phonetic speech features in human superior temporal gyrus using ultra-high field 7T fMRI.
- 11:00 am – 12:00 am, 141.04/K28. Poster, T. LIU: Affective processing of face stimuli in human primary visual cortex.
MP2RAGE: challenges and artifacts of its use for layer-fMRI
The MP2RAGE sequence is very popular for 7T anatomical imaging and is very commonly used to acquire 0.7-1 mm resolution whole brain anatomical reference data. Aside of this common application, it can also be very helpful for layer-fMRI studies to obtain even higher resolution T1 maps in the range of 0.5mm iso. However, when optimizing MP2RAGE sequence parameters for layer-fMRI studies, there are a few things that might be helpful to keep in mind.
In this post, I would like to discuss the challenges of using the popular MP2RAGE sequence in layer-fMRI studies. Specifically I will discuss challenges/features regarding:
- Background noise.
- Foldover artifact at very high resolutions.
- Usage of double-partial Fourier imaging.
- Inflow into arterial vessels.
Continue reading “MP2RAGE: challenges and artifacts of its use for layer-fMRI”
Example analysis pipeline of layer-VASO
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)
How many layers should I extract?
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.
