Jekyll2026-06-01T04:22:26+00:00https://mindlabresearch.github.io/feed.xmlblankMultimodel insights into neuopsychiatric disorders. ggseg visualization with Freesurfer data in R2020-05-16T01:59:00+00:002020-05-16T01:59:00+00:00https://mindlabresearch.github.io/blog/2020/ggseg-viz-freesurfer

Purpose of this Post

I enjoy finding new ways to visualize data in a meaningful (and also aesthetically pleasing) way. Finding the right tools in order to get your message across adds value, but can be challenging. When I learned about this visualization toolbox with the multitude of options it has, I had to learn how to use it – and it’s actually quite simple!

The links to both the blog and GitHub pages below are extremely informative (so please also refer to them)! Here, I wanted to outline the installation process I used in order to get the program running in RStudio (Part 1) and provide some examples of things I have done with the toolbox to potentially spark some inspiration (Part 2)!

Getting Started

Source

All the credits for this awesome toolbox go to Dr. Athanasia Mowinckel & Dr. Didac Vidal Pineiro at the Center for Lifespan Changes in Brain and Cognition, University of Oslo. Be sure to check out Dr. Mowinckel’s blog, which has a great overview of the different options available with the toolbox.

Note: There seem to be some updates happening at the moment with the toolbox, so this may not work 100% for you, but it worked for me in May of 2020. Updates to come if this changes.

Compatibility – What I Used

  • Operating System: Windows 10
  • R version: 4.0.0
  • RStudio version: 1.2.5033

Installation Guide

Libraries to Install Prior to ggseg

library(ggplot2)
library(devtools)
library(tidyselect)
library(magrittr)
library(rmarkdown)
library(stringr)
library(stringi)
library(tibble)
library(dplyr)
library(purrr)
library(pandocfilters)
library(tidyr)

If some libraries are missing, use the command below (using “tidyr” as an example):

install.packages("tidyr")

Installing ggseg

On the GitHub website, they state to use:

install.packages("remotes")
remotes::install_github("LCBC-UiO/ggseg", build_vignettes = TRUE)

Currently, there is a bug that is being fixed in the program with the Vignettes, so you will need to modify the code until this is fixed to:

install.packages("remotes")
remotes::install_github("LCBC-UiO/ggseg", build_vignettes = FALSE)

Personally, I found that devtools works for installing the program without any problems. Since I already installed devtools above, just enter the following in RStudio:

devtools::install_github("LCBC-UiO/ggseg", build_vignettes = FALSE)
library(ggseg)

This will take a little bit of time and install version 1.5.4. You should see this in your window as:

building 'ggseg_1.5.4.tar.gz'

Atlases

After installing ggseg, there are two default atlases pre-installed based on the default FreeSurfer output from running recon-all:

  • Desikan-Killany cortical atlas (dk)
  • Subcortical segmentation atlas (aseg)

Additionally, the following atlases are available, and the links show examples for how to use the atlases. These are beyond the scope of this post; I will focus just on the default atlases included in the package.

  • Yeo – both 7 and 17 network data
  • Desterieux – the 2009 atlas
  • Chen – both thickness and area maps
  • Schaefer – both 17 and 7 networks
  • Glasser – full atlas
  • JHU – white tract atlas
  • Tracula – white tract atlas
  • ICBM – white tract atlas
  • HO – Harvard-Oxford cortical (FSL)
  • DefaultExtra – extra 2D view for dk, p/a division

To install the extra atlases, first install ggsegExtra:

devtools::install_github("LCBC-UiO/ggsegExtra", build_vignettes = FALSE)
library(ggsegExtra)

To check which atlases are available, enter the following command:

ggseg_atlas_repos()

Install the atlas you would like to use (using the Glasser atlas as an example):

devtools::install_github("LCBC-UiO/ggsegGlasser")
library(ggsegGlasser)

Now you should have ggseg installed and ready to go without any problems!

]]>Installing Freesurfer Using Enhanced Mode Ubuntu 18.04 for Hyper-V on Windows 102020-03-30T01:59:00+00:002020-03-30T01:59:00+00:00https://mindlabresearch.github.io/blog/2020/installing-freesurferPrerequisites

Before getting started, I first installed Ubuntu within Hyper-V on my Windows 10 computer following the instructions below:
Using Enhanced Mode Ubuntu 18.04 for Hyper-V on Windows 10 - Scott Hanselman’s Blog

Link to FreeSurfer Installation Page

While the link below is extremely well-written and helpful, I noticed a few steps during my installation process using Ubuntu for Hyper-V that required additional steps, not listed on the page. This guide is meant to help others using the same system with the installation procedure – described in detail below.
DownloadAndInstall - Free Surfer Wiki

Installation Steps

1. Download FreeSurfer

Download FreeSurfer for Linux from the download page while logged into your Ubuntu VM.

File to download:
freesurfer-Linux-centos6_x86_64-stable-pub-v6.0.0.tar.gz

Link to download:
Download FreeSurfer
(Note: You may need to be logged into your Gmail account to download the file.)

2. Change Permissions

Ensure you have permissions to write into /usr/local/, where you will unpack the FreeSurfer package.

sudo chmod -R 777 /usr/local/

3. Unpack FreeSurfer Package

Unpack the package in the directory where you saved it. For example:

cd /home/shaas/Downloads
tar -C /usr/local -xzvf freesurfer-Linux-centos6_x86_64-stable-pub-v6.0.0.tar.gz

4. Install Additional Necessary Packages

sudo apt-get -y install bc binutils libgomp1 perl psmisc sudo tar tcsh unzip uuid-dev vim-common libjpeg62-dev

5. Setup & Configuration

To begin using FreeSurfer, open a terminal window, define an environment variable called FREESURFER_HOME (set to the location where FreeSurfer was installed), and source the setup script. This sourcing needs to be done every time you open a new terminal window, or you can add the lines below to your default setup file (e.g., .bashrc or .cshrc).

export FREESURFER_HOME=/usr/local/freesurfer
source $FREESURFER_HOME/SetUpFreeSurfer.sh

If done correctly, you should see output similar to:

Setting up environment for FreeSurfer/FS-FAST (and FSL)
FREESURFER_HOME /usr/local/freesurfer
FSFAST_HOME     /usr/local/freesurfer/fsfast
FSF_OUTPUT_FORMAT nii
SUBJECTS_DIR    /usr/local/freesurfer/subjects
MNI_DIR         /usr/local/freesurfer/mni

6. Get a License to Use FreeSurfer

A license key must be obtained to make the FreeSurfer tools operational. The license is free and comes in the form of a license.txt file. Once obtained, copy it to your FreeSurfer installation directory (the location defined by the FREESURFER_HOME environment variable).

Get license here:
FreeSurfer Registration Form

After downloading in Ubuntu, copy the file to your FreeSurfer folder:

cp /home/shaas/Downloads/license.txt /usr/local/freesurfer

7. Additional Items to Install

sudo apt-get update
sudo apt-get upgrade
sudo apt-get install libtool autoconf build-essential pkg-config automake tcsh
]]>Meta-analysis Tools2015-11-20T01:59:00+00:002015-11-20T01:59:00+00:00https://mindlabresearch.github.io/blog/2015/meta-analysis-toolsIntroduction

Seed-based d Mapping (formerly “Signed Differential Mapping”) is a statistical technique for meta-analyzing studies on differences in brain activity or structure which used neuroimaging techniques such as fMRI, VBM, DTI or PET. The methods have been fully validated in several studies (see references below), and meta-analyses using this method have been already published at the highest quality journals.

sdm_screenshot

The method

An introduction of the method can be found in the SDM Reference Manual.

Briefly, some of the features are:

  • Possibility of combining studies from which only peak coordinates are available with studies from which SPM t-maps are available, highly increase the power of the meta-analysis.
  • Use of specific masks correlation templates for fMRI, PET, gray matter, white matter, fractional anisotropy, TBSS and cerebrospinal fluid to account for the anisotropy in the spatial covariance of the brain, highly increasing the accuracy of the recreation of the effect size maps.
  • Accounting for peaks’ effect-size and signed maps to counteract positive and negative differences.
  • Weighting the calculations for intra-study variance (i.e. studies with large sample sizes and/or lower error contribute more), inter-study heterogeneity and other optional weights.
  • Use of the random-effects general linear models to allow meta-analytical comparisons between groups, meta-regressions and use of covariates.
  • Complementary analyses such as exploration of heterogeneity (by means of heterogeneity maps, subgroup analyses and meta-regressions) and jackknife analyses (to assess thereplicability of the results)

Other useful tools within the website:

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