MIGRAINE: MRI graph reliability analysis and inference for connectomics

William Gray Roncal; Zachary H. Koterba; Disa Mhembere; Dean M. Kleissas; Joshua T. Vogelstein; Randal Burns; Anita R. Bowles; Dimitrios K. Donavos; Sephira Ryman; Rex E. Jung; Lei Wu; Vince Calhoun; R. Jacob Vogelstein

doi:10.1109/GlobalSIP.2013.6736878

MIGRAINE: MRI graph reliability analysis and inference for connectomics

William Gray Roncal, Zachary H. Koterba, Disa Mhembere, Dean M. Kleissas, Joshua T. Vogelstein, Randal Burns, Anita R. Bowles, Dimitrios K. Donavos, Sephira Ryman, Rex E. Jung, Lei Wu, Vince Calhoun, R. Jacob Vogelstein

Whiting School of Engineering

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

13 Scopus citations

Abstract

Currently, connectomes (e.g., functional or structural brain graphs) can be estimated in humans at ≈ 1 mm³ scale using a combination of diffusion weighted magnetic resonance imaging, functional magnetic resonance imaging and structural magnetic resonance imaging scans. This manuscript summarizes a novel, scalable implementation of open-source algorithms to rapidly estimate magnetic resonance connectomes, using both anatomical regions of interest (ROIs) and voxel-size vertices. To assess the reliability of our pipeline, we develop a novel non-parametric non-Euclidean reliability metric. Here we provide an overview of the methods used, demonstrate our implementation, and discuss available user extensions. We conclude with results showing the efficacy and reliability of the pipeline over previous state-of-the-art.

Original language	English (US)
Title of host publication	2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
Pages	313-316
Number of pages	4
DOIs	https://doi.org/10.1109/GlobalSIP.2013.6736878
State	Published - 2013
Event	2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Austin, TX, United States Duration: Dec 3 2013 → Dec 5 2013

Publication series

Name	2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings

Other

Other	2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013
Country/Territory	United States
City	Austin, TX
Period	12/3/13 → 12/5/13

Keywords

Connectomics
Magnetic resonance imaging
Network theory
Pipeline

ASJC Scopus subject areas

Information Systems
Signal Processing

Access to Document

10.1109/GlobalSIP.2013.6736878

Cite this

Roncal, W. G., Koterba, Z. H., Mhembere, D., Kleissas, D. M., Vogelstein, J. T., Burns, R., Bowles, A. R., Donavos, D. K., Ryman, S., Jung, R. E., Wu, L., Calhoun, V., & Vogelstein, R. J. (2013). MIGRAINE: MRI graph reliability analysis and inference for connectomics. In 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings (pp. 313-316). Article 6736878 (2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings). https://doi.org/10.1109/GlobalSIP.2013.6736878

MIGRAINE: MRI graph reliability analysis and inference for connectomics. / Roncal, William Gray; Koterba, Zachary H.; Mhembere, Disa et al.
2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings. 2013. p. 313-316 6736878 (2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings).

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

Roncal, WG, Koterba, ZH, Mhembere, D, Kleissas, DM, Vogelstein, JT, Burns, R, Bowles, AR, Donavos, DK, Ryman, S, Jung, RE, Wu, L, Calhoun, V & Vogelstein, RJ 2013, MIGRAINE: MRI graph reliability analysis and inference for connectomics. in 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings., 6736878, 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings, pp. 313-316, 2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013, Austin, TX, United States, 12/3/13. https://doi.org/10.1109/GlobalSIP.2013.6736878

Roncal WG, Koterba ZH, Mhembere D, Kleissas DM, Vogelstein JT, Burns R et al. MIGRAINE: MRI graph reliability analysis and inference for connectomics. In 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings. 2013. p. 313-316. 6736878. (2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings). doi: 10.1109/GlobalSIP.2013.6736878

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title = "MIGRAINE: MRI graph reliability analysis and inference for connectomics",

abstract = "Currently, connectomes (e.g., functional or structural brain graphs) can be estimated in humans at ≈ 1 mm3 scale using a combination of diffusion weighted magnetic resonance imaging, functional magnetic resonance imaging and structural magnetic resonance imaging scans. This manuscript summarizes a novel, scalable implementation of open-source algorithms to rapidly estimate magnetic resonance connectomes, using both anatomical regions of interest (ROIs) and voxel-size vertices. To assess the reliability of our pipeline, we develop a novel non-parametric non-Euclidean reliability metric. Here we provide an overview of the methods used, demonstrate our implementation, and discuss available user extensions. We conclude with results showing the efficacy and reliability of the pipeline over previous state-of-the-art.",

keywords = "Connectomics, Magnetic resonance imaging, Network theory, Pipeline",

author = "Roncal, {William Gray} and Koterba, {Zachary H.} and Disa Mhembere and Kleissas, {Dean M.} and Vogelstein, {Joshua T.} and Randal Burns and Bowles, {Anita R.} and Donavos, {Dimitrios K.} and Sephira Ryman and Jung, {Rex E.} and Lei Wu and Vince Calhoun and Vogelstein, {R. Jacob}",

year = "2013",

doi = "10.1109/GlobalSIP.2013.6736878",

language = "English (US)",

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AU - Koterba, Zachary H.

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AU - Kleissas, Dean M.

AU - Vogelstein, Joshua T.

AU - Burns, Randal

AU - Bowles, Anita R.

AU - Donavos, Dimitrios K.

AU - Ryman, Sephira

AU - Jung, Rex E.

AU - Wu, Lei

AU - Calhoun, Vince

AU - Vogelstein, R. Jacob

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AB - Currently, connectomes (e.g., functional or structural brain graphs) can be estimated in humans at ≈ 1 mm3 scale using a combination of diffusion weighted magnetic resonance imaging, functional magnetic resonance imaging and structural magnetic resonance imaging scans. This manuscript summarizes a novel, scalable implementation of open-source algorithms to rapidly estimate magnetic resonance connectomes, using both anatomical regions of interest (ROIs) and voxel-size vertices. To assess the reliability of our pipeline, we develop a novel non-parametric non-Euclidean reliability metric. Here we provide an overview of the methods used, demonstrate our implementation, and discuss available user extensions. We conclude with results showing the efficacy and reliability of the pipeline over previous state-of-the-art.

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KW - Magnetic resonance imaging

KW - Network theory

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BT - 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings

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ER -

MIGRAINE: MRI graph reliability analysis and inference for connectomics

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Keywords

ASJC Scopus subject areas

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