Detecting dynamic and genetic effects on brain structure using high-dimensional cortical pattern matching

Paul M. Thompson; Kiralee M. Hayashi; Greig De Zubicaray; Andrew L. Janke; Stephen E. Rose; James Semple; David M. Doddrell; Tyrone D. Cannon; Arthur W. Toga

doi:10.1109/ISBI.2002.1029297

Detecting dynamic and genetic effects on brain structure using high-dimensional cortical pattern matching

Paul M. Thompson
, Kiralee M. Hayashi
, Greig De Zubicaray
, Andrew L. Janke
, Stephen E. Rose
, James Semple
, David M. Doddrell
, Tyrone D. Cannon
, Arthur W. Toga

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

19 Scopus citations

Abstract

We briefly describe a set of algorithms to detect and visualize effects of disease and genetic factors on the brain. Extreme variations in cortical anatomy, even among normal subjects, complicate the detection and mapping of systematic effects on brain structure in human populations. We tackle this problem in two stages. First, we develop a cortical pattern matching approach, based on metrically covariant partial differential equations (PDEs), to associate corresponding regions of cortex in an MRI brain image database (N=102 scans). Second, these high-dimensional deformation maps are used to transfer within-subject cortical signals, including measures of gray matter distribution, shape asymmetries, and degenerative rates, to a common anatomic template for statistical analysis. We illustrate these techniques in two applications: (1) mapping dynamic patterns of gray matter loss in longitudinally scanned Alzheimer's disease patients; and (2) mapping genetic influences on brain structure. We extend statistics used widely in behavioral genetics to cortical manifolds. Specifically, we introduce methods based on h-squared distributed random fields to map hereditary influences on brain structure in human populations.

Original language	English
Title of host publication	2002 IEEE International Symposium on Biomedical Imaging, ISBI 2002 - Proceedings
Publisher	IEEE Computer Society
Pages	473-476
Number of pages	4
ISBN (Electronic)	078037584X
DOIs	https://doi.org/10.1109/ISBI.2002.1029297
State	Published - 2002
Externally published	Yes
Event	2022 IEEE International Symposium on Biomedical Imaging, ISBI 2002 - Washington, United States Duration: 7 Jul 2002 → 10 Jul 2002

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2002-January
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	2022 IEEE International Symposium on Biomedical Imaging, ISBI 2002
Country/Territory	United States
City	Washington
Period	7/07/02 → 10/07/02

Access to Document

10.1109/ISBI.2002.1029297

Cite this

Thompson, P. M., Hayashi, K. M., De Zubicaray, G., Janke, A. L., Rose, S. E., Semple, J., Doddrell, D. M., Cannon, T. D., & Toga, A. W. (2002). Detecting dynamic and genetic effects on brain structure using high-dimensional cortical pattern matching. In 2002 IEEE International Symposium on Biomedical Imaging, ISBI 2002 - Proceedings (pp. 473-476). Article 1029297 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2002-January). IEEE Computer Society. https://doi.org/10.1109/ISBI.2002.1029297

@inproceedings{39a245fb149248d2a5305fc5f2f208a2,

title = "Detecting dynamic and genetic effects on brain structure using high-dimensional cortical pattern matching",

abstract = "We briefly describe a set of algorithms to detect and visualize effects of disease and genetic factors on the brain. Extreme variations in cortical anatomy, even among normal subjects, complicate the detection and mapping of systematic effects on brain structure in human populations. We tackle this problem in two stages. First, we develop a cortical pattern matching approach, based on metrically covariant partial differential equations (PDEs), to associate corresponding regions of cortex in an MRI brain image database (N=102 scans). Second, these high-dimensional deformation maps are used to transfer within-subject cortical signals, including measures of gray matter distribution, shape asymmetries, and degenerative rates, to a common anatomic template for statistical analysis. We illustrate these techniques in two applications: (1) mapping dynamic patterns of gray matter loss in longitudinally scanned Alzheimer's disease patients; and (2) mapping genetic influences on brain structure. We extend statistics used widely in behavioral genetics to cortical manifolds. Specifically, we introduce methods based on h-squared distributed random fields to map hereditary influences on brain structure in human populations.",

author = "Thompson, \{Paul M.\} and Hayashi, \{Kiralee M.\} and \{De Zubicaray\}, Greig and Janke, \{Andrew L.\} and Rose, \{Stephen E.\} and James Semple and Doddrell, \{David M.\} and Cannon, \{Tyrone D.\} and Toga, \{Arthur W.\}",

note = "Publisher Copyright: {\textcopyright} 2002 IEEE.; 2022 IEEE International Symposium on Biomedical Imaging, ISBI 2002 ; Conference date: 07-07-2002 Through 10-07-2002",

year = "2002",

doi = "10.1109/ISBI.2002.1029297",

language = "English",

series = "Proceedings - International Symposium on Biomedical Imaging",

publisher = "IEEE Computer Society",

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Thompson, PM, Hayashi, KM, De Zubicaray, G, Janke, AL, Rose, SE, Semple, J, Doddrell, DM, Cannon, TD & Toga, AW 2002, Detecting dynamic and genetic effects on brain structure using high-dimensional cortical pattern matching. in 2002 IEEE International Symposium on Biomedical Imaging, ISBI 2002 - Proceedings., 1029297, Proceedings - International Symposium on Biomedical Imaging, vol. 2002-January, IEEE Computer Society, pp. 473-476, 2022 IEEE International Symposium on Biomedical Imaging, ISBI 2002, Washington, United States, 7/07/02. https://doi.org/10.1109/ISBI.2002.1029297

Detecting dynamic and genetic effects on brain structure using high-dimensional cortical pattern matching. / Thompson, Paul M.; Hayashi, Kiralee M.; De Zubicaray, Greig et al.
2002 IEEE International Symposium on Biomedical Imaging, ISBI 2002 - Proceedings. IEEE Computer Society, 2002. p. 473-476 1029297 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2002-January).

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

TY - GEN

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AU - Rose, Stephen E.

AU - Semple, James

AU - Doddrell, David M.

AU - Cannon, Tyrone D.

AU - Toga, Arthur W.

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AB - We briefly describe a set of algorithms to detect and visualize effects of disease and genetic factors on the brain. Extreme variations in cortical anatomy, even among normal subjects, complicate the detection and mapping of systematic effects on brain structure in human populations. We tackle this problem in two stages. First, we develop a cortical pattern matching approach, based on metrically covariant partial differential equations (PDEs), to associate corresponding regions of cortex in an MRI brain image database (N=102 scans). Second, these high-dimensional deformation maps are used to transfer within-subject cortical signals, including measures of gray matter distribution, shape asymmetries, and degenerative rates, to a common anatomic template for statistical analysis. We illustrate these techniques in two applications: (1) mapping dynamic patterns of gray matter loss in longitudinally scanned Alzheimer's disease patients; and (2) mapping genetic influences on brain structure. We extend statistics used widely in behavioral genetics to cortical manifolds. Specifically, we introduce methods based on h-squared distributed random fields to map hereditary influences on brain structure in human populations.

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ER -

Thompson PM, Hayashi KM, De Zubicaray G, Janke AL, Rose SE, Semple J et al. Detecting dynamic and genetic effects on brain structure using high-dimensional cortical pattern matching. In 2002 IEEE International Symposium on Biomedical Imaging, ISBI 2002 - Proceedings. IEEE Computer Society. 2002. p. 473-476. 1029297. (Proceedings - International Symposium on Biomedical Imaging). doi: 10.1109/ISBI.2002.1029297

Detecting dynamic and genetic effects on brain structure using high-dimensional cortical pattern matching

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