Search the site...

ORGANIZATION FOR HUMAN BRAIN MAPPING
  • BLOG
  • Tutorials
  • Media
  • Contributors
  • OHBM WEBSITE
  • BLOG
  • Tutorials
  • Media
  • Contributors
  • OHBM WEBSITE

A BRIEF OVERVIEW OF PERMUTATION TESTING WITH EXAMPLES

3/19/2018

4 Comments

 
By Tian Ge, BT Thomas Yeo, Anderson M Winkler

Permutation methods are a class of statistical tests that, under minimal assumptions, can provide exact control of false positives (i.e., type I error). The central assumption is simply that of exchangeability, that is, swapping data points keeps the data just as likely as the original. With the increasing availability of inexpensive large-scale computational resources and openly shared, large datasets, permutation methods are becoming popular in neuroimaging due to their flexibility and ease of concern about yielding nominal error rates than parametric tests, which rely on assumptions and/or approximations that may be difficult to meet in real data. This becomes even more important in the presence of multiple testing, in that assumptions may not be satisfied for each and every test, and the correlation across tests may be difficult to account for. However, even exchangeability can be violated in the presence of dependence among observations, and it may not always be clear what to permute. The aim of this blog post is to emphasize the relevance of linking the null hypothesis and the dependence structure within the data to what should be shuffled in a permutation test. We provide a few practical examples, and offer some glimpses of the theory along the way.


Example 1: Permutation mechanics

Let’s begin by reviewing the mechanics of a permutation test. Consider a comparison between two groups, for example whether hippocampal volume is different between subjects with Alzheimer’s disease (AD) and demographically matched cognitively normal controls (that is, a group with similar age, sex, education level, etc). If we assume that in both groups the hippocampal volumes are independent samples from a Gaussian distribution, a classical parametric two-sample t-test can be used to test for a difference between means of the two groups. However, this distributional assumption may not be true, and departures from this assumption can potentially lead to incorrect conclusions. In these circumstances, permutation tests perform better than parametric tests by providing a valid statistical test with much weaker assumptions. Specifically, under the null hypothesis that the hippocampal volume has no actual difference between AD cases and controls, the group membership (or the label of case and control) becomes arbitrary, that is, any subject from one group might as well have been from the other.

Read More
4 Comments

Q&A with OHBM Multi-Modal Imaging Task Force members

3/5/2018

0 Comments

 
By AmanPreet Badhwar, Nils Muhlert & Jeanette Mumford
At its best, multi-modal imaging offers rich insight into a many aspects of brain structure & function. At the same time, its development has been thwarted by challenges, for example simultaneous EEG-fMRI has additional safety concerns, and the EEG data requires extra analysis steps to account for artifacts from the magnetic field and rapidly changing field gradients. Despite these issues, there is increasing attention to the merits of this approach, with high profile journals dedicating special issues to multi-modal data fusion.

To find out about the promises and pitfalls of multi-modal imaging, we sent a series of questions to members of the OHBM Multi-Modal Imaging Task Force. This team is comprised of experts in different imaging domains, and aims to promote and develop multi-modal imaging. We found out the state of the field from Alain Dagher, neurologist and PET/fMRI expert in the Montreal Neurological Institute, Urs Ribary, cognitive neuroscientist and EEG/fMRI expert in British Columbia, Gitte Knudsen, neurologist and translational neurobiologist at Copenhagen University, and Shella Keilholz, physicist and fMRI expert at Emory University and Georgia Tech.
Picture
From left to right: Shella Keilholz, Alain Dagher, Gitte Knudsen and Urs Ribary

Read More
0 Comments

    BLOG HOME

    ​TUTORIALS

    ​MEDIA

    ​contributors

    ​OHBM WEBSITE

    ​

    OHBM OnDemand 
    ​Education Platform


    RSS Feed

    Archives

    January 2024
    December 2023
    November 2023
    October 2023
    September 2023
    August 2023
    July 2023
    June 2023
    May 2023
    April 2023
    March 2023
    January 2023
    December 2022
    October 2022
    September 2022
    August 2022
    July 2022
    June 2022
    May 2022
    April 2022
    March 2022
    January 2022
    December 2021
    November 2021
    October 2021
    September 2021
    August 2021
    July 2021
    June 2021
    May 2021
    April 2021
    March 2021
    February 2021
    January 2021
    December 2020
    November 2020
    October 2020
    September 2020
    June 2020
    May 2020
    April 2020
    March 2020
    February 2020
    January 2020
    December 2019
    November 2019
    October 2019
    September 2019
    August 2019
    July 2019
    June 2019
    May 2019
    April 2019
    March 2019
    February 2019
    January 2019
    December 2018
    November 2018
    October 2018
    August 2018
    July 2018
    June 2018
    May 2018
    April 2018
    March 2018
    February 2018
    January 2018
    December 2017
    November 2017
    October 2017
    September 2017
    August 2017
    July 2017
    June 2017
    May 2017
    April 2017
    March 2017
    February 2017
    January 2017
    December 2016
    November 2016
    October 2016
    September 2016
    August 2016
    July 2016
    June 2016
    May 2016
    April 2016

stay connected with ohbm!


become a member

Telephone

952-646-2029

Email ohbm

EMAIL BLOG TEAM
Header image created by Thiebaut de Schotten & Batrancourt  
www.brainconnectivitybehaviour.eu