BRAIN Initiative: Theories, Models and Methods for Analysis of Complex Data from the Brain

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PI: Chung, Moo K

Email: MCHUNG@STAT.WISC.EDU

Institution: University of Wisconsin-Madison

Title: BRAIN Initiative: Theories, Models and Methods for Analysis of Complex Data from the Brain

The permutation test is the most widely used nonparametric test procedure in brain imaging. It is known as the exact test in statistics since the distribution of the test statistic under the null hypothesis can be exactly computed if we can calculate all possible values of the test statistic under every possible permutation. Unfortunately, generating every possible permutation for large-scale brain image datasets such as HCP and ADNI with thousands of images is not practical. Many previous attempts at speeding up the permutation test rely on various approximation strategies such as estimating the tail distribution with a known distribution. Our tool called Rapid Acceleration will rapidly accelerate the permutation test without any type of approximate strategies by exploiting the algebraic structure of the permutation test. The method is applied to large-scale brain imaging database HCP in localizing the group differences a well as more accurate estimation of twin correlations in ACE model. Datatype: Brain images (fMRI, DTI, MRI).  Website: http://pages.stat.wisc.edu/~mchung/twins

Grant #: EB022856

Status: Completed

Deliverables:

2021 Brain PI Meeting

Update:

Link to Poster:

Demo:

 

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