Rapid acceleration of the permutation test via transpositions

Moo K. Chung, Linhui Xie, Shih Gu Huang, Yixian Wang, Jingwen Yan, Li Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The permutation test is an often used test procedure for determining statistical significance in brain network studies. Unfortunately, generating every possible permutation for large-scale brain imaging datasets such as HCP and ADNI with hundreds of subjects is not practical. Many previous attempts at speeding up the permutation test rely on various approximation strategies such as estimating the tail distribution with known parametric distributions. In this study, we propose the novel transposition test that exploits the underlying algebraic structure of the permutation group. The method is applied to a large number of diffusion tensor images in localizing the regions of the brain network differences.

Original languageEnglish (US)
Title of host publicationConnectomics in NeuroImaging - 3rd International Workshop, CNI 2019, Held in Conjunction with MICCAI 2019, Proceedings
EditorsMarkus D. Schirmer, Ai Wern Chung, Archana Venkataraman, Islem Rekik, Minjeong Kim
PublisherSpringer
Pages42-53
Number of pages12
ISBN (Print)9783030323905
DOIs
StatePublished - Jan 1 2019
Externally publishedYes
Event3rd International Workshop on Connectomics in NeuroImaging, CNI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: Oct 13 2019Oct 13 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11848 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Workshop on Connectomics in NeuroImaging, CNI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period10/13/1910/13/19

Fingerprint

Permutation Test
Transposition
Brain
Statistical Significance
Permutation group
Algebraic Structure
Tensors
Tail
Permutation
Tensor
Imaging
Imaging techniques
Approximation

Keywords

  • Online statistics computation
  • Permutation group
  • Permutation test
  • Structural brain networks
  • Transposition test

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Chung, M. K., Xie, L., Huang, S. G., Wang, Y., Yan, J., & Shen, L. (2019). Rapid acceleration of the permutation test via transpositions. In M. D. Schirmer, A. W. Chung, A. Venkataraman, I. Rekik, & M. Kim (Eds.), Connectomics in NeuroImaging - 3rd International Workshop, CNI 2019, Held in Conjunction with MICCAI 2019, Proceedings (pp. 42-53). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11848 LNCS). Springer. https://doi.org/10.1007/978-3-030-32391-2_5

Rapid acceleration of the permutation test via transpositions. / Chung, Moo K.; Xie, Linhui; Huang, Shih Gu; Wang, Yixian; Yan, Jingwen; Shen, Li.

Connectomics in NeuroImaging - 3rd International Workshop, CNI 2019, Held in Conjunction with MICCAI 2019, Proceedings. ed. / Markus D. Schirmer; Ai Wern Chung; Archana Venkataraman; Islem Rekik; Minjeong Kim. Springer, 2019. p. 42-53 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11848 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Chung, MK, Xie, L, Huang, SG, Wang, Y, Yan, J & Shen, L 2019, Rapid acceleration of the permutation test via transpositions. in MD Schirmer, AW Chung, A Venkataraman, I Rekik & M Kim (eds), Connectomics in NeuroImaging - 3rd International Workshop, CNI 2019, Held in Conjunction with MICCAI 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11848 LNCS, Springer, pp. 42-53, 3rd International Workshop on Connectomics in NeuroImaging, CNI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019, Shenzhen, China, 10/13/19. https://doi.org/10.1007/978-3-030-32391-2_5
Chung MK, Xie L, Huang SG, Wang Y, Yan J, Shen L. Rapid acceleration of the permutation test via transpositions. In Schirmer MD, Chung AW, Venkataraman A, Rekik I, Kim M, editors, Connectomics in NeuroImaging - 3rd International Workshop, CNI 2019, Held in Conjunction with MICCAI 2019, Proceedings. Springer. 2019. p. 42-53. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-32391-2_5
Chung, Moo K. ; Xie, Linhui ; Huang, Shih Gu ; Wang, Yixian ; Yan, Jingwen ; Shen, Li. / Rapid acceleration of the permutation test via transpositions. Connectomics in NeuroImaging - 3rd International Workshop, CNI 2019, Held in Conjunction with MICCAI 2019, Proceedings. editor / Markus D. Schirmer ; Ai Wern Chung ; Archana Venkataraman ; Islem Rekik ; Minjeong Kim. Springer, 2019. pp. 42-53 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{6c489cff665846ae9b7570ff1a629d9b,
title = "Rapid acceleration of the permutation test via transpositions",
abstract = "The permutation test is an often used test procedure for determining statistical significance in brain network studies. Unfortunately, generating every possible permutation for large-scale brain imaging datasets such as HCP and ADNI with hundreds of subjects is not practical. Many previous attempts at speeding up the permutation test rely on various approximation strategies such as estimating the tail distribution with known parametric distributions. In this study, we propose the novel transposition test that exploits the underlying algebraic structure of the permutation group. The method is applied to a large number of diffusion tensor images in localizing the regions of the brain network differences.",
keywords = "Online statistics computation, Permutation group, Permutation test, Structural brain networks, Transposition test",
author = "Chung, {Moo K.} and Linhui Xie and Huang, {Shih Gu} and Yixian Wang and Jingwen Yan and Li Shen",
year = "2019",
month = "1",
day = "1",
doi = "10.1007/978-3-030-32391-2_5",
language = "English (US)",
isbn = "9783030323905",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "42--53",
editor = "Schirmer, {Markus D.} and Chung, {Ai Wern} and Archana Venkataraman and Islem Rekik and Minjeong Kim",
booktitle = "Connectomics in NeuroImaging - 3rd International Workshop, CNI 2019, Held in Conjunction with MICCAI 2019, Proceedings",

}

TY - GEN

T1 - Rapid acceleration of the permutation test via transpositions

AU - Chung, Moo K.

AU - Xie, Linhui

AU - Huang, Shih Gu

AU - Wang, Yixian

AU - Yan, Jingwen

AU - Shen, Li

PY - 2019/1/1

Y1 - 2019/1/1

N2 - The permutation test is an often used test procedure for determining statistical significance in brain network studies. Unfortunately, generating every possible permutation for large-scale brain imaging datasets such as HCP and ADNI with hundreds of subjects is not practical. Many previous attempts at speeding up the permutation test rely on various approximation strategies such as estimating the tail distribution with known parametric distributions. In this study, we propose the novel transposition test that exploits the underlying algebraic structure of the permutation group. The method is applied to a large number of diffusion tensor images in localizing the regions of the brain network differences.

AB - The permutation test is an often used test procedure for determining statistical significance in brain network studies. Unfortunately, generating every possible permutation for large-scale brain imaging datasets such as HCP and ADNI with hundreds of subjects is not practical. Many previous attempts at speeding up the permutation test rely on various approximation strategies such as estimating the tail distribution with known parametric distributions. In this study, we propose the novel transposition test that exploits the underlying algebraic structure of the permutation group. The method is applied to a large number of diffusion tensor images in localizing the regions of the brain network differences.

KW - Online statistics computation

KW - Permutation group

KW - Permutation test

KW - Structural brain networks

KW - Transposition test

UR - http://www.scopus.com/inward/record.url?scp=85075671590&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85075671590&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-32391-2_5

DO - 10.1007/978-3-030-32391-2_5

M3 - Conference contribution

AN - SCOPUS:85075671590

SN - 9783030323905

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 42

EP - 53

BT - Connectomics in NeuroImaging - 3rd International Workshop, CNI 2019, Held in Conjunction with MICCAI 2019, Proceedings

A2 - Schirmer, Markus D.

A2 - Chung, Ai Wern

A2 - Venkataraman, Archana

A2 - Rekik, Islem

A2 - Kim, Minjeong

PB - Springer

ER -