Information-theoretic clustering of neuroimaging metrics related to cognitive decline in the elderly

Madelaine Daianu; Greg Ver Steeg; Adam Mezher; Neda Jahanshad; Talia M. Nir; Xiaoran Yan; Gautam Prasad; Kristina Lerman; Aram Galstyan; Paul M. Thompson

doi:10.1007/978-3-319-42016-5_2

Information-theoretic clustering of neuroimaging metrics related to cognitive decline in the elderly

Madelaine Daianu, Greg Ver Steeg, Adam Mezher, Neda Jahanshad, Talia M. Nir, Xiaoran Yan, Gautam Prasad, Kristina Lerman, Aram Galstyan, Paul M. Thompson

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

2 Scopus citations

Abstract

As Alzheimer’s disease progresses, there are changes in metrics of brain atrophy and network breakdown derived from anatomical or diffusion MRI. Neuroimaging biomarkers of cognitive decline are crucial to identify, but few studies have investigated how sets of biomarkers cluster in terms of the information they provide. Here, we evaluated more than 700 frequently studied diffusion and anatomical measures in 247 elderly participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We used a novel unsupervised machine learning technique - CorEx - to identify groups of measures with high multivariate mutual information; we computed latent factors to explain correlations among them. We visualized groups of measures discovered by CorEx in a hierarchical structure and determined how well they predict cognitive decline. Clusters of variables significantly predicted cognitive decline, including measures of cortical gray matter, and correlated measures of brain networks derived from graph theory and spectral graph theory.

Original language	English
Title of host publication	Medical Computer Vision
Subtitle of host publication	Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers
Editors	Michael Kelm, Henning Müller, Bjoern Menze, Shaoting Zhang, Dimitris Metaxas, Georg Langs, Albert Montillo, Weidong Cai
Publisher	Springer Verlag
Pages	13-23
Number of pages	11
ISBN (Print)	9783319420158
DOIs	https://doi.org/10.1007/978-3-319-42016-5_2
State	Published - 2016
Externally published	Yes
Event	International Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI - Germany, Germany Duration: 9 Oct 2015 → 9 Oct 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9601 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI
Country/Territory	Germany
City	Germany
Period	9/10/15 → 9/10/15

Keywords

Brain connectivity
Diffusion weighted imaging
Gray matter
Machine learning
Spectral graph theory

Access to Document

10.1007/978-3-319-42016-5_2

Cite this

Daianu, M., Steeg, G. V., Mezher, A., Jahanshad, N., Nir, T. M., Yan, X., Prasad, G., Lerman, K., Galstyan, A., & Thompson, P. M. (2016). Information-theoretic clustering of neuroimaging metrics related to cognitive decline in the elderly. In M. Kelm, H. Müller, B. Menze, S. Zhang, D. Metaxas, G. Langs, A. Montillo, & W. Cai (Eds.), Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers (pp. 13-23). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9601 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-42016-5_2

Daianu, Madelaine ; Steeg, Greg Ver ; Mezher, Adam et al. / Information-theoretic clustering of neuroimaging metrics related to cognitive decline in the elderly. Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers. editor / Michael Kelm ; Henning Müller ; Bjoern Menze ; Shaoting Zhang ; Dimitris Metaxas ; Georg Langs ; Albert Montillo ; Weidong Cai. Springer Verlag, 2016. pp. 13-23 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{25d7b17f9af24075984b2d9b920afb06,

title = "Information-theoretic clustering of neuroimaging metrics related to cognitive decline in the elderly",

abstract = "As Alzheimer{\textquoteright}s disease progresses, there are changes in metrics of brain atrophy and network breakdown derived from anatomical or diffusion MRI. Neuroimaging biomarkers of cognitive decline are crucial to identify, but few studies have investigated how sets of biomarkers cluster in terms of the information they provide. Here, we evaluated more than 700 frequently studied diffusion and anatomical measures in 247 elderly participants from the Alzheimer{\textquoteright}s Disease Neuroimaging Initiative (ADNI). We used a novel unsupervised machine learning technique - CorEx - to identify groups of measures with high multivariate mutual information; we computed latent factors to explain correlations among them. We visualized groups of measures discovered by CorEx in a hierarchical structure and determined how well they predict cognitive decline. Clusters of variables significantly predicted cognitive decline, including measures of cortical gray matter, and correlated measures of brain networks derived from graph theory and spectral graph theory.",

keywords = "Brain connectivity, Diffusion weighted imaging, Gray matter, Machine learning, Spectral graph theory",

author = "Madelaine Daianu and Steeg, {Greg Ver} and Adam Mezher and Neda Jahanshad and Nir, {Talia M.} and Xiaoran Yan and Gautam Prasad and Kristina Lerman and Aram Galstyan and Thompson, {Paul M.}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; International Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI ; Conference date: 09-10-2015 Through 09-10-2015",

year = "2016",

doi = "10.1007/978-3-319-42016-5_2",

language = "English",

isbn = "9783319420158",

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

publisher = "Springer Verlag",

pages = "13--23",

editor = "Michael Kelm and Henning M{\"u}ller and Bjoern Menze and Shaoting Zhang and Dimitris Metaxas and Georg Langs and Albert Montillo and Weidong Cai",

booktitle = "Medical Computer Vision",

address = "Germany",

}

Daianu, M, Steeg, GV, Mezher, A, Jahanshad, N, Nir, TM, Yan, X, Prasad, G, Lerman, K, Galstyan, A & Thompson, PM 2016, Information-theoretic clustering of neuroimaging metrics related to cognitive decline in the elderly. in M Kelm, H Müller, B Menze, S Zhang, D Metaxas, G Langs, A Montillo & W Cai (eds), Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9601 LNCS, Springer Verlag, pp. 13-23, International Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI, Germany, Germany, 9/10/15. https://doi.org/10.1007/978-3-319-42016-5_2

Information-theoretic clustering of neuroimaging metrics related to cognitive decline in the elderly. / Daianu, Madelaine; Steeg, Greg Ver; Mezher, Adam et al.
Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers. ed. / Michael Kelm; Henning Müller; Bjoern Menze; Shaoting Zhang; Dimitris Metaxas; Georg Langs; Albert Montillo; Weidong Cai. Springer Verlag, 2016. p. 13-23 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9601 LNCS).

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

TY - GEN

T1 - Information-theoretic clustering of neuroimaging metrics related to cognitive decline in the elderly

AU - Daianu, Madelaine

AU - Steeg, Greg Ver

AU - Mezher, Adam

AU - Jahanshad, Neda

AU - Nir, Talia M.

AU - Yan, Xiaoran

AU - Prasad, Gautam

AU - Lerman, Kristina

AU - Galstyan, Aram

AU - Thompson, Paul M.

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - As Alzheimer’s disease progresses, there are changes in metrics of brain atrophy and network breakdown derived from anatomical or diffusion MRI. Neuroimaging biomarkers of cognitive decline are crucial to identify, but few studies have investigated how sets of biomarkers cluster in terms of the information they provide. Here, we evaluated more than 700 frequently studied diffusion and anatomical measures in 247 elderly participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We used a novel unsupervised machine learning technique - CorEx - to identify groups of measures with high multivariate mutual information; we computed latent factors to explain correlations among them. We visualized groups of measures discovered by CorEx in a hierarchical structure and determined how well they predict cognitive decline. Clusters of variables significantly predicted cognitive decline, including measures of cortical gray matter, and correlated measures of brain networks derived from graph theory and spectral graph theory.

AB - As Alzheimer’s disease progresses, there are changes in metrics of brain atrophy and network breakdown derived from anatomical or diffusion MRI. Neuroimaging biomarkers of cognitive decline are crucial to identify, but few studies have investigated how sets of biomarkers cluster in terms of the information they provide. Here, we evaluated more than 700 frequently studied diffusion and anatomical measures in 247 elderly participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We used a novel unsupervised machine learning technique - CorEx - to identify groups of measures with high multivariate mutual information; we computed latent factors to explain correlations among them. We visualized groups of measures discovered by CorEx in a hierarchical structure and determined how well they predict cognitive decline. Clusters of variables significantly predicted cognitive decline, including measures of cortical gray matter, and correlated measures of brain networks derived from graph theory and spectral graph theory.

KW - Brain connectivity

KW - Diffusion weighted imaging

KW - Gray matter

KW - Machine learning

KW - Spectral graph theory

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U2 - 10.1007/978-3-319-42016-5_2

DO - 10.1007/978-3-319-42016-5_2

M3 - Conference contribution

AN - SCOPUS:84981356518

SN - 9783319420158

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

SP - 13

EP - 23

BT - Medical Computer Vision

A2 - Kelm, Michael

A2 - Müller, Henning

A2 - Menze, Bjoern

A2 - Zhang, Shaoting

A2 - Metaxas, Dimitris

A2 - Langs, Georg

A2 - Montillo, Albert

A2 - Cai, Weidong

PB - Springer Verlag

T2 - International Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI

Y2 - 9 October 2015 through 9 October 2015

ER -

Daianu M, Steeg GV, Mezher A, Jahanshad N, Nir TM, Yan X et al. Information-theoretic clustering of neuroimaging metrics related to cognitive decline in the elderly. In Kelm M, Müller H, Menze B, Zhang S, Metaxas D, Langs G, Montillo A, Cai W, editors, Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers. Springer Verlag. 2016. p. 13-23. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-42016-5_2

Information-theoretic clustering of neuroimaging metrics related to cognitive decline in the elderly

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Keywords

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