Finding ROI of the double layers in M1

In this post I want to describe the guidelines that helped me to find the right spot of primary motor cortex (M1) that has a double-layer pattern during a conventional finger tapping task.

The motor cortex is an excellent model system to debug-layer fMRI methodology for multiple reasons:

  1. It has a consistent folding pattern across people.
  2. Its folding pattern is convoluted across one axis only. Hence, it is possible to use thicker slices with higher in-plane resolution.
  3. With 4mm, its is the thickest part of the cortex compared to all other areas. Hence, layer analysis can be done even with 1.2 mm voxels.
  4. It has an expected double layer structure, with two separate peaks. The separability of the peaks can be used as a measure of functional specificity.
  5. It is very close to the RF-receive coils and has high tSNR.
  6. It is easy to shim.

One tricky part, however, is to find the right location of the double layer feature.

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Gradient Temperature

tSNR changes across time

I got interested in gradient temperature because of the weird effect that tSNR seemed to increase over time.

Upon posing this effect on Twitter, PracitalfMRI and Ben Poser suggested that it might be due to gradient temperature. So I learned how to track it with as described below.

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GRAPPA kernel size

Almost every modern fMRI protocol (at SIEMENS scanners) uses GRAPPA. However, only very few people pay a lot of attention on optimal usage of the GRAPPA auto-callibration data. I realized the importance of optimizing GRAPPA parameters when doing high-resolution EPI. At high resolutions, GRAPPA-related noise can become an increasingly important limitation.  This is especially true with the low bandwidth that the body gradient coils force us to use.

In this blog-post I will explain how the GRAPPA kernel-size affects the fMRI data quality, how you can change it, how you can find out which kernel-size was used, and I will descrive simple software tools to identify regions that might benefit from adaptations of the GRAPPA-kernel size.

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How to deal with Ghosts in high-res EPI

Background

Ghost are the biggest limitation in high res fMRI. Similar to low resolution fMRI, ghosts in sub-millimeter EPI are arising from mismatch of k-space lines. This mismatch can be associated with (A) the actual readout itself or (B) inappropriate GRAPPA auto calibration data.

Here I try to make notes of strategies that I found helpful to minimize ghosts.

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