Multi-site meta-analysis of morphometry

Neda Jahanshad; Joshua Faskowitz; Gennady Roshchupkin; Derrek P. Hibar; Boris A. Gutman; Nicholas J. Tustison; Hieab H.H. Adams; Wiro J. Niessen; Meike W. Vernooij; M. Arfan Ikram; Marcel P. Zwiers; Alejandro Arias Vasquez; Barbara Franke; Jennifer L. Kroll; Benson Mwangi; Jair C. Soares; Alex Ing; Sylvane Desrivieres; Gunter Schumann; Narelle K. Hansell; Greig I. De Zubicaray; Katie L. McMahon; Nicholas G. Martin; Margaret J. Wright; Paul M. Thompson

doi:10.1109/TCBB.2019.2914905

Multi-site meta-analysis of morphometry

Neda Jahanshad, Joshua Faskowitz, Gennady Roshchupkin, Derrek P. Hibar, Boris A. Gutman, Nicholas J. Tustison, Hieab H.H. Adams, Wiro J. Niessen, Meike W. Vernooij, M. Arfan Ikram, Marcel P. Zwiers, Alejandro Arias Vasquez, Barbara Franke, Jennifer L. Kroll, Benson Mwangi, Jair C. Soares, Alex Ing, Sylvane Desrivieres, Gunter Schumann, Narelle K. HansellGreig I. De Zubicaray, Katie L. McMahon, Nicholas G. Martin, Margaret J. Wright, Paul M. Thompson

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

Genome-wide association studies GWAS link full genome data to a handful of traits. However, in neuroimaging studies, there is an almost unlimited number of traits that can be extracted for full image-wide big data analyses. Large populations are needed to achieve the necessary power to detect statistically significant effects, emphasizing the need to pool data across multiple studies. Neuroimaging consortia, e.g., ENIGMA and CHARGE, are now analyzing MRI data from over 30,000 individuals. Distributed processing protocols extract harmonized features at each site, and pool together only the cohort statistics using meta analysis to avoid data sharing. To date, such MRI projects have focused on single measures such as hippocampal volume, yet voxelwise analyses e.g., tensor-based morphometry; TBM may help better localize statistical effects. This can lead to 10¹³ tests for GWAS and become underpowered. We developed an analytical framework for multi-site TBM by performing multi-channel registration to cohort-specific templates. Our results highlight the reliability of the method and the added power over alternative options while preserving single site specificity and opening the doors for well-powered image-wide genome-wide discoveries.

Original language	English
Article number	3370702
Pages (from-to)	1508-1514
Number of pages	7
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume	16
Issue number	5
DOIs	https://doi.org/10.1109/TCBB.2019.2914905
State	Published - Sep 2019
Externally published	Yes

Keywords

ENIGMA
imaging genetics
mass univariate
meta-analysis
multi-channel registration
multi-site
voxelwise

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10.1109/TCBB.2019.2914905

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Jahanshad, N., Faskowitz, J., Roshchupkin, G., Hibar, D. P., Gutman, B. A., Tustison, N. J., Adams, H. H. H., Niessen, W. J., Vernooij, M. W., Ikram, M. A., Zwiers, M. P., Vasquez, A. A., Franke, B., Kroll, J. L., Mwangi, B., Soares, J. C., Ing, A., Desrivieres, S., Schumann, G., ... Thompson, P. M. (2019). Multi-site meta-analysis of morphometry. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16(5), 1508-1514. Article 3370702. https://doi.org/10.1109/TCBB.2019.2914905

@article{4451ac61d3cd4994a6ed3bd44aea90fa,

title = "Multi-site meta-analysis of morphometry",

abstract = "Genome-wide association studies GWAS link full genome data to a handful of traits. However, in neuroimaging studies, there is an almost unlimited number of traits that can be extracted for full image-wide big data analyses. Large populations are needed to achieve the necessary power to detect statistically significant effects, emphasizing the need to pool data across multiple studies. Neuroimaging consortia, e.g., ENIGMA and CHARGE, are now analyzing MRI data from over 30,000 individuals. Distributed processing protocols extract harmonized features at each site, and pool together only the cohort statistics using meta analysis to avoid data sharing. To date, such MRI projects have focused on single measures such as hippocampal volume, yet voxelwise analyses e.g., tensor-based morphometry; TBM may help better localize statistical effects. This can lead to 1013 tests for GWAS and become underpowered. We developed an analytical framework for multi-site TBM by performing multi-channel registration to cohort-specific templates. Our results highlight the reliability of the method and the added power over alternative options while preserving single site specificity and opening the doors for well-powered image-wide genome-wide discoveries.",

keywords = "ENIGMA, imaging genetics, mass univariate, meta-analysis, multi-channel registration, multi-site, voxelwise",

author = "Neda Jahanshad and Joshua Faskowitz and Gennady Roshchupkin and Hibar, {Derrek P.} and Gutman, {Boris A.} and Tustison, {Nicholas J.} and Adams, {Hieab H.H.} and Niessen, {Wiro J.} and Vernooij, {Meike W.} and Ikram, {M. Arfan} and Zwiers, {Marcel P.} and Vasquez, {Alejandro Arias} and Barbara Franke and Kroll, {Jennifer L.} and Benson Mwangi and Soares, {Jair C.} and Alex Ing and Sylvane Desrivieres and Gunter Schumann and Hansell, {Narelle K.} and {De Zubicaray}, {Greig I.} and McMahon, {Katie L.} and Martin, {Nicholas G.} and Wright, {Margaret J.} and Thompson, {Paul M.}",

year = "2019",

month = sep,

doi = "10.1109/TCBB.2019.2914905",

language = "English",

volume = "16",

pages = "1508--1514",

journal = "IEEE/ACM Transactions on Computational Biology and Bioinformatics",

issn = "1545-5963",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "5",

}

Jahanshad, N, Faskowitz, J, Roshchupkin, G, Hibar, DP, Gutman, BA, Tustison, NJ, Adams, HHH, Niessen, WJ, Vernooij, MW, Ikram, MA, Zwiers, MP, Vasquez, AA, Franke, B, Kroll, JL, Mwangi, B, Soares, JC, Ing, A, Desrivieres, S, Schumann, G, Hansell, NK, De Zubicaray, GI, McMahon, KL, Martin, NG, Wright, MJ & Thompson, PM 2019, 'Multi-site meta-analysis of morphometry', IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 16, no. 5, 3370702, pp. 1508-1514. https://doi.org/10.1109/TCBB.2019.2914905

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T1 - Multi-site meta-analysis of morphometry

AU - Jahanshad, Neda

AU - Faskowitz, Joshua

AU - Roshchupkin, Gennady

AU - Hibar, Derrek P.

AU - Gutman, Boris A.

AU - Tustison, Nicholas J.

AU - Adams, Hieab H.H.

AU - Niessen, Wiro J.

AU - Vernooij, Meike W.

AU - Ikram, M. Arfan

AU - Zwiers, Marcel P.

AU - Vasquez, Alejandro Arias

AU - Franke, Barbara

AU - Kroll, Jennifer L.

AU - Mwangi, Benson

AU - Soares, Jair C.

AU - Ing, Alex

AU - Desrivieres, Sylvane

AU - Schumann, Gunter

AU - Hansell, Narelle K.

AU - De Zubicaray, Greig I.

AU - McMahon, Katie L.

AU - Martin, Nicholas G.

AU - Wright, Margaret J.

AU - Thompson, Paul M.

PY - 2019/9

Y1 - 2019/9

N2 - Genome-wide association studies GWAS link full genome data to a handful of traits. However, in neuroimaging studies, there is an almost unlimited number of traits that can be extracted for full image-wide big data analyses. Large populations are needed to achieve the necessary power to detect statistically significant effects, emphasizing the need to pool data across multiple studies. Neuroimaging consortia, e.g., ENIGMA and CHARGE, are now analyzing MRI data from over 30,000 individuals. Distributed processing protocols extract harmonized features at each site, and pool together only the cohort statistics using meta analysis to avoid data sharing. To date, such MRI projects have focused on single measures such as hippocampal volume, yet voxelwise analyses e.g., tensor-based morphometry; TBM may help better localize statistical effects. This can lead to 1013 tests for GWAS and become underpowered. We developed an analytical framework for multi-site TBM by performing multi-channel registration to cohort-specific templates. Our results highlight the reliability of the method and the added power over alternative options while preserving single site specificity and opening the doors for well-powered image-wide genome-wide discoveries.

AB - Genome-wide association studies GWAS link full genome data to a handful of traits. However, in neuroimaging studies, there is an almost unlimited number of traits that can be extracted for full image-wide big data analyses. Large populations are needed to achieve the necessary power to detect statistically significant effects, emphasizing the need to pool data across multiple studies. Neuroimaging consortia, e.g., ENIGMA and CHARGE, are now analyzing MRI data from over 30,000 individuals. Distributed processing protocols extract harmonized features at each site, and pool together only the cohort statistics using meta analysis to avoid data sharing. To date, such MRI projects have focused on single measures such as hippocampal volume, yet voxelwise analyses e.g., tensor-based morphometry; TBM may help better localize statistical effects. This can lead to 1013 tests for GWAS and become underpowered. We developed an analytical framework for multi-site TBM by performing multi-channel registration to cohort-specific templates. Our results highlight the reliability of the method and the added power over alternative options while preserving single site specificity and opening the doors for well-powered image-wide genome-wide discoveries.

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KW - imaging genetics

KW - mass univariate

KW - meta-analysis

KW - multi-channel registration

KW - multi-site

KW - voxelwise

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DO - 10.1109/TCBB.2019.2914905

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JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics

IS - 5

M1 - 3370702

ER -

Multi-site meta-analysis of morphometry

Abstract

Keywords

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