Statistical Methods for Processing Neuroimaging Data from Two Different Sites with a Down Syndrome Population Application

Davneet S. Minhas; Zixi Yang; John Muschelli; Charles M. Laymon; Joseph M. Mettenburg; Matthew D. Zammit; Sterling Johnson; Chester A. Mathis; Ann D. Cohen; Benjamin L. Handen; William E. Klunk; Ciprian M. Crainiceanu; Bradley T. Christian; Dana L. Tudorascu

doi:10.1007/978-3-030-50153-2_28

Statistical Methods for Processing Neuroimaging Data from Two Different Sites with a Down Syndrome Population Application

Davneet S. Minhas, Zixi Yang, John Muschelli, Charles M. Laymon, Joseph M. Mettenburg, Matthew D. Zammit, Sterling Johnson, Chester A. Mathis, Ann D. Cohen, Benjamin L. Handen, William E. Klunk, Ciprian M. Crainiceanu, Bradley T. Christian, Dana L. Tudorascu

Bloomberg School of Public Health

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

Abstract

Harmonization of magnetic resonance imaging (MRI) and positron emission tomography (PET) scans from multi-scanner and multi-site studies presents a challenging problem. We applied the Removal of Artificial Voxel Effect by Linear regression (RAVEL) method to normalize T1-MRI intensities collected on two different scanners across two different sites as part of the Neurodegeneration in Aging Down syndrome (NiAD) study. The effects on FreeSurfer regional cortical thickness and volume outcome measures, in addition to FreeSurfer-based regional quantification of amyloid PET standardized uptake value ratio (SUVR) outcomes, were evaluated. A neuroradiologist visually assessed the accuracy of FreeSurfer hippocampus segmentations with and without the application of RAVEL. Quantitative results demonstrated that the application of RAVEL intensity normalization prior to running FreeSurfer significantly impacted both FreeSurfer volume and cortical thickness outcome measures. Visual assessment demonstrated that the application of RAVEL significantly improved FreeSurfer hippocampal segmentation accuracy. The RAVEL intensity normalization had little impact on PET SUVR measures.

Original language	English (US)
Title of host publication	Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings
Editors	Marie-Jeanne Lesot, Susana Vieira, Marek Z. Reformat, João Paulo Carvalho, Anna Wilbik, Bernadette Bouchon-Meunier, Ronald R. Yager
Publisher	Springer
Pages	367-379
Number of pages	13
ISBN (Print)	9783030501525
DOIs	https://doi.org/10.1007/978-3-030-50153-2_28
State	Published - 2020
Event	18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020 - Lisbon, Portugal Duration: Jun 15 2020 → Jun 19 2020

Publication series

Name	Communications in Computer and Information Science
Volume	1239 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020
Country/Territory	Portugal
City	Lisbon
Period	6/15/20 → 6/19/20

Keywords

Harmonization
MRI
PET

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-3-030-50153-2_28

Cite this

Minhas, D. S., Yang, Z., Muschelli, J., Laymon, C. M., Mettenburg, J. M., Zammit, M. D., Johnson, S., Mathis, C. A., Cohen, A. D., Handen, B. L., Klunk, W. E., Crainiceanu, C. M., Christian, B. T., & Tudorascu, D. L. (2020). Statistical Methods for Processing Neuroimaging Data from Two Different Sites with a Down Syndrome Population Application. In M.-J. Lesot, S. Vieira, M. Z. Reformat, J. P. Carvalho, A. Wilbik, B. Bouchon-Meunier, & R. R. Yager (Eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings (pp. 367-379). (Communications in Computer and Information Science; Vol. 1239 CCIS). Springer. https://doi.org/10.1007/978-3-030-50153-2_28

Statistical Methods for Processing Neuroimaging Data from Two Different Sites with a Down Syndrome Population Application. / Minhas, Davneet S.; Yang, Zixi; Muschelli, John et al.
Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings. ed. / Marie-Jeanne Lesot; Susana Vieira; Marek Z. Reformat; João Paulo Carvalho; Anna Wilbik; Bernadette Bouchon-Meunier; Ronald R. Yager. Springer, 2020. p. 367-379 (Communications in Computer and Information Science; Vol. 1239 CCIS).

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

Minhas, DS, Yang, Z, Muschelli, J, Laymon, CM, Mettenburg, JM, Zammit, MD, Johnson, S, Mathis, CA, Cohen, AD, Handen, BL, Klunk, WE, Crainiceanu, CM, Christian, BT & Tudorascu, DL 2020, Statistical Methods for Processing Neuroimaging Data from Two Different Sites with a Down Syndrome Population Application. in M-J Lesot, S Vieira, MZ Reformat, JP Carvalho, A Wilbik, B Bouchon-Meunier & RR Yager (eds), Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings. Communications in Computer and Information Science, vol. 1239 CCIS, Springer, pp. 367-379, 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020, Lisbon, Portugal, 6/15/20. https://doi.org/10.1007/978-3-030-50153-2_28

Minhas DS, Yang Z, Muschelli J, Laymon CM, Mettenburg JM, Zammit MD et al. Statistical Methods for Processing Neuroimaging Data from Two Different Sites with a Down Syndrome Population Application. In Lesot MJ, Vieira S, Reformat MZ, Carvalho JP, Wilbik A, Bouchon-Meunier B, Yager RR, editors, Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings. Springer. 2020. p. 367-379. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-50153-2_28

Minhas, Davneet S. ; Yang, Zixi ; Muschelli, John et al. / Statistical Methods for Processing Neuroimaging Data from Two Different Sites with a Down Syndrome Population Application. Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings. editor / Marie-Jeanne Lesot ; Susana Vieira ; Marek Z. Reformat ; João Paulo Carvalho ; Anna Wilbik ; Bernadette Bouchon-Meunier ; Ronald R. Yager. Springer, 2020. pp. 367-379 (Communications in Computer and Information Science).

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abstract = "Harmonization of magnetic resonance imaging (MRI) and positron emission tomography (PET) scans from multi-scanner and multi-site studies presents a challenging problem. We applied the Removal of Artificial Voxel Effect by Linear regression (RAVEL) method to normalize T1-MRI intensities collected on two different scanners across two different sites as part of the Neurodegeneration in Aging Down syndrome (NiAD) study. The effects on FreeSurfer regional cortical thickness and volume outcome measures, in addition to FreeSurfer-based regional quantification of amyloid PET standardized uptake value ratio (SUVR) outcomes, were evaluated. A neuroradiologist visually assessed the accuracy of FreeSurfer hippocampus segmentations with and without the application of RAVEL. Quantitative results demonstrated that the application of RAVEL intensity normalization prior to running FreeSurfer significantly impacted both FreeSurfer volume and cortical thickness outcome measures. Visual assessment demonstrated that the application of RAVEL significantly improved FreeSurfer hippocampal segmentation accuracy. The RAVEL intensity normalization had little impact on PET SUVR measures.",

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Statistical Methods for Processing Neuroimaging Data from Two Different Sites with a Down Syndrome Population Application

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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