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.

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Registration of high-resolution data

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.

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Quick analysis pipeline of getting layer fMRI profiles without anatomical reference data

This is a step-by-step description on how to obtain layer profiles from any high-resolution fMRI dataset. It is based on manual delineated ROIs and does not require the tricky analysis steps including distortion correction, registration to whole brain “anatomical” datasets, or automatic tissue type segmentation. Hence this is a very quick way for a first glance of the freshly acquired data.

This post shows how you can get from activation maps to layer-profiles in 10 min. In a quick and dirty way.

The important steps are: 1.) Upscaling, 2.) Manual delineation of GM, 3.) Calculation of cortical depths in ROI, 4.) Extracting functional data based on calculated cortical depths.

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Getting layers in EPI space

Overview

Big steps:

  1. Manual aligment of MP2RAGE with EPI (optional when MP2RAGE is acquired in same session)
  2. ANTS alignment of MP2RAGE and EPI. (part of anatomical_maser.sh, see github)
  3. Running Freesurfer on MP2RAGE data in EPI space. (part of anatomical_maser.sh, see github)
  4. Using SUMA to get fine samples tissue borders in EPI-voxel space (in oblique space) (part of anatomical_maser.sh, see github)
  5. Manual correction of Freesurfer GM-ribbon
  6. calculating layers from GM-ribbon in neuroDebian.

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