10.17.1. SurfLayers Demo 1

Introduction

We briefly describe the SurfLayers Demo, which combines AFNI+SUMA functionality to investigate surface layers in MRI data. This Demo was presented at OHBM 2021:

Creating Layered Surfaces to Visualize with AFNI + SUMA, with applications to laminar fMRI
by Torrisi S, Lauren P, Taylor PA, Park S, Feinberg D, Glen DR.

To run this demo, your system’s AFNI version should be at least 21.1.14.

The Demo of scripts+data can be downloaded by running:

@Install_SURFLAYERS_DEMO1

The above command will both download and unpack the SurfLayers Demo within the directory from which it is run. There is a descriptive README.txt for users to ignore read.

This demo makes use of and presents several new (at the time) features within AFNI and SUMA. Firstly, these scripts utilize the SurfLayers and quickspecSL programs, to estimate intermediate surface layers. Secondly, they utilize the clipping plane mode within SUMA, whereby one can focus on subregions within the viewer and investigate nested surface layers.

Each script uses terminal commands to “drive” both the afni and suma GUIs, so you can visualize your data. That is in fact one of the purposes of these new tools (and of AFNI in general): to keep users close to their data, to understand it better. The GUIs also “talk” to one another, sending information back and forth in realtime, so you can really explore your data in fun and informative ways.

For suggestions on applying SUMA’s “Clipping plane” functionality, please see this part of the documentation:
... which contains a useful cheatsheet of keystrokes.

Scripts 1 and 2: basic MRI surface

These scripts use data from the AFNI Bootcamp, namely within the AFNI_data6/FT_analyis/ directory (which is part of the standard CD.tgz Bootcamp data package):

  • the FT/SUMA/* data, which were created by running FreeSurfer’s recon-all on the subject’s anatomical, followed by AFNI’s @SUMA_Make_Spec_FS to create NIFTI volumes and standardized surface meshes.

  • the results directory made by running the s05.ap.uber script, which contains an afni_proc.py command. (One could also run s05.ap.uber.NL there, which runs an afni_proc.py command that makes using on nonlinear alignment between the subject and template space, for more detailed matching).

These scripts are not really doing laminar work, per se, because this data isn’t high enough resolution. However, these scripts do give a good intro to the functionality and viewing, at a resolution many people are used to working.

Running script s01*

../../_images/img_SL_s01.png

Running script s02*

../../_images/img_SL_s02.png

Scripts 3 and 4: laminar FMRI hemisphere

These scripts utilize data contained within the demo itself—and this is a real, laminar FMRI dataset. The dataset2 directory contains one anatomical (MP2RAGE) hemisphere and a 7T fingertapping dataset using accelerated GRASE sequence.

Running script s03*

../../_images/img_SL_s03.png

Running script s04*

../../_images/img_SL_s04.png

Scripts 5 and 6: laminar FMRI patch

These scripts also utilize laminar FMRI data contained within the demo itself (also a 7T accelerated GRASE dataset with an MP2RAGE structural scan). The dataset3 directory contains a left calcarine patch that was drawn on retinotopic (meridian-mapping) functional results on a spherical surface.

Running script s05*

../../_images/img_SL_s05.png

Running script s06*

../../_images/img_SL_s06.png

Questions/Contact

For questions/comments/suggestions, please contact:

Salvatore (Sam) Torrisi

torrisi __at__ berkeley.edu

Daniel Glen

glend __at__ mail.nih.gov

Paul Taylor

paul.taylor __at__ nih.gov

... and/or use the ever-popular AFNI Messageboard: