Modeling the brain morphology distribution in the general aging population

W. Huizinga; D. H J Poot; G. Roshchupkin; E. E. Bron; M. A. Ikram; M. W. Vernooij; D. Rueckert; W. J. Niessen; S. Klein

doi:10.1117/12.2207228

Modeling the brain morphology distribution in the general aging population

W. Huizinga^*, D. H J Poot, G. Roshchupkin, E. E. Bron, M. A. Ikram, M. W. Vernooij, D. Rueckert, W. J. Niessen, S. Klein

^*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

3 Citations (Scopus)

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Abstract

Both normal aging and neurodegenerative diseases such as Alzheimer's disease cause morphological changes of the brain. To better distinguish between normal and abnormal cases, it is necessary to model changes in brain morphology owing to normal aging. To this end, we developed a method for analyzing and visualizing these changes for the entire brain morphology distribution in the general aging population. The method is applied to 1000 subjects from a large population imaging study in the elderly, from which 900 were used to train the model and 100 were used for testing. The results of the 100 test subjects show that the model generalizes to subjects outside the model population. Smooth percentile curves showing the brain morphology changes as a function of age and spatiotemporal atlases derived from the model population are publicly available via an interactive web application at agingbrain.bigr.nl.

Original language	English
Title of host publication	Medical Imaging 2016
Subtitle of host publication	Biomedical Applications in Molecular, Structural, and Functional Imaging
Editors	Barjor Gimi, Andrzej Krol
Publisher	SPIE
Pages	1-7
Volume	9788
ISBN (Electronic)	978-1-510600232
DOIs	https://doi.org/10.1117/12.2207228
Publication status	Published - 2016
Event	Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging - San Diego, United States Duration: 1 Mar 2016 → 3 Mar 2016

Publication series

Name	Proceedings of SPIE
Volume	9788
ISSN (Electronic)	1605-7422

Conference

Conference	Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging
Country/Territory	United States
City	San Diego
Period	1/03/16 → 3/03/16

Keywords

LMS method
Non-rigid groupwise registration
Partial least squares regression
Spatiotemporal atlas
Statistical modeling

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10.1117/12.2207228

97880IFinal published version, 1.98 MB

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Huizinga, W., Poot, D. H. J., Roshchupkin, G., Bron, E. E., Ikram, M. A., Vernooij, M. W., Rueckert, D., Niessen, W. J., & Klein, S. (2016). Modeling the brain morphology distribution in the general aging population. In B. Gimi, & A. Krol (Eds.), Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging (Vol. 9788, pp. 1-7). Article 97880I (Proceedings of SPIE; Vol. 9788). SPIE. https://doi.org/10.1117/12.2207228

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title = "Modeling the brain morphology distribution in the general aging population",

abstract = "Both normal aging and neurodegenerative diseases such as Alzheimer's disease cause morphological changes of the brain. To better distinguish between normal and abnormal cases, it is necessary to model changes in brain morphology owing to normal aging. To this end, we developed a method for analyzing and visualizing these changes for the entire brain morphology distribution in the general aging population. The method is applied to 1000 subjects from a large population imaging study in the elderly, from which 900 were used to train the model and 100 were used for testing. The results of the 100 test subjects show that the model generalizes to subjects outside the model population. Smooth percentile curves showing the brain morphology changes as a function of age and spatiotemporal atlases derived from the model population are publicly available via an interactive web application at agingbrain.bigr.nl.",

keywords = "LMS method, Non-rigid groupwise registration, Partial least squares regression, Spatiotemporal atlas, Statistical modeling",

author = "W. Huizinga and Poot, {D. H J} and G. Roshchupkin and Bron, {E. E.} and Ikram, {M. A.} and Vernooij, {M. W.} and D. Rueckert and Niessen, {W. J.} and S. Klein",

year = "2016",

doi = "10.1117/12.2207228",

language = "English",

volume = "9788",

series = "Proceedings of SPIE",

publisher = "SPIE",

pages = "1--7",

editor = "Barjor Gimi and Andrzej Krol",

booktitle = "Medical Imaging 2016",

address = "United States",

note = "Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging ; Conference date: 01-03-2016 Through 03-03-2016",

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Huizinga, W, Poot, DHJ, Roshchupkin, G, Bron, EE, Ikram, MA, Vernooij, MW, Rueckert, D, Niessen, WJ & Klein, S 2016, Modeling the brain morphology distribution in the general aging population. in B Gimi & A Krol (eds), Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging. vol. 9788, 97880I, Proceedings of SPIE, vol. 9788, SPIE, pp. 1-7, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, San Diego, United States, 1/03/16. https://doi.org/10.1117/12.2207228

Modeling the brain morphology distribution in the general aging population. / Huizinga, W.; Poot, D. H J; Roshchupkin, G. et al.
Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging. ed. / Barjor Gimi; Andrzej Krol. Vol. 9788 SPIE, 2016. p. 1-7 97880I (Proceedings of SPIE; Vol. 9788).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Roshchupkin, G.

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AU - Vernooij, M. W.

AU - Rueckert, D.

AU - Niessen, W. J.

AU - Klein, S.

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N2 - Both normal aging and neurodegenerative diseases such as Alzheimer's disease cause morphological changes of the brain. To better distinguish between normal and abnormal cases, it is necessary to model changes in brain morphology owing to normal aging. To this end, we developed a method for analyzing and visualizing these changes for the entire brain morphology distribution in the general aging population. The method is applied to 1000 subjects from a large population imaging study in the elderly, from which 900 were used to train the model and 100 were used for testing. The results of the 100 test subjects show that the model generalizes to subjects outside the model population. Smooth percentile curves showing the brain morphology changes as a function of age and spatiotemporal atlases derived from the model population are publicly available via an interactive web application at agingbrain.bigr.nl.

AB - Both normal aging and neurodegenerative diseases such as Alzheimer's disease cause morphological changes of the brain. To better distinguish between normal and abnormal cases, it is necessary to model changes in brain morphology owing to normal aging. To this end, we developed a method for analyzing and visualizing these changes for the entire brain morphology distribution in the general aging population. The method is applied to 1000 subjects from a large population imaging study in the elderly, from which 900 were used to train the model and 100 were used for testing. The results of the 100 test subjects show that the model generalizes to subjects outside the model population. Smooth percentile curves showing the brain morphology changes as a function of age and spatiotemporal atlases derived from the model population are publicly available via an interactive web application at agingbrain.bigr.nl.

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Y2 - 1 March 2016 through 3 March 2016

ER -

Modeling the brain morphology distribution in the general aging population

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Keywords

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