Conformal Welding for Brain-Intelligence Analysis

Liqun Yang; Muhammad Razib; Kenia Chang He; Tianren Yang; Zhong Lin Lu; Xianfeng Gu; Wei Zeng

doi:10.1007/978-3-030-33720-9_28

Conformal Welding for Brain-Intelligence Analysis

Liqun Yang
, Muhammad Razib
, Kenia Chang He
, Tianren Yang
, Zhong Lin Lu
, Xianfeng Gu
, Wei Zeng

Computer Science

Florida International University
NYU Shanghai
New York University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this work, we present a geometric method to explore the relationship between brain anatomical structure and human intelligence based on conformal welding theory. We first generate the anatomical atlas on the structural MRI data; then, compute the signature for each cortical region by welding the conformal maps of the region and its complement domain along the common boundary, and combine all the region signature as that for the whole brain; and finally, use the signatures for shape visualization and classification using the learning methods. The signature is global, intrinsic to surface and curve geometry, and invariant to conformal transformations; and the computation is efficient through solving sparse linear systems. Experiments on real data set with 243 subjects demonstrate the efficacy of the proposed method and concluded that the conformal welding signature of cortical surface can classify human intelligence with a competitive accuracy rate compared with traditional features.

Original language	English
Title of host publication	Advances in Visual Computing - 14th International Symposium on Visual Computing, ISVC 2019, Proceedings
Editors	George Bebis, Bahram Parvin, Richard Boyle, Darko Koracin, Daniela Ushizima, Sek Chai, Shinjiro Sueda, Xin Lin, Aidong Lu, Daniel Thalmann, Chaoli Wang, Panpan Xu
Publisher	Springer
Pages	368-380
Number of pages	13
ISBN (Print)	9783030337193
DOIs	https://doi.org/10.1007/978-3-030-33720-9_28
State	Published - 2019
Event	14th International Symposium on Visual Computing, ISVC 2019 - Nevada, United States Duration: Oct 7 2019 → Oct 9 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11844 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Symposium on Visual Computing, ISVC 2019
Country/Territory	United States
City	Nevada
Period	10/7/19 → 10/9/19

Keywords

Brain structure
Conformal welding
Human intelligence

Access to Document

10.1007/978-3-030-33720-9_28

Cite this

Yang, L., Razib, M., He, K. C., Yang, T., Lu, Z. L., Gu, X., & Zeng, W. (2019). Conformal Welding for Brain-Intelligence Analysis. In G. Bebis, B. Parvin, R. Boyle, D. Koracin, D. Ushizima, S. Chai, S. Sueda, X. Lin, A. Lu, D. Thalmann, C. Wang, & P. Xu (Eds.), Advances in Visual Computing - 14th International Symposium on Visual Computing, ISVC 2019, Proceedings (pp. 368-380). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11844 LNCS). Springer. https://doi.org/10.1007/978-3-030-33720-9_28

Yang, Liqun ; Razib, Muhammad ; He, Kenia Chang et al. / Conformal Welding for Brain-Intelligence Analysis. Advances in Visual Computing - 14th International Symposium on Visual Computing, ISVC 2019, Proceedings. editor / George Bebis ; Bahram Parvin ; Richard Boyle ; Darko Koracin ; Daniela Ushizima ; Sek Chai ; Shinjiro Sueda ; Xin Lin ; Aidong Lu ; Daniel Thalmann ; Chaoli Wang ; Panpan Xu. Springer, 2019. pp. 368-380 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{574d6420ecb2449b999fbdb50b8a5c2f,

title = "Conformal Welding for Brain-Intelligence Analysis",

abstract = "In this work, we present a geometric method to explore the relationship between brain anatomical structure and human intelligence based on conformal welding theory. We first generate the anatomical atlas on the structural MRI data; then, compute the signature for each cortical region by welding the conformal maps of the region and its complement domain along the common boundary, and combine all the region signature as that for the whole brain; and finally, use the signatures for shape visualization and classification using the learning methods. The signature is global, intrinsic to surface and curve geometry, and invariant to conformal transformations; and the computation is efficient through solving sparse linear systems. Experiments on real data set with 243 subjects demonstrate the efficacy of the proposed method and concluded that the conformal welding signature of cortical surface can classify human intelligence with a competitive accuracy rate compared with traditional features.",

keywords = "Brain structure, Conformal welding, Human intelligence",

author = "Liqun Yang and Muhammad Razib and He, \{Kenia Chang\} and Tianren Yang and Lu, \{Zhong Lin\} and Xianfeng Gu and Wei Zeng",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 14th International Symposium on Visual Computing, ISVC 2019 ; Conference date: 07-10-2019 Through 09-10-2019",

year = "2019",

doi = "10.1007/978-3-030-33720-9\_28",

language = "English",

isbn = "9783030337193",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "368--380",

editor = "George Bebis and Bahram Parvin and Richard Boyle and Darko Koracin and Daniela Ushizima and Sek Chai and Shinjiro Sueda and Xin Lin and Aidong Lu and Daniel Thalmann and Chaoli Wang and Panpan Xu",

booktitle = "Advances in Visual Computing - 14th International Symposium on Visual Computing, ISVC 2019, Proceedings",

}

Yang, L, Razib, M, He, KC, Yang, T, Lu, ZL, Gu, X & Zeng, W 2019, Conformal Welding for Brain-Intelligence Analysis. in G Bebis, B Parvin, R Boyle, D Koracin, D Ushizima, S Chai, S Sueda, X Lin, A Lu, D Thalmann, C Wang & P Xu (eds), Advances in Visual Computing - 14th International Symposium on Visual Computing, ISVC 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11844 LNCS, Springer, pp. 368-380, 14th International Symposium on Visual Computing, ISVC 2019, Nevada, United States, 10/7/19. https://doi.org/10.1007/978-3-030-33720-9_28

Conformal Welding for Brain-Intelligence Analysis. / Yang, Liqun; Razib, Muhammad; He, Kenia Chang et al.
Advances in Visual Computing - 14th International Symposium on Visual Computing, ISVC 2019, Proceedings. ed. / George Bebis; Bahram Parvin; Richard Boyle; Darko Koracin; Daniela Ushizima; Sek Chai; Shinjiro Sueda; Xin Lin; Aidong Lu; Daniel Thalmann; Chaoli Wang; Panpan Xu. Springer, 2019. p. 368-380 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11844 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Conformal Welding for Brain-Intelligence Analysis

AU - Yang, Liqun

AU - Razib, Muhammad

AU - He, Kenia Chang

AU - Yang, Tianren

AU - Lu, Zhong Lin

AU - Gu, Xianfeng

AU - Zeng, Wei

PY - 2019

Y1 - 2019

N2 - In this work, we present a geometric method to explore the relationship between brain anatomical structure and human intelligence based on conformal welding theory. We first generate the anatomical atlas on the structural MRI data; then, compute the signature for each cortical region by welding the conformal maps of the region and its complement domain along the common boundary, and combine all the region signature as that for the whole brain; and finally, use the signatures for shape visualization and classification using the learning methods. The signature is global, intrinsic to surface and curve geometry, and invariant to conformal transformations; and the computation is efficient through solving sparse linear systems. Experiments on real data set with 243 subjects demonstrate the efficacy of the proposed method and concluded that the conformal welding signature of cortical surface can classify human intelligence with a competitive accuracy rate compared with traditional features.

AB - In this work, we present a geometric method to explore the relationship between brain anatomical structure and human intelligence based on conformal welding theory. We first generate the anatomical atlas on the structural MRI data; then, compute the signature for each cortical region by welding the conformal maps of the region and its complement domain along the common boundary, and combine all the region signature as that for the whole brain; and finally, use the signatures for shape visualization and classification using the learning methods. The signature is global, intrinsic to surface and curve geometry, and invariant to conformal transformations; and the computation is efficient through solving sparse linear systems. Experiments on real data set with 243 subjects demonstrate the efficacy of the proposed method and concluded that the conformal welding signature of cortical surface can classify human intelligence with a competitive accuracy rate compared with traditional features.

KW - Brain structure

KW - Conformal welding

KW - Human intelligence

UR - https://www.scopus.com/pages/publications/85076254764

U2 - 10.1007/978-3-030-33720-9_28

DO - 10.1007/978-3-030-33720-9_28

M3 - Conference contribution

AN - SCOPUS:85076254764

SN - 9783030337193

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

SP - 368

EP - 380

BT - Advances in Visual Computing - 14th International Symposium on Visual Computing, ISVC 2019, Proceedings

A2 - Bebis, George

A2 - Parvin, Bahram

A2 - Boyle, Richard

A2 - Koracin, Darko

A2 - Ushizima, Daniela

A2 - Chai, Sek

A2 - Sueda, Shinjiro

A2 - Lin, Xin

A2 - Lu, Aidong

A2 - Thalmann, Daniel

A2 - Wang, Chaoli

A2 - Xu, Panpan

PB - Springer

T2 - 14th International Symposium on Visual Computing, ISVC 2019

Y2 - 7 October 2019 through 9 October 2019

ER -

Yang L, Razib M, He KC, Yang T, Lu ZL, Gu X et al. Conformal Welding for Brain-Intelligence Analysis. In Bebis G, Parvin B, Boyle R, Koracin D, Ushizima D, Chai S, Sueda S, Lin X, Lu A, Thalmann D, Wang C, Xu P, editors, Advances in Visual Computing - 14th International Symposium on Visual Computing, ISVC 2019, Proceedings. Springer. 2019. p. 368-380. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-33720-9_28

Conformal Welding for Brain-Intelligence Analysis

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